Richard Socher from You.com (and before that, Stanford, MetaMind, Salesforce) joins Host Pieter Abbeel to discuss the future of search, LLMs, AGI, You.com, Metamind, AIX Ventures.

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Raluca Ada Popa from UC Berkeley, Opaque Systems, PreVeil joins Host Pieter Abbeel to discuss data privacy and AI, cloud enclaves, confidential and encrypted computing, differential privacy, PreVeil, and Opaque Systems.

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Aidan Gomez, co-founder and CEO of Cohere joins Host Pieter Abbeel to discuss transformers, large language models, command and instruct models, prompt engineering, LLMs competitive landscape.

Aidan is one of the researchers behind Attention is All You Need: the original transformer neural network paper which has proven the most impactful innovation in neural nets, and really all of AI in the past 10 years. In 2019, Aidan founded Cohere which is building the tools and APIs to make NLP part of every developers toolkit.

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Alexandr Wang from Scale AI joins Host Pieter Abbeel to discuss data, labeling, foundation models, LLMs, truthfulness, reinforcement learning from human feedback (RLHF), and AI for defense.

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Chelsea Finn joins Host Pieter Abbeel to discuss distribution shift, meta-learning, editing LLMs, single-life RL, and what can AI not (yet) do today.

Chelsea is a renowned expert in the field of robotics and artificial intelligence. She is an Assistant Professor in the Computer Science Department and the Electrical Engineering Department at Stanford University and is also a research scientist at Google Brain. Her research focuses on developing algorithms for robots and other intelligent systems that can learn from experience and adapt to new situations. She is a recipient of numerous awards, including the NSF CAREER Award, the MIT Technology Review 35 Innovators Under 35 Award, and the Sloan Research Fellowship.

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Yoshua Bengio joins Host Pieter Abbeel to discuss large language models, higher level cognition, causality, responsible AI, and human creativity.

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Last week, we were honored to have Professor Geoff Hinton join the show for a wide-ranging discussion inspired by insights gleaned from Hinton’s journey in academia, as well as past 10 years with Google Brain. The episode covers how existing neural networks and backpropagation models operate differently than how the brain actually works; the AlexNet/Abstract.html">ImageNet breakthrough moment; the purpose of sleep; and why it’s better to grow our computers than manufacture them.

As you might recall, we also gave our audience an opportunity to contribute questions for Geoff via Twitter. We received so many amazing questions from our audience that we had to break down our time with Geoff into two parts! In this episode, we’ll discuss some of these questions with Geoff.

Tune in to get Geoff’s answers to the following questions AND MORE:

Are you concerned with AI becoming too successful?

What is the connection between mania and genius?

What childhood experiences shaped him the most?

What is next in AI?

What should PhD students focus on?

How conscious do you think today's neural nets are?

How important is embodiment for intelligence?

How does the brain work?

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Over the past ten years, AI has experienced breakthrough after breakthrough in everything from computer vision to speech recognition, protein folding prediction, and so much more.

Many of these advancements hinge on the deep learning work conducted by our guest, Geoff Hinton, who has fundamentally changed the focus and direction of the field. A recipient of the Turing Award, the equivalent of the Nobel prize for computer science, he has over half a million citations of his work.

Hinton has spent about half a century on deep learning, most of the time researching in relative obscurity. But that all changed in 2012 when Hinton and his students showed deep learning is better at image recognition than any other approaches to computer vision, and by a very large margin. That result, that moment, known as the Abstract.html">ImageNet moment, changed the whole AI field. Pretty much everyone dropped what they had been doing and switched to deep learning.

Geoff joins Pieter in our two-part season finale for a wide-ranging discussion inspired by insights gleaned from Hinton’s journey from academia to Google Brain. The episode covers how existing neural networks and backpropagation models operate differently than how the brain actually works; the purpose of sleep; and why it’s better to grow our computers than manufacture them.

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Originally created to be an open domain conversational AI, your own ‘artificial BFF' that you could chat with when your real friends weren’t available — fast forward six years, and Hugging Face is the leading Natural Language Processing (NLP) start-up. The company is currently valued at $2B with over 10,000 companies using the open-source AI platform - including Bing, Apple, and Monzo.

Our guest, Clement Delangue, CEO of Hugging Face, believes in the sustainable long-term benefits of open-science and open-source collaboration and has built a community that fosters this goal. And while Hugging Face was initially mainly aimed at NLP, recently it is expanding to more machine learning domains, including computer vision and reinforcement learning.

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As we all re-entered society over the last year, after months of WFH sweatpants and t-shirts, a personal stylist would have been helpful.

Well, now there’s AI for that - THE YES. By taking a quick style quiz, you’ll have daily product and brand recommendations and a personalized feed displaying exactly what you’re looking for — in your size, budget and preferences. The more you say “yes”, the more personalized your feed gets. Right now, the company only offers women clothes but may expand in the future.

The CTO of THE YES, Amit Aggarwal, joins the show to share how the company uses ML to create millions of unique stores daily for each one of their users.

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