Comment by nerdponx
1 year ago
Ironically it's probably a lot closer to what a super-human AGI would look like in practice, compared to just an LLM alone.
1 year ago
Ironically it's probably a lot closer to what a super-human AGI would look like in practice, compared to just an LLM alone.
Right. To me, this is the "agency" thing, that I still feel like is somewhat missing in contemporary AI, despite all the focus on "agents".
If I tell an "agent", whether human or artificial, to win at chess, it is a good decision for that agent to decide to delegate that task to a system that is good at chess. This would be obvious to a human agent, so presumably it should be obvious to an AI as well.
This isn't useful for AI researchers, I suppose, but it's more useful as a tool.
(This may all be a good thing, as giving AIs true agency seems scary.)
If this was part of the offering: “we can recognise requests and delegate them to appropriate systems,” I’d understand and be somewhat impressed but the marketing hype is missing this out.
Most likely because they want people to think the system is better than it is for hype purposes.
I should temper my level of impressed with only if it’s doing this dynamically . Hardcoding recognition of chess moves isn’t exactly a difficult trick to pull given there’s like 3 standard formats…
You're speaking like it's confirmed. Do you have any proof?
Again, the comment you initially responded to was not talking about faking it by using a chess engine. You were the one introducing that theory.
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Fair!
So… we’re at expert systems again?
That’s how the AI winter started last time.
What is an "expert system" to you? In AI they're just series of if-then statements to encode certain rules. What non-trivial part of an LLM reaching out to a chess AI does that describe?
The initial LLM acts as an intention detection mechanism switch.
To personify LLM way too much:
It sees that a prompt of some kind wants to play chess.
Knowing this it looks at the bag of “tools” and sees a chess tool. It then generates a response which eventually causes a call to a chess AI (or just chess program, potentially) which does further processing.
The first LLM acts as a ton of if-then statements, but automatically generated (or brute-forcly discovered) through training.
You still needed discrete parts for this system. Some communication protocol, an intent detection step, a chess execution step, etc…
I don’t see how that differs from a classic expert system other than the if statement is handled by a statistical model.