Talking Machines

By Tote Bag Productions

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Category: Technology

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Subscribers: 583
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Episodes: 110

Description

Talking Machines is your window into the world of machine learning. Your hosts, Katherine Gorman and Neil Lawrence, bring you clear conversations with experts in the field, insightful discussions of industry news, and useful answers to your questions. Machine learning is changing the questions we can ask of the world around us, here we explore how to ask the best questions and what to do with the answers.

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Episode Date
Gods and Robots
Sep 09, 2021
Responsibility, Risk, and Publishing
Aug 19, 2021
ICML 2021: Test of Time(ly) Award
Jul 24, 2021
Learning with Less, Invisible Labor and Combating Anti-Blackness
Jul 09, 2021
Let's Reflect
Jun 13, 2020
Predicting Floods and Really Doing Good
May 29, 2020
ICLR: accessible, inclusive, virtual
May 14, 2020
Humans in the Loop and Outside of the Classroom
May 01, 2020
The Evolution of ML and Furry Little Animals
Apr 16, 2020
Talking Machines Live and Understanding Modeling Viruses
Apr 03, 2020
Prioritizing Problems and 100 episodes
Mar 20, 2020
The Great AI Fallacy
Mar 05, 2020
If a Machine Could Predict Your Death, Should it?
Feb 20, 2020
Predicting the Decade and Distributing Conferences
Feb 06, 2020
Debating Project Debater and Hello NeurIPS
Nov 21, 2019
De-Enchanting AI with the Law
Nov 07, 2019
How to Ask an Actionable Question
Oct 25, 2019
Children are the Future and Ada Lovelace Day
Oct 10, 2019
News from Neil and Updates from DALI
Sep 26, 2019
A Cooperative Path to Artificial Intelligence
Sep 13, 2019
What Does Red Sound Like
Aug 30, 2019
Not What But Why
Aug 15, 2019
Idea Pandemics and Workshop Walkthrough
Aug 01, 2019
PosterSession.ai and Deep Quaggles
Jul 18, 2019
The View from Addis Ababa
Jul 04, 2019
DSA Addis Ababa and ICML Los Angeles
Jun 21, 2019
Data Trusts and Citation Trends
Jun 06, 2019
Reproducibly and Revisiting History
May 23, 2019
Insights from AISTATS
May 10, 2019
The Deep End of Deep Learning
Apr 25, 2019
Exploring MARS and Getting back to Bayesics
Apr 11, 2019
The Sweetness of a Bitter Lesson and Bringing ML and Healthcare Closer
Mar 28, 2019
Slowed Down Conferences and Even More Summer Schools
Mar 14, 2019
Jupyter Notebooks and Modern Model Distribution
Feb 28, 2019
Real World Real Time and Five Papers for Mike Tipping
Feb 15, 2019
The Bezos Paradox and Machine Learning Languages
Feb 01, 2019
Being Global Bit by Bit
Jan 17, 2019
The Possibility Of Explanation and The End of Season Four
Nov 29, 2018
Neural Information Processing Systems and Distributed Internal Intelligence Systems
Nov 16, 2018
Data Driven Ideas and Actionable Privacy
Nov 01, 2018
AI for Good and The Real World
Oct 18, 2018
Systems Design and Tools for Transparency
Oct 05, 2018
How to Research in Hype and CIFAR's Strategy
Sep 20, 2018
Troubling Trends and Climbing Mountains
Sep 07, 2018
Gaussian Processes, Grad School, and Richard Zemel
Aug 23, 2018
Long Term Fairness
Aug 09, 2018
Simulated Learning and Real World Ethics
Jul 27, 2018
ICML 2018 with Jennifer Dy
Jul 12, 2018
Aspirational Asimov and How to Survive a Conference
Jun 28, 2018
Explanations and Reviews
Jun 14, 2018
Statements on Statements
May 31, 2018
