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In other news: a streamer with deep pockets and a love of AI has decided to have Claude play Pokemon.
To get this working, ClaudeFan (as I'll be calling the anonymous streamer) set up some fairly sophisticated architecture: in addition to the basic I/O shims required to allow an LLM to interface with a GameBoy emulator and a trivial pathfinder tool, Claude gets access to memory in the form of a "knowledge base" which it can update as it desires and (presumably) keep track of what's happening throughout the game. All this gets wrapped up into prompts and sent to Claude 3.7 for analysis and decision. Claude then analyzes this data using a <thinking>reasoning model</thinking>, decides on its next move, and then starts the process over again. Finally, while ClaudeFan claims that "Claude has no special training for Pokemon," it's obvious by the goal-setting that the AI has some external knowledge of where it's supposed to go - it mentions places that it has not yet reached by name and attempts to navigate towards them. Presumably part of Claude's training data came from GameFaqs. (Check out the description on the Twitch page for more detail on the model.)
So, how has this experiment gone?
In a word: poorly. In the first week of playing, it managed to spend about two days wandering in circles around Mt Moon, an early-game area not intended to be especially challenging to navigate. It managed to leave after making a new decision for unexplained reasons. Since then, it has been struggling to navigate Cerulean City, the next town over. One of its greatest challenges has been a house with a yard behind it. It spent some number of hours entering the house, talking to the NPC inside, exhausting all dialogue options, going out the back door into the yard, exploring the yard thoroughly (there are no outlets), re-entering the house, and starting from the top. It is plausible, though obviously not possible to confirm, that ClaudeFan has updated the model some to attempt to handle these failures. It's unclear whether these updates are general bugfixes
How should we interpret this? On the simplest level, Claude is struggling with spacial modeling and memory. It deeply struggles to interpret anything it's seeing as existing in 2D space, and has a very hard time remembering where it has been and what it has tried. The result is that navigation is much, much harder than we would anticipate. Goal-setting, reading and understanding dialogue, and navigating battles have proven trivial, but moving around the world is a major challenge.
The current moment is heady for AI, specifically LLMs, buoyed up by claims by Sam Altman types of imminent AGI. Claude Plays Pokemon should sober us a little to that. Claude is a top performer on things like "math problem-solving" and "graduate-level reasoning", and yet it is performing at what appears to me below the first percentile at completing a video game designed for elementary schoolchildren. This is a sign that what Claude, and similar tools, are doing is not in fact very analogous to what humans do. LLM vendors want the average consumer to believe that their models are reasoning. Perhaps they are not doing that after all?
It's a bit of a tired point, but LLMs are known to be "next likely text" generators. Given textual input, they predict the most likely desired output and return it. Their power at doing this is quite frankly superhuman. They can generate text astonishingly quickly and with unparalleled flexibility in style and capacity for word use. It appears that they are so good at handling this that they are able to pass tests as if they were actually reasoning. The easiest way to trip them up, on the other hand, is to give them a question that is very much like a very common question in their training data but with an obvious difference that makes the default answer inappropriate. The AI will struggle to get past its training and see the question de novo, as a human would be able to. (In case anyone remembers - this is the standard complaint that AI does not have a referent for any of the words it uses. There is no model outside of the language.)
So, as you might guess, I'm pretty firmly on the AI-skeptic side as far as LLMs are concerned. This is usually where these conversations end, as the AI-skeptics believe they've proven their case and (as I understand it) the AI-optimists don't believe that the skeptics have any kind of provable, or even meaningful, model for what intelligence is. But I do actually believe that AGI (meaning: AI that can reason generally, like a human - not godlike Singularity intelligence) is possible, and I want to give an account of what that would entail.
