This Week in Machine Learning & Artificial Intelligence (AI) Podcast

By Sam Charrington

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Description

This Week in Machine Learning & AI is the most popular podcast of its kind. TWiML & AI caters to a highly-targeted audience of machine learning & AI enthusiasts. They are data scientists, developers, founders, CTOs, engineers, architects, IT & product leaders, as well as tech-savvy business leaders. These creators, builders, makers and influencers value TWiML as an authentic, trusted and insightful guide to all that’s interesting and important in the world of machine learning and AI. Technologies covered include: machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, deep learning and more.

Episode Date
Omni-Channel Customer Experiences with Vince Jeffs - TWiML Talk #154
00:43:41
In this, the final episode of our PegaWorld series I’m joined by Vince Jeffs, Senior Director of Product Strategy for AI and Decisioning at Pegasystems. Vince and I had a great talk about the role AI and advanced analytics will play in defining future customer experiences. We do this in the context provided by one of his presentations from the conference, which explores four technology scenarios from Pegasystems’ innovation labs. These look at a connected car experience, the use of deep learning for diagnostics, dynamic notifications, and continuously optimized marketing. We also get into an interesting discussion about how much is too much when it comes to hyperpersonalized experiences, and how businesses can manage this challenge. The notes for this show can be found at twimlai.com/talk/154. For more information on the Pegaworld series, visit twimlai.com/pegaworld2018.
Jun 21, 2018
Workforce Intelligence for Automation & Productivity with Michael Kempe - TWiML Talk #153
00:36:53
In this episode of our PegaWorld series, I’m joined by Michael Kempe, chief operating officer at global share registry and financial services provider Link Market Services. In the interview, Michael and I dig into Link’s use of workforce intelligence software to allow it to track and analyze the performance of its workforce and business processes. Michael and I discuss some of the initial challenges associated with implementing this type of system, including skepticism amongst employees, and how it ultimately sets the stage for the Link’s broader use of machine learning, AI and so called “robotic process automation” to increase workforce productivity. The notes for this show can be found at twimlai.com/talk/153. For more information on our PegaWorld series, visit twimlai.com/pegaworld2018.
Jun 20, 2018
Data Platforms for Decision Automation at Scotiabank with Jim Saleh - TWiML Talk #152
00:33:20
In this show, part of our PegaWorld 18 series, I'm joined by Jim Saleh, Senior Director of process and decision automation at Scotiabank. Jim is tasked with helping the bank transition from a world where customer interactions are based on historical analytics to one where they’re based on real-time decisioning and automation. In our conversation we discuss what’s required to deliver real-time decisioning, starting from the ground up with the data platform. In this vein we explore topics like data lakes, data warehouses, integration, and more, and the effort required to take advantage of these. The notes for this show can be found at twimlai.com/talk/152. For more info on our PegaWorld 2018 series, visit twimlai.com/pegaworld2018.
Jun 19, 2018
Towards the Self-Driving Enterprise with Kirk Borne - TWiML Talk #151
00:42:10
In this show, the first of our PegaWorld 18 series, I'm joined by Kirk Borne, Principal Data Scientist at management consulting firm Booz Allen Hamilton. In our conversation, Kirk shares his views on automation as it applies to enterprises and their customers. We discuss his experiences evangelizing data science within the context of a large organization, and the role of AI in helping organizations achieve automation. Along the way Kirk, shares a great analogy for intelligent automation, comparing it to an autonomous vehicle . We covered a ton of ground in this chat, which I think you’ll get a kick out of. The notes for this show can be found at twimlai.com/talk/151. For more info about our PegaWorld 2018 Series, visit twimlai.com/pegaworld2018.
Jun 18, 2018
How a Global Energy Company Adopts ML & AI with Nicholas Osborn - TWiML Talk #150
00:47:39
On today’s show I’m excited to share this interview with Nick Osborn, a longtime listener of the show and Leader of the Global Machine Learning Project Management Office at AES Corporation, a Fortune 200 power company. Nick and I met at my AI Summit a few weeks back, and after a brief chat about some of the things he was up to at AES, I knew I needed to get him on the show! In this interview, Nick and I explore how AES is implementing machine learning across multiple domains at the company. We dig into several examples falling under the Natural Language, Computer Vision, and Cognitive Assets categories he’s established for his projects. Along the way we cover some of the key podcast episodes that helped Nick discover potentially applicable ML techniques, and how those are helping his team broaden the use of machine learning at AES. This was a fun and informative conversation that has a lot to offer. Thanks, Nick! The notes for this episode can be found at twimlai.com/talk/150.
Jun 14, 2018
Problem Formulation for Machine Learning with Romer Rosales - TWiML Talk #149
00:51:28
In this episode, i'm joined by Romer Rosales, Director of AI at LinkedIn. We begin with a discussion of graphical models and approximate probability inference, and he helps me make an important connection in the way I think about that topic. We then review some of the applications of machine learning at LinkedIn, and how what Romer calls their ‘holistic approach’ guides the evolution of ML projects at LinkedIn. This leads us into a really interesting discussion about problem formulation and selecting the right objective function for a given problem. We then talk through some of the tools they’ve built to scale their data science efforts, including large-scale constrained optimization solvers, online hyperparameter optimization and more. This was a really fun conversation, that I’m sure you’ll enjoy! The notes for this show can be found at twimlai.com/talk/149.
Jun 11, 2018
AI for Materials Discovery with Greg Mulholland - TWiML Talk #148
00:42:23
In this episode I’m joined by Greg Mulholland, Founder and CEO of Citrine Informatics, which is applying AI to the discovery and development of new materials. Greg and I start out with an exploration of some of the challenges of the status quo in materials science, and what’s to be gained by introducing machine learning into this process. We discuss how limitations in materials manifest themselves, and Greg shares a few examples from the company’s work optimizing battery components and solar cells. We dig into the role and sources of data used in applying ML in materials, and some of the unique challenges to collecting it, and discuss the pipeline and algorithms Citrine uses to deliver its service. This was a fun conversation that spans physics, chemistry, and of course machine learning, and I hope you enjoy it. The notes for this show can be found at twimlai.com/talk/148.
Jun 07, 2018
Data Innovation & AI at Capital One with Adam Wenchel - #TWiML Talk 147
00:46:22
In this episode I’m joined by Adam Wenchel, vice president of AI and Data Innovation at Capital One, to discuss how Machine Learning & AI are being integrated into their day-to-day practices, and how those advances benefit the customer. In our conversation, we look into a few of the many applications of AI at the bank, including fraud detection, money laundering, customer service, and automating back office processes. Adam describes some of the challenges of applying ML in financial services and how Capital One maintains consistent portfolio management practices across the organization. We also discuss how the bank has organized to scale their machine learning efforts, and the steps they’ve taken to overcome the talent shortage in the space. The notes for this show can be found at twimlai.com/talk/147.
Jun 04, 2018
Deep Gradient Compression for Distributed Training with Song Han - TWiML Talk #146
00:47:01
On today’s show I chat with Song Han, assistant professor in MIT’s EECS department, about his research on Deep Gradient Compression. In our conversation, we explore the challenge of distributed training for deep neural networks and the idea of compressing the gradient exchange to allow it to be done more efficiently. Song details the evolution of distributed training systems based on this idea, and provides a few examples of centralized and decentralized distributed training architectures such as Uber’s Horovod, as well as the approaches native to Pytorch and Tensorflow. Song also addresses potential issues that arise when considering distributed training, such as loss of accuracy and generalizability, and much more. The notes for this show can be found at twimlai.com/talk/146.
May 31, 2018
Masked Autoregressive Flow for Density Estimation with George Papamakarios - TWiML Talk #145
00:35:55
In this episode, University of Edinburgh Phd student George Papamakarios and I discuss his paper “Masked Autoregressive Flow for Density Estimation.” George walks us through the idea of Masked Autoregressive Flow, which uses neural networks to produce estimates of probability densities from a set of input examples. We discuss some of the related work that’s laid the groundwork for his research, including Inverse Autoregressive Flow, Real NVP and Masked Auto-encoders. We also look at the properties of probability density networks and discuss some of the challenges associated with this effort. The notes for this show can be found at twimlai.com/talk/145.
May 28, 2018
Training Data for Computer Vision at Figure Eight with Qazaleh Mirsharif - TWiML Talk #144
00:22:55
For today’s show, the last in our TrainAI series, I'm joined by Qazaleh Mirsharif, a machine learning scientist working on computer vision at Figure Eight. Qazaleh and I caught up at the TrainAI conference to discuss a couple of the projects she’s worked on in that field, namely her research into the classification of retinal images and her work on parking sign detection from Google Street View images. The former, which attempted to diagnose diseases like diabetic retinopathy using retinal scan images, is similar to the work I spoke with Ryan Poplin about on TWiML Talk #122. In my conversation with Qazaleh we focus on how she built her datasets for each of these projects and some of the key lessons she’s learned along the way. The notes for this show can be found at twimlai.com/talk/144. For series details, visit twimlai.com/trainai2018.
May 25, 2018
Agile Data Science with Sarah Aerni - TWiML Talk #143
00:39:34
Today we continue our TrainAI series with Sarah Aerni, Director of Data Science at Salesforce Einstein. Sarah and I sat down at the TrainAI conference to discuss her talk “Notes from the Field: The Platform, People, and Processes of Agile Data Science.” Sarah and I dig into the concept of agile data science, exploring what it means to her and how she’s seen it done at Salesforce and other places she’s worked. We also dig into the notion of machine learning platforms, which is also a keen area of interest for me. We discuss some of the common elements we’ve seen in ML platforms, and when it makes sense for an organization to start building one. The notes for this show can be found at twimlai.com/talk/143. For more details on the TrainAI series, visit twimlai.com/trainai2018
May 24, 2018
Tensor Operations for Machine Learning with Anima Anandkumar - TWiML Talk #142
00:36:02
In this episode of our TrainAI series, I sit down with Anima Anandkumar, Bren Professor at Caltech and Principal Scientist with Amazon Web Services. Anima joined me to discuss the research coming out of her “Tensorlab” at CalTech. In our conversation, we review the application of tensor operations to machine learning and discuss how an example problem–document categorization–might be approached using 3 dimensional tensors to discover topics and relationships between topics. We touch on multidimensionality, expectation maximization, and Amazon products Sagemaker and Comprehend. Anima also goes into how to tensorize neural networks and apply our understanding of tensor algebra to do perform better architecture searches. The notes for this show can be found at twimlai.com/talk/142. For series info, visit twimlai.com/trainai2018
May 23, 2018
Deep Learning for Live-Cell Imaging with David Van Valen - TWiML Talk #141
00:38:37
In today’s show, I sit down with David Van Valen, assistant professor of Bioengineering & Biology at Caltech. David joined me after his talk at the Figure Eight TrainAI conference to chat about his research using image recognition and segmentation techniques in biological settings. In particular, we discuss his use of deep learning to automate the analysis of individual cells in live-cell imaging experiments. We had a really interesting discussion around the various practicalities he’s learned about training deep neural networks for image analysis, and he shares some great insights into which of the techniques from the deep learning research have worked for him and which haven’t. If you’re a fan of our Nerd Alert shows, you’ll really like this one. Enjoy! The notes for this show can be found at twimlai.com/talk/141. For more information on this series, visit twimlai.com/trainai2018.
May 22, 2018
Checking in with the Master w/ Garry Kasparov - TWiML Talk #140
00:34:40
In this episode I’m joined by legendary chess champion, author, and fellow at the Oxford Martin School, Garry Kasparov. Garry and I sat down after his keynote at the Figure Eight Train AI conference in San Francisco last week. Garry and I discuss his bouts with the chess-playing computer Deep Blue–which became the first computer system to defeat a reigning world champion in their 1997 rematch–and how that experience has helped shaped his thinking on artificially intelligent systems. We explore his perspective on the evolution of AI, the ways in which chess and Deep Blue differ from Go and Alpha Go, and the significance of DeepMind’s Alpha Go Zero. We also talk through his views on the relationship between humans and machines, and how he expects it to change over time. The notes for this show can be found at twimlai.com/talk/140. For more information on this series, visit twimlai.com/trainai2018.
May 21, 2018
Exploring AI-Generated Music with Taryn Southern - TWiML Talk #139
00:34:09
In this episode I’m joined by Taryn Southern - a singer, digital storyteller and Youtuber, whose upcoming album I AM AI will be produced completely with AI based tools. Taryn and I explore all aspects of what it means to create music with modern AI-based tools, and the different processes she’s used to create her singles Break Free, Voices in My Head, and more. She also provides a rundown of the many tools she’s used in this space, including Google Magenta, Watson Beat, AMPer, Landr and more. This was a super fun interview that I think you’ll get a kick out of. The notes for this show can be found at twimlai.com/talk/139
May 17, 2018
Practical Deep Learning with Rachel Thomas - TWiML Talk #138
00:45:52
In this episode, i'm joined by Rachel Thomas, founder and researcher at Fast AI. If you’re not familiar with Fast AI, the company offers a series of courses including Practical Deep Learning for Coders, Cutting Edge Deep Learning for Coders and Rachel’s Computational Linear Algebra course. The courses are designed to make deep learning more accessible to those without the extensive math backgrounds some other courses assume. Rachel and I cover a lot of ground in this conversation, starting with the philosophy and goals behind the Fast AI courses. We also cover Fast AI’s recent decision to switch to their courses from Tensorflow to Pytorch, the reasons for this, and the lessons they’ve learned in the process. We discuss the role of the Fast AI deep learning library as well, and how it was recently used to held their team achieve top results on a popular industry benchmark of training time and training cost by a factor of more than ten. The notes for this show can be found at twimlai.com/talk/138
May 14, 2018
Kinds of Intelligence w/ Jose Hernandez-Orallo - TWiML Talk #137
00:45:28
In this episode, I'm joined by Jose Hernandez-Orallo, professor in the department of information systems and computing at Universitat Politècnica de València and fellow at the Leverhulme Centre for the Future of Intelligence, working on the Kinds of Intelligence Project. Jose and I caught up at NIPS last year after the Kinds of Intelligence Symposium that he helped organize there. In our conversation, we discuss the three main themes of the symposium: understanding and identifying the main types of intelligence, including non-human intelligence, developing better ways to test and measure these intelligences, and understanding how and where research efforts should focus to best benefit society. The notes for this show can be found at twimlai.com/talk/137.
May 10, 2018
Taming arXiv with Natural Language Processing w/ John Bohannon - TWiML Talk #136
00:55:41
In this episode i'm joined by John Bohannan, Director of Science at AI startup Primer. As you all may know, a few weeks ago we released my interview with Google legend Jeff Dean, which, by the way, you should definitely check if you haven’t already. Anyway, in that interview, Jeff mentions the recent explosion of machine learning papers on arXiv, which I responded to jokingly by asking whether Google had already developed the AI system to help them summarize and track all of them. While Jeff didn’t have anything specific to offer, a listener reached out and let me know that John was in fact already working on this problem. In our conversation, John and I discuss his work on Primer Science, a tool that harvests content uploaded to arxiv, sorts it into natural topics using unsupervised learning, then gives relevant summaries of the activity happening in different innovation areas. We spend a good amount of time on the inner workings of Primer Science, including their data pipeline and some of the tools they use, how they determine “ground truth” for training their models, and the use of heuristics to supplement NLP in their processing. The notes for this show can be found at twimlai.com/talk/136
May 07, 2018
Epsilon Software for Private Machine Learning with Chang Liu - TWiML Talk #135
00:47:39
In this episode, our final episode in the Differential Privacy series, I speak with Chang Liu, applied research scientist at Georgian Partners, a venture capital firm that invests in growth stage business software companies in the US and Canada. Chang joined me to discuss Georgian’s new offering, Epsilon, a software product that embodies the research, development and lessons learned helps in helping their portfolio companies deliver differentially private machine learning solutions to their customers. In our conversation, Chang discusses some of the projects that led to the creation of Epsilon, including differentially private machine learning projects at BlueCore, Work Fusion and Integrate.ai. We explore some of the unique challenges of productizing differentially private ML, including business, people and technology issues. Finally, Chang provides some great pointers for those who’d like to further explore this field. The notes for this show can be found at twimlai.com/talk/135
May 04, 2018
Scalable Differential Privacy for Deep Learning with Nicolas Papernot - TWiML Talk #134
01:00:50
In this episode of our Differential Privacy series, I'm joined by Nicolas Papernot, Google PhD Fellow in Security and graduate student in the department of computer science at Penn State University. Nicolas and I continue this week’s look into differential privacy with a discussion of his recent paper, Semi-supervised Knowledge Transfer for Deep Learning From Private Training Data. In our conversation, Nicolas describes the Private Aggregation of Teacher Ensembles model proposed in this paper, and how it ensures differential privacy in a scalable manner that can be applied to Deep Neural Networks. We also explore one of the interesting side effects of applying differential privacy to machine learning, namely that it inherently resists overfitting, leading to more generalized models. The notes for this show can be found at twimlai.com/talk/134.
