The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
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799 afleveringen, van nieuw naar oud
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Cell Exploration with ML at the Allen Institute w/ Jianxu Chen - #383
15 juni 2020 · 44 min
Today we’re joined by Jianxu Chen, a scientist at the Allen Institute for Cell Science. Lees meer
At the latest GTC conference, Jianxu presented his work on the Allen Cell Explorer Toolkit, an open-source project that allows users to do 3D segmentation of intracellular structures in fluorescence microscope images at high resolutions, making the images more accessible for data analysis. We discuss three of the major components of the toolkit: the cell image analyzer, the image generator, and the image visualizer
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Neural Arithmetic Units & Experiences as an Independent ML Researcher with Andreas Madsen - #382
11 juni 2020 · 32 min
Today we’re joined by Andreas Madsen, an independent researcher based in Denmark. While we caught up with Andreas to discuss his ICLR spotlight paper, “Neural Arithmetic Units,” we also spend time exploring his experience as an independent researcher, discussing the difficulties of working with limited resources, the importance of finding peers to collaborate with, and tempering expectations of getting papers accepted to conferences -- something that might take a few tries to get right. Lees meer
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2020: A Critical Inflection Point for Responsible AI with Rumman Chowdhury - #381
8 juni 2020 · 1 uur 2 min
Today we’re joined by Rumman Chowdhury, Managing Director and Global Lead of Responsible AI at Accenture. In our conversation with Rumman, we explored questions like: Lees meer
• Why is now such a critical inflection point in the application of responsible AI?
• How should engineers and practitioners think about AI ethics and responsible AI?
• Why is AI ethics inherently personal and how can you define your own personal approach?
• Is the implementation of AI governance necessarily authoritarian? -
Panel: Advancing Your Data Science Career During the Pandemic - #380
4 juni 2020 · 1 uur 7 min
Today we’re joined by Ana Maria Echeverri, Caroline Chavier, Hilary Mason, and Jacqueline Nolis, our guests for the recent Advancing Your Data Science Career During the Pandemic panel. Lees meer
In this conversation, we explore ways that Data Scientists and ML/AI practitioners can continue to advance their careers despite current challenges. Our panelists provide concrete tips, advice, and direction for those just starting out, those affected by layoffs, and those just wanting to move forward in their careers.
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On George Floyd, Empathy, and the Road Ahead
2 juni 2020 · 6 min
Visit twimlai.com/blacklivesmatter for resources to support organizations pushing for social equity like Black Lives Matter, and groups offering relief for those jailed for exercising their rights to peaceful protest.
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Engineering a Less Artificial Intelligence with Andreas Tolias - #379
28 mei 2020 · 46 min
Today we’re joined by Andreas Tolias, Professor of Neuroscience at Baylor College of Medicine. Lees meer
We caught up with Andreas to discuss his recent perspective piece, “Engineering a Less Artificial Intelligence,” which explores the shortcomings of state-of-the-art learning algorithms in comparison to the brain. The paper also offers several ideas about how neuroscience can lead the quest for better inductive biases by providing useful constraints on representations and network architecture.
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Rethinking Model Size: Train Large, Then Compress with Joseph Gonzalez - #378
25 mei 2020 · 52 min
Today we’re joined by Joseph Gonzalez, Assistant Professor in the EECS department at UC Berkeley. Lees meer
In our conversation, we explore Joseph’s paper “Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers,” which looks at compute-efficient training strategies for models. We discuss the two main problems being solved; 1) How can we rapidly iterate on variations in architecture? And 2) If we make models bigger, is it really improving any efficiency?
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The Physics of Data with Alpha Lee - #377
21 mei 2020 · 34 min
Today we’re joined by Alpha Lee, Winton Advanced Fellow in the Department of Physics at the University of Cambridge. Our conversation centers around Alpha’s research which can be broken down into three main categories: data-driven drug discovery, material discovery, and physical analysis of machine learning. We discuss the similarities and differences between drug discovery and material science, his startup, PostEra which offers medicinal chemistry as a service powered by machine learning, and much more Lees meer
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Is Linguistics Missing from NLP Research? w/ Emily M. Bender - #376 🦜
18 mei 2020 · 53 min
Today we’re joined by Emily M. Bender, Professor of Linguistics at the University of Washington. Lees meer
Our discussion covers a lot of ground, but centers on the question, "Is Linguistics Missing from NLP Research?" We explore if we would be making more progress, on more solid foundations, if more linguists were involved in NLP research, or is the progress we're making (e.g. with deep learning models like Transformers) just fine?
