
24 segments available
I had a lot of fun chatting with Shane Legg - Founder & Chief AGI Scientist, Google DeepMind! We discuss: * Why he expects AGI around 2028 * How to align superhuman models * What new architectures needed for AGI * Has Deepmind sped up capabilities or safety more? * Why multimodality will be next big landmark * & much more 𝐄𝐏𝐈𝐒𝐎𝐃𝐄 𝐋𝐈𝐍𝐊𝐒 * Transcript: https://www.dwarkeshpatel.com/p/shane-legg * Apple Podcasts: https://podcasts.apple.com/us/podcast/shane-legg-deepmind-founder-2028-agi-new-architectures/id1516093381?i=1000632720307 * Spotify: https://open.spotify.com/episode/0Ru2CtaJqsQ5mpA5dqHWAK?si=4AsglwIZQpqht7p9Wpc_CA * Twitter: https://twitter.com/dwarkesh_sp/status/1717566262472237134 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 00:00:00 - Measuring AGI 00:11:41 - Do we need new architectures? 00:16:26 - Is search needed for creativity? 00:19:19 - Superhuman alignment 00:29:58 - Impact of Deepmind on safety vs capabilities 00:34:03 - Timelines 00:41:24 - Multimodality
Shane Legg discusses the complexities of measuring progress towards Artificial General Intelligence (AGI). He emphasizes that AGI is about generality, requiring a broad range of cognitive tests to evaluate performance against human capabilities. Legg highlights the difficulty in establishing a comprehensive set of benchmarks that truly reflect human cognitive abilities.
"Today I have the pleasure of interviewing Shane Legg, who is the founder and the Chief AGI scientist of Google DeepMind. Shane, welcome to the podcast. Thank you. It's a pleasure being here. Firs..."
In this segment, Shane Legg critiques existing benchmarks for AI, such as MMLU, noting their inadequacies in measuring aspects of human cognition like episodic memory and understanding streaming video. He explains the limitations of language models and the need for more comprehensive testing to capture the breadth of human cognitive functions.
"then for all practical purposes, you have an AGI. Let's get more concrete. We measure the performance of these large language models on MMLU and other benchmarks. What is missing from the benchma..."
Legg explores the concept of sample efficiency in AI, relating it to human learning capabilities. He discusses how current models learn from vast amounts of data but lack the rapid learning associated with human episodic memory. He expresses optimism about addressing these shortcomings through architectural advancements in AI.
"The thing you're referring to with episodic memory, would it be fair to call that sample efficiency or is that a different thing? It's very much related to sample efficiency. It's one of the thin..."
Shane Legg outlines his vision for validating AGI, emphasizing the need for a suite of diverse tests that cover various cognitive tasks. He suggests an adversarial approach to testing, where attempts are made to find gaps in AI performance compared to human capabilities, marking the threshold for achieving AGI.
"improve and probably be adequately solved. Going back to the original question of how do you measure when human level AI has arrived or has gone beyond it. As you mentioned, there's these other s..."
In this segment, Legg reflects on his earlier research regarding intelligence measurement and the importance of a reference machine. He discusses the shift from a purely mathematical definition of intelligence to a more practical approach that considers human intelligence as a meaningful benchmark for developing AGI.
"A lot of your earlier research, at least the ones I could find, emphasized that AI should be able to manipulate and succeed in a variety of open-ended environments. It almost sounds like a video..."
Legg addresses the need for architectural changes in AI to incorporate episodic memory and improve learning efficiency. He contrasts the current models' capabilities with human cognitive processes, suggesting that a comprehensive system should effectively balance rapid learning and deep generalization.
"obviously, because it exists in the world. We know that human intelligence is very, very powerful because it's affected the world profoundly in countless ways. And we know if human level intelli..."
Shane Legg discusses the relevance of DeepMind's domain-specific models, like AlphaFold, in the context of AGI development. He clarifies that while these projects may not directly contribute to AGI, they provide valuable insights and advancements that could inform future AGI research.
"I think it'll be architectural in nature because the current architectures don't really have what you need to do this. They basically have a context window, which is very, very fluid, of course,..."
Legg elaborates on Richard Sutton's 'Bitter Lesson' essay, emphasizing the importance of scaling both search and learning in AI systems. He argues that while current LLMs excel at learning, they lack the search capabilities necessary for true creativity. This segment highlights the need for AI to explore possibilities beyond mere data mimicry to achieve innovative outcomes.
"you could do a compression test and you could see if it fills in words and a sample of text and that could measure intelligence. And funnily enough, that's basically how the LLMs are trained. At ..."
In this insightful discussion, Shane Legg explains that true creativity in AI requires the ability to search through a space of possibilities. He uses the example of AlphaGo's Move 37 to illustrate how unexpected yet plausible moves can lead to innovative solutions. Legg stresses that current language models need to incorporate search mechanisms to transcend their training data.
"And I think that's what we're seeing today actually, that these incredibly powerful foundation models are incredibly good sequence predictors that are compressing the world based on all this dat..."
Shane Legg addresses the potential training methods for newer models at Google DeepMind, hinting at the necessity of integrating search capabilities. He acknowledges the scaling and training approaches common in the field while suggesting that DeepMind has its unique techniques to enhance model performance.
"hidden gems. That's what creativity is. Current language models don't really do that. They really are mimicking the data. They are mimicking all the human ingenuity and everything, which they have..."
In this segment, Legg discusses the challenges of aligning human-level and superhuman AIs. He emphasizes the importance of developing ethical frameworks and robust reasoning capabilities in AI systems to ensure they act in alignment with human values. Legg outlines the current efforts at DeepMind to address safety and ethical considerations in AI development.