May 03, 2018
Differential Privacy at Bluecore with Zahi Karam - TWiML Talk #133
00:39:14
In this episode of our Differential Privacy series, I'm joined by Zahi Karam, Director of Data Science at Bluecore, whose retail marketing platform specializes in personalized email marketing. I sat down with Zahi at the Georgian Partners portfolio conference last year, where he gave me my initial exposure to the field of differential privacy, ultimately leading to this series. Zahi shared his insights into how differential privacy can be deployed in the real world and some of the technical and cultural challenges to doing so. We discuss the Bluecore use case in depth, including why and for whom they build differentially private machine learning models. The notes for this show can be found at twimlai.com/talk/133
May 01, 2018
Differential Privacy Theory & Practice with Aaron Roth - TWiML Talk #132
00:44:11
In the first episode of our Differential Privacy series, I'm joined by Aaron Roth, associate professor of computer science and information science at the University of Pennsylvania. Aaron is first and foremost a theoretician, and our conversation starts with him helping us understand the context and theory behind differential privacy, a research area he was fortunate to begin pursuing at its inception. We explore the application of differential privacy to machine learning systems, including the costs and challenges of doing so. Aaron discusses as well quite a few examples of differential privacy in action, including work being done at Google, Apple and the US Census Bureau, along with some of the major research directions currently being explored in the field. The notes for this show can be found at twimlai.com/talk/132.
Apr 30, 2018
Optimal Transport and Machine Learning with Marco Cuturi - TWiML Talk #131
00:33:26
In this episode, i’m joined by Marco Cuturi, professor of statistics at Université Paris-Saclay. Marco and I spent some time discussing his work on Optimal Transport Theory at NIPS last year. In our discussion, Marco explains Optimal Transport, which provides a way for us to compare probability measures. We look at ways Optimal Transport can be used across machine learning applications, including graphical, NLP, and image examples. We also touch on GANs, or generative adversarial networks, and some of the challenges they present to the research community. The notes for this show can be found at twimlai.com/talk/131.
Apr 26, 2018
Collecting and Annotating Data for AI with Kiran Vajapey - TWiML Talk #130
00:41:21
In this episode, I’m joined by Kiran Vajapey, a human-computer interaction developer at Figure Eight. In this interview, Kiran shares some of what he’s has learned through his work developing applications for data collection and annotation at Figure Eight and earlier in his career. We explore techniques like data augmentation, domain adaptation, and active and transfer learning for enhancing and enriching training datasets. We also touch on the use of Imagenet and other public datasets for real-world AI applications. If you like what you hear in this interview, Kiran will be speaking at my AI Summit April 30th and May 1st in Las Vegas and I’ll be joining Kiran at the upcoming Figure Eight TrainAI conference, May 9th&10th in San Francisco. The notes for this show can be found at twimlai.com/talk/130
Apr 23, 2018
Autonomous Aerial Guidance, Navigation and Control Systems with Christopher Lum - TWiML Talk #129
00:54:06
Ok, In this episode, I'm joined by Christopher Lum, Research Assistant Professor in the University of Washington’s Department of Aeronautics and Astronautics. Chris also co-heads the University’s Autonomous Flight Systems Lab, where he and his students are working on the guidance, navigation, and control of unmanned systems. In our conversation, we discuss some of the technical and regulatory challenges of building and deploying Unmanned Autonomous Systems. We also talk about some interesting work he’s doing on evolutionary path planning systems as well as an Precision Agriculture use case. Finally, Chris shares some great starting places for those looking to begin a journey into autonomous systems research. The notes for this show can be found at twimlai.com/talk/129.
Apr 19, 2018
Infrastructure for Autonomous Vehicles with Missy Cummings - TWiML Talk #128
00:43:20
In this episode, I’m joined by Missy Cummings, head of Duke University’s Humans and Autonomy Lab and professor in the department of mechanical engineering. In addition to being an accomplished researcher, Missy also became one of the first female fighter pilots in the US Navy following the repeal of the Combat Exclusion Policy in 1993. We discuss Missy’s research into the infrastructural and operational challenges presented by autonomous vehicles, including cars, drones and unmanned aircraft. We also cover trust, explainability, and interactions between humans and AV systems. This was an awesome interview and i'm glad we’re able to bring it to you! The notes for this show can be found at twimlai.com/talk/128.
Apr 16, 2018
Hyper-Personalizing the Customer Experience w/ AI with Rob Walker - TWiML Talk #127
00:42:42
In this episode, we're joined by Rob Walker, Vice President of decision management and analytics at Pegasystems, a leading provider of software for customer engagement and operational excellence. Rob and I discuss what’s required for enterprises to fully realize the vision of providing a hyper-personalized customer experience, and how machine learning and AI can be used to determine the next best action an organization should take to optimize sales, service, retention, and risk at every step in the customer relationship. Along the way we dig into a couple of key areas, specifically some of the techniques his organization uses to allow customers to manage the tradeoff between model performance and transparency, particularly in light of new laws like GDPR, and how all this ties to an enterprise’s ability to manage bias and ethical issues when deploying ML. We cover a lot of ground in this one and I think you’ll find Rob’s perspective really interesting. The notes for this show can be found at twimlai.com/talk/127.
Apr 12, 2018
Information Extraction from Natural Document Formats with David Rosenberg - TWiML Talk #126
00:46:50
In this episode, I’m joined by David Rosenberg, data scientist in the office of the CTO at financial publisher Bloomberg, to discuss his work on “Extracting Data from Tables and Charts in Natural Document Formats.” Bloomberg is dealing with tons of financial and company data in pdfs and other unstructured document formats on a daily basis. To make meaning from this information more efficiently, David and his team have implemented a deep learning pipeline for extracting data from the documents. In our conversation, we dig into the information extraction process, including how it was built, how they sourced their training data, why they used LaTeX as an intermediate representation and how and why they optimize on pixel-perfect accuracy. There’s a lot of interesting info in this show and I think you’re going to enjoy it. The notes for this show can be found at twimlai.com/talk/126.
Apr 09, 2018
Human-in-the-Loop AI for Emergency Response & More w/ Robert Munro - TWiML Talk #125
00:49:52
In this episode, I chat with Rob Munro, CTO of the newly branded Figure Eight, formerly known as CrowdFlower. Figure Eight’s Human-in-the-Loop AI platform supports data science & machine learning teams working on autonomous vehicles, consumer product identification, natural language processing, search relevance, intelligent chatbots, and more. Rob and I had a really interesting discussion covering some of the work he’s previously done applying machine learning to disaster response and epidemiology, including a use case involving text translation in the wake of the catastrophic 2010 Haiti earthquake. We also dig into some of the technical challenges that he’s encountered in trying to scale the human-in-the-loop side of machine learning since joining Figure Eight, including identifying more efficient approaches to image annotation as well as the use of zero shot machine learning to minimize training data requirements. Finally, we briefly discuss Figure Eight’s upcoming TrainAI conference, which takes place on May 9th & 10th in San Francisco. Train AI you can join me and Rob, along with a host of amazing speakers like Garry Kasparov, Andrej Karpathy, Marti Hearst and many more and receive hands-on AI, machine learning and deep learning training through real-world case studies on practical machine learning applications. For more information on TrainAI, head over to figure-eight.com/train-ai, and be sure to use code TWIMLAI for 30% off your registration! For those of you listening to this on or before April 6th, Figure Eight is offering an even better deal on event registration. Use the code figure-eight to register for only 88 dollars. The notes for this show can be found at twimlai.com/talk/125.
Apr 05, 2018
Systems and Software for Machine Learning at Scale with Jeff Dean - TWiML Talk #124
00:56:41
In this episode I’m joined by Jeff Dean, Google Senior Fellow and head of the company’s deep learning research team Google Brain, who I had a chance to sit down with last week at the Googleplex in Mountain View. As you’ll hear, I was very excited for this interview, because so many of Jeff’s contributions since he started at Google in ‘99 have touched my life and work. In our conversation, Jeff and I dig into a bunch of the core machine learning innovations we’ve seen from Google. Of course we discuss TensorFlow, and its origins and evolution at Google. We also explore AI acceleration hardware, including TPU v1, v2 and future directions from Google and the broader market in this area. We talk through the machine learning toolchain, including some things that Googlers might take for granted, and where the recently announced Cloud AutoML fits in. We also discuss Google’s process for mapping problems across a variety of domains to deep learning, and much, much more. This was definitely one of my favorite conversations, and I'm pumped to be able to share it with you. The notes for this show can be found at twimlai.com/talk/124.
Apr 02, 2018
Semantic Segmentation of 3D Point Clouds with Lyne Tchapmi - TWiML Talk #123
00:38:07
In this episode I’m joined by Lyne Tchapmi, PhD student in the Stanford Computational Vision and Geometry Lab, to discuss her paper, “SEGCloud: Semantic Segmentation of 3D Point Clouds.” SEGCloud is an end-to-end framework that performs 3D point-level segmentation combining the advantages of neural networks, trilinear interpolation and fully connected conditional random fields. In our conversation, Lyne and I cover the ins and outs of semantic segmentation, starting from the sensor data that we’re trying to segment, 2d vs 3d representations of that data, and how we go about automatically identifying classes. Along the way we dig into some of the details, including how she obtained a more fine grain labeling of points from sensor data and the transition from point clouds to voxels. The notes for this show can be found at twimlai.com/talk/123.
Mar 29, 2018
Predicting Cardiovascular Risk Factors from Eye Images with Ryan Poplin - TWiML Talk #122
00:43:23
In this episode, I'm joined by Google Research Scientist Ryan Poplin, who recently co-authored the paper “Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning.” In our conversation, Ryan details his work training a deep learning model to predict various patient risk factors for heart disease, including some surprising ones like age and gender. We also dive into some interesting findings he discovered with regards to multi-task learning, as well as his use of an attention mechanisms to provide explainability. This was a really interesting discussion that I think you’ll really enjoy! The notes for this show can be found at twimlai.com/talk/122.
Mar 26, 2018
Reproducibility and the Philosophy of Data with Clare Gollnick - TWiML Talk #121
00:39:27
In this episode, i'm joined by Clare Gollnick, CTO of Terbium Labs, to discuss her thoughts on the “reproducibility crisis” currently haunting the scientific landscape. For a little background, a “Nature” survey in 2016 showed that "more than 70% of researchers have tried and failed to reproduce another scientist's experiments, and more than half have failed to reproduce their own experiments." Clare gives us her take on the situation, and how it applies to data science, along with some great nuggets about the philosophy of data and a few interesting use cases as well. We also cover her thoughts on Bayesian vs Frequentist techniques and while we’re at it, the Vim vs Emacs debate. No, actually I’m just kidding on that last one. But this was indeed a very fun conversation that I think you’ll enjoy! For the complete show notes, visit twimlai.com/talk/121.
Mar 22, 2018
Surveying the Connected Car Landscape with GK Senthil - TWiML Talk #120
00:30:17
In this episode, I’m joined by GK Senthil, director & chief product owner for innovation at Toyota Connected. GK and I spoke about some of the potential opportunities and challenges for smart cars. We discussed Toyota’s recently announced partnership with Amazon to embed Alexa in vehicles, and more generally the approach they’re taking to get connected car technology up to par with smartphones and other intelligent devices we use on a daily basis. We cover in-car voice recognition and touch on the ways ML & AI need to be developed to be useful in vehicles, as well as the approaches to getting there. The notes for this show can be found at twimlai.com/talk/120
Mar 19, 2018
Adversarial Attacks Against Reinforcement Learning Agents with Ian Goodfellow & Sandy Huang
00:50:06
In this episode, I’m joined by Ian Goodfellow, Staff Research Scientist at Google Brain and Sandy Huang, Phd Student in the EECS department at UC Berkeley, to discuss their work on the paper Adversarial Attacks on Neural Network Policies. If you’re a regular listener here you’ve probably heard of adversarial attacks, and have seen examples of deep learning based object detectors that can be fooled into thinking that, for example, a giraffe is actually a school bus, by injecting some imperceptible noise into the image. Well, Sandy and Ian’s paper sits at the intersection of adversarial attacks and reinforcement learning, another area we’ve discussed quite a bit on the podcast. In their paper, they describe how adversarial attacks can also be effective at targeting neural network policies in reinforcement learning. Sandy gives us an overview of the paper, including how changing a single pixel value can throw off performance of a model trained to play Atari games. We also cover a lot of interesting topics relating to adversarial attacks and RL individually, and some related areas such as hierarchical reward functions and transfer learning. This was a great conversation that I’m really excited to bring to you! For complete show notes, head over to twimlai.com/talk/119
Mar 15, 2018
Towards Abstract Robotic Understanding with Raja Chatila - TWiML Talk #118
00:49:03
In this episode, we're joined by Raja Chatila, director of Intelligent Systems and Robotics at Pierre and Marie Curie University in Paris, and executive committee chair of the IEEE global initiative on ethics of intelligent and autonomous systems. Raja and I had a great chat about his research, which deals with robotic perception and discovery. We discuss the relationship between learning and discovery, particularly as it applies to robots and their environments, and the connection between robotic perception and action. We also dig into the concepts of affordances, abstract teachings, meta-reasoning and self-awareness as they apply to intelligent systems. Finally, we touch on the issue of values and ethics of these systems. The notes for this show can be found at twimlai.com/talk/118.
Mar 12, 2018
Discovering Exoplanets w/ Deep Learning with Chris Shallue - TWiML Talk #117
00:46:30
Earlier this week, I had a chance to speak with Chris Shallue, Senior Software Engineer on the Google Brain Team, about his project and paper on “Exploring Exoplanets with Deep Learning.” This is a great story. Chris, inspired by a book he was reading, reached out on a whim to a Harvard astrophysics researcher, kicking off a collaboration and side project eventually leading to the discovery of two new planets outside our solar system. In our conversation, we walk through the entire process Chris followed to find these two exoplanets, including how he researched the domain as an outsider, how he sourced and processed his dataset, and how he built and evolved his models. Finally, we discuss the results of his project and his plans for future work in this area. This podcast is being published in parallel with Google’s release of the source code and data that Chris developed and used, which we’ll link to below, so if what you hear inspires you to dig into this area, you’ve got a nice head start. This was a really interesting conversation, and I'm excited to share it with you! The notes for this show can be found at twimlai.com/talk/117 The corresponding blog post for this project can be found at https://research.googleblog.com/2018/03/open-sourcing-hunt-for-exoplanets.html
Mar 08, 2018
Learning Active Learning with Ksenia Konyushkova - TWiML Talk #116
00:33:02
In this episode, I speak with Ksenia Konyushkova, Ph.D. student in the CVLab at Ecole Polytechnique Federale de Lausanne in Switzerland. Ksenia and I connected at NIPS in December to discuss her interesting research into ways we might apply machine learning to ease the challenge of creating labeled datasets for machine learning. The first paper we discuss is “Learning Active Learning from Data,” which suggests a data-driven approach to active learning that trains a secondary model to identify the unlabeled data points which, when labeled, would likely have the greatest impact on our primary model’s performance. We also discuss her paper “Learning Intelligent Dialogs for Bounding Box Annotation,” in which she trains an agent to guide the actions of a human annotator to more quickly produce bounding boxes. TWiML Online Meetup Update Join us Tuesday, March 13th for the March edition of the Online Meetup! Sean Devlin will be doing an in-depth review of reinforcement learning and presenting the Google DeepMind paper, "Playing Atari with Deep Reinforcement Learning." Head over to twimlai.com/meetup to learn more or register. Conference Update Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML. Early price ends February 2! The notes for this show can be found at https://twimlai.com/talk/116.
Mar 05, 2018
Machine Learning Platforms at Uber with Mike Del Balso - TWiML Talk #115
00:50:10
In this episode, I speak with Mike Del Balso, Product Manager for Machine Learning Platforms at Uber. Mike and I sat down last fall at the Georgian Partners Portfolio conference to discuss his presentation “Finding success with machine learning in your company.” In our discussion, Mike shares some great advice for organizations looking to get value out of machine learning. He also details some of the pitfalls companies run into, such as not have proper infrastructure in place for maintenance and monitoring, not managing their expectations, and not putting the right tools in place for data science and development teams. On this last point, we touch on the Michelangelo platform, which Uber uses internally to build, deploy and maintain ML systems at scale, and the open source distributed TensorFlow system they’ve created, Horovod. This was a very insightful interview, so get your notepad ready! Vote on our #MyAI Contest! Over the past few weeks, you’ve heard us talk quite a bit about our #MyAI Contest, which explores the role we see for AI in our personal lives! We received some outstanding entries, and now it’s your turn to check them out and vote for a winner. Do this by visiting our contest page at https://twimlai.com/myai. Voting remains open until Sunday, March 4th at 11:59 PM Eastern time. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. The notes for this show can be found at twimlai.com/talk/115.