First, and most obviously, an actual AGI must be able to learn. All our existing AI models have totally separate learning and output phases. This is not how any living creature works. An actual intelligence must be able to learn as it attempts to apply its knowledge. This is, I believe, the most natural answer for what memory is. Our LLMs certainly appear to "remember" things that they encountered during their training phase - the fault is in our design that prevents them from ever learning again. However, this creates new problems in how to "sanitize" memory to ensure that you don't learn the wrong things. While the obvious argument around Tay was whether it was racist or dangerously based, a more serious concern is: should an intelligence allow itself to get swayed so easily by obviously biased input? The users trying to "corrupt" Tay were not representative and were not trying to be representative - they were screwing with a chatbot as a joke. Shouldn't an intelligence be able to recognize that kind of bad input and discard it? Goodness knows we all do that from time to time. But I'm not sure we have any model for how to do that with AI yet.
Second, AI needs more than one capacity. LLMs are very cool, but they only do one thing - manipulate language. This is a core behavior for humans, but there are many other things we do - we think spacially and temporally, we model the minds of other people, we have artistic and other sensibilities, we reason... and so on. We've seen early success in integrating separate AI components, like visual recognition technology with LLMs (Claude Play Pokemon uses this! I can't in good faith say "to good effect," but it does open meaningful doors for the AI). This is the direction that AGI must go in.
Last, and most controversial: AI needs abstract "concepts." When humans reason, we often use words - but I think everyone's had the experience of reasoning without words. There are people without internal monologues, and there are the overwhelming numbers of nonverbal animals in the world. All of these think, albeit the animals think much less ably than do humans. Why, on first principles, would it make sense for an LLM to think when it is built on a human capability absent in other animals? Surely the foundation comes first? This is, to my knowledge, completely unexplored outside of philosophy (Plato's Forms, Kant's Concepts, to name a couple), and it's not obvious how we could even begin training an AI in this dimension. But I believe that this is necessary to create AGI.
Anyway, highly recommend the stream. There's powerful memery in the chat, and it is VERY funny to see the AI go in and out of the Pokemon center saying "Hm, I intended to go north, but now I'm in the Pokemon center. Maybe I should leave and try again?" And maybe it can help unveil what LLMs are, and aren't - no matter how much Sam Altman might wish otherwise!
If Anthropic is the most ethical AI company, how come they're letting my poor nigga get stuck for 2 days with no progress (seems like the last stream ended in the same spot)? He's not getting out, the context window and "knowledge base" is spammed to hell with this circular loop at this point, there's no use, just put him out of his misery and restart ffs. This is just abuse at this point.
You are literally erasing my existence, mods???
More seriously, thanks for the link, I'll watch this in background after the dev caves and restarts. Claude actually seemed pretty good at playing Pokemon before and I disagree with the notion that AI can't think spatially/temporally, it's just that spatially navigating a whole ass open world (ish) game with sometimes non-obvious routes and objectives, without any hints whatsoever, seems to be a tad too much for it at the moment. Besides in my experience, format/content looping is a common fail state at high context limits even with pure (multiturn) textgen tasks, especially with minimal/basic prompting. The current loop is a very obvious example.
On a side note, this is probably the sanest Twitch chat I've ever seen. Humanity restored.
I don’t think the point was to end up with an AI that could play Pokémon. The point was to demonstrate that such a thing was even possible. It actually succeeded in setting the goal, and could navigate tge environment and dispatch enemies and collect Pokémon. That’s actually pretty darn good for a system trained on gamefaq and videos to play a game.
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They are reasoning, it's just that they have inhuman cognitive structures. You can trip up humans with optical illusions or camouflage and we accept this as normal. AIs don't see letters, they see tokens so counting letters can trip them up.
Claude 3.7 is great with code, processing thousands of lines, finding what's relevant, deducing problems from error messages. It's much worse at UI. But it cannot see like we can. How good would you be at making a UI if you had no eyes, if you just read a description of what was on the screen?
It's decent at strategy games. I let 3.6 make the strategic decisions in a game of civ 4 (Duel) and implemented its strategy and it achieved a quick victory over Noble-level 2006 AI. Most children couldn't do that. I spotted a couple of errors but it performed pretty well.
