Basically the conclusion is LLMs don't have world models. For work that's basically done on a screen, you can make world models. Harder for other context for example visual context.
For a screen (coding, writing emails, updating docs) -> you can create world models with episodic memories that can be used as background context before making a new move (action). Many professions rely partially on email or phone (voice) so LLMs can be trained for world models in these context. Just not every context.
The key is giving episodic memory to agents with visual context about the screen and conversation context. Multiple episodes of similar context can be used to make the next move. That's what I'm building on.
That's missing a big chunk of the post: it's not just about visible / invisible information, but also the game theory dynamics of a specific problem and the information within it. (Adversarial or not? Perfect information or asymmetrical?)
All the additional information in the world isn't going to help an LLM-based AI conceal its poker-betting strategy, because it fundamentally has no concept of its adversarial opponent's mind, past echoes written in word form.
Cliche allegory of the cave, but LLM vs world is about switching from training artificial intelligence on shadows to the objects casting the shadows.
Sure, you have more data on shadows in trainable form, but it's an open question on whether you can reliably materialize a useful concept of the object from enough shadows. (Likely yes for some problems, no for others)
I do understand what you're saying, but that's impossible to resonate with real-world context, as in the real world, each person not only plays politics but also, to a degree, follows their own internal world model for self-reflection created by experience. It's highly specific and constrained to the context each person experiences.
Game theory, at the end of the day, is also a form of teaching points that can be added to an LLM by an expert. You're cloning the expert's decision process by showing past decisions taken in a similar context. This is very specific but still has value in a business context.
That was the crux of the post for me: the assertion that there are classes of problems for which no amount of expert behavior cloning will result in dynamic expert decision making, because a viable approach to expert deciding isn't trained in the former.
The biggest problem is internal knowledge and external knowledge systems are completely different. One reason internal knowledge is different it is very specific business context and/or it's value prop for the business that allows charging clients for access.
To bridge this gap, the best approach is to train agents to your use case. Agents need to be students -> interns -> supervised -> independent before they can be useeful for your business.
Marketing line: Atom is your conversational AI agent that automates complex workflows through natural language chat. Now with Computer Use Agent capabilities, Atom can see and interact with your desktop applications, automate repetitive tasks, and create visual workflows that bridge web services with local desktop software.
I'm working on a superpowered version of Siri/Alexa that can manage finances, notes, meetings, research, automation, and communication - including email/Slack
shameless plug - started a subreddit r/shouldibuythisproduct to ask other members with history if a product is worth buying. Sometimes just knowing a brand is trustworthy or not is enough to make a good buying decision along with product specs. https://www.reddit.com/r/shouldibuythisproduct/
good question: here's a response I made on the reddit
People might think that this might be another system that might get gamed. If someone tries hard enough, they can play the long game, and game the system. The point is user karma and user history should be enough to make it harder to game the system. Once there is enough traffic, there will strong moderation and minimum posting/commenting requirements
Also another response to bribing the influencers of the system:
Bribe could work but as long as the product is not crap, I think there is no perfect model. The influencers will still have to worry about keeping a reputation and if they put their name on a crappy product they will lose their reputation. People will start pointing this out. Karma might make them stand out and face more criticism for being in the limelight. You also make a good point. Maybe it should be shouldibuythisbrand?
People might think that this might be another system that might get gamed. If someone tries hard enough, they can play the long game, and game the system. The point is user karma and user history will should be enough to make it harder to game the system. Once there is enough traffic, there will strong moderation and minimum posting/commenting requirements
The intentions are good, but I see some immediate problems:
For one, there is no guarantee that someone on that subreddit will have purchased the same product on Amazon. This is especially an issue because reviews will become more important for products that are more niche or bought less frequently.
Secondly, the user base of the subreddit will be small unless you can make a convincing argument for why Amazon reviews are broken. Then you have to get that argument in front of a large number of Amazon buyers.
Finally, because of the small user base, this will be easier to game than Amazon reviews because karma is easier to spoof than buyer reviews. Someone could become a big fish in the small pond of the subreddit and influence more purchases.
It might be a good idea to reach out to some consumer watchdog groups to help with getting some traction under this project
People might think that this might be another system that might get gamed. If someone tries hard enough, they can play the long game, and game the system. The point is user karma and user history will should be enough to make it harder to game the system. Once there is enough traffic, there will strong moderation and minimum posting/commenting requirements
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