Mar 01, 2018
Inverse Programming for Deeper AI with Zenna Tavares - TWiML Talk #114
00:29:40
For today’s show, the final episode of our Black in AI Series, I’m joined by Zenna Tavares, a PhD student in the both the department of Brain and Cognitive Sciences and the Computer Science and Artificial Intelligence Lab at MIT. I spent some time with Zenna after his talk at the Strange Loop conference titled “Running Programs in Reverse for Deeper AI.” Zenna shares some great insight into his work on program inversion, an idea which lies at the intersection of Bayesian modeling, deep-learning, and computational logic. We set the stage with a discussion of inverse graphics and the similarities between graphic inversion and vision inversion. We then discuss the application of these techniques to intelligent systems, including the idea of parametric inversion. Last but not least, zenna details how these techniques might be implemented, and discusses his work on ReverseFlow, a library to execute tensorflow programs backwards, and Sigma.jl a probabilistic programming environment implemented in the dynamic programming language Julia. This talk packs a punch, and I’m glad to share it with you. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. The notes for this show can be found at twimlai.com/talk/114. For complete contest details, visit twimlai.com/myai. For complete series details, visit twimlai.com/blackinai2018
Feb 26, 2018
Statistical Relational Artificial Intelligence with Sriraam Natarajan - TWiML Talk #113
00:48:49
In this episode, I speak with Sriraam Natarajan, Associate Professor in the Department of Computer Science at UT Dallas. While at NIPS a few months back, Sriraam and I sat down to discuss his work on Statistical Relational Artificial Intelligence. StarAI is the combination of probabilistic & statistical machine learning techniques with relational databases. We cover systems learning on top of relational databases and making predictions with relational data, with quite a few examples from the healthcare field. Sriraam and his collaborators have also developed BoostSRL, a gradient-boosting based approach to learning different types of statistical relational models. We briefly touch on this, along with other implementation approaches. Join the #MyAI Discussion! As a TWiML listener, you probably have an opinion on the role AI will play in our lives, and we want to hear your take. Sharing your thoughts takes two minutes, can be done from anywhere, and qualifies you to win some great prizes. So hit pause, and jump on over twimlai.com/myai right now to share or learn more. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. The notes for this show can be found at twimlai.com/talk/113. For complete contest details, visit twimlai.com/myai.
Feb 23, 2018
Classical Machine Learning for Infant Medical Diagnosis with Charles Onu - TWiML Talk #112
00:48:47
In this episode, part 4 in our Black in AI series, i'm joined by Charles Onu, Phd Student at McGill University in Montreal & Founder of Ubenwa, a startup tackling the problem of infant mortality due to asphyxia. Using SVMs and other techniques from the field of automatic speech recognition, Charles and his team have built a model that detects asphyxia based on the audible noises the child makes upon birth. We go into the process he used to collect his training data, including the specific methods they used to record samples, and how their samples will be used to maximize accuracy in the field. We also take a deep dive into some of the challenges of building and deploying the platform and mobile application. This is a really interesting use case, which I think you’ll enjoy. Join the #MyAI Discussion! As a TWiML listener, you probably have an opinion on the role AI will play in our lives, and we want to hear your take. Sharing your thoughts takes two minutes, can be done from anywhere, and qualifies you to win some great prizes. So hit pause, and jump on over twimlai.com/myai right now to share or learn more. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. The notes for this show can be found at twimlai.com/talk/112. For complete contest details, visit twimlai.com/myai. For complete series details, visit twimlai.com/blackinai2018.
Feb 20, 2018
Learning "Common Sense" and Physical Concepts with Roland Memisevic - TWiML Talk #111
00:33:44
In today’s episode, I’m joined by Roland Memisevic, co-founder, CEO, and chief scientist at Twenty Billion Neurons. Roland joined me at the RE•WORK Deep Learning Summit in Montreal to discuss the work his company is doing to train deep neural networks to understand physical actions. In our conversation, we dig into video analysis and understanding, including how data-rich video can help us develop what Roland calls comparative understanding, or AI “common sense”. We briefly touch on the implications of AI/ML systems having comparative understanding, and how Roland and his team are addressing problems like getting properly labeled training data. Enter Our #MyAI Contest! Are you looking forward to the role AI will play in your life, or in your children’s lives? Or, are you afraid of what’s to come, and the changes AI will bring? Or, maybe you’re skeptical, and don’t think we’ll ever really achieve enough with AI to make a difference? In any case, if you’re a TWiML listener, you probably have an opinion on the role AI will play in our lives, and we want to hear your take. Sharing your thoughts takes two minutes, can be done from anywhere, and qualifies you to win some great prizes. So hit pause, and jump on over twimlai.com/myai right now to share or learn more. The notes for this show can be found at twimlai.com/talk/111.
Feb 15, 2018
Trust in Human-Robot/AI Interactions with Ayanna Howard - TWiML Talk #110
00:47:33
In this episode, the third in our Black in AI series, I speak with Ayanna Howard, Chair of the Interactive School of Computing at Georgia Tech. Ayanna joined me for a lively discussion about her work in the field of human-robot interaction. We dig deep into a couple of major areas she’s active in that have significant implications for the way we design and use artificial intelligence, namly pediatric robotics and human-robot trust. That latter bit is particularly interesting, and Ayanna provides a really interesting overview of a few of her experiments, including a simulation of an emergency situation, where, well, I don’t want to spoil it, but let’s just say as the actual intelligent beings, we need to make some better decisions. Enjoy! Are you looking forward to the role AI will play in your life, or in your children’s lives? Or, are you afraid of what’s to come, and the changes AI will bring? Or, maybe you’re skeptical, and don’t think we’ll ever really achieve enough with AI to make a difference? As a TWiML listener, you probably have an opinion on the role AI will play in our lives, and we want to hear your take. Sharing your thoughts takes two minutes, can be done from anywhere, and qualifies you to win some great prizes. So hit pause, and jump on over twimlai.com/myai right now to share or learn more. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. The notes for this show can be found at twimlai.com/talk/110. For complete contest details, visit twimlai.com/myai. For complete series details, visit twimlai.com/blackinai2018.
Feb 13, 2018
Data Science for Poaching Prevention and Disease Treatment with Nyalleng Moorosi - TWiML Talk #109
00:54:20
For today’s show, I'm joined by Nyalleng Moorosi, Senior Data Science Researcher at The Council for Scientific & Industrial Research or CSIR, in Pretoria, South Africa. In our discussion, we discuss two major projects that Nyalleng is apart of at the CSIR, one, a predictive policing use case, which focused on understanding and preventing rhino poaching in Kruger National Park, and the other, a healthcare use case which focuses on understanding the effects of a drug treatment that was causing pancreatic cancer in South Africans. Along the way we talk about the challenges of data collection, data pipelines and overcoming sparsity. This was a really interesting conversation that I’m sure you’ll enjoy. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. The notes for this show can be found at twimlai.com/talk/109. For complete contest details, visit twimlai.com/myaicontest. For complete series details, visit twimlai.com/blackinai2018.
Feb 08, 2018
Security and Safety in AI: Adversarial Examples, Bias and Trust w/ Moustapha Cissé - TWiML Talk #108
00:50:59
In this episode I’m joined by Moustapha Cissé, Research Scientist at Facebook AI Research Lab (or FAIR) Paris. Moustapha’s broad research interests include the security and safety of AI systems, and we spend some time discussing his work on adversarial examples and systems that are robust to adversarial attacks. More broadly, we discuss the role of bias in datasets, and explore his vision for models that can identify these biases and adjust the way they train themselves in order to avoid taking on those biases. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. Early price ends February 2! The notes for this show can be found at twimlai.com/talk/108. For complete contest details, visit twimlai.com/myaicontest. For complete series details, visit twimlai.com/blackinai2018.
Feb 06, 2018
Peering into the Home w/ Aerial.ai's Wifi Motion Analytics - TWiML Talk #107
00:44:15
In this episode I’m joined by Michel Allegue and Negar Ghourchian of Aerial.ai. Aerial is doing some really interesting things in the home automation space, by using wifi signal statistics to identify and understand what’s happening in our homes and office environments. Michel, the CTO, describes some of the capabilities of their platform, including its ability to detect not only people and pets within the home, but surprising characteristics like breathing rates and patterns. He also gives us a look into the data collection process, including the types of data needed, how they obtain it, and how it is parsed. Negar, a senior data scientist with Aerial, describes the types of models used, including semi-supervised, unsupervised and signal processing based models, and how they’ve scaled their platform, and provides us with some real-world use cases. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. Early price ends February 2! The notes for this show can be found at twimlai.com/talk/107. For complete contest details, visit twimlai.com/myaicontest. For complete series details, visit twimlai.com/aiathome.
Feb 02, 2018
Physiology-Based Models for Fitness and Training w/ Firstbeat with Ilkka Korhonen - TWiML Talk #106
00:38:54
In this episode i'm joined by Ilkka Korhonen, Vice President of Technology at Firstbeat, a company whose algorithms are embedded in fitness watches from companies like Garmin and Suunto and which use your heartbeat data to offer personalized insights into stress, fitness, recovery and sleep patterns. We cover a ton about Firstbeat in the conversation, including how they transform the sensor readings into more actionable data, their use of a digital physiological model of the human body, how they use sensor data to identify and predict physiological changes within the body, and some of the opportunities that Firstbeat has to further apply ML in the future. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. Early price ends February 2! The notes for this show can be found at twimlai.com/talk/106. For complete contest details, visit twimlai.com/myaicontest. For complete series details, visit twimlai.com/aiathome.
Feb 02, 2018
Machine Learning for Signal Processing Applications w/ Stuart Feffer & Brady Tsai - TWiML Talk #105
00:39:47
In this episode, I'm joined by Stuart Feffer, co-founder and CEO of Reality AI, which provides tools and services for engineers working with sensors and signals, and Brady Tsai, Business Development Manager at Koito, which develops automotive lighting solutions for car manufacturers. Stuart and Brady joined me at CES a few weeks ago after they announced a partnership to bring Adaptive Driving Beam, or ADB, headlights to North America. Brady explains what exactly ADB technology is and how it works, while Stuart walks me through the technical aspects of not only this partnership, but of the reality AI platform as a whole. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. Early price ends February 2! The notes for this show can be found at twimlai.com/talk/105. For complete contest details, visit twimlai.com/myaicontest. For complete series details, visit twimlai.com/aiathome.
Feb 01, 2018
Personalizing the Ferrari Challenge Experience w/ Intel AI - TWiML Talk #104
00:40:39
In this episode, I'm joined by Andy Keller and Emile Chin-Dickey to discuss Intel's partnership with the Ferrari Challenge North American Series. Andy is a Deep Learning Data Scientist at Intel and Emile is Senior Manager of Marketing Partnerships at the company. In this show, Emile gives us a high-level overview of the Ferrari Challenge partnership and the goals of the collaboration. Andy & I then dive into the AI aspects of the project, including how the training data was collected, the techniques they used to perform fine-grained object detection in the video streams, how they built the analytics platform, some of the remaining challenges with this project, and more! Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. Early price ends February 2! The notes for this show can be found at twimlai.com/talk/104. For complete contest details, visit twimlai.com/myaicontest. For complete series details, visit twimlai.com/aiathome.
Jan 31, 2018
Deep Learning for 3D Sensors and Cameras in Lighthouse with Alex Teichman - TWiML Talk #103
00:45:19
In this episode, I sit down with Alex Teichman, CEO and Co-Founder of Lighthouse, a company taking a new approach to the in-home smart camera. Alex and I dig into what exactly the Lighthouse product is, and all the interesting stuff inside, including its combination of 3D sensing, computer vision, and NLP. We also talk about Alex’s process for building the Lighthouse network architecture, they tech stack the product is based on, and some things that surprised him in their efforts to get AI into a consumer product. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. Early price ends February 2! The notes for this show can be found at twimlai.com/talk/103. For complete contest details, visit twimlai.com/myaicontest. For complete series details, visit twimlai.com/aiathome.
Jan 30, 2018
Computer Vision for Cozmo, the Cutest Toy Robot Everrrrr! with Andrew Stein - TWiML Talk #102
00:47:13
In this episode, I'm joined by Andrew Stein, computer vision engineer at consumer robotics company Anki, and his partner in crime Cozmo, a toy robot with tons of personality. Andrew joined me during the hustle and bustle of CES a few weeks ago to give me some insight into how Cozmo works, plays, and learns, and how he’s different from other consumer robots you may know, such as the Roomba. We discuss the types of algorithms that help power Cozmo, such as facial detection and recognition, 3D pose recognition, reasoning, and even some simple emotional AI. We also cover Cozmo’s functionality and programmability, including a cool feature called Code Lab. This was a really fun interview, and you’ll be happy to know there’s a companion video starring Cozmo himself right here: https://youtu.be/jUkacU1I0QI. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. Early price ends February 2! The notes for this show can be found at twimlai.com/talk/102. For complete contest details, visit twimlai.com/myaicontest. For complete series details, visit twimlai.com/aiathome.
Jan 30, 2018
Expectation Maximization, Gaussian Mixtures & Belief Propagation, OH MY! w/ Inmar Givoni - Talk #101
00:48:57
In this episode i'm joined by Inmar Givoni, Autonomy Engineering Manager at Uber ATG, to discuss her work on the paper Min-Max Propagation, which was presented at NIPS last month in Long Beach. Inmar and I get into a meaty discussion about graphical models, including what they are and how they’re used, some of the challenges they present for both training and inference, and how and where they can be best applied. Then we jump into an in-depth look at the key ideas behind the Min-Max Propagation paper itself, including the relationship to the broader domain of belief propagation and ideas like affinity propagation, and how all these can be applied to a use case example like the makespan problem. This was a really fun conversation! Enjoy! Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML. Visit twimlai.com/ainy2018 for registration details. Early price ends February 2!
Jan 26, 2018
A Linear-Time Kernel Goodness-of-Fit Test - NIPS Best Paper '17 - TWiML Talk #100
00:23:40
In this episode, I speak with Arthur Gretton, Wittawat Jitkrittum, Zoltan Szabo and Kenji Fukumizu, who, alongside Wenkai Xu authored the 2017 NIPS Best Paper Award winner “A Linear-Time Kernel Goodness-of-Fit Test.” In our discussion, we cover what exactly a “goodness of fit” test is, and how it can be used to determine how well a statistical model applies to a given real-world scenario. The group and I the discuss this particular test, the applications of this work, as well as how this work fits in with other research the group has recently published. Enjoy! In our discussion, we cover what exactly a “goodness of fit” test is, and how it can be used to determine how well a statistical model applies to a given real-world scenario. The group and I the discuss this particular test, the applications of this work, as well as how this work fits in with other research the group has recently published. Enjoy! This is your last chance to register for the RE•WORK Deep Learning and AI Assistant Summits in San Francisco, which are this Thursday and Friday, January 25th and 26th. These events feature leading researchers and technologists like the ones you heard in our Deep Learning Summit series last week. The San Francisco will event is headlined by Ian Goodfellow of Google Brain, Daphne Koller of Calico Labs, and more! Definitely check it out and use the code TWIMLAI for 20% off of registration. The notes for this show can be found at twimlai.com/talk/100.
Jan 24, 2018
Solving Imperfect-Information Games with Tuomas Sandholm - NIPS ’17 Best Paper - TWiML Talk #99
00:29:19
In this episode I speak with Tuomas Sandholm, Carnegie Mellon University Professor and Founder and CEO of startups Optimized Markets and Strategic Machine. Tuomas, along with his PhD student Noam Brown, won a 2017 NIPS Best Paper award for their paper “Safe and Nested Subgame Solving for Imperfect-Information Games.” Tuomas and I dig into the significance of the paper, including a breakdown of perfect vs imperfect information games, the role of abstractions in game solving, and how the concept of safety applies to gameplay. We discuss how all these elements and techniques are applied to poker, and how the algorithm described in this paper was used by Noam and Tuomas to create Libratus, the first AI to beat top human pros in No Limit Texas Hold’em, a particularly difficult game to beat due to its large state space. This was a fascinating interview that I'm really excited to share with you all. Enjoy! This is your last chance to register for the RE•WORK Deep Learning and AI Assistant Summits in San Francisco, which are this Thursday and Friday, January 25th and 26th. These events feature leading researchers and technologists like the ones you heard in our Deep Learning Summit series last week. The San Francisco will event is headlined by Ian Goodfellow of Google Brain, Daphne Koller of Calico Labs, and more! Definitely check it out and use the code TWIMLAI for 20% off of registration. The notes for this show can be found at twimlai.com/talk/99
Jan 22, 2018
Separating Vocals in Recorded Music at Spotify with Eric Humphrey - TWiML Talk #98
00:28:24
In today’s show, I sit down with Eric Humphrey, Research Scientist in the music understanding group at Spotify. Eric was at the Deep Learning Summit to give a talk on Advances in Deep Architectures and Methods for Separating Vocals in Recorded Music. We discuss his talk, including how Spotify's large music catalog enables such an experiment to even take place, the methods they use to train algorithms to isolate and remove vocals from music, and how architectures like U-Net and Pix2Pix come into play when building his algorithms. We also hit on the idea of “creative AI,” Spotify’s attempt at understanding music content at scale, optical music recognition, and more. This show is part of a series of shows recorded at the RE•WORK Deep Learning Summit in Montreal back in October. This was a great event and, in fact, their next event, the Deep Learning Summit San Francisco is right around the corner on January 25th and 26th, and will feature more leading researchers and technologists like the ones you’ll hear here on the show this week, including Ian Goodfellow of Google Brain, Daphne Koller of Calico Labs, and more! Definitely check it out and use the code TWIMLAI for 20% off of registration. The notes for this show can be found at twimlai.com/talk/98
Jan 19, 2018
Accelerating Deep Learning with Mixed Precision Arithmetic with Greg Diamos - TWiML Talk #97
00:40:37
In this show I speak with Greg Diamos, senior computer systems researcher at Baidu. Greg joined me before his talk at the Deep Learning Summit, where he spoke on “The Next Generation of AI Chips.” Greg’s talk focused on some work his team was involved in that accelerates deep learning training by using mixed 16-bit and 32-bit floating point arithmetic. We cover a ton of interesting ground in this conversation, and if you’re interested in systems level thinking around scaling and accelerating deep learning, you’re really going to like this one. And of course, if you like this one, you’re also going to like TWiML Talk #14 with Greg’s former colleague, Shubho Sengupta, which covers a bunch of related topics. This show is part of a series of shows recorded at the RE•WORK Deep Learning Summit in Montreal back in October. This was a great event and, in fact, their next event, the Deep Learning Summit San Francisco is right around the corner on January 25th and 26th, and will feature more leading researchers and technologists like the ones you’ll hear here on the show this week, including Ian Goodfellow of Google Brain, Daphne Koller of Calico Labs, and more! Definitely check it out and use the code TWIMLAI for 20% off of registration.