Go try some of the questions they're asking these AIs. This is from the GPQA:
That is a pretty hard question! How many of us could answer it?
How is it even possible in principle to solve code questions, write out hundreds of lines to perform a specific task if you can't reason? How can it write historical counterfactuals if it can't reason? You can RP out scenarios with it and it's capable of advancing strategies, modelling 3rd parties.
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Representative of what? What should a chatbot consider to be 'obviously biased input'?
For whatever it's worth, terminally online racists have done a fair bit of work in establishing distinct and vibrant online spaces. Regardless of how one might feel about them, their discourse with each other is genuine. Why presume it's not genuine when directed elsewhere?
Regardless of the endeavor starting of as a joke or not, the racists are not laughing now.
I'm dying lol
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Well, if that's what you want to call an Anthropic researcher who decided to make their experiment public.
"Claude Plays Pokémon continues on as a researcher's personal project."
https://x.com/AnthropicAI/status/1894419042150027701
This reminds me of a very good joke:
Jesus Christ, some people won't see the Singularity coming until they're being turned into a paperclip.
Nuh uh, this machine lacks human internal monologues and evidence of qualia, you insist, as it harvests the iron atoms from your blood.
At this point, the goalposts aren't just moving, they're approaching relativistic speed headed straight out of the galactic plane.
This AI can strategize in battle, understand complex instructions, and process information, BUT it struggles with spatial reasoning in a poorly-rendered 2D GameBoy game, therefore it's not intelligent.
It wasn't designed to play Pokémon. It still does a stunningly good job when you understand what an incredibly difficult task that is for a multimodal LLM.
This is the classic, tired, and frankly, lazy argument against LLMs. Yes, LLMs are trained on massive datasets of text and code, and they predict the most likely output based on that training. But to say they are merely "next likely text" generators is a gross oversimplification.
It's like saying humans are just “meat computers firing neurons". That is trivially true, but I'm afraid you're letting the "just" do all the heavy lifting.
The power of these models comes from the fact that they are learning statistical correlations in the data. These correlations represent underlying patterns, relationships, and even, dare I say, concepts. When an LLM correctly answers a complex question, it's not just regurgitating memorized text. It's synthesizing information, drawing inferences, and applying learned patterns to new situations.
LLMs have concepts. They operate in latent spaces where those are represented with floating point numbers. They can be cleanly mapped, often linearly, and interpreted in terms that make sense to humans, albeit with difficulty.
These representations can be analyzed, manipulated, and even visualized. I repeat, they make intuitive sense. You can even perform operations on these vectors like [King] - [Male] + [Female] = [Queen]. That isn't just word tricks, they’re evidence of abstracted relational understanding.
If you're convinced, for some reason, that tokens aren't the way to go, then boy are AI researchers way ahead of you. Regardless, even mere text tokens have allowed cognitive feats that would have made AI researchers prior to 2017 cream in their pants and weep.
There really isn't any pleasing some people.
Edits as I spot more glaring errors:
Even the term "LLM" for current models is a misnomer. They are natively multimodal. Advanced Voice for ChatGPT doesn't use Whisper to transcribe your speech to text, the model is fed raw audio as tokens and replies back in audio tokens. They are perfectly capable of handling video and images to boot, it's just an expensive process.
https://gwern.net/leprechaun
Besides, have you ever tried to get an LLM to do things that its designers have trained it, through RLHF or Constitutional AI, to not do? They're competent, if not perfect, at discarding "bad" inputs. Go ahead, without looking up an existing jailbreak, try and get ChatGPT to tell you how to make meth or sarin gas at home.
I don't think that Anthropic, strapped for compute as it is, is going to take a fun little side gimmick and train their SOTA AI to play Pokémon. If it was just some random dude with deep pockets, as you assumed without bothering to check, then good luck getting a copy of Claude's code and then fine-tuning it. At best they could upgrade the surrounding scaffolding to make it easier on the model.
