Richard Sutton

Student of mind and nature, libertarian, chess player, cancer survivor. @ Keen, UAlberta, Amii, The Royal Society, Turing Award

[Richard Sutton @RichardSSutton]

AI researchers seek to understand intelligence well enough to create beings of greater intelligence than current humans.

Reaching this profound intellectual milestone will enrich our economies and challenge our societal institutions. It will be unprecedented and transformational, but also a continuation of trends that are thousands of years old.

People have always created tools and been changed by them; this is what humans do.

The next big step is to understand ourselves.

This is a quest grand and glorious, and quintessentially human.

[Richard Sutton @RichardSSutton]

My friends from the former DeepMind Alberta have gone on to do RL for financial trading with great success!

[Martin Schmid @Lifrordi]

We are looking for more amazing researchers and engineers to join our team. EquiLibre Technologies is a frontier AI trading research lab. We train our own foundational models, use our own RL algorithms, and trade our own money.

The proof is in the pudding. [THREAD]

https://t.co/sRHFJQfle6

https://t.co/zqi7GOXNdd

[Matt Ridley @mattwridley]

“A clear pattern of suppressing dissent, silencing critics, and burying the truth”

Tulsi Gabbard’s revelations about Anthony Fauci are a gigantic scandal.

Millions dead, billions deceived, one highly paid individual pardoned.

[Richard Sutton @RichardSSutton]

Michael Jordan brings a strong and thoughtful perspective on AI:
https://t.co/0EVjoDW5Hd

[Shailesh @0xThoughtVector]

I disagree with him but goddamn this is a banger.

[Richard Sutton @RichardSSutton]

@roninmccar41032 @BetaTomorrow The long-term impact of artificial intelligence will depend on our culture's response.

[Richard Sutton @RichardSSutton]

A new and possibly controversial perspective:
In this video, I explain the sense in which generative AI trained by supervised learning is incapable of making novel discoveries.
https://t.co/zin5QbbT9N

The text of the speech:

AI Creativity and Discovery

Good day ladies and gentlemen. I regret that I am unable to be with you all today to engage in a back-and-forth discussion, but I am nevertheless pleased to be able to share with you, via this recording, some high-level thoughts about the current and future state of artificial intelligence, and in particular about AI’s relationship to science and mathematics, which is, as I understand it, the central focus of this meeting and of the SAIR Foundation.

I would like to start with an old joke; I am sure you have heard it before. It is the one about the researcher whose work is being evaluated, and the review comes back, and says “This work is both novel and good. Unfortunately, the parts that are good are not novel, and the parts that are novel are not good.”

My first point about AI is that this assessment applies exactly to large parts of AI as we know it today. Not all of today’s AI, but a large part of it. Pretty much all of what we mean by “Generative AI”---which includes large language models, and the images and video models, and even the new methods for learning world models. All of these AIs take large numbers of examples and produce a “model” which behaves similar to the examples, that is, which generates text like people, or images like artists or nature, and videos like we find on the internet. Don’t get me wrong, Generative AI can be extremely useful. No doubt about that. But the assessment of the joke still applies. These systems can produce output that is both novel and good, but not at the same time.

In many ways this is just absolutely not a problem. When we ask an AI for an answer from the internet, or to summarize a document, we don’t want it to be novel. We are happy if the quality of the answer, the goodness, comes from the source material—from the people who wrote the document or the articles on the internet. If the AI’s answer is novel it means it is going beyond the source material, adding something beyond it. This is what we call “hallucinations”. In most cases, we don’t like it when the AI makes something up, when it adds something novel.

One exception, of course, is when we are looking not for facts or reality, but for fiction and entertainment. We might ask for a bedtime story for a child, or an image based on existing images on the internet but which is nevertheless different and distinct from them. In these cases, it is never easy for us to know how creative the AI is actually being, as we do not know how close the AI’s story, poem, or image is to the source material. In a real practical sense we can not know this because the internet is too big, the possible sources that the AI may draw upon are too numerous.

When we ask for a fiction or novelty, the AI can give it to us because its processing is in part stochastic. Every decision can go multiple ways and will go different ways and produce a different trajectory every time. The trajectory can be random—and thus novel—or it can be based on the training data—and thus “good” because the training data is good, sourced from people or reality. Thus, the trajectory is either novel or good—based on randomness or based on data—but never both at the same time.