Jan 17, 2018
Composing Graphical Models With Neural Networks with David Duvenaud - TWiML Talk #96
00:37:31
In this episode, we hear from David Duvenaud, assistant professor in the Computer Science and Statistics departments at the University of Toronto. David joined me after his talk at the Deep Learning Summit on “Composing Graphical Models With Neural Networks for Structured Representations and Fast Inference.” In our conversation, we discuss the generalized modeling and inference framework that David and his team have created, which combines the strengths of both probabilistic graphical models and deep learning methods. He gives us a walkthrough of his use case which is to automatically segment and categorize mouse behavior from raw video, and we discuss how the framework is applied here and for other use cases. We also discuss some of the differences between the frequentist and bayesian statistical approaches. The notes for this show can be found at twimlai.com/talk/96
Jan 15, 2018
Embedded Deep Learning at Deep Vision with Siddha Ganju - TWiML Talk #95
00:35:45
In this episode we hear from Siddha Ganju, data scientist at computer vision startup Deep Vision. Siddha joined me at the AI Conference a while back to chat about the challenges of developing deep learning applications “at the edge,” i.e. those targeting compute- and power-constrained environments.In our conversation, Siddha provides an overview of Deep Vision’s embedded processor, which is optimized for ultra-low power requirements, and we dig into the data processing pipeline and network architecture process she uses to support sophisticated models in embedded devices. We dig into the specific the hardware and software capabilities and restrictions typical of edge devices and how she utilizes techniques like model pruning and compression to create embedded models that deliver needed performance levels in resource constrained environments, and discuss use cases such as facial recognition, scene description and activity recognition. Siddha's research interests also include natural language processing and visual question answering, and we spend some time discussing the latter as well.
Jan 12, 2018
Neuroevolution: Evolving Novel Neural Network Architectures - TWiML Talk #94
00:47:37
Today, I'm joined by Kenneth Stanley, Professor in the Department of Computer Science at the University of Central Florida and senior research scientist at Uber AI Labs. Kenneth studied under TWiML Talk #47 guest Risto Miikkulainen at UT Austin, and joined Uber AI Labs after Geometric Intelligence, the company he co-founded with Gary Marcus and others, was acquired in late 2016. Kenneth’s research focus is what he calls Neuroevolution, applies the idea of genetic algorithms to the challenge of evolving neural network architectures. In this conversation, we discuss the Neuroevolution of Augmenting Topologies (or NEAT) paper that Kenneth authored along with Risto, which won the 2017 International Society for Artificial Life’s Award for Outstanding Paper of the Decade 2002 - 2012. We also cover some of the extensions to that approach he’s created since, including, HyperNEAT, which can efficiently evolve very large networks with connectivity patterns that look more like those of the human and that are generally much larger than what prior approaches to neural learning could produce, and novelty search, an approach which unlike most evolutionary algorithms has no defined objective, but rather simply searches for novel behaviors. We also cover concepts like “Complexification” and “Deception”, biology vs computation including differences and similarities, and some of his other work including his book, and NERO, a video game complete with Real-time Neuroevolution. This is a meaty “Nerd Alert” interview that I think you’ll really enjoy.
Jan 11, 2018
A Quantum Computing Primer and Implications for AI with Davide Venturelli - TWiML Talk #93
00:35:52
Today, I'm joined by Davide Venturelli, science operations manager and quantum computing team lead for the Universities Space Research Association’s Institute for Advanced Computer Science at NASA Ames. Davide joined me backstage at the NYU Future Labs AI Summit a while back to give me some insight into a topic that I’ve been curious about for some time now, quantum computing. We kick off our discussion about the core ideas behind quantum computing, including what it is, how it’s applied and the ways it relates to computing as we know it today. We discuss the practical state of quantum computers and what their capabilities are, and the kinds of things you can do with them. And of course, we explore the intersection between AI and quantum computing, how quantum computing may one day accelerate machine learning, and how interested listeners can get started down the quantum rabbit hole. The notes for this show can be found at twimlai.com/talk/93
Jan 08, 2018
Learning State Representations with Yael Niv - TWiML Talk #92
00:49:02
This week on the podcast we’re featuring a series of conversations from the NIPs conference in Long Beach, California. I attended a bunch of talks and learned a ton, organized an impromptu roundtable on Building AI Products, and met a bunch of great people, including some former TWiML Talk guests. In this episode I speak with Yael Niv, professor of neuroscience and psychology at Princeton University. Yael joined me after her invited talk on “Learning State Representations.” In this interview Yael and I explore the relationship between neuroscience and machine learning. In particular, we discusses the importance of state representations in human learning, some of her experimental results in this area, and how a better understanding of representation learning can lead to insights into machine learning problems such as reinforcement and transfer learning. Did I mention this was a nerd alert show? I really enjoyed this interview and I know you will too. Be sure to send over any thoughts or feedback via the show notes page at twimlai.com/talk/92.
Dec 22, 2017
Philosophy of Intelligence with Matthew Crosby - TWiML Talk #91
00:31:43
This week on the podcast we’re featuring a series of conversations from the NIPs conference in Long Beach, California. I attended a bunch of talks and learned a ton, organized an impromptu roundtable on Building AI Products, and met a bunch of great people, including some former TWiML Talk guests.This time around i'm joined by Matthew Crosby, a researcher at Imperial College London, working on the Kinds of Intelligence Project. Matthew joined me after the NIPS Symposium of the same name, an event that brought researchers from a variety of disciplines together towards three aims: a broader perspective of the possible types of intelligence beyond human intelligence, better measurements of intelligence, and a more purposeful analysis of where progress should be made in AI to best benefit society. Matthew’s research explores intelligence from a philosophical perspective, exploring ideas like predictive processing and controlled hallucination, and how these theories of intelligence impact the way we approach creating artificial intelligence. This was a very interesting conversation, i'm sure you’ll enjoy.
Dec 21, 2017
Geometric Deep Learning with Joan Bruna & Michael Bronstein - TWiML Talk #90
00:41:28
This week on the podcast we’re featuring a series of conversations from the NIPs conference in Long Beach, California. I attended a bunch of talks and learned a ton, organized an impromptu roundtable on Building AI Products, and met a bunch of great people, including some former TWiML Talk guests. This time around I'm joined by Joan Bruna, Assistant Professor at the Courant Institute of Mathematical Sciences and the Center for Data Science at NYU, and Michael Bronstein, associate professor at Università della Svizzera italiana (Switzerland) and Tel Aviv University. Joan and Michael join me after their tutorial on Geometric Deep Learning on Graphs and Manifolds. In our conversation we dig pretty deeply into the ideas behind geometric deep learning and how we can use it in applications like 3D vision, sensor networks, drug design, biomedicine, and recommendation systems. This is definitely a Nerd Alert show, and one that will get your multi-dimensional neurons firing. Enjoy!
Dec 20, 2017
AI at the NASA Frontier Development Lab with Sara Jennings, Timothy Seabrook and Andres Rodriguez
00:38:57
This week on the podcast we’re featuring a series of conversations from the NIPs conference in Long Beach, California. I attended a bunch of talks and learned a ton, organized an impromptu roundtable on Building AI Products, and met a bunch of great people, including some former TWiML Talk guests. In this episode i'm joined by Sara Jennings, Timothy Seabrook and Andres Rodriguez to discuss NASA’s Frontier Development Lab or FDL. The FDL is an intense 8-week applied AI research accelerator, focused on tackling knowledge gaps useful to the space program. In our discussion, Sara, producer at the FDL, provides some insight into its goals and structure. Timothy, a researcher at FDL, describes his involvement with the program, including some of the projects he worked on while on-site. He also provides a look into some of this year’s FDL projects, including Planetary Defense, Solar Storm Prediction, and Lunar Water Location. Last but not least, Andres, Sr. Principal Engineer at Intel's AIPG, joins us to detail Intel’s support of the FDL, and how the various elements of the Intel AI stack supported the FDL research. This is a jam packed conversation, so be sure to check the show notes page at twimlai.com/talk/89 for all the links and tidbits from this episode.
Dec 19, 2017
Using Deep Learning and Google Street View to Estimate Demographics with Timnit Gebru
00:32:13
This week on the podcast we’re featuring a series of conversations from the NIPs conference in Long Beach, California. I attended a bunch of talks and learned a ton, organized an impromptu roundtable on Building AI Products, and met a bunch of great people, including some former TWiML Talk guests. In this episode I sit down with Timnit Gebru, postdoctoral researcher at Microsoft Research in the Fairness, Accountability, Transparency and Ethics in AI, or FATE, group. Timnit is also one of the organizers behind the Black in AI group, which held a very interesting symposium and poster session at NIPS. I’ll link to the group’s page in the show notes. I’ve been following Timnit’s work for a while now and was really excited to get a chance to sit down with her and pick her brain. We packed a ton into this conversation, especially keying in on her recently released paper “Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US”. Timnit describes the pipeline she developed for this research, and some of the challenges she faced building and end-to-end model based on google street view images, census data and commercial car vendor data. We also discuss the role of social awareness in her work, including an explanation of how domain adaptation and fairness are related and her view of the major research directions in the domain of fairness. The notes for this show can be found at twimlai.com/talk/88 For series information, visit twimlai.com/nips2017
Dec 19, 2017
Integrative Learning for Robotic Systems with Aaron Ames - TWiML Talk #87
00:49:24
This week on the podcast we’re featuring a series of conversations from the AWS re:Invent conference in Las Vegas. I had a great time at this event getting caught up on the latest and greatest machine learning and AI products and services announced by AWS and its partners. Today we’re joined by Aaron Ames, Professor of Mechanical & Civil Engineering at Caltech. Aaron joined me before his talk at the Deep Learning Summit “Eye, Robot: Computer Vision and Autonomous Robotics” and I had a ton of questions for him. While he considers himself a “hardware guy”, we got into a great discussion centered around the intersection of Robotics and ML Inference. We cover a range of topics, including Boston Dynamics backflipping robot (If you haven't seen it, check out the show notes), Humanoid Robotics, His work on motion primitives and transitions and he even gives us a few predictions on the future of robotics.
Dec 15, 2017
Visual Recognition in the Cloud for Law Enforcement with Chris Adzima - TWiML Talk #86
00:37:40
This week on the podcast we’re featuring a series of conversations from the AWS re:Invent conference in Las Vegas. I had a great time at this event getting caught up on the latest and greatest machine learning and AI products and services announced by AWS and its partners. In this episode we’re joined by Chris Adzima, Senior Information Analyst for the Washington County Sheriff’s Department. While Chris is not a traditional data scientist, he comes to us with a very interesting use case using AWS’s Rekognition. Chris is using Rekognition to identify suspects in the Portland area by running their mugshots through the software. In our conversation, he details how he is using Rekognition, while giving us few use cases along the way. We discuss how bias affects the work he is doing, and how they try to remove it from their process, not only from a software developer standpoint, but from a law enforcement standpoint and what his next steps are with the Rekognition software. This was a pretty interesting discussion, i’m sure you’ll enjoy it!
Dec 14, 2017
Embodied Visual Learning with Kristen Grauman - TWiML Talk #85
00:41:33
This week on the podcast we’re featuring a series of conversations from the AWS re:Invent conference in Las Vegas. I had a great time at this event getting caught up on the latest and greatest machine learning and AI products and services announced by AWS and its partners. This time around we’re joined by Kristen Grauman, a professor in the department of computer science at UT Austin. Kristen specializes in Computer Vision and joined me leading up to her talk at the Deep Learning Summit “Learning where to look in video”. Kristen & I cover the details from her talk, like exploring how a vision system can learn how to move and where to look. Kristen considers how an embodied vision system can internalize the link between “how I move” and “what I see”, explore policies for learning to look around actively, and learn to mimic human videographer tendencies, automatically deciding where to look in unedited 360 degree video. The notes for this show can be found at twimlai.com/talk/85. For series details, visit twimlai.com/reinvent.
Dec 13, 2017
Real-Time Machine Learning in the Database with Nikita Shamgunov - TWiML Talk #84
00:42:51
This week on the podcast we’re featuring a series of conversations from the AWS re:Invent conference in Las Vegas. I had a great time at this event getting caught up on the latest and greatest machine learning and AI products and services announced by AWS and its partners. In this episode, I’ll be speaking with Nikita Shamgunov, co-founder and CEO of MemSQL, a company offering a distributed, memory-optimized data warehouse of the same name. Nikita and I take a deep dive into some of the features of their recently released 6.0 version, which supports built-in vector operations like dot product and euclidean distance to enable machine learning use cases like real-time image recognition, visual search and predictive analytics for IoT. We also discuss how to architect enterprise machine learning solutions around the data warehouse by including components like data lakes and Spark. Finally, we touch on some of the performance advantages MemSQL has seen by implementing vector operations using Intel’s latest AVX2 and AVX512 instruction sets. Make sure you check out the show notes at twimlai.com/talk/84
Dec 12, 2017
re:Invent Roundup Roundtable - TWiML Talk # 83
01:08:26
This week on the podcast we’re featuring a series of conversations from the AWS re:Invent conference in Las Vegas. I had a great time at this event getting caught up on the latest and greatest machine learning and AI products and services announced by AWS and its partners. If you missed the news coming out of re:Invent and want to know more about what one of the biggest AI platform providers is up to, you’ll want to say tuned, because we’ll discuss many of their new offerings in this episode, a Roundtable discussion I held with Dave McCrory VP of Software Engineering at Wise.io at GE Digital and Lawrence Chung, engagement lead at ThingLogix. We cover all of AWS’ most important news, including the new SageMaker and DeepLens, their Rekognition and Transcription services, Alexa for Business, GreenGrass ML and more. This kind of discussion is something a little new for the show, and is a bit reminiscent of my days covering news here on the podcast, so I hope you enjoy it!
Dec 11, 2017
Driving Customer Loyalty with Predictive and Conversational AI with Sherif Mityas - TWiML Talk #82
00:37:32
This week on the podcast we’re running a series of shows consisting of conversations with some of the impressive speakers from an event called the AI Summit in New York City. The theme of the conference, and the series, is AI in the Enterprise, and I think you’ll find it really interesting in that it includes a mix of both technical and case-study-oriented discussions. To close out our AI Summit New York Series, I speak with Sherif Mityas, head of Technology, Digital and Strategy at restaurant chain TGI Fridays. Sherif joins us to discuss how Fridays is utilizing conversational AI to enhance customer loyalty. Sherif wants Friday’s to be known as a tech company that happens to sell burgers and beer, and in this conversation we get an in-depth look at the technology landscape they’ve put in place to move the company in this direction. Sherif also shares some of the things on the horizon for Friday’s, as well as some of what they’ve learned along the way. Be sure to share your feedback or questions on the show notes page, which you’ll find at twimlai.com/talk/82.
Dec 08, 2017
Innovation Factories for AI in FInancial Services with Thierry Derungs - TWiML Talk #81
00:40:56
This week on the podcast we’re running a series of shows consisting of conversations with some of the impressive speakers from an event called the AI Summit in New York City. The theme of the conference, and the series, is AI in the Enterprise, and I think you’ll find it really interesting in that it includes a mix of both technical and case-study-oriented discussions. Today’s show continues our discussion of enterprise AI, with a conversation with Thierry Derungs, Chief Digital Officer at BNP Paribas, a multinational bank headquartered in Paris. Thierry joined me to discuss how BNP uses AI and some of the opportunities that have arisen with the changing AI landscape. We also discuss the innovation process that BNP has used to introduce AI to the bank, via what they call innovation incubators or “factories”. The notes for this show can be found at twimlai.com/talk/81.
Dec 07, 2017
Block-Sparse Kernels for Deep Neural Networks with Durk Kingma - TWiML Talk #80
00:45:16
The show is part of a series that I’m really excited about, in part because I’ve been working to bring them to you for quite a while now. The focus of the series is a sampling of the interesting work being done over at OpenAI, the independent AI research lab founded by Elon Musk, Sam Altman and others. In this show I’m joined by Jonas Schneider, Robotics Technical Team Lead at OpenAI. This episode features Durk Kingma, a Research Scientist at OpenAI. Although Durk is probably best known for his pioneering work on variational autoencoders, he joined me this time to talk through his latest project on block sparse kernels, which OpenAI just published this week. Block sparsity is a property of certain neural network representations, and OpenAI’s work on developing block sparse kernels helps make it more computationally efficient to take advantage of them. In addition to covering block sparse kernels themselves and the background required to understand them, we also discuss why they’re important and walk through some examples of how they can be used. I’m happy to present another fine Nerd Alert show to close out this OpenAI Series, and I know you’ll enjoy it! To find the notes for this show, visit twimlai.com/talk/80 For more info on this series, visit twimlai.com/openai
Dec 07, 2017
AI for Customer Service and Marketing at Aeromexico with Brian Gross - TWiML Talk #79
00:30:23
This week on the podcast we’re running a series of shows consisting of conversations with some of the impressive speakers from an event called the AI Summit in New York City. The theme of the conference, and the series, is AI in the Enterprise, and I think you’ll find it really interesting in that it includes a mix of both technical and case-study-oriented discussions. Today I'm joined by Brian Gross, Head of Digital Innovation for the Mexico City-based airline AeroMexico. AeroMexico is using AI techniques like neural nets to build a chatbot that responds to its customer’s inquiries. In our conversation, Brian describes how he views the chatbot landscape, shares his thoughts on the platform requirements that established enterprises like AeroMexico have for chatbots, and describes how AeroMexico plans to stay ahead of the curve. Be sure post any feedback or questions you may have to the show notes page, which you’ll find at twimlai.com/talk/79. For more info on this series, visit twimlai.com/aisummit.