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Disrupting DeepFakes: Adversarial Attacks Against Conditional Image Translation Networks with Nataniel Ruiz - #375
14 mei 2020 · 43 min
Today we’re joined by Nataniel Ruiz, a PhD Student at Boston University. Lees meer
We caught up with Nataniel to discuss his paper “Disrupting DeepFakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systems.” In our conversation, we discuss the concept of this work, as well as some of the challenging parts of implementing this work, potential scenarios in which this could be deployed, and the broader contributions that went into this work.
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Understanding the COVID-19 Data Quality Problem with Sherri Rose - #374
11 mei 2020 · 44 min
Today we’re joined by Sherri Rose, Associate Professor at Harvard Medical School. We cover a lot of ground in our conversation, including the intersection of her research with the current COVID-19 pandemic, the importance of quality in datasets and rigor when publishing papers, and the pitfalls of using causal inference. Lees meer
We also touch on Sherri’s work in algorithmic fairness, the shift she’s seen in fairness conferences covering these issues in relation to healthcare research, and a few recent pape
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The Whys and Hows of Managing Machine Learning Artifacts with Lukas Biewald - #373
7 mei 2020 · 55 min
Today we’re joined by Lukas Biewald, founder and CEO of Weights & Biases, to discuss their new tool Artifacts, an end to end pipeline tracker. In our conversation, we explore Artifacts’ place in the broader machine learning tooling ecosystem through the lens of our eBook “The definitive guide to ML Platforms” and how it fits with the W&B model management platform. We discuss also discuss what exactly “Artifacts” are, what the tool is tracking, and take a look at the onboarding process for users. Lees meer
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Language Modeling and Protein Generation at Salesforce with Richard Socher - #372
4 mei 2020 · 42 min
Today we’re joined Richard Socher, Chief Scientist and Executive VP at Salesforce. Richard and his team have published quite a few great projects lately, including CTRL: A Conditional Transformer Language Model for Controllable Generation, and ProGen, an AI Protein Generator, both of which we cover in-depth in this conversation. We also explore the balancing act between investments, product requirement research and otherwise at a large product-focused company like Salesforce. Lees meer
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AI Research at JPMorgan Chase with Manuela Veloso - #371
30 april 2020 · 47 min
Today we’re joined by Manuela Veloso, Head of AI Research at J.P. Morgan Chase. Since moving from CMU to JP Morgan Chase, Manuela and her team established a set of seven lofty research goals. In this conversation we focus on the first three: building AI systems to eradicate financial crime, safely liberate data, and perfect client experience. We also explore Manuela’s background, including her time CMU in the ‘80s, or as she describes it, the “mecca of AI,” and her founding role with RoboCup. Lees meer
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Panel: Responsible Data Science in the Fight Against COVID-19 - #370
29 april 2020 · 58 min
In this discussion, we explore how data scientists and ML/AI practitioners can responsibly contribute to the fight against coronavirus and COVID-19. Four experts: Rex Douglass, Rob Munro, Lea Shanley, and Gigi Yuen-Reed shared a ton of valuable insight on the best ways to get involved. Lees meer
We've gathered all the resources that our panelists discussed during the conversation, you can find those at twimlai.com/talk/370.
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Adversarial Examples Are Not Bugs, They Are Features with Aleksander Madry - #369
27 april 2020 · 41 min
Today we’re joined by Aleksander Madry, Faculty in the MIT EECS Department, to discuss his paper “Adversarial Examples Are Not Bugs, They Are Features.” In our conversation, we talk through what we expect these systems to do, vs what they’re actually doing, if we’re able to characterize these patterns, and what makes them compelling, and if the insights from the paper will help inform opinions on either side of the deep learning debate. Lees meer
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AI for Social Good: Why "Good" isn't Enough with Ben Green - #368
23 april 2020 · 42 min
Today we’re joined by Ben Green, PhD Candidate at Harvard and Research Fellow at the AI Now Institute at NYU. Lees meer
Ben’s research is focused on the social and policy impacts of data science, with a focus on algorithmic fairness and the criminal justice system. We discuss his paper ‘Good' Isn't Good Enough,’ which explores the 2 things he feels are missing from data science and machine learning research; A grounded definition of what “good” actually means, and the absence of a “theory of change.
