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Claude AI playing Pokemon shows AGI is still a long ways off
(Read this on Substack for some funny pictures)
Evaluating AI is hard. One of the big goals of AI is to create something that could functionally act like a human -- this is commonly known as “Artificial General Intelligence” (AGI). The problem with testing AI’s is that their intelligence is often “spiky”, i.e. it’s really good in some areas but really bad in others, so any single test is likely to be woefully inadequate. Computers have always been very good at math, and even something as simple as a calculator could easily trounce humans when it comes to doing simple arithmetic. This has been true for decades if not over a century. But calculators obviously aren’t AGI. They can do one thing at a superhuman level, but are useless for practically anything else.
LLMs like chatGPT and Claude are more like calculators than AI hype-meisters would like to let on. When they burst onto the scene in late 2022, they certainly seemed impressively general. You could ask them a question on almost any topic, and they’d usually give a coherent answer so long as you excused the occasional hallucinations. They also performed quite well on human measurements of intelligence, such as college level exams, the SAT, and IQ tests.. If LLMs could do well on the definitive tests of human intelligence, then certainly AGI was only months or even weeks away, right? The problem is that LLMs are still missing quite a lot of things that would make them practically useful for most tasks. In the words of Microsoft’s CEO, they’re “generating basically no value”. There’s some controversy over whether the relative lack of current applications is a short-term problem that will be solved soon, or if it’s indicative of larger issues. Claude’s performance playing Pokemon Red points quite heavily toward the latter explanation.
First, the glass-half-full view: The ability for Claude to play Pokemon at all is highly impressive at baseline. If we were just looking for any computer algorithm to play games, then TAS speedruns have existed for a while, but that would be missing the point. While AI playing a children’s video game isn’t exactly Kasparov vs Deep Blue, the fact it’s built off of something as general as an LLM is remarkable. It has rudimentary vision to see the screen and respond to events that occur as they come into the field of view. It interacts with the game through a bespoke button-entering system built by the developer. It interprets a coordinate system to plan to move to different squares on the screen. It accomplishes basic tasks like battling and rudimentary navigation in ways that are vastly superior to random noise. It’s much better than monkeys randomly plugging away at typewriters. This diagram by the dev shows how it works
I have a few critiques that likely aren’t possible for a single developer, but would still be good to keep in mind when/if capabilities improve. The goal should be to play the game like a player would, so it shouldn’t be able to read directly from the RAM, and instead it should only rely on what it can see on the screen. It also shouldn’t need to have a bespoke button-entering system designed at all and should instead do this using something like ChatGPT’s Operator. There should be absolutely no game-specific hints given, and ideally its training data wouldn’t have Pokemon Red (or even anything Pokemon-related) included. That said, though, this current iteration is still a major step forward.
Oh God it’s so bad
Now the glass-half-empty view: It sucks. It’s decent enough at the battles which have very few degrees of freedom, but it’s enormously buffoonish at nearly everything else. There’s an absurdist comedy element to the uncanny valley AI that’s good enough to seem like it’s almost playing the game as a human would, but bad enough that it seems like it’s severely psychotic and nonsensical in ways similar to early LLMs writing goofy Harry Potter fanfiction. Some of the best moments include it erroneously thinking it was stuck and writing a letter to Anthropic employees demanding they reset the game, to developing an innovative new tactic for faster navigation called the “blackout strategy” where it tries to commit suicide as quickly as possible to reset to the most recently visited Pokemon center… and then repeating this in the same spot over and over again. This insanity also infects its moment-to-moment thinking, from hallucinating that any rock could be a Geodude in disguise (pictured at the top of this article), to thinking it could judge a Jigglypuff’s level solely by its girth.