Dec 06, 2017
Scaling AI for the Enterprise with Mazin Gilbert - TWiML Talk #78
00:51:58
This week on the podcast we’re running a series of shows consisting of conversations with some of the impressive speakers from an event called the AI Summit in New York City. The theme of the conference, and the series, is AI in the Enterprise, and I think you’ll find it really interesting in that it includes a mix of both technical and case-study-oriented discussions. My guest this time around is Mazin Gilbert, vice president of advanced technology & architecture with AT&T. Mazin and I have a really interesting discussion on what’s really required to scale AI in the enterprise, and you’ll learn about a new open source project that AT&T is working on to allow any enterprise to do this. You already know by now that I geek out when it comes to talking about the intersection of machine learning and cloud computing, and this conversation is no exception. Be sure to let us know what you think by posting your comments or questions to the show notes page at twimlai.com/talk/78. For more info on this series, visit twimlai.com/aisummit
Dec 05, 2017
Scaleable Distributed Deep Learning with Hillery Hunter - TWiML Talk #77
00:41:21
This week on the podcast we’re running a series of shows consisting of conversations with some of the impressive speakers from an event called the AI Summit in New York City. The theme of the conference, and the series, is AI in the Enterprise, and I think you’ll find it really interesting in that it includes a mix of both technical and case-study-oriented discussions. My guest for this first show in the series is, Hillery Hunter, IBM Fellow & Director of the Accelerated Cognitive Infrastructure group at IBM’s T.J. Watson Research Center. Hillery and I met a few weeks back in New York and I'm really glad that we were able to get her on the show. Hillery joins us to discuss her team's research into distributed deep learning, which was recently released as the PowerAI Distributed Deep Learning Communication Library or DDL. In my conversation with Hillery, we discuss the purpose and technical architecture of the DDL, it’s ability to offer fully synchronous distributed training of deep learning models, the advantages of its Multi-Ring Topology, and much more. This is for sure a nerd alert pod, especially for the performance and hardware geeks among us . Be sure post any feedback or questions you may have to the show notes page, which you’ll find at twimlai.com/talk/77. For more info on this series, visit twimlai.com/aisummit
Dec 04, 2017
Robotics at OpenAI with Jonas Schneider - TWiML Talk #76
00:47:42
The show is part of a series that I’m really excited about, in part because I’ve been working to bring them to you for quite a while now. The focus of the series is a sampling of the interesting work being done over at OpenAI, the independent AI research lab founded by Elon Musk, Sam Altman and others. In this show I’m joined by Jonas Schneider, Robotics Technical Team Lead at OpenAI. While in San Francisco a few months ago, I spent some time with Jonas at the OpenAI office, during which we covered a lot of interesting ground around OpenAI’s work in robotics. We discuss OpenAI Gym, which was the first project he worked on at OpenAI, as well as how they approach setting up the infrastructure for their experimental work, including how they’ve set up a Robots-as-a-Service environment for their researchers and how they use the open source Kubernetes project to manage their compute environment. Check it out and let us know what you think! To find the notes for this show, visit twimlai.com/talk/76 For more info on this series, visit twimlai.com/openai
Dec 01, 2017
AI Robustness and Safety with Dario Amodei - TWiML Talk #75
00:39:04
The show is part of a series that I’m really excited about, in part because I’ve been working to bring them to you for quite a while now. The focus of the series is a sampling of the interesting work being done over at OpenAI, the independent AI research lab founded by Elon Musk, Sam Altman and others. In this episode i'm joined by Dario Amodei, Team Lead for Safety Research at OpenAI. While in San Francisco a few months ago, I spent some time at the OpenAI office, during which I sat down with Dario to chat about the work happening at OpenAI around AI safety. Dario and I dive into the two areas of AI safety that he and his team are focused on--robustness and alignment. We also touch on his research with the Google DeepMind team, the OpenAI Universe tool, and how human interactions can be incorporated into reinforcement learning models. This was a great conversation, and along with the other shows in this series, this is a nerd alert show! To find the notes for this show, visit twimlai.com/talk/75 For more info on this series, visit twimlai.com/openai
Nov 30, 2017
Towards Artificial General Intelligence with Greg Brockman - TWiML Talk #74
00:58:15
The show is part of a series that I’m really excited about, in part because I’ve been working to bring them to you for quite a while now. The focus of the series is a sampling of the interesting work being done over at OpenAI, the independent AI research lab founded by Elon Musk, Sam Altman and others. In this episode, I’m joined by Greg Brockman, OpenAI Co-Founder and CTO. Greg and I touch on a bunch of topics in the show. We start with the founding and goals of OpenAI, before diving into a discussion on Artificial General Intelligence, what it means to achieve it, and how we going about doing so safely and without bias. We also touch on how to massively scale neural networks and their training training and the evolution of computational frameworks for AI. This conversation is not only informative and nerd alert worthy, but we cover some very important topics, so please take it all in, enjoy, and send along your feedback! To find the notes for this show, visit twimlai.com/talk/74 For more info on this series, visit twimlai.com/openai
Nov 28, 2017
Exploring Black Box Predictions with Sam Ritchie - TWiML Talk #73
00:40:13
This week, we’ll be featuring a series of shows recorded from Strange Loop, a great developer-focused conference that takes place every year right in my backyard! The conference is a multi-disciplinary melting pot of developers and thinkers across a variety of fields, and we’re happy to be able to bring a bit of it to those of you who couldn’t make it in person! In this episode, I speak with Sam Ritchie, a software engineer at Stripe. I caught up with Sam RIGHT after his talk at the conference, where he covered his team’s work on explaining black box predictions. In our conversation, we discuss how Stripe uses black box predictions for fraud detection, and he gives a few use case scenarios. We discuss Stripe’s approach for explaining those predictions as well as other approaches, and briefly mention Carlos Guestrin’s work on LIME paper, which he and I discuss in TWiML Talk #7. The notes for this show can be found at twimlai.com/talk/73 For more series info, visit twimlai.com/STLoop
Nov 25, 2017
Experimental Creative Writing with the Vectorized Word - Allison Parish - TWIML Talk #72
00:30:00
This week, we’ll be featuring a series of shows recorded from Strange Loop, a great developer-focused conference that takes place every year right in my backyard! The conference is a multi-disciplinary melting pot of developers and thinkers across a variety of fields, and we’re happy to be able to bring a bit of it to those of you who couldn’t make it in person! In this episode, I speak with Allison Parrish, Poet and Professor at NYU in the Interactive Telecommunications dept. Allison’s work centers around generated poetry, via artificial intelligence and machine learning. She joins me prior to her conference talk on “Experimental Creative Writing with the Vectorized Word”. In our time together, we discuss some of her research into computational poetry generation, actually performing AI-produced poetry, and some of the methods and processes she uses for generating her work. Allison’s work centers around generated poetry, via artificial intelligence and machine learning. She joins me prior to her conference talk on “Experimental Creative Writing with the Vectorized Word”. In our time together, we discuss some of her research into computational poetry generation, actually performing AI-produced poetry, and some of the methods and processes she uses for generating her work. The notes for this show can be found at twimlai.com/talk/72 For more series info, visit twimlai.com/STLoop
Nov 24, 2017
The Biological Path Towards Strong AI - Matthew Taylor - TWiML Talk #71
00:40:18
This week, we’ll be featuring a series of shows recorded from Strange Loop, a great developer-focused conference that takes place every year right in my backyard! The conference is a multi-disciplinary melting pot of developers and thinkers across a variety of fields, and we’re happy to be able to bring a bit of it to those of you who couldn’t make it in person! In this episode, I speak with Matthew Taylor, Open Source Manager at Numenta. You might remember hearing a bit about Numenta from an interview I did with Francisco Weber of Cortical.io, for TWiML Talk #10, a show which remains the most popular show on the podcast. Numenta is basically trying to reverse-engineer the neocortex, and use what they learn to develop a neocortical theory for biological and machine intelligence called Hierarchical Temporal Memory. Matt joined me at the conference to discuss his talk “The Biological Path Towards Strong AI”. In our conversation, we discuss the basics of HTM, it’s biological inspiration, and how it differs from traditional neural network models including deep learning. This is a Nerd Alert show, and after you listen I would encourage you to check out the conversation with Francisco which we’ll link to in the show notes. The notes for this show can be found at twimlai.com/talk/71 For series information, visit twimlai.com/stloop
Nov 22, 2017
Pytorch: Fast Differentiable Dynamic Graphs in Python with Soumith Chintala - TWiML Talk #70
00:44:30
This week, we’ll be featuring a series of shows recorded from Strange Loop, a great developer-focused conference that takes place every year right in my backyard! The conference is a multi-disciplinary melting pot of developers and thinkers across a variety of fields, and we’re happy to be able to bring a bit of it to those of you who couldn’t make it in person! In this show I speak with Soumith Chintala, a Research Engineer in the Facebook AI Research Lab (FAIR). Soumith joined me at Strange Loop before his talk on Pytorch, the deep learning framework. In this talk we discuss the market evolution of deep learning frameworks and tools, different approaches to programming deep learning frameworks, Facebook’s motivation for investing in Pytorch, and much more. This was a fun interview, I hope you enjoy! The notes for this show can be found at twimlai.com/talk/70 For series information, visit twimlai.com/stloop
Nov 21, 2017
Accessible Machine Learning for the Enterprise Developer with Ryan Sevey & Jason Montgomery
00:47:10
This week, we’ll be featuring a series of shows recorded from Strange Loop, a great developer-focused conference that takes place every year right in my backyard! The conference is a multi-disciplinary melting pot of developers and thinkers across a variety of fields, and we’re happy to be able to bring a bit of it to those of you who couldn’t make it in person! In this show you'll hear from Nexosis founders Ryan Sevey and Jason Montgomery. Ryan, Jason and I discuss how they got their start by applying ML to identify cheaters in video games, the application of ML for time-series data analysis, and of course the Nexosis Machine Learning API. Of course, if you like what you hear, they invite you to get your free Nexosis API key and discover what they can bring to your next project at nexosis.com/twiml. The notes for this show can be found at twimlai.com/talk/69 For series information, visit twimlai.com/stloop
Nov 20, 2017
Bridging the Gap Between Academic and Industry Careers with Ross Fadely - TWiML Talk #68
00:21:07
We close out our NYU Future Labs AI Summit interview series with Ross Fadely, a New York based AI lead with Insight Data Science. Insight is an interesting company offering a free seven week post-doctoral training fellowship helping individuals to bridge the gap between academia and careers in data science, data engineering and AI. Ross joined me backstage at the Future Labs Summit after leading a Machine Learning Primer for attendees. Our conversation explores some of the knowledge gaps that Insight has identified in folks coming out of academia, and how they structure their program to address them. If you find yourself looking to make this transition, you’ll definitely want to check out this episode. The notes for this show can be found at twimlai.com/talk/68 For series information, visit twimlai.com/ainexuslab2
Nov 16, 2017
The Limitations of Human-in-the-Loop AI with Dennis Mortensen - TWiML Talk #67
00:37:34
We continue our NYU Future Labs AI Summit interview series with Dennis Mortensen, founder and CEO of X.ai, a company whose AI-based personal assistant Amy helps users with scheduling meetings. I caught up with Dennis backstage at the Future Labs event a few weeks ago, right before he went on stage to talk about “Investing in AI from the Startup POV.” Dennis gave shares some great insight into building an AI-first company, not to mention his vision for the future of scheduling, something no one actually enjoys doing, and his thoughts on the future of human-AI interaction. This was a fun interview, which I’m sure you’ll enjoy. A quick warning though… This might not be a show to listen to in the car with the kiddos, as this episode does contain a few expletives. The notes for this show can be found at twimlai.com/talk/67 For series information, visit twimlai.com/ainexuslab2
Nov 13, 2017
Nexus Lab Cohort 2 - Second Mind - TWiML Talk #66
00:23:24
The podcast you’re about to hear is the fourth of a series of shows recorded at the NYU Future Labs AI Summit last week in New York City. In this show, I speak with Kul Singh, CEO and Founder of Second Mind. Second Mind is building an integration platform for businesses that allows them to bring augmented intelligence to voice conversations. We talk to Kul about the concept behind Second Mind, and how the company combines ambient listening with a low-latency matching system to help users eliminate an estimated 2.5 hours of manual searches per day! The notes for this show can be found at twimlai.com/talk/66 For series information, visit twimlai.com/ainexuslab2
Nov 09, 2017
Nexus Lab Cohort 2 - Bite.ai - TWiML Talk #65
00:28:51
The podcast you’re about to hear is the second of a series of shows recorded at the NYU Future Labs AI Summit last week in New York City.In this episode, you’ll hear from Bite.ai, a startup founded by Vinay Anantharaman and Michal Wolski, founders who met working at Clarifai, another NYU Future Labs alumni, whose CEO Matt Zeiler I interviewed on TWiML Talk #22(Link on show notes page). Bite is using convolutional neural networks and other machine learning to help computers understand and reason about food. Their product is the app Bitesnap, which provides users with detailed nutritional information about the food they’re about to eat using just a photo and a serving size. We dive into the details of their app and service, the machine learning models and pipeline that enable it, and how they plan to compete with other apps targeting dieters, and more! The notes for this show can be found at twimlai.com/talk/65 For series information, visit twimlai.com/ainexuslab2.