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The Evolution of Evolutionary AI with Risto Miikkulainen - #367
20 april 2020 · 38 min
Today we’re joined by Risto Miikkulainen, Associate VP of Evolutionary AI at Cognizant AI. Risto joined us back on episode #47 to discuss evolutionary algorithms, and today we get an update on the latest on the topic. In our conversation, we discuss use cases for evolutionary AI and the latest approaches to deploying evolutionary models. We also explore his paper “Better Future through AI: Avoiding Pitfalls and Guiding AI Towards its Full Potential,” which digs into the historical evolution of AI. Lees meer
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Neural Architecture Search and Google’s New AutoML Zero with Quoc Le - #366
16 april 2020 · 54 min
Today we’re super excited to share our recent conversation with Quoc Le, a research scientist at Google. Quoc joins us to discuss his work on Google’s AutoML Zero, semi-supervised learning, and the development of Meena, the multi-turn conversational chatbot. Lees meer
This was a really fun conversation, so much so that we decided to release the video! April 16th at 12 pm PT, Quoc and Sam will premiere the video version of this interview on Youtube, and answer your questions in the chat. We’ll see you there!
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Automating Electronic Circuit Design with Deep RL w/ Karim Beguir - #365
13 april 2020 · 35 min
Today we’re joined by return guest Karim Beguir, Co-Founder and CEO of InstaDeep. In our conversation, we chat with Karim about InstaDeep’s new offering, DeepPCB, an end-to-end platform for automated circuit board design. We discuss challenges and problems with some of the original iterations of auto-routers, how Karim defines circuit board “complexity,” the differences between reinforcement learning being used for games and in this use case, and their spotlight paper from NeurIPS. Lees meer
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Neural Ordinary Differential Equations with David Duvenaud - #364
9 april 2020 · 49 min
Today we’re joined by David Duvenaud, Assistant Professor at the University of Toronto, to discuss his research on Neural Ordinary Differential Equations, a type of continuous-depth neural network. In our conversation, we talk through a few of David’s papers on the subject. We discuss the problem that David is trying to solve with this research, the potential that ODEs have to replace “the backbone” of the neural networks that are used to train today, and David’s approach to engineering. Lees meer
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The Measure and Mismeasure of Fairness with Sharad Goel - #363
6 april 2020 · 48 min
Today we’re joined by Sharad Goel, Assistant Professor at Stanford. Sharad, who also has appointments in the computer science, sociology, and law departments, has spent recent years focused on applying ML to understanding and improving public policy. In our conversation, we discuss Sharad’s extensive work on discriminatory policing, and The Stanford Open Policing Project. We also dig into Sharad’s paper “The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning.” Lees meer
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Simulating the Future of Traffic with RL w/ Cathy Wu - #362
2 april 2020 · 35 min
Today we’re joined by Cathy Wu, Assistant Professor at MIT. We had the pleasure of catching up with Cathy to discuss her work applying RL to mixed autonomy traffic, specifically, understanding the potential impact autonomous vehicles would have on various mixed-autonomy scenarios. To better understand this, Cathy built multiple RL simulations, including a track, intersection, and merge scenarios. We talk through how each scenario is set up, how human drivers are modeled, the results, and much more. Lees meer
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Consciousness and COVID-19 with Yoshua Bengio - #361
30 maart 2020 · 49 min
Today we’re joined by one of, if not the most cited computer scientist in the world, Yoshua Bengio, Professor at the University of Montreal and the Founder and Scientific Director of MILA. We caught up with Yoshua to explore his work on consciousness, including how Yoshua defines consciousness, his paper “The Consciousness Prior,” as well as his current endeavor in building a COVID-19 tracing application, and the use of ML to propose experimental candidate drugs. Lees meer
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Geometry-Aware Neural Rendering with Josh Tobin - #360
26 maart 2020 · 27 min
Today we’re joined by Josh Tobin, Co-Organizer of the machine learning training program Full Stack Deep Learning. We had the pleasure of sitting down with Josh prior to his presentation of his paper Geometry-Aware Neural Rendering at NeurIPS. Josh's goal is to develop implicit scene understanding, building upon Deepmind's Neural scene representation and rendering work. We discuss challenges, the various datasets used to train his model, and the similarities between VAE training and his process, and mor Lees meer
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The Third Wave of Robotic Learning with Ken Goldberg - #359