Really, I think it is okay if the output of Generative AI is never good and novel at the same time. For the researcher in the joke this is a devastating criticism, but for most things it is not, and for Generative AI it is not. Generative AI is meant to be a mimic. This is what supervised learning is for. Generative AI can be extremely useful, even when it just mimics, if it is faster, or cheaper, or smaller, or more customizable, or more copy-able, than the thing being mimicked. It is okay if Generative AI cannot be both novel and good at the same time. It is still a transformative technology.

But it is a limitation. And remember we are here to use AI for science and mathematics, and for these areas the assessment of the reviewer in the joke is devastating. For these areas we need true creativity and discovery. Generative AI—or Mimicking AI—will never get where us there. For these we need something more, and indeed we have something more in other parts of AI. We have many AI systems which can give us more. We have AlphaGo with its world-changing move 37, or AlphaZero with its brilliant original chess-playing style. We have GT-Sophy that drives simulated racecars better than any human. We have AlphaFold and AlphaProof and Claude-Code, which have brought true advances in science, mathematics, and programming. We have RL-Lyft which optimizes the assignment of cars to passengers in the ride-hailing business. All these systems have found things that are both novel and good. And, truth be told, some language models have been augmented in ways that make them more than Generative AI based on supervised learning.

All these systems have some additional features that make them capable of true creativity and true discovery. It is important for us to recognize what this is—and that it is not present in ordinary, garden-variety Generative AI. It is something that can not come from just supervised learning, from learning from examples. What is it? Well, it is a simple thing, a commonsense thing. It is not new. We have many names for it, but unfortunately none of them are very good names. I will call it Discovery. Basically, Discovery is just the idea of trying many things and seeing which of them work, then keeping those that worked the best. Evolution by natural selection works this way. The scientific method works this way. And just ordinary life and learning works this way. We try things and remember what works. What could be more obvious? In this behavioral case, psychology has two names for it— “instrumental learning” and “operant conditioning”—and in machine learning it is what we mean by “reinforcement learning”. We also see the idea of Discovery in planning and combinatorial search—anything that involves the idea of “generate and test”.

The essence of Discovery is to combine three steps:
1. Variation,
2. Evaluation, and
3. Selective retention.

Of course, I am not the first to say this. I am not the first to point out that this combination of steps is key to science, to evolution by natural selection, and to animal behavior. I think particularly of papers by Donald Campbell, by Daniel Dennett, and by Gary Cziko. What is new in my remarks is to directly relate the idea of Discovery to modern AI to help us see that it is not present in supervised learning or Generative AI—in particular, that Discovery is not present in backpropagation or gradient descent.

Let me say explicitly what is missing from Generative AI. As we have remarked, these systems do have a stochastic aspect, so they do generate a variety of trajectories and behavior. What is missing is the Evaluation step. The generator was pre-trained by supervised learning, leaving no way at runtime to Evaluate what it generates. And of course without Evaluation there can be no Selective retention, and thus no Discovery. The variation can bring novelty, but without evaluation there is no Discovery, and arguably, no creativity. That is, I would say that creativity requires that the new things generated be Evaluated. Without evaluation, and retention of the best, there is nothing created. The novelty flickers into existence but, if its value is unrecognized, it flickers away and is lost.

In many cases, Evaluation is done by people to make a discovery. As when we have Generative AI make many pictures for us, and then we pick the one that we like the best. The human+AI system completes the discovery.
In many other cases, the Evaluation comes from a clear objective. Some moves lead to checkmate, some steps lead to a proof, some actions result in high reward, some genotypes make more copies, some theories explain the data better.

Some prefer the Variation step to be called Blind variation, where “blind” here means that it is uninformed, a shot in the dark. It does not need to be completely uninformed; a good scientist does not select theories to test at random. But neither can it be completely informed and determined. There must be some uncertainty about where the answer lies in order for there to be a discovery. In practice, the variation is partly informed and partly blind, but it is the blind part that corresponds to the discovery.