Nov 08, 2017
Nexus Lab Cohort 2 - Bowtie - TWiML Talk #64
00:27:02
The podcast you’re about to hear is the second of a series of shows recorded at the NYU Future Labs AI Summit last week in New York City. In this episode, I speak with Ron Fisher and Mike Wang, who, along with Vivek Sudarsan founded Bowtie Labs, a 24/7 AI-based receptionist designed to help businesses in the beauty, wellness, and fitness industries increase retail conversion rates. I’ve talked with a few startups in the conversational space recently and one common theme seems to be quickly outgrowing commercial conversational platforms. Ron and Mike shared their own experiences with decision, and shared some of the challenges they’re trying to overcome with their ML models, as well as some of the techniques they use to make their system as responsive as possible. The notes for this show can be found at twimlai.com/talk/64 For Series information, visit twimlai.com/ainexuslab2
Nov 07, 2017
AI Nexus Lab Cohort 2 - Mt. Cleverest - TWiML Talk #63
00:33:59
The podcast you’re about to hear is the first of a series of shows recorded at the NYU Future Labs AI Summit last week in New York City. My guests this time around are James Villarrubia and Bernie Prat, CEO and COO respectively, of Mt. Cleverest, an online service for teachers and students, that can take any text via the web, and generate a quiz along with answers based on the content supplied. To do this, Bernie and James employ a pretty sophisticated natural language understanding pipeline, which we discuss in this interview. We also touch on the challenges they face in generating correct question answers, how they fine tune their ML models to improve those answers over time, and more. The notes for this show can be found at twimlai.com/talk/63 For Series information, visit twimlai.com/nexuslabs2
Nov 06, 2017
Learning to Learn, and other Opportunities in Machine Learning with Graham Taylor - TWiML Talk #62
00:38:41
The podcast you’re about to hear is the third of a series of shows recorded at the Georgian Partners Portfolio Conference last week in Toronto. My guest this time is Graham Taylor, professor of engineering at the University of Guelph, who keynoted day two of the conference. Graham leads the Machine Learning Research Group at Guelph, and is affiliated with Toronto’s recently formed Vector Institute for Artificial Intelligence. Graham and I discussed a number of the most important trends and challenges in artificial intelligence, including the move from predictive to creative systems, the rise of human-in-the-loop AI, and how modern AI is accelerating with our ability to teach computers how to learn-to-learn. The notes for this show can be found at twimlai.com/talk/62. For series info, visit twimlai.com/GPPC2017
Nov 03, 2017
Building Conversational Application for Financial Services with Kenneth Conroy - TWiML Talk #61
00:38:52
The podcast you’re about to hear is the second of a series of shows recorded at the Georgian Partners Portfolio Conference last week in Toronto. My guest for this interview is Kenneth Conroy, VP of data science at Vancouver, Canada-based Finn.ai, a company building a chatbot system for banks. Kenneth and I spoke about how Finn.AI built its core conversational platform. We spoke in depth about the requirements and challenges of conversational applications, and how and why they transitioned off of a commercial chatbot platform--in their case API.ai--and built their own custom platform based on deep learning, word2vec and other natural language understanding technologies. The notes for this show can be found at https://twimlai.com/talk/61
Nov 01, 2017
Fighting Fraud with Machine Learning at Shopify with Solmaz Shahalizadeh - TWiML Talk #60
00:37:26
The podcast you’re about to hear is the first of a series of shows recorded at the Georgian Partners Portfolio Conference last week in Toronto. My guest for this show is Solmaz Shahalizadeh, Director of Merchant Services Algorithms at Shopify. Solmaz gave a great talk at the GPPC focused on her team’s experiences applying machine learning to fight fraud and improve merchant satisfaction. Solmaz and I dig into, step-by-step, the process they used to transition from a legacy, rules-based fraud detection system system to a more scalable, flexible one based on machine learning models. We discuss the importance of well-defined project scope; tips and traps when selecting features to train your models; and the various models, transformations and pipelines the Shopify team selected; and how they use PMML to make their Python models available to their Ruby-on-Rails web application. The notes for this show can be found at twimlai.com/talk/60 For Series info, visit twimlai.com/GPPC2017
Oct 30, 2017
Modeling Human Drivers for Autonomous Vehicles with Katie Driggs-Campbell - TWiML Talk #59
00:34:43
We are back with our third show this week, episode 3 of our Autonomous Vehicles Series. My guest this time is Katie Driggs-Campbell, PostDoc in the Intelligent Systems Lab at Stanford University’s Department of Aeronautics and Astronautics. Katie joins us to discuss her research into human behavioral modeling and control systems for self-driving vehicles. Katie also gives us some insight into her process for collecting training data, how social nuances come into play for self-driving cars, and more. The notes for this show can be found at twimlai.com/talk/59 For Series info, visit twimlai.com/av2017
Oct 27, 2017
Perception Models for Self-Driving Cars with Jianxiong Xiao - TWiML Talk #58
00:43:15
We are back with our second show this week, episode 2 of our Autonomous Vehicles Series. This time around we are joined by Jianxiong Xiao of AutoX, a company building computer vision centric solutions for autonomous vehicles. Jianxiong, a PhD graduate of MIT’s CSAIL Lab, joins me to discuss the different layers of the autonomous vehicle stack and the models for machine perception currently used in self-driving cars. If you’re new to the autonomous vehicles space I’m confident you’ll learn a ton, and even if you know the space in general, you’ll get a glimpse into why Jianxiong thinks AutoX’s direct perception approach is superior to end-to-end processing or mediated perception. The notes for this show can be found at twimlai.com/talk/58 For Series info, visit twimlai.com/av2017
Oct 25, 2017
Training Data for Autonomous Vehicles - Daryn Nakhuda - TWiML Talk #57
00:49:27
The episode you are about to hear is the first of a new series of shows on Autonomous Vehicles. We all know that self-driving cars is one of the hottest topics in ML & AI, so we had to dig a little deeper into the space. To get us started on this journey, I’m excited to present this interview with Daryn Nakhuda, CEO and Co-Founder of MightyAI. Daryn and I discuss the many challenges of collecting training data for autonomous vehicles, along with some thoughts on human-powered insights and annotation, semantic segmentation, and a ton more great stuff. For the notes for this show, Visit twimlai.com/talk/57. For series info, visit twimlai.com/AV2017
Oct 23, 2017
Human Factors in Machine Intelligence with James Guszcza - TWiML Talk #56
00:46:07
As you all know, a few weeks ago, I spent some time in SF at the Artificial Intelligence Conference. I sat down with James Guszcza, US Chief Data Scientist at Deloitte Consulting to talk about human factors in machine intelligence. James was in San Francisco to give a talk at the O’Reilly AI Conference on “Why AI needs human-centered design.” We had an amazing chat, in which we explored the many reasons why the human element is so important in ML and AI, along with useful ways to build algorithms and models that reflect this human element, while avoiding out problems like group-think and bias. This was a very interesting conversation. I enjoyed it a ton, and I’m sure you will too! The notes for this episode can be found at twimlai.com/talk/56
Oct 16, 2017
Intel Nervana Devcloud with Naveen Rao & Scott Apeland - TWiML Talk #51
00:38:48
In this episode, I talk to Naveen Rao, VP and GM of Intel’s AI Products Group, and Scott Apeland, director of Intel’s Developer Network. It's been a few months since we last spoke to Naveen, so he gives us a quick update on what Intel’s been up to and we discuss his perspective on some recent developments in the AI ecosystem. Scott and I dig into Intel Nervana’s new DevCloud offering, which was announced at the conference. We also discuss the Intel Nervana AI Academy, a new portal offering hands-on learning tools and other resources for various aspects of machine learning and AI. The notes for this show can be found at twimlai.com/talk/51
Oct 06, 2017
AI-Powered Conversational Interfaces with Paul Tepper - TWiML Talk #52
00:37:36
The show you’re about to hear is part of a series of shows recorded in San Francisco at the Artificial Intelligence Conference. My guest for this show is Paul Tepper, worldwide head of cognitive innovation and product manager for machine learning & AI at Nuance Communications. Paul gave a talk at the conference on critical factors in building successful AI-powered conversational interfaces. We covered a bunch of topics, like voice UI design, behavioral biometrics and a ton of other interesting things that Nuance has in the works. The notes for this show can be found at twimlai.com/talk/52
Oct 06, 2017
ML Use Cases at Think Big Analytics with Mo Patel and Laura Frølich - TWiML Talk #54
00:46:11
The show you’re about to hear is part of a series of shows recorded in San Francisco at the Artificial Intelligence Conference. This time around, I speak with Mo Patel, practice director of AI & deep learning and Laura Frølich, data scientist, of Think Big Analytics. Mo and Laura joined me at the AI conference after their session on “Training vision models with public transportation datasets.” We talked over a bunch of use cases they’ve worked on involving image analysis and deep learning, including an assisted driving system. We also talk through a bunch of practical challenges faced when working on real machine learning problems, like feature detection, data augmentation, and training data. The notes for this show can be found at twimlai.com/talk/54
Oct 06, 2017
Ray: A Distributed Computing Platform for Reinforcement Learning with Ion Stoica - TWiML Talk #55
00:28:53
The show you’re about to hear is part of a series of shows recorded in San Francisco at the Artificial Intelligence Conference. In this episode, I talk with Ion Stoica, professor of computer science & director of the RISE Lab at UC Berkeley. Ion joined us after he gave his talk “Building reinforcement learning applications with Ray.” We dive into Ray, a new distributed computing platform for RL, as well as RL generally, along with some of the other interesting projects RISE Lab is working on, like Clipper & Tegra. This was a pretty interesting talk. Enjoy! The notes for this show can be found at twimlai.com/talk/55
Oct 05, 2017
Topological Data Analysis with Gunnar Carlsson - TWiML Talk #53
00:34:21
The show you’re about to hear is part of a series of shows recorded in San Francisco at the Artificial Intelligence Conference. My guest for this show is Gunnar Carlsson, professor emeritus of mathematics at Stanford University and president and co-founder of machine learning startup Ayasdi. Gunnar joined me after his session at the conference on “Topological data analysis as a framework for machine intelligence.” In our talk, we take a super deep dive on the mathematical underpinnings of TDA and its practical application through software. Nerd Alert! The notes for this show can be found at twimlai.com/talk/53
Oct 03, 2017
Bayesian Optimization for Hyperparameter Tuning with Scott Clark - TWiML Talk #50
00:49:29
As you all know, a few weeks ago, I spent some time in SF at the Artificial Intelligence Conference. While I was there, I had just enough time to sneak away and catch up with Scott Clark, Co-Founder and CEO of Sigopt, a company whose software is focused on automatically tuning your model’s parameters through Bayesian optimization. We dive pretty deeply into that process through the course of this discussion, while hitting on topics like Exploration vs Exploitation, Bayesian Regression, Heterogeneous Configuration Models and Covariance Kernels. I had a great time and learned a ton, but be forewarned, this is most definitely a Nerd Alert show! Notes for this show can be found at twimlai.com/talk/50
Oct 02, 2017
Symbolic and Sub-Symbolic Natural Language Processing with Jonathan Mugan - TWiML Talk #49
00:45:20
Like last week’s interview with Bruno Goncalves, this week’s interview was also recorded at the last O’Reilly AI Conference back in New York in June. Also like last week’s show, this week’s is also focused on Natural Language Processing and I think you’ll enjoy it. I’m joined by Jonathan Mugan, co-founder and CEO of Deep Grammar, a company that is building a grammar checker using deep learning and what they call deep symbolic processing. This interview is a great complement to my conversation with Bruno, and we cover a variety of topics from both the sub-symbolic and symbolic schools of NLP, such as attention mechanisms like sequence to sequence, and ontological approaches like WordNet, synsets, FrameNet, and SUMO. You can find the notes for this show at twimlai.com/talk/49
Sep 25, 2017
Word2Vec & Friends with Bruno Gonçalves - TWiML Talk #48
00:33:43
This week i'm bringing you an interview from Bruno Goncalves, a Moore-Sloan Data Science Fellow at NYU. As you’ll hear in the interview, Bruno is a longtime listener of the podcast. We were able to connect at the NY AI conference back in June after I noted on a previous show that I was interested in learning more about word2vec. Bruno graciously agreed to come on the show and walk us through an overview of word embeddings, word2vec and related ideas. He provides a great overview of not only word2vec, related NLP concepts such as Skip Gram, Continuous Bag of Words, Node2Vec and TFIDF. Notes for this show can be found at twimlai.com/talk/48.
Sep 19, 2017
Evolutionary Algorithms in Machine Learning with Risto Miikkulainen - TWiML Talk #47
01:00:40
My guest this week is Risto Miikkulainen, professor of computer science at UT-Austin and vice president of Research at Sentient Technologies. Risto came locked and loaded to discuss a topic that we've received a ton of requests for -- evolutionary algorithms. During our talk we discuss some of the things Sentient is working on in the financial services and retail fields, and we dig into the technology behind it, evolutionary algorithms, which is also the focus of Risto’s research at UT. I really enjoyed this interview and learned a ton, and I’m sure you will too! Notes for this show can be found at twimlai.com/talk/47.
Sep 11, 2017
Agile Machine Learning with Jennifer Prendki - TWiML Talk #46
00:50:46
My guest this week is Jennifer Prendki. That name might sound familiar, as she was one of the great speakers from my Future of Data Summit back in May. At the time, Jennifer was senior data science manager and principal data scientist at Walmart Labs, but she's since moved on to become head of data science at Atlassian. Back at the summit, Jennifer gave an awesome talk on what she calls Data Mixology, the slides for which you can find on the show notes page. My conversation with Jennifer begins with a recap of that talk. After that, we shift our focus to some of the practices she helped develop and implement at Walmart around the measurement and management of machine learning models in production, and more generally, building agile processes and teams for machine learning. The notes for this show can be found at twimlai.com/talk/46
Sep 05, 2017
LSTMs, Plus a Deep Learning History Lesson with Jürgen Schmidhuber - TWiML Talk #44
01:06:24
This week we have a very special interview to share with you! Those of you who’ve been receiving my newsletter for a while might remember that while in Switzerland last month, I had the pleasure of interviewing Jurgen Schmidhuber, in his lab IDSIA, which is the Dalle Molle Institute for Artificial Intelligence Research in Lugano, Switzerland, where he serves as Scientific Director. In addition to his role at IDSIA, Jurgen is also Co-Founder and Chief Scientist of NNaisense, a company that is using AI to build large-scale neural network solutions for “superhuman perception and intelligent automation.” Jurgen is an interesting, accomplished and in some circles controversial figure in the AI community and we covered a lot of very interesting ground in our discussion, so much so that I couldn't truly unpack it all until I had a chance to sit with it after the fact. We talked a bunch about his work on neural networks, especially LSTM’s, or Long Short-Term Memory networks, which are a key innovation behind many of the advances we’ve seen in deep learning and its application over the past few years. Along the way, Jurgen walks us through a deep learning history lesson that spans 50+ years. It was like walking back in time with the 3 eyed raven. I know you’re really going to enjoy this one, and by the way, this is definitely a nerd alert show! For the show notes, visit twimlai.com/talk/44
Aug 28, 2017
Machine Teaching for Better Machine Learning with Mark Hammond - TWiML Talk #43
01:09:00
Today’s show, which concludes the first season of the Industrial AI Series, features my interview with Bonsai co-founder and CEO Mark Hammond. I sat down with Mark at Bonsai HQ a few weeks ago and we had a great discussion while I was there. We touched on a ton of subjects throughout this talk, including his starting point in Artificial intelligence, how Bonsai came about & more. Mark also describes the role of what he calls “machine teaching” in delivering practical machine learning solutions, particularly for enterprise or industrial AI use cases. This was one of my favorite conversations, I know you’ll enjoy it! The notes for this show can be found at twimlai.com/talk/43
Aug 21, 2017
Marrying Physics-Based and Data-Driven ML Models with Josh Bloom - TWiML Talk #42
00:55:33
Recently I had a chance to catch up with a friend and friend of the show, Josh Bloom, vice president of data & analytics at GE Digital. If you’ve been listening for a while, you already know that Josh was on the show around this time last year, just prior to the acquisition of his company Wise.io by GE Digital. It was great to catch up with Josh on his journey within GE, and the work his team is doing around Industrial AI, now that they’re part of the one of the world’s biggest industrial companies. We talk about some really interesting things in this show, including how his team is using autoencoders to create training datasets, and how they incorporate knowledge of physics and physical systems into their machine learning models. The notes for this show can be found at twimlai.com/talk/42.
Aug 14, 2017
Cognitive Biases in Data Science with Drew Conway - TWiML Talk #39
00:36:50
This show features my interview with Drew Conway, whose Wrangle keynote could have been called “Confessions of a CIA Data Scientist.” The focus of our interview, and of Drew’s presentation, is an interesting set of observations he makes about the role of cognitive biases in data science. If your work involves making decisions or influencing behavior based on data-driven analysis--and it probably does or will--you’re going to want to hear what he has to say. A quick note before we dive in: As is the case with my other field recordings, there’s a bit of unavoidable background noise in this interview. Sorry about that! The show notes for this episode can be found at https://twimlai.com/talk/39
Aug 05, 2017
Data Pipelines at Zymergen with Airflow with Erin Shellman - TWiML Talk #41
00:36:13
The show you’re listening to features my interview with Erin Shellman. Erin is a statistician and data science manager with Zymergen, a company using robots and machine learning to engineer better microbes. If you’re wondering what exactly that means, I was too, and we talk about it in the interview. Our conversation focuses on Zymergen’s use of Apache Airflow, an open-source data management platform originating at Airbnb, that Erin and her team uses to create reliable, repeatable data pipelines for its machine learning applications. A quick note before we dive in: As is the case with my other field recordings, there’s a bit of unavoidable background noise in this interview. Sorry about that! The show notes for this episode can be found at https://twimlai.com/talk/41
Aug 05, 2017
Web Scale Engineering for Machine Learning with Sharath Rao - TWiML Talk #40
00:32:06
The show you’re about to listen to features my interview with Sharath Rao, Tech Lead Manager & Machine Learning Engineer at Instacart I reached out to Sharath about being on the show and was blown away when he replied that not only had he heard about the show, but that he was a fan and an avid listener. My conversation with him digs into some of the practical lessons and patterns he’s learned by building production-ready, web-scale data products based on machine learning models, including the search and recommendation systems at Instacart. We also spend a few minutes discussing our upcoming TWiML Paper Reading Meetup! A quick note before we dive in: As is the case with my other field recordings, there’s a bit of unavoidable background noise in this interview. Sorry about that! The show notes for this episode can be found at https://twimlai.com/talk/40.
Aug 04, 2017
Deep Learning for Warehouse Operations with Calvin Seward - TWiML Talk #38
00:48:16
This week, I’m happy to bring you my interview with Calvin Seward, a research scientist with Berlin, Germany based Zalando. While our American listeners might not know the name Zalando, they’re one of the largest e-commerce companies in Europe with a focus on fashion and shoes. Calvin is a research scientist there, while also pursuing his doctorate studies at Johannes Kepler University in Linz, Austria. Our discussion, which continues our Industrial AI series, focuses on how Calvin’s team tackled an interesting warehouse optimization problem using deep learning. Calvin also gives his thoughts on the distinction between AI and ML, and the four P’s that he focuses on: Prestige, Products, Paper, and Patents. The notes for this show can be found at https://twimlai.com/talk/38.