All these attempts are streamed on Twitch, and they could make for hilarious viewing if it wasn’t so gosh darn slow. There’s a big lag in between its actions as the agent does each round of thinking. Something as simple as running from a random encounter, which would take a human no more than a few seconds, can last up to a full minute as Claude slowly thinks about pressing ‘A’ for the introductory text “A wild Zubat has appeared!”, then thinks again about moving its cursor to the right, then thinks again about moving its cursor down, and then thinks one last time about pressing ‘A’ again to run from the battle. Even in the best of times, everything is covered in molasses. The most likely reaction anyone would have to watching this would likely be boredom after the novelty wears off in a few minutes. As such, the best way to “watch” this insanity is on a second monitor, or to just hear the good parts second-hand from people who watched it themselves.
Is there an AI that can watch dozens of hours of boring footage and only pick out the funny parts?
By far the worst aspect, though, is Claude’s inability to navigate. It gets trapped in loops very easily, and is needlessly distracted by any objects it sees. The worst example of this so far has been its time in Mount Moon, which is a fairly (though not entirely) straightforward level that most kids probably beat in 15-30 minutes. Claude got trapped there for literal days, with its typical loop being going down a ladder, wandering around a bit, finding the ladder again, going back up the ladder, wandering around a bit, finding the ladder, going back down again, repeat. It’s like watching a sitcom of a man with a 7 second memory.
There’s supposed to be a second AI (Critique Claude) to help evaluate actions from time to time, but it’s mostly useless since LLMs are inherently yes-men, so when he's talking to the very deluded and hyperfixated main Claude he just goes with it. Even when he disagrees, main Claude acts like a belligerent drunk and simply ignores him.
In the latest iteration, the dev created a tool for storing long-term memories. I’m guessing the hope was that Claude would write down that certain ladders were dead-ends and thus should be ignored, which would have gone a long way towards fixing the navigation issues. However, it appears to have backfired: while Claude does indeed record some information about dead-ends, he has a tendency to delete those entries fairly quickly which renders them pointless. Worse, it seems to have made Claude remember that his “blackout strategy” “succeeded” in getting out of Mount Moon, prompting it to double, triple, and quadruple down on it. I’m sure there’s some dark metaphor in the development of long-term memory leading to Claude chaining suicides.
What does this mean for AGI predictions?
Watching this trainwreck has been one of the most lucid negative advertisements for LLMs I’ve seen. A lot of the perceptions about when AGI might arrive are based on the vibes people get by watching what AI can do. LLMs can seem genuinely godlike when they spin up a full stack web app in <15 seconds, but the vibes come crashing back down to Earth when people see Claude bumbling around in circles for days in a simplistic video game made for children.
The “strawberry” test had been a frequent concern for early LLMs that often claimed the word only contained 2 R’s. The problem has been mostly fixed by now, but there’s questions to be asked in how this was done. Was it resolved by LLMs genuinely becoming smarter, or did the people making LLMs cheat a bit by hardcoding special logic for these types of questions. If it’s the latter, then problems would tend to arise when the AI encounters the issue in a novel format, as Gary Marcus recently showed. But of course, the obvious followup question is “does this matter”? So what if LLMs can’t do the extremely specific task of counting letters if they can do almost everything else? It might be indicative of some greater issue… or it might not.
But it’s a lot harder to doubt that game playing is an irrelevant metric. Pokemon Red is a pretty generous test for many reasons: There’s no punishment for long delays between actions. It’s a children’s game, so it’s not very hard. The creator is using a mod for coloring to make it easier to see (this is why Jigglypuff’s eyes look a bit screwy in the picture above). Yet despite all this, Claude still sucks. If it can’t even play a basic game, how could anyone expect LLMs to do regular office work, for, say, $20,000 a month? The long-term memory and planning just isn’t there yet, and that’s not exactly a trivial problem to solve.
It’s possible that Claude will beat pokemon this year, probably through some combination of brute-force and overfitting knowledge to the game at hand. However, I find it fairly unlikely (<50% chance) that by the end of 2025 there will be an AI that exists that can 1) be able to play Pokemon at the level of a human child, i.e. beat the game, able to do basic navigation, not have tons of lag in between trivial actions, and 2) be genuinely general (putting the G in AGI) and not just overfit to Pokemon, with evidence coming from being able to achieve similar results in similar games like Fire Emblem, Dragon Quest, early Final Fantasy titles, or whatever else.
