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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.
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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