Jul 31, 2017
Deep Robotic Learning with Sergey Levine - TWiML Talk #37
00:49:12
This week we continue our Industrial AI series with Sergey Levine, an Assistant Professor at UC Berkeley whose research focus is Deep Robotic Learning. Sergey is part of the same research team as a couple of our previous guests in this series, Chelsea Finn and Pieter Abbeel, and if the response we’ve seen to those shows is any indication, you’re going to love this episode! Sergey’s research interests, and our discussion, focus in on include how robotic learning techniques can be used to allow machines to acquire autonomously acquire complex behavioral skills. We really dig into some of the details of how this is done and I found that our conversation filled in a lot of gaps for me from the interviews with Pieter and Chelsea. By the way, this is definitely a nerd alert episode! Notes for this show can be found at twimlai.com/talk/37
Jul 24, 2017
Smart Buildings & IoT with Yodit Stanton - TWiML Talk #36
00:56:10
After a brief hiatus, the Industrial AI Series is making its triumphant return! Our guest this week is Yodit Stanton, a self-described Data Nerd, and the Founder & CEO of Opensensors.io. OpenSensors.io is a real-time data exchange for IoT, that enables anyone to publish and subscribe to real time open data in order to build higher order smart systems and better understand the world around them. Our discussion focuses on Smart Buildings and how they’re enabled by IoT and machine learning techniques. The notes for this show can be found at twimlai.com/talk/36
Jul 17, 2017
Enhancing Customer Experiences With Emotional AI with Rana El Kaliouby - TWiML Talk #35
00:33:40
My guest for this show is Rana el Kaliouby. Rana is co-founder and CEO of Affectiva. Affectiva, as Rana puts it, "is on a mission to humanize technology by bringing in artificial emotional intelligence". If you liked my conversation about Emotional AI with Pascale Fung from last year’s O’Reilly AI conference, you’re going to love this one. My conversation with Rana kind of picks up where the previous one left off, with a focus on how her company is bringing Artificial Emotional Intelligence services to market. Rana and her team have developed a machine learning / computer vision platform that can use the camera on any device to read your facial expressions in real time, then maps it to an emotional state. Using data science to mine the world’s largest emotion repository, Affectiva has collected over 5.5 million pieces of emotional expression data to date, from laptop, driving, cellular interactions. Understanding the importance of personal privacy, Rana and her Co-Founder Rosalind Wright Picard have vowed to shy away from partnerships that would subject consumers to unknowing surveillance, a commendable effort. The notes for this show can be found at https://twimlai.com/talk/35
Jul 05, 2017
Intel Nervana Update + Productizing AI Research with Naveen Rao And Hanlin Tang - TWiML Talk #31
00:42:21
I talked about Intel’s acquisition of Nervana Systems on the podcast when it happened almost a year ago, so I was super excited to have an opportunity to sit down with Nervana co-founder Naveen Rao, who now leads Intel’s newly formed AI Products Group, for the first show in our O'Reilly AI series. We talked about how Intel plans to extend its leadership position in general purpose compute into the AI realm by delivering silicon designed specifically for AI, end-to-end solutions including the cloud, enterprise data center, and the edge; and tools that let customers quickly productize and scale AI-based solutions. I also spoke with Hanlin Tang, an algorithms engineer at Intel’s AIPG, about two tools announced at the conference: version 2.0 of Intel Nervana’s deep learning framework Neon and Nervana Graph, a new toolset for expressing and running deep learning applications as framework and hardware-independent computational graphs. Nervana Graph in particular sounds like a very interesting project, not to mention a smart move for Intel, and I’d encourage folks to take a look at their Github repo. The show notes for this page can be found at https://twimlai.com/talk/31
Jul 05, 2017
Expressive AI - Generated Music With Google's Performance RNN - Doug Eck - TWiML Talk #32
00:46:31
My guest for this second show in our O’Reilly AI series is Doug Eck of Google Brain. Doug did a keynote at the O’Reilly conference on Magenta, Google’s project for melding machine learning and the arts. Magenta’s goal is to produce open-source tools and models that help people in their personal creative processes. Doug’s research starts with using so-called “generative” machine learning models to create engaging media. Additionally, he is working on how to bring other aspects of the creative process into play. We talk about the newly announced Performance RNN project, which uses neural networks to create expressive, AI-generated music. We also touch on QuickDraw, a project by Google AI Experiments, in which users as Doug describes it, “play Pictionary” with a visual classifier. We dig into what he foresees as possibilities for Magenta, machine learning models eventually developing storylines, generative models for media and creative coding. The notes for this episode can be found at https://twimlai.com/talk/32.
Jul 05, 2017
The Power Of Probabilistic Programming with Ben Vigoda - TWiML Talk #33
00:42:43
My guest for this third episode in the O'Reilly AI series is Ben Vigoda. Ben is the founder and CEO of Gamalon, a DARPA-funded startup working on Bayesian Program Synthesis. We dive into what exactly this means and how it enables what Ben calls idea learning in the show. Gamalon's first application structures unstructured data — input a paragraph or phrase of unstructured text and output a structured spreadsheet/database row or API call. This can be applicable to a wide range of data challenges, including enterprise product and customer information, AI or digital assistant, and many others. Before Gamalon, Ben was co-founder and CEO of Lyric Semiconductor, Inc., which created the first microprocessor architectures dedicated for statistical machine learning. The company was based on his PhD thesis at MIT and acquired by Analog Devices. In today’s talk we are discussing probabilistic programming, his new approach to deep learning, posterior distribution, and the difference between sampling methods and variational methods and how solvers work in the system. Nerd alert: We go pretty deep in this discussion. The notes for this show can be found at https://twimlai.com/talk/33
Jul 05, 2017
Video Object Detection At Scale with Reza Zadeh - TWiML Talk #34
00:52:41
My guest for the fourth show in the O'Reilly AI Series is Reza Zadeh. Reza is an adjunct professor of computational mathematics at Stanford University and founder and CEO of the startup Matroid. Reza has a background in machine translation and distributed machine learning, along with having helped build Apache Spark, and the"Who to Follow" feature on Twitter, which is based on a chapter from his PhD thesis. Our conversation focused on some of the challenges and approaches to scaling deep learning, both in general and in the context of his company’s video object detection service. Our conversation focused on some of the challenges and approaches to scaling deep learning, both in general and in the context of his company’s video object detection service. We also spoke about the advancement of computer vision technologies, using CPU's, GPU's, the upcoming shift to TPU's and we get below the surface on Apache Spark.
Jul 05, 2017
Natural Language Understanding for Amazon Alexa with Zornitsa Kozareva - TWiML Talk #30
00:56:53
Our guest this week is Zornitsa Kozareva, Manager of Machine Learning with Amazon Web Services Deep Learning, where she leads a group focused on natural language processing and dialogue systems for products like Alexa and Lex, the latter of which we introduce in the podcast. We spend most of our time talking through the architecture of modern Natural Language Understanding systems, including the role of deep learning, and some of the various ways folks are working to overcome the challenges in this field, such as understanding human intent. If you’re interested in this field she mentions the AWS Chatbot Challenge, which you’ve still got a couple more weeks to participate in. The notes for this show can be found at twimlai.com/talk/30.
Jun 29, 2017
Robotic Perception and Control with Chelsea Finn - TWiML Talk #29
00:56:08
This week we continue our series on industrial applications of machine learning and AI with a conversation with Chelsea Finn, a PhD student at UC Berkeley. Chelsea’s research is focused on machine learning for robotic perception and control. Despite being early in her career, Chelsea is an accomplished researcher with more than 14 published papers in the past 2 years, on subjects like Deep Visual Foresight , Model-Agnostic Meta-Learning and Visuomotor Learning to name a few, all of which we discuss in the show, along with topics like zero-shot, one-shot and few-shot learning. I’d also like to give a shout out to Shreyas, a listener who wrote in to request that we interview a current PhD student about their journey and experiences. Chelsea and I spend some time at the end of the interview talking about this, and she has some great advice for current and prospective PhD students but also independent learners in the field. During this part of the discussion I wonder out loud if any listeners would be interested in forming a virtual paper reading club of some sort. I’m not sure yet exactly what this would look like, but please drop a comment in the show notes if you’re interested. I'm going to once again deploy the Nerd Alert for this episode; Chelsea and I really dig deep into these learning methods and techniques, and this conversation gets pretty technical at times, to the point that I had a tough time keeping up myself. The notes for this page can be found at twimlai.com/talk/29
Jun 23, 2017
Reinforcement Learning Deep Dive with Pieter Abbeel - TWiML Talk #28
00:54:19
This week our guest is Pieter Abbeel, Assistant Professor at UC Berkeley, Research Scientist at OpenAI, and Cofounder of Gradescope. Pieter has an extensive background in AI research, going way back to his days as Andrew Ng’s first PhD student at Stanford. His research today is focused on deep learning for robotics. During this conversation, Pieter and I really dig into reinforcement learning, a technique for allowing robots (or AIs) to learn through their own trial and error. Nerd alert!! This conversation explores cutting edge research with one of the leading researchers in the field and, as a result, it gets pretty technical at times. I try to uplevel it when I can keep up myself, so hang in there. I promise that you’ll learn a ton if you keep with it. The notes for this show can be found at twimlai.com/talk/28
Jun 17, 2017
Intelligent Autonomous Robots with Ilia Baranov - TWiML Talk #27
00:55:47
Our first guest in the Industrial AI series is Ilia Baranov, engineering manager at Clearpath Robotics. Ilia is responsible for setting the engineering direction for all of Clearpath’s research platforms. Ilia likes to describe his role at the company as “both enabling and preventing the robot revolution.” He’s a longtime contributor to the Open Source Robotics Community and ROS, an open source robotic operating system. He is the also the managing engineer of the PR2 support team at Clearpath and leads the technical demonstration group. In our conversation we cover a lot of ground, including what it really means to field autonomous robots, the use of autonomous robots in research and industrial environments, the different approaches and challenges to achieving autonomy, and much more! The notes for this show are available at twimlai.com/talk/27, and for more information on the Industrial AI Series, visit twimlai.com/IndustrialAI.
Jun 09, 2017
Global AI Trends with Ben Lorica - TWiML Talk #26
00:57:22
This week I’ve invited my friend Ben Lorica onto the show. Ben is Chief Data Scientist for O’Reilly Media, and Program Director of Strata Data & the O'Reilly A.I. conference. Ben has worked on analytics and machine learning in the finance and retail industries, and serves as an advisor for nearly a dozen startups. In his role at O’Reilly he’s responsible for the content for 7 major conferences around the world each year. In the show we discuss all of that, touching on how publishers can take advantage of machine learning and data mining, how the role of “data scientist” is evolving and the emergence of the machine learning engineer, and a few of the hot technologies, trends and companies that he’s seeing arise around the world. The notes for this show can be found at twimlai.com/talk/26
Jun 02, 2017
Offensive vs Defensive Data Science with Deep Varma - TWiML Talk #25
00:56:30
This week on the show my guest is Deep Varma, Vice President of Data Engineering at real estate startup Trulia. Deep has run data engineering teams in silicon valley for well over a decade, and is now responsible for the engineering efforts supporting Trulia’s Big Data Technology Platform, which encompasses everything from Data acquisition & management to Data Science & Algorithms. In the show we discuss all of that, with an emphasis on Trulia’s data engineering pipeline and their personalization platform, as well how they use computer vision, deep learning and natural language generation to deliver their product. Along the way, Deep offers great insights into what he calls offensive vs defensive data science, and the difference between data-driven decision making vs products. Another great interview, and i'm sure you’ll enjoy it. The notes for this show can be found at twimlai.com/talk/25 Subscribe! iTunes ➙ https://itunes.apple.com/us/podcast/this-week-in-machine-learning/id1116303051?mt=2 Soundcloud ➙ https://soundcloud.com/twiml Google Play ➙ http://bit.ly/2lrWlJZ Stitcher ➙ http://www.stitcher.com/s?fid=92079&refid=stpr RSS ➙ https://twimlai.com/feed Lets Connect! Twimlai.com ➙ https://twimlai.com/contact Twitter ➙ https://twitter.com/twimlai Facebook ➙ https://Facebook.com/Twimlai Medium ➙ https://medium.com/this-week-in-machine-learning-ai
May 26, 2017
Reinforcement Learning: The Next Frontier of Gaming with Danny Lange - TWiML Talk #24
00:57:19
My guest on the show this week is Danny Lange, VP for Machine Learning & AI at video game technology developer Unity Technologies. Danny is well traveled in the world of ML and AI, and has had a hand in developing machine learning platforms at companies like Uber, Amazon and Microsoft. In this conversation we cover a bunch of topics, including How ML & AI are being used in gaming, the importance of reinforcement learning in the future of game development, the intersection between AI and AR/VR and the next steps in natural character interaction. The notes for this show can be found at twimlai.com/talk/24
May 20, 2017
Integrating Psycholinguistics into AI with Dominique Simmons - TWiML Talk #23
01:02:16
I think you’re really going to enjoy today’s show. Our guest this week is Dominique Simmons, Applied research Scientist at AI tools vendor Dimensional Mechanics. Dominique brings an interesting background in Cognitive Psychology and psycholinguistics to her work and research in AI and, well, to this podcast. In our conversation, we cover the implications of cognitive psychology for neural networks and AI systems, and in particular how an understanding of human cognition impacts the development of AI models for media applications. We also discuss her research into multimodal training of AI models, and how our understanding of the human brain has influenced this work. We also explore the debate around the biological plausibility of machine learning and AI models. It was a great conversation. The show notes can be found at twimlai.com/talk/23.
May 12, 2017
Deep Neural Nets for Visual Recognition with Matt Zeiler - TWiML Talk #22
00:24:19
Today we bring you our final interview from backstage at the NYU FutureLabs AI Summit. Our guest this week is Matt Zeiler. Matt graduated from the University of Toronto where he worked with deep learning researcher Geoffrey Hinton and went on to earn his PhD in machine learning at NYU, home of Yann Lecun. In 2013 Matt’s founded Clarifai, a startup whose cloud-based visual recognition system gives developers a way to integrate visual identification into their own products, and whose initial image classification algorithm achieved top 5 results in that year’s ImageNet competition. I caught up with Matt after his talk “From Research to the Real World”. Our conversation focused on the birth and growth of Clarifai, as well as the underlying deep neural network architectures that enable it. If you’ve been listening to the show for a while, you’ve heard me ask several guests how they go about evolving the architectures of their deep neural networks to enhance performance. Well, in this podcast Matt gives the most satisfying answer I’ve received to date by far. Check it out. I think you’ll enjoy it. The show notes can be found at twimlai.com/talk/22.
May 05, 2017
Engineering the Future of AI with Ruchir Puri - TWiML Talk #21
00:25:06
Today we bring you the second of three interviews we did backstage from the NYU FutureLabs AI Summit, this time with Ruchir Puri. Ruchir is the Chief Architect at IBM Watson as well as an IBM Fellow. I caught up with Ruchir after his talk on “engineering the Future of AI for Businesses”. Our conversation focused on cognition and reasoning, and we explored what these concepts represent, how enterprises really want to consume them, and how IBM Watson seeks to deliver them. The show notes can be found at twimlai.com/talk/21.
Apr 28, 2017
Selling AI to the Enterprise with Kathryn Hume - TWiML Talk #20
00:24:50
This week's guest is Kathryn Hume. Kathryn is the President of Fast Forward Labs, which is an independent machine intelligence research company that helps organizations accelerate their data science and machine intelligence capabilities. If Fast Forward Labs sounds familiar, that's because we had their founder, Hilary Mason on a few months ago. We’ll link to that in the show notes. My discussion with Kathryn focused on AI adoption within the enterprise. She shared several really interesting examples of the kinds of things she’s seeing enterprises do with machine learning and AI, and we discussed a few of the various challenges enterprises face and some of the lessons her company has learned in helping them. I really enjoyed our conversation and I know you will too! You can find the notes for todays show here: https://twimlai.com/talk/20
Apr 21, 2017
From Particle Physics to Audio AI with Scott Stephenson - TWiML Talk #19
00:59:21
This week my guest is Scott Stephenson. Scott is co-Founder & CEO of Deepgram, which has developed an AI-based platform for indexing and searching audio and video. Scott and I cover a ton of interesting topics including applying machine learning techniques to particle physics, his time in a lab 2 miles below the surface of the earth, applying neural networks to audio, and the Deep Learning Framework Kur that his company open-sourced. The show notes can be found at twimlai.com/talk/19.
Apr 14, 2017
(4/5) Behold.ai - Increasing Efficiency of Healthcare Insurance Billing with NLP - TWiML Talk #18
00:16:31
This week I'm on location at NYU/ffVC AI NexusLab startup accelerator, speaking with founders from the 5 companies in the program's inaugural batch. This interview is with Behold.ai, which uses computer vision and natural language processing techniques to bring efficiencies to the world of healthcare insurance billing. The notes for this series can be found at twimlai.com/nexuslab. Thanks to Future Labs at NYU Tandon and ffVenture Capital for sponsoring the series!
Apr 07, 2017
(5/5) AlphaVertex - Creating a Worldwide Financial Knowledge Graph - TWiML Talk #18
00:26:14
This week I'm on location at NYU/ffVC AI NexusLab startup accelerator, speaking with founders from the 5 companies in the program's inaugural batch. This interview is with AlphaVertex, a FinTech startup creating a worldwide financial knowledge graph to help investors predict stock prices. The notes for this series can be found at twimlai.com/nexuslab. Thanks to Future Labs at NYU Tandon and ffVenture Capital for sponsoring the series!
Apr 07, 2017
(3/5) Cambrian Intelligence - Using AI to Simplify the Programming of Robots - TWiML Talk #18
00:23:20
This week I'm on location at NYU/ffVC AI NexusLab startup accelerator, speaking with founders from the 5 companies in the program's inaugural batch. This interview is with Cambrian Intelligence, a company using AI to simplify the programming of industrial robots for the automotive industry. The notes for this series can be found at twimlai.com/nexuslab. Thanks to Future Labs at NYU Tandon and ffVenture Capital for sponsoring the series!
Apr 07, 2017
(2/5) Klustera - Location-Based Intelligence for Smarter Marketing - TWiML Talk #18
00:22:11
This week I'm on location at NYU/ffVC AI NexusLab startup accelerator, speaking with founders from the 5 companies in the program's inaugural batch. This interview is with Klustera, a company applying location-based intelligence and machine learning to help brands execute smarter marketing campaigns. The notes for this series can be found at twimlai.com/nexuslab. Thanks to Future Labs at NYU Tandon and ffVenture Capital for sponsoring the series!