23 maart 2020 · 1 uur 2 min
Today we’re joined by Ken Goldberg, professor of engineering at UC Berkeley, focused on robotic learning. In our conversation with Ken, we chat about some of the challenges that arise when working on robotic grasping, including uncertainty in perception, control, and physics. We also discuss his view on the role of physics in robotic learning, and his thoughts on potential robot use cases, from the use of robots in assisting in telemedicine, agriculture, and even robotic Covid-19 testing. Lees meer
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Learning Visiolinguistic Representations with ViLBERT w/ Stefan Lee - #358
18 maart 2020 · 28 min
Today we’re joined by Stefan Lee, an assistant professor at Oregon State University. In our conversation, we focus on his paper ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks. We discuss the development and training process for this model, the adaptation of the training process to incorporate additional visual information to BERT models, where this research leads from the perspective of integration between visual and language tasks. Lees meer
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Upside-Down Reinforcement Learning with Jürgen Schmidhuber - #357
16 maart 2020 · 34 min
Today we’re joined by Jürgen Schmidhuber, Co-Founder and Chief Scientist of NNAISENSE, the Scientific Director at IDSIA, as well as a Professor of AI at USI and SUPSI in Switzerland. Jürgen’s lab is well known for creating the Long Short-Term Memory (LSTM) network, and in this conversation, we discuss some of the recent research coming out of his lab, namely Upside-Down Reinforcement Learning. Lees meer
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SLIDE: Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning with Beidi Chen - #356
12 maart 2020 · 32 min
Beidi Chen is part of the team that developed a cheaper, algorithmic, CPU alternative to state-of-the-art GPU machines. They presented their findings at NeurIPS 2019 and have since gained a lot of attention for their paper, SLIDE: In Defense of Smart Algorithms Over Hardware Acceleration for Large-Scale Deep Learning Systems. Beidi shares how the team took a new look at deep learning with the case of extreme classification by turning it into a search problem and using locality-sensitive hashing. Lees meer
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Advancements in Machine Learning with Sergey Levine - #355
9 maart 2020 · 43 min
Today we're joined by Sergey Levine, an Assistant Professor at UC Berkeley. We last heard from Sergey back in 2017, where we explored Deep Robotic Learning. Sergey and his lab’s recent efforts have been focused on contributing to a future where machines can be “out there in the real world, learning continuously through their own experience.” We caught up with Sergey at NeurIPS 2019, where Sergey and his team presented 12 different papers -- which means a lot of ground to cover! Lees meer
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Secrets of a Kaggle Grandmaster with David Odaibo - #354
5 maart 2020 · 41 min
Imagine spending years learning ML from the ground up, from its theoretical foundations, but still feeling like you didn’t really know how to apply it. That’s where David Odaibo found himself in 2015, after the second year of his PhD. David’s solution was Kaggle, a popular platform for data science competitions. Lees meer
Fast forward four years, and David is now a Kaggle Grandmaster, the highest designation, with particular accomplishment in computer vision competitions, and co-founder and CTO of Analytical
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NLP for Mapping Physics Research with Matteo Chinazzi - #353
3 maart 2020 · 35 min
Predicting the future of science, particularly physics, is the task that Matteo Chinazzi, an associate research scientist at Northeastern University focused on in his paper Mapping the Physics Research Space: a Machine Learning Approach. In addition to predicting the trajectory of physics research, Matteo is also active in the computational epidemiology field. His work in that area involves building simulators that can model the spread of diseases like Zika or the seasonal flu at a global scale. Lees meer
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Metric Elicitation and Robust Distributed Learning with Sanmi Koyejo - #352
27 februari 2020 · 56 min
The unfortunate reality is that many of the most commonly used machine learning metrics don't account for the complex trade-offs that come with real-world decision making. This is one of the challenges that Sanmi Koyejo, assistant professor at the University of Illinois, has dedicated his research to address. Sanmi applies his background in cognitive science, probabilistic modeling, and Bayesian inference to pursue his research which focuses broadly on “adaptive and robust machine learning.” Lees meer
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High-Dimensional Robust Statistics with Ilias Diakonikolas - #351
24 februari 2020 · 36 min
