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.
>Doner kebab or döner kebab[a] is a Turkish
(what is turkish)->(parse lots of potentially relevant/irrelevant context because I have no way of knowing which if any of this informs the doner kebab before I've looked at it)
>dish made of meat
(what is meat) -> (parse lots of potentially ir/relevant context because I don't know if the specific origin/chemistry/mechanics or whether maillard reactions are important before I learn about them)
>cooked on a vertical rotisserie.
(what is a rotisserie) -> etc etc etc
Seems significantly less efficient than just having the various (how to cook > meat, tools > rotisserie, how to cook > seasoning > tomato; lettuce; cabbage; onion with sumac; fresh or pickled cucumber or chili; various sauces, etc) just already built in to the weights.
I'm just playing devil's advocate here.
Yes, but still "how to cook" is not atomic. It involves knowing how to move stuff, how to measure, what "cooked" looks like in different environment (i.e. different lighting) or variations in ingredients, how to recover from specific failures (i.e. a good cook can fix accidentally adding too much salt, by counter-balancing with an ingredient that absorbs the extra salt). And this is only one skill.
It's a bit how deep image neural nets work, where simply detecting shape primitives is not enough, the net is also the connection and relation between those primitives.
Even saying, the AI should just have the "cooking" or "coding" skill, trivializes the problem.
> Humans spend 99% of their life on boring repeating tasks
But we are also non-stop unconciously learning about the world non-stop, from the analgous stream of inputs and seeing the immediate result/feedback. Even looking at static picture is like over-training a specific dataset.
Just boiling water would be difficult. Do I just add heat until I see bubbles? Or should I have a world model in which I understand that boiling water will be of varying temperatures at varying altitudes and given different liquids.
Because if the recipe just says "boil for 10 minutes" but the thing being cooked really needs a temperature of 212F for 10 minutes, the thing isn't going to be cooked if you're not actually at 212 for 10.
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.