Ilya Sutskever — We're moving from the age of scaling to the age of research
Ilya & I discuss SSI’s strategy, the problems with pre-training, how to improve the generalization of AI models, and how to ensure AGI goes well.
Watch on YouTube; read the transcript.
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Timestamps
(00:00:00) – Explaining model jaggedness
(00:09:39) - Emotions and value functions
(00:18:49) – What are we scaling?
(00:25:13) – Why humans generalize better than models
(00:35:45) – SSI’s plan to straight-shot superintelligence
(00:46:47) – SSI’s model will learn from deployment
(00:55:07) – How to think about powerful AGIs
(01:18:13) – “We are squarely an age of research company”
(01:20:23) – Self-play and multi-agent
(01:32:42) – Research taste
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The exciting, perilous journey toward AGI | Ilya Sutskever
Just weeks before the management shakeup at OpenAI rocked Silicon Valley and made international news, the company's cofounder and chief scientist Ilya Sutskever explored the transformative potential of artificial general intelligence (AGI), highlighting how it could surpass human intelligence and profoundly transform every aspect of life. Hear his take on the promises and perils of AGI — and his optimistic case for how unprecedented collaboration will ensure its safe and beneficial development.
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What is Digital Life? with OpenAI Co-Founder & Chief Scientist Ilya Sutskever
Each iteration of ChatGPT has demonstrated remarkable step function capabilities. But what’s next? Ilya Sutskever, Co-Founder & Chief Scientist at OpenAI, joins Sarah Guo and Elad Gil to discuss the origins of OpenAI as a capped profit company, early emergent behaviors of GPT models, the token scarcity issue, next frontiers of AI research, his argument for working on AI safety now, and the premise of Superalignment. Plus, how do we define digital life?
Ilya Sutskever is Co-founder and Chief Scientist of OpenAI. He leads research at OpenAI and is one of the architects behind the GPT models. He co-leads OpenAI's new "Superalignment" project, which tries to solve the alignment of superintelligences in 4 years. Prior to OpenAI, Ilya was co-inventor of AlexNet and Sequence to Sequence Learning. He earned his Ph.D in Computer Science from the University of Toronto.
Show Links:
Ilya Sutskever | LinkedIn
Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @ilyasut
Show Notes:
(00:00) - Early Days of AI Research
(06:51) - Origins of Open Ai & CapProfit Structure
(13:46) - Emergent Behaviors of GPT Models
(17:55) - Model Scale Over Time & Reliability
(22:23) - Roles & Boundaries of Open-Source in the AI Ecosystem (28:22) - Comparing AI Systems to Biological & Human Intelligence (30:52) - Definition of Digital Life
(32:59) - Super Alignment & Creating Pro Human AI
(39:01) - Accelerating & Decelerating Forces
Ilya Sutskever (OpenAI Chief Scientist) — Why next-token prediction could surpass human intelligence
I went over to the OpenAI offices in San Fransisco to ask the Chief Scientist and cofounder of OpenAI, Ilya Sutskever, about:
* time to AGI
* leaks and spies
* what's after generative models
* post AGI futures
* working with Microsoft and competing with Google
* difficulty of aligning superhuman AI
Watch on YouTube. Listen on Apple Podcasts, Spotify, or any other podcast platform. Read the full transcript here. Follow me on Twitter for updates on future episodes.
Timestamps
(00:00) - Time to AGI
(05:57) - What’s after generative models?
(10:57) - Data, models, and research
(15:27) - Alignment
(20:53) - Post AGI Future
(26:56) - New ideas are overrated
(36:22) - Is progress inevitable?