Now let us briefly go all the way to modern deep learning, to the backpropagation algorithm. At first it might seem that backpropagation is incapable of discovery because it is deterministic and thus incapable of variation. But this is not correct. The weight updates of backprop are deterministic, but the weights are initialized to small random values. The random initialization is often downplayed, but in fact it is a necessary form of variation; it must be done properly to get good performance. In backprop this Variation is done once, at network initialization, so its effect is temporary, and later the network may lose its ability to learn. This is the weakness of deep learning that is alleviated with a new algorithm that my group presented in Nature a couple of years ago. Our “continual backpropagation” made one small change: every so often a less-used neuron would be re-initialized to small random weights. This allows the variation to continue and plasticity to be retained.

Although there is much more to be said about Creativity and Discovery, this is the key point: they are more than supervised learning, more than pattern recognition, more than prediction, and more than world modeling. Those things are important, but they alone will not bring us to discovery. Discovery requires Evaluation from a person or from an explicit goal, and only in the latter case will we attain full autonomy.

So that is my call to arms. If we want the full power of AI scientists, then we should share the goals with them so they can create, evaluate, discover, and in these ways fully participate in achieving the goals. Let’s be bold! Let’s fully automate Creativity and Discovery!

[Richard Sutton @RichardSSutton]

@yoavgo A bandwagon has little appreciation for precise language and nuance.

[Richard Sutton @RichardSSutton]

The question is about discovery. If an LLM (trained purely by supervised learning to mimic its input) is fed talk of discovery, then it will talk more about discovery, but it won't do discovery. Perhaps we can agree on the answer to that limited question?
Anyway, I hope we are getting closer to the question. It requires a little bit of nuance. Markus Buehler and I were careful not to claim a limitation of LLMs in general as they are not even a well defined concept.

[Seyed Mohammad Marandi @s_m_marandi]

But Western media is silent.

[Richard Sutton @RichardSSutton]

Or perhaps you will prefer this short form of the video:
https://t.co/moeZjBcN0r

[Zhijing Jin @ZhijingJin]

Just wrapped a fascinating debate at @UpperBoundConf on "Could a Machine Be a Person?"🤖
Big thanks to co-panelists @RichardSSutton, Geoffrey Rockwell & @katragram, and moderator @alonamarie, for a rich discussion on AI personhood, agency & moral responsibility. #AI #Ethics

[Chirasmita Mallick @chirasmita16]

@RichardSSutton wrote "The Bitter Lesson" in 2019.

Silicon Valley read it and scaled LLMs to the moon.

Sutton says they got it wrong. 🧵

[Richard Sutton @RichardSSutton]

The bitter lesson in 26 words:

Don’t be distracted by human knowledge, as AI has been historically.
Instead focus on methods for creating knowledge that scale with computation, like search and learning.

[Beijing Daily @DailyBeijing]

A tailor-made "kindergarten" for robots. Turing Award winner and a principal founder of reinforcement learning, Richard Sutton, has signed an agreement with a #Beijing-based company, collaborating to build a "Robot Kindergarten", planned to be located in Shougang Park in Beijing.

[sorina @robot_in_space2]

Our Open Ant is open-source!

Have fun with it! 😊

https://t.co/yCNjtPQENU

[Richard Sutton @RichardSSutton]

If you are interested, you can learn a bit more about me from this video portrait from the Heidelberg Laureates Forum: https://t.co/8KDUOOOg09

[Joseph Modayil @JosephModayil]

A recent paper answered a question I had for over twenty years: how does a brain organize the sense of smell? This mouse study shows a 1 dimensional spatial code gives a brain map for ~1000 different smell sensors. This raises so many more questions. https://t.co/jSdBPNFY1F

[Furkan Gözükara @FurkanGozukara]

Amazing. No words to describe this tune. Emotional and to the point. New Iranian LEGO movie : We Share the Same Pain 💔 😭🥹

via Brick Beat Battalion

[Richard Sutton @RichardSSutton]

I am definitely going to this...

[RL in Big Worlds @rlc_bigworlds]

We are proud to have an amazing line-up of speakers!

They will present their works, which incorporate the constraint that the world is bigger than the agent and impossible to anticipate, observe, or model perfectly.

We are also looking forward to the panel discussion!