Comment by XCSme
2 months ago
Yup, you still need knowledge. Even if you have access to all the data and tools, you still need to know what to search for, what tools to use and to understand what the user is asking.
Our computers can already do everything, have access to all the tools and information, yet they still need a human/intelligence to use it and apply to specific problems.
Even defining the problem requires knowledge.
As for the tools, if the model has access to 1000 tools, how would it know which one to use if it doesn't have any knowledge itself?
What if I ask for "table tennis spin" it had a "magnus effect calculator", how would it know to make the connection between the two?
Model can use tools to get that knowledge. In your example, read Wikipedia page about table tennis. Imagine a reasoning engine with a big enough context, that knows nothing. A path built from first principles to understand "table tennis spin" — does not look very long for me.
How would it know about Wikipedia and when to use it? From the tool description? If we had 100k such tools, then that wouldn't even fit in the context.
This is only one example, plus if the topic is more complex, maybe it had to search/learn everything (what is table tennis, what is spin, what is a human, what is a ball), etc. So it would be like spawning a baby human, have it spend an (instant) life learning about the world before providing an answer. Maybe this could work in 10 yesrs, if models get stronger with huge context lengths and almost instant data retrieval. Is it the best way to go about things though? Most animals have most of their core abilities embedded in their DNA and "instincts". A cat doesn't have to learn what a bird is in order to hunt it, it's already "embedded" in its neural pathways, or even deeper, at a full-body level. Those type of systems are a lot more efficient than the learned ones. Maybe the best future AI, will have everything already embedded, instead of just being a strong reasoning machine. All AI responses should be instant and like "reflexes" instead of reasoned steps.
Imagine you only know how to cook (use fry pan skill) and know how to cook omelette (recipe). You get the task to cook doner kebab. How many Wikipedia pages do you need to read to get a good understanding? I guess its max 5.
I think grounding your abstract problem to an example makes it more trivial, than it sounds in general.
> How would it know about Wikipedia and when to use it?
2 general concepts "You have to get good understanding of subject area before you do actions" + "Wikipedia is a good source of knowledge of subject areas" will get a model there.
> spawning a baby human, have it spend an (instant) life learning
Humans spend 99% of their life on boring repeating tasks, not learning anything, just navigating on heuristics.
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It probably can't use all the truth in its content window, not yet anyway.
E.g. you put a graph in its content window, and you ask it to find a Hamiltonian cycle, can it do it?
Probably this could be a next step in the future for more powerful AIs, a layer that abstracts the facts in its content window away, and a layer that solves this types of abstractions.