LLMs are pretty good right now at a narrow slice of tasks, but they’re missing a big chunk of the human brain that would allow them to accomplish most tasks. Perhaps this can be remedied through additional “scaffolding”, and I expect “scaffolding” of various types to be a big part of what gives AI more mainstream appeal over the next few years (think stuff like Deep Research). Perhaps scaffolding alone is insufficient and we need a much bigger breakthrough to make AI reasonably agentic. In any case, there will probably be a generic game-playing AI at some point in the next decade… just don’t expect it to be done by the end of the year. This is the type of thing that will take some time to play out.
LLMs have intelligence, what they don't have is advanced spatial skills and visual comprehension. Claude Sonnet 3.7 is designed to code first and foremost, secondarily as a writer/conversationalist. Game-playing and consoling in Minecraft cathedrals (which Sonnet does quite well) is a tertiary capability that they didn't even aim for but are testing anyway. They didn't try to make it good at this, unsurprisingly it's not that great at it.
I had Sonnet play through a civ 4 game where I implemented its strategy and tactics, it was perfectly capable of reacting to text inputs but didn't really understand the pictures, where units were in relation to eachother. If you give it the inputs in the medium it understands best, text, it's pretty capable. When these AIs struggle with strawberry, that's because their tokenizer can't properly count letters. They never see a single letter.
Have a look at what they do with code in minecraft: https://youtube.com/watch?v=FCnQvdypW_I
It's a long video but you can take a general look at what they build. Can you build a house by coding it in? OK, they're not that great at stairs. But they are directly coding things in. I bet 98% of the planet couldn't do this specific niche skill that humans have no aptitude for, coding in architecture in minecraft. That's not a tool in our arsenal. It doesn't discredit our intelligence because we're bad at things we're not supposed to do in mediums unintuitive to us.
Now consider Alphago. You can't beat it, nobody can. A few people can beat Google's 7 year old AI Starcraft pro, not very many though (and this is the version downgraded to non vastly superhuman speed).
That Sonnet can kind of play Pokemon is proof in my mind that AGI is imminent. It proves that Sonnet's intelligence generalizes out into domains it wasn't remotely designed for, even crippled by the visual input barrier. Combined with the specialized bots, we have both 'extremely broad' and 'extremely capable'. All that remains is marrying the strengths of both approaches together, scaling up, working on visuals and tweaking.
Consider just today another AI agent arrived, it seems pretty capable: https://x.com/LamarDealMaker/status/1898454061277458498
Would you be willing to state here what your predictions are for when you think we're likely to have AGI? It might also be good to have a definition of AGI, or whatever metric that could be used to judge success. For instance, "I predict we'll have AGI by 2028, and it will lead to >5% annual GDP growth or >10% unemployment thereafter"... something like that?
AI-boosters seem to think any piece of evidence points to AGI being imminent. I just can't imagine how someone would look at Claude's performance playing Pokemon and see it as a good sign.
I think by 2027. Executives in Anthropic, OpenAI seem to give that as their date.
Defining AGI is tricky. I think the key element is take-off. AI right now is best at coding. You use code to make AI. Recursive self-improvement is the name of the game, AGI will be when you have bots performing the tasks that AI researchers do now in collating data and training models (not in the partial sense like how synthetic data is used today but in a holistic sense where the main intellectual work would be done by AIs). And AGI will be ephemeral because superintelligence will happen immediately afterwards and things will get very crazy very quickly, the world power structure will change fundamentally. Nobody will be asking 'what does this mean for unemployment' because that will be the least of our concerns. We will know AGI when we see it.
Little things like mapping the relations between objects in space don't disprove intelligence. If you presented Pokemon like a text adventure game, Claude would have no problem winning. The intelligence is there, that's what they're working on. Advanced vision isn't there, people don't particularly need AIs to play Pokemon, they're needed for writing code.
And yes I am heavily, heavily invested in AI companies, so I have some skin in the game.
Text adventure games exist. Has anyone tried pitting Claude against one?
I tried with Colossal Cave, but it's in the training set.
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