Today we’re joined by Ilias Diakonikolas, faculty in the CS department at the University of Wisconsin-Madison, and author of the paper Distribution-Independent PAC Learning of Halfspaces with Massart Noise, recipient of the NeurIPS 2019 Outstanding Paper award. The paper is regarded as the first progress made around distribution-independent learning with noise since the 80s. In our conversation, we explore robustness in ML, problems with corrupt data in high-dimensional settings, and of course, the paper. Lees meer
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How AI Predicted the Coronavirus Outbreak with Kamran Khan - #350
19 februari 2020 · 51 min
Today we’re joined by Kamran Khan, founder & CEO of BlueDot, and professor of medicine and public health at the University of Toronto. BlueDot has been the recipient of a lot of attention for being the first to publicly warn about the coronavirus that started in Wuhan. How did the company’s system of algorithms and data processing techniques help flag the potential dangers of the disease? In our conversation, Kamran talks us through how the technology works, its limits, and the motivation behind the wor Lees meer
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Turning Ideas into ML Powered Products with Emmanuel Ameisen - #349
17 februari 2020 · 42 min
Today we’re joined by Emmanuel Ameisen, machine learning engineer at Stripe, and author of the recently published book “Building Machine Learning Powered Applications; Going from Idea to Product.” In our conversation, we discuss structuring end-to-end machine learning projects, debugging and explainability in the context of models, the various types of models covered in the book, and the importance of post-deployment monitoring. Lees meer
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Algorithmic Injustices and Relational Ethics with Abeba Birhane - #348
13 februari 2020 · 41 min
Today we’re joined by Abeba Birhane, PhD Student at University College Dublin and author of the recent paper Algorithmic Injustices: Towards a Relational Ethics, which was the recipient of the Best Paper award at the 2019 Black in AI Workshop at NeurIPS. In our conversation, break down the paper and the thought process around AI ethics, the “harm of categorization,” how ML generally doesn’t account for the ethics of various scenarios and how relational ethics could solve the issue, and much more. Lees meer
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AI for Agriculture and Global Food Security with Nemo Semret - #347
10 februari 2020 · 1 uur 4 min
Today we’re excited to kick off our annual Black in AI Series joined by Nemo Semret, CTO of Gro Intelligence. Gro provides an agricultural data platform dedicated to improving global food security, focused on applying AI at macro scale. In our conversation with Nemo, we discuss Gro’s approach to data acquisition, how they apply machine learning to various problems, and their approach to modeling. Lees meer
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Practical Differential Privacy at LinkedIn with Ryan Rogers - #346
7 februari 2020 · 34 min
Today we’re joined by Ryan Rogers, Senior Software Engineer at LinkedIn, to discuss his paper “Practical Differentially Private Top-k Selection with Pay-what-you-get Composition.” In our conversation, we discuss how LinkedIn allows its data scientists to access aggregate user data for exploratory analytics while maintaining its users’ privacy through differential privacy, and the connection between a common algorithm for implementing differential privacy, the exponential mechanism, and Gumbel noise. Lees meer
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Networking Optimizations for Multi-Node Deep Learning on Kubernetes with Erez Cohen - #345
5 februari 2020 · 32 min
Today we conclude the KubeCon ‘19 series joined by Erez Cohen, VP of CloudX & AI at Mellanox, who we caught up with before his talk “Networking Optimizations for Multi-Node Deep Learning on Kubernetes.” In our conversation, we discuss NVIDIA’s recent acquisition of Mellanox, the evolution of technologies like RDMA and GPU Direct, how Mellanox is enabling Kubernetes and other platforms to take advantage of the recent advancements in networking tech, and why we should care about networking in Deep Lea Lees meer
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Managing Research Needs at the University of Michigan using Kubernetes w/ Bob Killen - #344
3 februari 2020 · 25 min
Today we’re joined by Bob Killen, Research Cloud Administrator at the University of Michigan. In our conversation, we explore how Bob and his group at UM are deploying Kubernetes, the user experience, and how those users are taking advantage of distributed computing. We also discuss if ML/AI focused Kubernetes users should fear that the larger non-ML/AI user base will negatively impact their feature needs, where gaps currently exist in trying to support these ML/AI users’ workloads, and more! Lees meer
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Scalable and Maintainable Workflows at Lyft with Flyte w/ Haytham AbuelFutuh and Ketan Umare - #343
30 januari 2020 · 45 min
