It can also take part of a problem, choose a lead at random, think through the results, and step back if that doesn’t work. A forward pass in an LLM doesn’t do that, yet.
Basically it seems to me that LLMs may have part of what makes us good at inventing ideas, but they’re missing part of that process.
Think about how often narrowly specialized academics reinvent a bad version of something that already has an elaborately understood theory in another field. (a meme example is when a medical researcher working on insulin metabolism reinvented the trapezoid rule for integration - but of course many more mundane cases happen every day). It would be a great opportunity to avoid this by checking with an LLM. Arguably if that researcher fed his idea to an LLM as it exists today, the LLM would have recognized that this is just the trapezoid rule. In fact I also use it for such "sounding board" where it gives me good googleable terms like, "what you're looking for is called an XYZ".
1. It is very difficult for me to tell you about my context as a user within low dimension variables.
2. I do not understand my situation in the universe to be able to tell AI.
3. I dont have a vocabulary with AI. Internet i feel aced this with shared HTTP protocol to consistently share agreed upon state. For ex within Uber I am a very narrow request response universe with.. POST phone, car, gps(a,b,c,d), now, payment.
But as a student wanting to learn algorithms how do I pass that I'm $age $internet-type from $place and prefer graphical explanations of algorithms, have tried but gotten scared of that thick book and these $milestones-cs50, know $python upto $proficiency(which again is a fractal variable with research papers on how to define for learning).
Similarly how do I help you understand what stage my startup idea is beyond low traction, but want to know have $networks/(VC, devs, sales) APIs, have $these successful partnerships with such evidence $attendance, $sales. Who should I speak to? Could you pls write the needful in mails and engage in partnership with other bots under $budget.
Even in the real world this vocabulary is in smaller pockets as our contexts are too different.
4. Learning assumes knowledge exists as a global forever variable in a wider than we understand universe. $meteor being a non maskable interrupt to the power supply at unicorn temperatures in a decade. Similarly one time trends in disposable $companies that $ecosystem uses to learn. I'm in a desert village with with absent electricity might mean those machines never reach me and perhaps most people don't have a basic phone in the world to be able to share state. Their local power mafia politics and absent governance might mean the pdf AI recommends i read might or might not help.
I don't know how this will evolve but to think of the possibilities has been so interesting. It's like computers can talk to us easily and they're such smart babies on day 1 and "folks we aren't able to put right, enough, cheap data in" is perhaps the real bottleneck to how much usefulness we are being able to uncover.