The Futility of Artificial Carpenters and Further Reading
May 17, 2018
Economies, Work and AI
May 03, 2018
Explainability and the Inexplicable
Apr 19, 2018
Good Data Practice Rules
Apr 05, 2018
Can an AI Practitioner Fix a Radio?
Mar 22, 2018
Natural vs Artificial Intelligence and Doing Unexpected Work
Mar 08, 2018
Scientific Rigor and Turning Information into Action
Feb 22, 2018
Code Review for Community Change
Feb 08, 2018
The Pace of Change and The Public View of ML
Oct 05, 2017
The Long View and Learning in Person
Sep 21, 2017
Machine Learning in the Field and Bayesian Baked Goods
Sep 08, 2017
Data Science Africa with Dina Machuve
Aug 10, 2017
The Church of Bayes and Collecting Data
Jul 28, 2017
Getting a Start in ML and Applied AI at Facebook
Jul 13, 2017
Bias Variance Dilemma for Humans and the Arm Farm
Jun 29, 2017
Overfitting and Asking Ecological Questions with ML
Jun 15, 2017
Graphons and "Inferencing"
May 25, 2017
Hosts of Talking Machines: Neil Lawrence and Ryan Adams
Apr 27, 2017
ANGLICAN and Probabilistic Programming
Sep 01, 2016
Eric Lander and Restricted Boltzmann Machines
Aug 18, 2016
Generative Art and Hamiltonian Monte Carlo
Aug 04, 2016
Perturb-and-MAP and Machine Learning in the Flint Water Crisis
Jul 21, 2016
Automatic Translation and t-SNE
Jul 07, 2016
Fantasizing Cats and Data Numbers
Jun 16, 2016
Spark and ICML
Jun 02, 2016
Computational Learning Theory and Machine Learning for Understanding Cells
May 19, 2016
Sparse Coding and MADBITS
May 05, 2016
Remembering David MacKay
Apr 21, 2016
Machine Learning and Society
Apr 08, 2016
Software and Statistics for Machine Learning
Mar 24, 2016
Machine Learning in Healthcare and The AlphaGo Matches
Mar 10, 2016
AI Safety and The Legacy of Bletchley Park
Feb 25, 2016
Robotics and Machine Learning Music Videos
Feb 11, 2016
OpenAI and Gaussian Processes
Jan 28, 2016
Real Human Actions and Women in Machine Learning
Jan 14, 2016
Open Source Releases and The End of Season One
Nov 22, 2015
Probabilistic Programming and Digital Humanities
Nov 05, 2015
Workshops at NIPS and Crowdsourcing in Machine Learning
Oct 22, 2015
Machine Learning Mastery and Cancer Clusters
Oct 08, 2015
Data from Video Games and The Master Algorithm
Sep 24, 2015
Strong AI and Autoencoders
Sep 10, 2015
Active Learning and Machine Learning in Neuroscience
Aug 27, 2015
Machine Learning in Biology and Getting into Grad School
Aug 13, 2015
Machine Learning for Sports and Real Time Predictions
Jul 30, 2015
Really Really Big Data and Machine Learning in Business
Jul 16, 2015
Solving Intelligence and Machine Learning Fundamentals
Jul 02, 2015
Working With Data and Machine Learning in Advertising
Jun 18, 2015
The Economic Impact of Machine Learning and Using The Kernel Trick on Big Data
Jun 04, 2015
How We Think About Privacy and Finding Features in Black Boxes
May 21, 2015
Interdisciplinary Data and Helping Humans Be Creative
May 07, 2015
Starting Simple and Machine Learning in Meds
Apr 23, 2015
Spinning Programming Plates and Creative Algorithms
Apr 09, 2015
The Automatic Statistician and Electrified Meat
Mar 26, 2015
The Future of Machine Learning from the Inside Out
Mar 13, 2015
The History of Machine Learning from the Inside Out
Feb 26, 2015
Using Models in the Wild and Women in Machine Learning
Feb 12, 2015
Common Sense Problems and Learning about Machine Learning
Jan 29, 2015
Machine Learning and Magical Thinking
Jan 15, 2015
Hello World!
Jan 01, 2015