Today we kick off our KubeCon ‘19 series joined by Haytham AbuelFutuh and Ketan Umare, a pair of software engineers at Lyft. We caught up with Haytham and Ketan at KubeCo, where they were presenting their newly open-sourced, cloud-native ML and data processing platform, Flyte. We discuss what prompted Ketan to undertake this project and his experience building Flyte, the core value proposition, what type systems mean for the user experience, how it relates to Kubeflow and how Flyte is used across Lyft. Lees meer
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Causality 101 with Robert Osazuwa Ness - #342
27 januari 2020 · 40 min
Today Robert Osazuwa Ness, ML Research Engineer at Gamalon and Instructor at Northeastern University joins us to discuss Causality, what it means, and how that meaning changes across domains and users, and our upcoming study group based around his new course sequence, “Causal Modeling in Machine Learning," for which you can find details at twimlai.com/community. Lees meer
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PaccMann^RL: Designing Anticancer Drugs with Reinforcement Learning w/ Jannis Born - #341
23 januari 2020 · 42 min
Today we’re joined by Jannis Born, Ph.D. student at ETH & IBM Research Zurich, to discuss his “PaccMann^RL” research. Jannis details how his background in computational neuroscience applies to this research, how RL fits into the goal of anticancer drug discovery, the effect DL has had on his research, and of course, a step-by-step walkthrough of how the framework works to predict the sensitivity of cancer drugs on a cell and then discover new anticancer drugs. Lees meer
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Social Intelligence with Blaise Aguera y Arcas - #340
20 januari 2020 · 48 min
Today we’re joined by Blaise Aguera y Arcas, a distinguished scientist at Google. We had the pleasure of catching up with Blaise at NeurIPS last month, where he was invited to speak on “Social Intelligence.” In our conversation, we discuss his role at Google, and his team’s approach to machine learning, and of course his presentation, in which he touches discussing today’s ML landscape, the gap between AI and ML/DS, the difference between intelligent systems and true intelligence, and much more. Lees meer
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Music & AI Plus a Geometric Perspective on Reinforcement Learning with Pablo Samuel Castro - #339
16 januari 2020 · 45 min
Today we’re joined by Pablo Samuel Castro, Staff Research Software Developer at Google. We cover a lot of ground in our conversation, including his love for music, and how that has guided his work on the Lyric AI project, and a few of his papers including “A Geometric Perspective on Optimal Representations for Reinforcement Learning” and “Estimating Policy Functions in Payments Systems using Deep Reinforcement Learning.” Lees meer
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Trends in Computer Vision with Amir Zamir - #338
14 januari 2020 · 1 uur 37 min
Today we close out AI Rewind 2019 joined by Amir Zamir, who recently began his tenure as an Assistant Professor of Computer Science at the Swiss Federal Institute of Technology. Amir joined us back in 2018 to discuss his CVPR Best Paper winner, and in today’s conversation, we continue with the thread of Computer Vision. In our conversation, we discuss quite a few topics, including Vision-for-Robotics, the expansion of the field of 3D Vision, Self-Supervised Learning for CV Tasks, and much more! Lees meer
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Trends in Natural Language Processing with Nasrin Mostafazadeh - #337
9 januari 2020 · 1 uur 13 min
Today we continue the AI Rewind 2019 joined by friend-of-the-show Nasrin Mostafazadeh, Senior AI Research Scientist at Elemental Cognition. We caught up with Nasrin to discuss the latest and greatest developments and trends in Natural Language Processing, including Interpretability, Ethics, and Bias in NLP, how large pre-trained models have transformed NLP research, and top tools and frameworks in the space. Lees meer
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Trends in Fairness and AI Ethics with Timnit Gebru - #336
6 januari 2020 · 50 min
Today we keep the 2019 AI Rewind series rolling with friend-of-the-show Timnit Gebru, a research scientist on the Ethical AI team at Google. A few weeks ago at NeurIPS, Timnit joined us to discuss the ethics and fairness landscape in 2019. In our conversation, we discuss diversification of NeurIPS, with groups like Black in AI, WiML and others taking huge steps forward, trends in the fairness community, quite a few papers, and much more. Lees meer
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Trends in Reinforcement Learning with Chelsea Finn - #335
2 januari 2020 · 1 uur 8 min
Today we continue to review the year that was 2019 via our AI Rewind series, and do so with friend of the show Chelsea Finn, Assistant Professor in the CS Department at Stanford University. Chelsea’s research focuses on Reinforcement Learning, so we couldn’t think of a better person to join us to discuss the topic. In this conversation, we cover topics like Model-based RL, solving hard exploration problems, along with RL libraries and environments that Chelsea thought moved the needle last year. Lees meer