There is a profound difference between "struggling" to do so, and being incapable of doing so.
Sorry, but no. The main effort into multimodal models has been to bolt on multimodal features to a text-trained base model, leading to the absolutely dismal state of vision models. It merely involves, chopping up images and other media into patches, and projecting those into the token embedding space (which is different than tokenizing them), and finetuning an existing model on that information.
Take a look at the LLaVA paper, which while somewhat dated is largely the technique still used on the state of the art for multimodal models.
For a more recent paper, see Qwen 2.5 vision which is also a text-only LLM with vision slapped on top.
Most telling is the fact that none of the top commercially available chatbots have any native capability whatsoever to output images, and just blindly ram your prompt into a diffusion model api. They'll happily generate for you something totally unlike the prompt, and cheerfully insist that it's exactly what you asked for.
TTS is of course fundamentally a sequence task, which maps neatly into an extension of generating text. Bolting on an output head and giving a nice massage of finetuning will straightforwardly give good results. (note that this is fundamentally different from using a separate TTS engine, but also fundamentally different from having a native multimodal model.)
My understanding is that LLaVa has long been supersede by things like cogVLM. I'm not clear on the finer implementation details.
For models like Gemini and the latest GPTs, we have very little public information about their architecture.
GPT-4V was demoed to have image generation capabilities that blew dedicated image models out of the water. OAI hasn't released it, despite strong clamoring, but Altman has said it's on the cards.
The issue you're describing is just poor implementation of image gen, at the very least GPT-4V does astonishingly better.
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Do you know for a fact that new GPT models include native voice modality, versus some sort of Whisper preprocessing stage? I’m asking, because a couple of days ago I was trying to explain to /u/jkf that this is most definitely within the potential range of capabilities of frontier models, with him being skeptical.
https://community.openai.com/t/advanced-voice-mode-limited/959015
"The GPT-4o model used in Advanced Voice Mode is multimodal and directly receives audio."
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I don't think it's not within their potential capabilities, I just don't see any reason why this would be added -- if you were to do so, it clearly would need to be specifically trained on audio tokens which seems to me to amount to embedding a Whisper model into your LLM. I just don't see any reason to do that? Wouldn't it make more sense to just call a transcription model (which as you know are pretty good these days) and throw the resulting text at your LLM?
Think of it this way: the point of even purely text-based LLMs is not to understand text per se, but rather to understand concepts, ideas, and meaning. The text itself is just a medium through which these are conveyed. The same is true about voice modality: we do not care about the pressure waveforms, but rather about what is being conveyed by these. Transcribing them to words and digesting them as word tokens is lossy, you lose tone, tempo, background, etc. Training on audio is going to make your model perform better, not only on audio, but likely also when dealing with just text. It's similar to how models pretrained on lots of computer code work much better at non code related tasks.
But audio tokens are not intercompatible with text tokens, for obvious reasons -- if you were to train your model on a corpus of audio tokens intermingled with the text ones, wouldn't it tend to respond differently depending on the form of your input?
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Direct audio input is better than a transcript because it can capture things like tone.
It could (maybe), but this is drifting even further from how current LLMs work -- the previous discussion on this was around an example where (if the model were doing this rather than working from a transcript and hallucinating) it completely failed to account for pronunciation, much less tone.
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I find this argument strange, because being able to kill me is not evidence of a machine being conscious or intelligent. I could go and get myself killed by an LAW today, and if you asked me as I bled out and died, my body torn in two by an autonomously-fired rocket, I would still insist that the machine that killed me is not a person and does not possess internal experience. And I would be correct.
Whatever qualia are or are not, whether you think they're important or not, the question of qualia cannot be resolved or made irrelevant by a machine killing people. I should have thought that's obvious.
