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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!
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.
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."
Thanks!
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