"I think it's fair to say we're roughly doing the sorts of scaling and training that you see many people in the field doing but we have our own take on it and our own different tricks and technique..."
Shane Legg shares insights on how AI systems should approach ethical decision-making. He compares the process to human reasoning, advocating for a model that evaluates options and consequences thoughtfully. Legg argues that AI must develop a deep understanding of ethics to make sound decisions aligned with human values.
"fundamentally a highly ethical value aligned system from the get go. How do you do that? Maybe this is slightly naive, but this is my take on it — How do people do it? If you have a really diffic..."
In this thought-provoking segment, Legg outlines the requirements for creating a profoundly ethical AI system. He stresses the need for a comprehensive understanding of ethics, world models, and reasoning capabilities. Legg argues that without these elements, AI cannot consistently act in an ethical manner.
"thing. When you sample from a foundation model at the moment, it's blurting out the first thing. It's like System 1, if you like, from psychology, from Kahneman et al. That's not good enough. And..."
Shane Legg concludes by discussing the importance of training AI systems on human ethics. He emphasizes the need for AI to understand ethical principles as well as a skilled ethicist. Legg highlights the societal responsibility to define the values that AI should uphold, marking it as a crucial step in the development of ethical AI.
"And then you set it up in such a way that it applies this reasoning and this understanding of ethics to analyze the different options which are in front of it and then execute on which is the mo..."
In this segment, Legg outlines the necessity of training AI systems on human ethics to ensure they understand and apply ethical principles effectively. He highlights the importance of defining specific ethical values for AI to follow and the societal role in establishing these values. The discussion touches on the potential for creating a set of ethics that can mitigate fears surrounding AGI behavior.
"systems. The way I think about it is this: to have a profoundly ethical AI system, it also has to be very, very capable. It needs a really good world model, a really good understanding of ethics, ..."
Legg emphasizes the importance of verifying AI's reasoning processes to ensure it adheres to ethical principles. He suggests that continuous oversight and rigorous testing of AI's understanding of ethics are crucial for maintaining ethical behavior. This segment explores the challenges of ensuring AI systems consistently follow the ethical guidelines set by human experts.
"in terms of the behavior of these AGI systems. And then what you do is you engineer the system to actually follow these things so that every time it makes a decision, it does an analysis using a..."
Shane Legg reflects on DeepMind's impact on AGI safety versus capabilities. He discusses the challenges of hiring for AGI safety roles and the importance of openly addressing safety concerns within the company. Legg shares insights on how DeepMind has contributed to the credibility of AGI safety discussions and the broader implications for the AI community.
"Do you have some sort of framework for that at Google DeepMind? This is not so much a Google DeepMind perspective on this. This is my take on how I think we need to do this kind of thing. There a..."
In this segment, Legg addresses the debate over whether DeepMind has accelerated AI capabilities more than safety measures. He acknowledges the complexities of assessing the impact of DeepMind on the AI landscape and discusses the interplay between safety and capability advancements. Legg reflects on the historical context of AGI and the evolving dynamics of the AI research community.
"I've been worried about AGI safety for a long time, well before DeepMind. But it was always really hard to hire people to work on AGI safety, particularly in the early days. Back in 2013 or so, ..."
Shane Legg shares his reasoning behind the prediction of achieving AGI by 2028. He discusses the exponential growth of computational power and data, and how these factors contribute to the development of scalable algorithms. Legg reflects on the trends that support his prediction and acknowledges the uncertainties inherent in research, emphasizing the potential for significant advancements in the coming years.
"you had a blog post where you said — “I’ve decided to once again leave my prediction for when human level AGI will arrive unchanged. That is, I give it a log-normal distribution with a mean of 202..."
Shane Legg outlines his expectations for AI models leading up to 2028, predicting they will mature, become more factual, and develop multimodal capabilities. He anticipates a surge of impressive applications, while acknowledging the potential for misuse. This segment highlights the transformative impact of AI advancements on various industries.
"then. You often hit unexpected problems in research and science and sometimes things take longer than you expect. If we're in 2029 and it hasn't happened yet, if there was a problem that caused i..."
Legg discusses various research directions within DeepMind aimed at improving AI safety, including interpretability and deliberative dialogue. He expresses optimism about these approaches, particularly those that facilitate alignment with increasingly powerful systems. This segment emphasizes the importance of safety in the development of AGI.
"anything is just loads of great applications for the coming years. There can be some misuse cases as well. I'm sure somebody will come up with something to do with these models that is quite unh..."
In this segment, Shane Legg addresses two critical areas for improvement in large language models (LLMs): episodic memory and System 2 thinking. He explains how these elements can enhance the models' problem-solving capabilities and factual accuracy, which are essential for their applications in various fields.
"to scale the alignment to increasingly powerful systems. I think things of that kind of flavor have quite a lot of promise in my opinion, but that's kind of quite a broad category. There are man..."
Shane Legg predicts that the next significant milestone in AI will be achieving full multimodal capabilities. He envisions a future where models can understand and process various forms of data, such as text, images, and video, leading to a richer interaction with the world. This segment highlights the transformative potential of multimodal AI.
"limits their applications in many ways. The final question is this. You've been in this field for over a decade, much longer than many others, and you've seen different landmarks like ImageNet an..."
Legg discusses the early stages of multimodal AI and its potential to revolutionize applications beyond text. He emphasizes that as models begin to process diverse data types, new possibilities will emerge, many of which are currently unimaginable. This segment captures the excitement surrounding the future of AI technology.
"into the world in a much more powerful way. Do you mind if I ask a follow-up on that? ChatGPT just released their multimodal feature and you, in DeepMind, you had the Gato paper, where you have t..."