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To put the obvious counterpoint out there, Claude was never actually designed to play video games at all, and has gotten decent at doing so in a couple of months. The drawbacks are still there: navigation sucks, it’s kinda so, it likes to suicide, etc., but even then, the system is no designed to play games at all.
To me, this is a success, as it’s demonstrating using information it has in its memory to make an informed decision about outcomes. It can meet a monster, read its name, knows its stats, and can think about whether or not its own stats are good enough to take it on. This is applied knowledge. Applied knowledge is one of the hallmarks of general understanding. If I can only apply a procedure if told to do so, I don’t understand it. If I can use that procedure in the context of solving a problem, I do understand it. Clause at minimum understands the meaning of the stats it sees: level, HP, stamina, strength, etc. and can understand that the ratio between the monster’s stats and its own are import, and understand that if the monster has better stats than the player, that the player will lose. That’s thinking strategically based on information at hand.
Claude didn't "get decent at playing" games in a couple of months. A human wrote a scaffold to let a very expensive text prediction model, along with a vision model, attempt to play a video game. A human constructed a memory system and knowledge transfer system, and wired up ways for the model to influence the emulator, read relevant RAM states, wedge all that stuff into its prompt, etc. So far this is mostly a construct of human engineering, which still collapses the moment it gets left to its own devices.
When you say it's "understanding" and "thinking strategically", what you really mean it that it's generating plausible-looking text that, in the small, resembles human reasoning. That's what these models are designed to do. But if you hide the text window and judge it by how it's behaving, how intelligent does it look, really? This is what makes it so funny, the model is slowly blundering around in dumb loops while producing volumes of eloquent optimistic narrative about its plans and how much progress it's making.
I'm not saying there isn't something there, but we live in a world where it's claimed that programmers will be obsolete in 2 years, people are fretting about superintelligent AI killing us all, openAI is planning to rent "phd level" AI agent "employees" to companies for large sums, etc. Maybe this is a sign that we should back up a bit.
This is something I don't understand. The LLM generates text that goes in the 'thinking' box, which purports to explain its 'thought' process. Why does anybody take that as actually granting insight into anything? Isn't that just the LLM doing the same thing the LLM does all the time by default, i.e. make up text to fill a prompt? Surely it's just as much meaningless gobbledygook as all text an LLM produces? I would expect that box to faithfully explain what's actually going on in the model just as much as an LLM is able to faithfully describe the outside world, i.e., not at all.
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What I mean by thinking strategically is exactly what makes the thing interesting. It’s not just creating plausible texts, but it understands how the game works. It understands that losing HP means losing a life, and thus if the HP of the enemy and its STR are too high for it to handle at a given level. In other words, it can contextualize that information and use it not only to understand, but to work toward a goal.
I’m not saying this is the highest standard. It’s about what a 3-4 year old can understand about a game of that complexity. And as a proof of concept, I think it shows that AI can reason a bit. Give this thing 10 years, a decent research budget, I think it could probably take on something like Morrowind. It’s slow, but I think given what it can do now, im pretty optimistic that an AI can make data driven decisions in a fairly short timeframe.
What makes things interesting is that the line between "creating plausible texts" and "understanding" is so fuzzy. For example, the sentence
will be much more plausible if the continuation is a number smaller than 125. "138" would be unlikely to be found in its training set. So in that sense, yes, it understands that attacks cause it to lose HP, that a Pokemon losing HP causes it to faint, etc. However, "work towards a goal" is where this seems to break down. These bits of disconnected knowledge have difficulty coming together into coherent behavior or goal-chasing. Instead you get something distinctly alien, which I've heard called "token pachinko". A model sampling from a distribution that encodes intelligence, but without the underlying mind and agency behind it. I honestly don't know if I'd call it reasoning or not.
It is very interesting, and I suspect that with no constraints on model size or data, you could get indistinguishable-from-intelligent behavior out of these models. But in practice, this is probably going to be seen as horrendously and impractically inefficient, once we figure out how actual reasoning works. Personally, I doubt ten years with this approach is going to get to AGI, and in fact, it looks like these models have been hitting a wall for a while now.
I think at some point, we’re talking about angels dancing on pins. Thought and thinking as qualia that other being experience is probably going to be hard. I would suggest that being able to create a heuristic based on information available and known laws of the universe in question constitutes at least an understanding of what the information means. Thinking that fighting a creature with higher STR and HP stats than your own is a pretty good child’s understanding of the same situation. It’s stronger, therefore I will likely faint if I fight that monster. Having the goal of “not wanting to faint” thus makes the decision heuristic of “if the monster’s statistics are better than yours, or your HP is too low, run away.” This is making a decision more or less.
A kid knows falling leads to skinned knees, and that falling happens when you’re up off the ground is doing the same sort of reasoning. I don’t want to skin my knees, so I’m not climbing the tree.
That's true, but if that leads to running from every battle, then you won't level up. Even little kids will realize that they're doing something wrong if they're constantly running. That's what I mean when I say it has a lot of disconnected knowledge, but it can't put it together to seek a goal.
One could argue that's an issue with its limited memory, possibly a fault of the scaffold injecting too much noise into the prompt. But I think a human with bad memory could do better, given tools like Claude has. I think the problem might be that all that knowledge is distilled from humans. The strategies it sees are adapted for humans with their long-term memory, spatial reasoning, etc. Not for an LLM with its limitations. And it can't learn or adapt, either, so it's doomed to fail, over and over.
I really think it will take something new to get past this. RL-based approaches might be promising. Even humans can't just learn by reading, they need to apply the knowledge for themselves, solve problems, fail and try again. But success in that area may be a long way away, and we don't know if the LLM approach of training on human data will ever get us to real intelligence. My suspicion is that if you only distill from humans, you'll be tethered to humans forever. That's probably a good thing from the safetyist perspective, though.
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