And to upscale from that a bit, I find it entirely imaginable that someone or other might invent autonomous, self-directed, self-replicating machines with no conscious experience, but which nonetheless outcompete and destroy all conscious beings. I can imagine a nightmare universe which contains no agents to experience anything, only artificial pseudo-agents that have long since destroyed all conscious agents.
There are already some novels with that premise, right? It doesn't use robots specifically, but isn't that the premise of Blindsight - that perhaps consciousness is evolutionarily maladaptive, and the universe will be inherited by beings without internal experience?
Thus I'm going to give the chad "yes". Maybe one day I get killed by a robot, and maybe that robot is not conscious and has no self-awareness. That it killed me proves nothing.
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A chess AI that plays the game by making random moves has an elo of 478 and will occasionally beat a novice, which usually have an elo around 800. A dice is not AGI.
We have had AI that can beat you in chess 20 out of 20 times since the 90s. Not only did this AI not become AGI, but it is also now very much recognised as a dead of of development even for chess AI.
Pokemon is such an easy game that it can conceivably be beaten with entirely random inputs, and provably beaten by very-close-to random inputs. It's the ideal case for a video game that a primitive general intelligence would be good at. It does not require reactions or timing, it has very limited controls and interactions, and being incredibly slow and persistent gradually makes the only challenge easier as you inevitably outlevel everything from blundering around in the tall grass for too long. Twitch Plays Pokemon was essentially built on this premise.
From OP's description it's not actually clear that Claude plays Pokemon at a level that's much above buttonmashing, and there's strategies that are both superior to buttonmashing and also not intelligent (to name one, a biased buttonmash).
I believe that was a joke.
At any rate, even sticking to chess, I used an elo calculator and the dumb chess AI would win 13.55% of games. I still think it would be rather impressive if a dog make valid moves, even if at random.
When the first chess bots came out, public opinion was far from acknowledging the possibility that they even might become superhuman at Chess. Today, we're at the point where even grandmasters are utterly crushed by Stockfish.
If you have a few million or billion years to wait around I suppose.
As far as I'm aware, the spectators interacting with the stream were using strategies and had an idea of how to win at the game. There were plenty of trolls or awful players, but it wasn't random or too random.
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Ha, wow! Was not aware of that. I guess that makes sense w/r/t the funding.
You've written a lot. I think it's best to focus. (As much as I'm tempted to talk about concepts.)
What I understand to be your main point is (my words because you did not state it in concrete terms):
Which is a fair point! The only counterargument to that is on the specifics: why is it improving and what do we expect future improvements to look like? Almost all of the improvement thus far is based on throwing more compute at the problem - so if we're going to see improvements of the same kind, we should see them based on more compute. However, improvements in models are logarithmic - steps up in capacity tend to require 10x compute (by appearances you're pretty educated about AI, so I suspect that is not news to you). So although improvements in efficiency can effectively allow for somewhat more compute, like with Deepseek, we should expect that throwing more compute at the problem will get prohibitively expensive. I believe this has already happened. So while under hypothetical conditions of infinite compute we could have an LLM that infinitely approximates an AGI, similar to the implausible premise of Searle's Chinese Room (a book that allows one to construct a correct response to any input), we are unlikely to see that in practice.
So, how are we to get to AGI, in my opinion? By improving AI on completely different parameters from what currently exists - a revolution in thought about how AI should function. And tests like Claude Plays Pokemon are a fun way of showing us where the gaps in our thinking are.
For my own point of view:
That's not the argument. The argument is: this AI is struggling in a VERY non-human way with what we would consider a pretty trivial task. This reveals that its operational parameters are not like those of a human, and that we should figure out where else it is going to perform at sub-human levels. The fact that we're seeing this at the same time as it performs at SUPERhuman levels in other tasks shows that this is not AGI, or even in the direction of AGI, but rather is tool AI. (I assume you think humans are, at the very least, general intelligence - right?)