Apr 07, 2017
(1/5) HelloVera - AI-Powered Customer Support - TWiML Talk #18
00:25:37
This week I'm on location at NYU/ffVC AI NexusLab startup accelerator, speaking with founders from the 5 companies in the program's inaugural batch. This interview is with HelloVera, a company applying artificial intelligence to the challenge of automating customer support experiences. The notes for this series can be found at https://twimlai.com/nexuslab. Thanks to Future Labs at NYU Tandon and ffVenture Capital for sponsoring the series!
Apr 07, 2017
Interactive Machine Learning Systems with Alekh Agarwal - TWiML Talk #17
00:35:03
This week my guest is Alekh Agarwal. Alekh is a researcher with Microsoft Research whose research is focused on Interactive Machine Learning. In our discussion, Alekh and I discuss various aspects of this exciting area of research such as active learning, reinforcement learning, contextual bandits and more.
Mar 31, 2017
Machine Learning in Cybersecurity with Evan Wright - TWiML Talk #16
01:05:31
This week my guest is Evan Wright, principal data scientist at cybersecurity startup Anomali. In my interview with Evan, he and I discussed about a number of topics surrounding the use of machine learning in cybersecurity. If Evan’s name sounds familiar, it’s because Evan was the winner of the O’Reilly Strata+Hadoop World ticket giveaway earlier this month. We met up at the conference last week and took advantage of the opportunity to record this show. Our conversation covers, among other topics, the three big problems in cybersecurity that ML can help out with, the challenges of acquiring ground truth in cybersecurity and some ways to accomplish it, and the use of decision trees, generative adversarial networks, and other algorithms in the field. The show notes can be found at twimlai.com/talk/16.
Mar 24, 2017
Domain Knowledge in Machine Learning Models for Sustainability with Stefano Ermon - TWiML Talk #15
00:55:55
My guest this week is Stefano Ermon, Assistant Professor of Computer Science at Stanford University, and Fellow at Stanford’s Woods Institute for the Environment. Stefano and I met at the Re-Work Deep Learning Summit earlier this year, where he gave a presentation on Machine Learning for Sustainability. Stefano and I spoke about a wide range of topics, including the relationship between fundamental and applied machine learning research, incorporating domain knowledge in machine learning models, dimensionality reduction, and his interest in applying ML & AI to addressing sustainability issues such as poverty, food security and the environment. The show notes can be found at twimlai.com/talk/15.
Mar 17, 2017
Scaling Deep Learning: Systems Challenges & More with Shubho Sengupta — TWiML Talk #14
01:13:58
This week my guest is Shubho Sengupta, Research Scientist at Baidu. I had the pleasure of meeting Shubho at the Rework Deep Learning Summit earlier this year, where he delivered a presentation on Systems Challenges for Deep Learning. We dig into this topic in the interview, and discuss a variety of issues including network architecture, productionalization, operationalization and hardware. The show notes can be found at twimlai.com/talk/14.
Mar 10, 2017
Understanding Deep Neural Nets with Dr. James McCaffrey - TWiML Talk #13
01:18:34
My guest this week is Dr. James McCaffrey, research engineer at Microsoft Research. James and I cover a ton of ground in this conversation, including recurrent neural nets (RNNs), convolutional neural nets (CNNs), long short term memory (LSTM) networks, residual networks (ResNets), generative adversarial networks (GANs), and more. We also discuss neural network architecture and promising alternative approaches such as symbolic computation and particle swarm optimization. The show notes can be found at twimlai.com/talk/13.
Mar 03, 2017
Brendan Frey - Reprogramming the Human Genome with AI - TWiML Talk #12
01:03:18
My guest this week is Brendan Frey, Professor of Engineering and Medicine at the University of Toronto and Co-Founder and CEO of the startup Deep Genomics. Brendan and I met at the Re-Work Deep Learning Summit in San Francisco last month, where he delivered a great presentation called “Reprogramming the Human Genome: Why AI is Needed.” In this podcast we discuss the application of AI to healthcare. In particular, we dig into how Brendan’s research lab and company are applying machine learning and deep learning to treating and preventing human genetic disorders. The show notes can be found at twimlai.com/talk/12
Feb 24, 2017
Hilary Mason - Building AI Products - TWiML Talk #11
00:19:40
My guest this time is Hilary Mason. Hilary was one of the first “famous” data scientists. I remember hearing her speak back in 2011 at the Strange Loop conference in St. Louis. At the time she was Chief Scientist for bit.ly. Nowadays she’s running Fast Forward Labs, which helps organizations accelerate their data science and machine intelligence capabilities through a variety of research and consulting offerings. Hilary presented at the O'Reilly AI conference on “practical AI product development” and she shares a lot of wisdom on that topic in our discussion. The show notes can be found at twimlai.com/talk/11.
Jan 25, 2017
Francisco Webber - Statistics vs Semantics for Natural Language Processing - TWiML Talk #10
00:49:23
My guest this time is Francisco Webber, founder and General Manager of artificial intelligence startup Cortical.io. Francisco presented at the O’Reilly AI conference on an approach to natural language understanding based on semantic representations of speech. His talk was called “AI is not a matter of strength but of intelligence.” My conversation with Francisco was a bit technical and abstract, but also super interesting. The show notes can be found at twimlai.com/talk/10.
Dec 03, 2016
Pascale Fung - Emotional AI: Teaching Computers Empathy - TWiML Talk #9
00:34:50
My guest this time is Pascale Fung, professor of electrical & computer engineering at Hong Kong University of Science and Technology. Pascale delivered a presentation at the recent O'Reilly AI conference titled "How to make robots empathetic to human feelings in real time," and I caught up with her after her talk to discuss teaching computers to understand and respond to human emotions. We also spend some time talking about the (information) theoretical foundations of modern approaches to speech understanding. The notes for this show can be found at twimlai.com/talk/9.
Nov 08, 2016
Diogo Almeida - Deep Learning: Modular in Theory, Inflexible in Practice - TWiML Talk #8
00:46:52
My guest this time is Diogo Almeida, senior data scientist at healthcare startup Enlitic. Diogo and I met at the O'Reilly AI conference, where he delivered a great presentation on in-the-trenches deep learning titled “Deep Learning: Modular in theory, inflexible in practice,” which we discuss in this interview. Diogo is also a past 1st place Kaggle competition winner, and we spend some time discussing the competition he competed in and the approach he took as well. The notes for this show can be found at twimlai.com/talk/8.
Oct 23, 2016
Carlos Guestrin - Explaining the Predictions of Machine Learning Models - TWiML Talk #7
00:32:30
My guest this time is Carlos Guestrin, the Amazon professor of Machine Learning at the University of Washington. Carlos and I recorded this podcast at a conference, shortly after Apple's acquisition of his company Turi. Our focus for this podcast is the explainability of machine learning algorithms. In particular, we discuss some interesting new research published by his team at U of W. The notes for this show can be found at twimlai.com/talk/7.
Oct 09, 2016
Angie Hugeback - Generating Training Data for Your ML Models - TWiML Talk #6
01:02:29
My guest this time is Angie Hugeback, who is principal data scientist at Spare5. Spare5 helps customers generate the high-quality labeled training datasets that are so crucial to developing accurate machine learning models. In this show, Angie and I cover a ton of the real-world practicalities of generating training datasets. We talk through the challenges faced by folks that need to label training data, and how to develop a cohesive system for achieving performing the various labeling tasks you’re likely to encounter. We discuss some of the ways that bias can creep into your training data and how to avoid that. And we explore the some of the popular 3rd party options that companies look at for scaling training data production, and how they differ. Spare5 has graciously sponsored this episode; you can learn more about them at spare5.com. The notes for this show can be found at twimlai.com/talk/6.
Sep 29, 2016
Joshua Bloom - Machine Learning for the Stars & Productizing AI - TWiML Talk #5
01:30:06
My guest this time is Joshua Bloom. Josh is professor of astronomy at the University of California, Berkeley and co-founder and Chief Technology Officer of machine learning startup Wise.io. In this wide-ranging interview you’ll learn how Josh and his research group at Berkeley pioneered the use of machine learning for the analysis of images from robotic infrared telescopes. We discuss the founding of his company, Wise.io, which uses machine learning to help customers deliver better customer support. That wasn’t where the company started though, and you’ll hear why and how they evolved to serve this market. We talk about his company’s technology stack and data science pipeline in fair detail, and discuss some of the key technology decisions they’ve made in building their product. We also discuss some interesting open research challenges in machine learning and AI. The notes for this show can be found at twimlai.com/talk/5.
Sep 22, 2016
Charles Isbell - Interactive AI, Plus Improving ML Education - TWiML Talk #4
01:08:29
My guest this time is Charles Isbell, Jr., Professor and Senior Associate Dean in the College of Computing at Georgia Institute of Technology. Charles and I go back a bit… in fact he’s the first AI researcher I ever met. His research focus is what he calls “interactive artificial intelligence,” a discipline of AI focused specifically on the interactions between AIs and humans. We explore what this means and some of the interesting research results in this field. One part of this discussion I found particularly interesting was the intersection between his AI research and marketing and behavioral economics. Beyond his research, Charles is well known in the ML and AI worlds for his popular Machine Learning course sequence on Udacity, which he teaches with Brown University professor Michael Littman, and for the Online Master’s of Science in Computer Science program that he helped launch at Georgia Tech. We also spend quite a bit of time talking about what’s really missing in machine learning education and how to make it more accessible. The notes for this show can be found at twimlai.com/talk/4.
Sep 10, 2016
Xavier Amatriain - Engineering Practical Machine Learning Systems - TWiML Talk #3
00:57:20
My guest this time is Xavier Amatriain. Xavier is a former researcher who went on to lead the machine learning recommendations team at Netflix, and is now the vice president of engineering at Quora, the Q&A site. We spend quite a bit of time digging into each of these experiences in the interview. Here are just a few of the things we cover in our discussion: Why Netflix invested $1 million in the Netflix Prize, but didn’t use the winning solution; What goes into engineering practical machine learning systems; The problem Xavier has with the deep learning hype; And, what the heck is a multi-arm bandit and how can it help us. The notes for this show can be found at https://twimlai.com/talk/3.
Aug 28, 2016
Siraj Raval - How to Build Confidence as an ML Developer - TWiML Talk #2
00:41:46
Siraj Raval is a machine learning hacker and teacher whose machine learning for hackers and fresh machine learning youtube series are fun, informative, high energy and practical ways to learn about a ton of machine learning and AI topics. I had a chance to catch up with Siraj in San Francisco recently, and we had a great discussion. Siraj has great advice on how to learn machine learning and build confidence as a machine learning developer, how to research and formulate projects, who to follow on Machine Learning twitter, and much more. The notes for this show can be found at https://twimlai.com/talk/2
Aug 21, 2016
This Week in ML & AI – 8/12/16: Another huge machine learning acquisition + AI in the Olympics
00:23:36
This Week in Machine Learning & AI brings you the week’s most interesting and important stories from the world of machine learning and artificial intelligence. This week we discuss Intel’s latest deep learning acquisition, AI in the Olympics, and how you can win a free ticket to the O’Reilly AI Conference in New York City. Plus a bunch more on This Week in Machine Learning & AI. The notes for this show can be found at twimlai.com/13.
Aug 15, 2016
This Week in ML & AI – 8/5/16: Apple Acquires Turi, the DARPA Hacker-Bot Challenge and More
00:24:55
This Week in Machine Learning & AI brings you the week’s most interesting and important stories from the world of machine learning and artificial intelligence. This week we look at Apple’s acquisition of machine learning startup Turi, DARPA’s autonomous hacker-bot challenge, and Comma.ai’s autonomous driving dataset. Plus, of course, tons more. Show notes for this episode can be found at twimlai.com/12.
Aug 06, 2016
Clare Corthell - Open Source Data Science Masters, Hybrid AI, Algorithmic Ethics - TWiML Talk #1
00:49:02
This Week in Machine Learning & AI brings you the week’s most interesting and important stories from the world of machine learning and artificial intelligence. We try something new this week with an interview of Clare Corthell, Founding Partner of Luminant Data, recorded live at the Wrangle Conference. We cover her background and what she’s been up to lately, the Open Source Data Science Masters project that she created, getting beyond the beginner’s plateau in machine learning and data science, hybrid AI, the top 3 lessons from her time as a consulting data scientist, and, a recurring topic both here on This Week in Machine Learning and AI and also at the conference: Algorithmic Ethics. The notes for this show can be found at https://twimlai.com/11.
Jul 31, 2016
This Week in ML & AI - 7/22/16: ML to Optimize Datacenters, Crazy New GPU from NVIDIA, Faster RNNs
00:25:19
This Week in Machine Learning & AI brings you the week’s most interesting and important stories from the world of machine learning and artificial intelligence. This week covers Google’s use of ML to cut data center power consumption, NVIDIA new ‘crazy, reckless’ GPU, and a new Layer Normalization technique that promises to reduce the training time for deep neural networks. Plus, a bunch more. Show notes for this episode can be found at twimlai.com/10.
Jul 24, 2016
This Week in ML & AI - 7/15/16: A Wingman AI for Pokémon Go and Wide & Deep Learning at Google
00:30:22
This Week in Machine Learning & AI brings you the week’s most interesting and important stories from the world of machine learning and artificial intelligence. This week's show features a conversation about public datasets, an AI-powered Pokémon Go Wingman, a new deep learning app for your iPhone, Google research into Wide & Deep learning models, plus a whole lot more. Show notes for this episode can be found at twimlai.com/9.
Jul 17, 2016
This Week in ML & AI - 7/8/16: A BS Meter for AI, Retrieval Models for Chatbots & Predatory Robots
00:29:28
This Week in Machine Learning & AI brings you the week’s most interesting and important stories from the world of machine learning and artificial intelligence. This week's show covers the White House’s AI Now workshop, tuning your AI BS meter, research on predatory robots, an AI that writes Python code, plus acquisitions, financing, technology updates and a bunch more. Show notes for this episode can be found at https://twimlai.com/8.
Jul 10, 2016
This Week in ML & AI - 7/1/16: Fatal Tesla Autopilot Crash, EU Outlawing Machine Learning & CVPR
00:35:36
This Week in Machine Learning & AI brings you the week’s most interesting and important stories from the world of machine learning and artificial intelligence. This week's show covers the first fatal Tesla autopilot crash, a new EU law that could prohibit machine learning, the AI that shot down a human fighter pilot (in simulation), the 2016 CVPR conference, 10 hot AI startups, the business implications of machine learning, cool chatbot projects and if you can believe it, even more. Show notes for this episode can be found at https://twimlai.com/7.
Jul 03, 2016
This Week in ML & AI - 6/24/16: Dueling Neural Networks at ICML, Plus Training a Robotic Housekeeper
00:25:40
This Week in Machine Learning & AI brings you the week’s most interesting and important stories from the world of machine learning and artificial intelligence. This week's show covers the International Conference on Machine Learning (ICML), new research on "dueling architectures" for reinforcement learning, AI safety for robots, plus top AI business deals, tech announcement, projects and more.
Jun 25, 2016
This Week in Machine Learning & AI - 6/17/16: Apple's New ML APIs, IBM Brings Deep Learning Thunder
00:24:32
This Week in Machine Learning & AI brings you the week’s most interesting and important stories from the world of machine learning and artificial intelligence. This week’s podcast digs into Apple's ML and AI announcements at WWDC, looks at IBM's new Deep Thunder offering, and discusses exciting new deep learning research from MIT, OpenAI and Google. Show notes available at https://twimlai.com/5.
Jun 18, 2016
This Week In Machine Learning & AI - 6/10/16: Self-Motivated AI, Plus A Kill-Switch for Rogue Bots
00:24:05
This Week in Machine Learning & AI brings you the week’s most interesting and important stories from the world of machine learning and artificial intelligence. This week’s podcast looks at new research on intrinsic motivation for AI systems, a kill-switch for intelligent agents, "knu" chips for machine learning, a screenplay made by a neural net, and more. Show notes and subscribe links at https://cloudpul.se/twiml/4.
Jun 11, 2016
This Week In Machine Learning & AI - 6/3/16: Facebook's DeepText, ML & Art, Artificial Assistants
00:24:51
This Week in Machine Learning & AI brings you the week’s most interesting and important stories from the world of machine learning and artificial intelligence. This week’s podcast looks at Facebooks' new DeepText engine, creating music & art with deep learning and Google Magenta, how to build artificial assistants and bots, and applying economics to machine learning models. For show notes visit: https://cloudpul.se/posts/twiml-facebooks-deeptext-ml-art-artificial-assistants
Jun 04, 2016
This Week In Machine Learning & AI - 5/27/16: The White House on AI & Aggressive Self-Driving Cars
00:25:53
This Week in Machine Learning & AI brings you the week's most interesting and important stories from the world of machine learning and artificial intelligence. This week's episode explores the White House workshops on AI, human bias in AI and machine learning models, a company working on machine learning for small datasets, plus the latest AI & ML news and a self-driving car that learned how to drive aggressively.
May 28, 2016
This Week In Machine Learning & AI - 5/20/16: AI at Google I/O, Amazon's Deep Learning DSSTNE
00:19:29
This Week In Machine Learning & AI - May 20, 2016. Google I/O, deep learning hardware and an AI to save you from conference call hell.
May 21, 2016