(41:27) - Future Breakthroughs
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Ilya Sutskever: The Mastermind Behind GPT-4 and the Future of AI
In this podcast episode, Ilya Sutskever, the co-founder and chief scientist at OpenAI, discusses his vision for the future of artificial intelligence (AI), including large language models like GPT-4. Sutskever starts by explaining the importance of AI research and how OpenAI is working to advance the field. He shares his views on the ethical considerations of AI development and the potential impact of AI on society. The conversation then moves on to large language models and their capabilities. Sutskever talks about the challenges of developing GPT-4 and the limitations of current models. He discusses the potential for large language models to generate a text that is indistinguishable from human writing and how this technology could be used in the future. Sutskever also shares his views on AI-aided democracy and how AI could help solve global problems such as climate change and poverty. He emphasises the importance of building AI systems that are transparent, ethical, and aligned with human values. Throughout the conversation, Sutskever provides insights into the current state of AI research, the challenges facing the field, and his vision for the future of AI. This podcast episode is a must-listen for anyone interested in the intersection of AI, language, and society. Timestamps: (00:04) Introduction of Craig Smith and Ilya Sutskever. (01:00) Sutskever's AI and consciousness interests. (02:30) Sutskever's start in machine learning with Hinton. (03:45) Realization about training large neural networks. (06:33) Convolutional neural network breakthroughs and imagenet. (08:36) Predicting the next thing for unsupervised learning. (10:24) Development of GPT-3 and scaling in deep learning. (11:42) Specific scaling in deep learning and potential discovery. (13:01) Small changes can have big impact. (13:46) Limits of large language models and lack of understanding. (14:32) Difficulty in discussing limits of language models. (15:13) Statistical regularities lead to better understanding of world. (16:33) Limitations of language models and hope for reinforcement learning. (17:52) Teaching neural nets through interaction with humans. (21:44) Multimodal understanding not necessary for language models. (25:28) Autoregressive transformers and high-dimensional distributions. (26:02) Autoregressive transformers work well on images. (27:09) Pixels represented like a string of text. (29:40) Large generative models learn compressed representations of real-world processes. (31:31) Human teachers needed to guide reinforcement learning process. (35:10) Opportunity to teach AI models more skills with less data. (39:57) Desirable to have democratic process for providing information. (41:15) Impossible to understand everything in complicated situations. Craig Smith Twitter: https://twitter.com/craigssEye on A.I. Twitter: https://twitter.com/EyeOn_AI
Ilya Sutskever explains the origins of deep learning
On the last episode (Ep.22) of Season One of The Robot Brains Podcast our guest is Ilya Sutskever. Ilya is the Co-Founder and Chief Scientist of OpenAI. As a PhD student at Toronto, Ilya was one of the authors on the 2012 AlexNet paper that completely changed the field of AI, resulting in the widespread adoption of deep learning, resulting in the avalanche of AI breakthroughs we’ve seen the past 10 years. After the AlexNet breakthrough in computer vision, at Google, among many other breakthroughs, Ilya showed that neural networks are unexpectedly great at machine translation, at least at the time it was unexpected, now it’s long become the norm to use neural nets for machine translation. Late 2015 Ilya left Google to co-found OpenAI, where he is Chief Scientist. Some of his breakthroughs include GPT, CLIP, DallE, Codex. Ilya’s academic work, less than 10 years out of his PhD, has ben cited over 250,000 times, reflecting his absolutely mind-blowing influence on the field. | SUBSCRIBE TO THE ROBOT BRAINS PODCAST TODAY | Visit therobotbrains.ai and follow us on Twitter @therobotbrains, Instagram @therobotbrains and YouTube TheRobotBrainsPodcast | Host: Pieter Abbeel | Executive Producers: Ricardo Reyes & Henry Tobias Jones | Audio Production: Kieron Matthew Banerji | Title Music: Alejandro Del Pozo Hosted on Acast. See acast.com/privacy for more information.
#94 – Ilya Sutskever: Deep Learning
Ilya Sutskever is the co-founder of OpenAI, is one of the most cited computer scientist in history with over 165,000 citations, and to me, is one of the most brilliant and insightful minds ever in the field of deep learning. There are very few people in this world who I would rather talk to and brainstorm with about deep learning, intelligence, and life than Ilya, on and off the mic.
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EPISODE LINKS:
Ilya’s Twitter: https://twitter.com/ilyasut
Ilya’s Website: https://www.cs.toronto.edu/~ilya/
This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.
Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.
OUTLINE:
00:00 – Introduction
02:23 – AlexNet paper and the ImageNet moment
08:33 – Cost functions
13:39 – Recurrent neural networks
16:19 – Key ideas that led to success of deep learning
19:57 – What’s harder to solve: language or vision?
29:35 – We’re massively underestimating deep learning
36:04 – Deep double descent
41:20 – Backpropagation
42:42 – Can neural networks be made to reason?
50:35 – Long-term memory
56:37 – Language models
1:00:35 – GPT-2
1:07:14 – Active learning
1:08:52 – Staged release of AI systems
1:13:41 – How to build AGI?
1:25:00 – Question to AGI
1:32:07 – Meaning of life
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