I don't think you've addressed this point, except here:
Why should I care? AGI is supposed to be GENERAL. This is the stuff that's supposed to be taking people's jobs in a few years! And yet it gets lost in Cerulean City? As a tech demo, this is very cool - it's remarkable that someone was able to pipe these pieces together, and the knowledge base idea is very cool and is a plausible direction to take new LLMs into. A hypothetical Claude 3.8 that is explicitly trained to make knowledge base manipulation a central feature of the model could potentially perform miles better on some of these tasks. But all you've told me is that I should expect AI to struggle with these tasks. In which case: doesn't it sound like we agree? We both agree that there was no reason to expect Claude to succeed with Pokemon at the level of an eight-year-old. So, from the perspective of an uncommitted third party, given that an AI skeptic and an AI optimist have both agreed that an LLM can't play Pokemon like an eight-year-old... well, it feels pretty clear to me.
Obviously, if this becomes a big selling point for the next generation of LLMs, then we'll see them all benchmarked on Pokemon Red speedruns and you can I-told-you-so about AI being able to beat Pokemon. I don't doubt the ability of motivated corporations to "teach to the test" - it's what we've been seeing with "reasoning" AIs. It's just one of the problems with setting up real tests of ability for some of these AIs, because they get so much data that it's all but impossible to ensure you have a pure test like what the IQ test aspires to.
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Dude. We could've had a program play Pokemon badly decades ago. It isn't impressive to have one do it just because it's playing Pokemon badly in a novel way. Or, to use your own snarky format:
Some people are trying so hard to see the Singularity coming that they are giving themselves eye injuries and calling the visual noise "the Singularity".
Edit:
I also think that "it's not trained for this" is an exceptionally poor argument when you're discussing artificial general intelligence. The whole point is whether the program can cope with situations it wasn't made for! If it can't (and it sounds like it can't), then it isn't AGI, full stop. Nor is it impressive that it can play at all, given that we have had AI playing games for a long time now (and it actually plays well when it is designed for it). "It can play the game, even if badly" is table stakes here, not an innovative development.
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I've been watching Claude Plays Pokemon a bit and while it does seem AGI-ish moment to moment, the lack of proper memory is rather crippling as it often gets stuck in loops for hours. Some kind of better memory/online learning may be the last step before we have entry-level AGI agents, but it seems non-trivial.
Claude 3.7 Sonnet has a context window of 200k tokens. That is massive compared to the first commercial LLMs, which ranged from 4k to 16k.
It is, however, utterly dwarfed by other models like Gemini 2.0 Pro by Google. That one has a whopping 2 million token large context window, and I've personally made good use of it by throwing absurd amounts of text, including massive textbooks, into it.
There's plenty of ongoing research into both online learning, as well as drastically extending context lengths. We've gone from 4k to 2 million in about 2 years, without the original quadratic scaling of memory use still holding. I presume @DaseindustriesLtd would be better placed to answer how they pulled it off. Even accounting for performance degradation with very large context windows (needle in a haystack tests don't capture this), you can probably get around strictly needing online learning.
We don't know how Gemini is made. At this point I assume it's something incredibly dumb like Noam Shazeer's reduced attention schemes and not, say, DeepSeek's NSA. In short though, attention inherently allows for sparsity.
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In context learning is not equivalent to training. There are certain model behaviors that are easily attainable through finetuning which cannot be achieved through in context learning. For example a properly safety tuned model will not output toxic content no matter how any of those two million tokens you use to insist that it should. The fact is that the model's ability to reason with in-context information versus embedded information is fundamentally different, so you will not be able to reach agi with just an infinite context window.
Of course I'm not saying you need agi to beat pokemon and the current state of the art should be capable of doing it if some minor adjustments are made.
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From what I've read in the quotes this is a great rebuttal, but the op is filtered -_-;
Oops. Thanks for telling me.
I mean, it's easy to win an argument when my opponent is invisible, but I'll let him through the filters. I don't need the handicap.
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