In the context of building a tool designed to share knowledge using Artificial Intelligence there's one fundamental question that's been bugging me, which is: 'how can a computer teach itself' - or to put it in a more grounded way: 'how does a computer know that 2+2=4 without being told / taught so by a third party (i.e. a human)?

Of course, the concept of knowledge and learning is no different when talking about machines as it is with humans, so the basic question is 'how do we learn stuff'? (and what is it to 'know' something?).

Listening to a podcast with Ilya Sutskever (one of the founders of OpenAI) he described very succinctly that humans learn in two ways:

• Learning from others

• Learning by trial and error (& patterns)

So in the context of a computer not being able to 'learn from someone else' then the only other way it can learn is by trial and error - or a more smarter version of trial and error being 'pattern matching'. For example, the computer may observe that 1+1 = 2, and therefore guess that the pattern follows that 2+2=4.

It's this 'guessing' that underpins the intelligence and knowledge of AI. Essentially all AI is doing is a very sophisticated very of guessing (aka probability) - and thanks to extremely large neural networks powering Large Language Models (LLMs) this 'guessing' is next-level guessing (aka highly technical and increasingly accurate probability modelling).

Other than listening to AI OG's like Ilya Sutskever for insights into how humans and machines learn, I've also started quizzing my 8 year-old daughter for insights into learning - as she is a teaching obsessive and has a healthy love of writing school lesson plans in her spare time.

Interestingly, her answer to the 'how do we know 2+2 = 4' question was bang on Sutskever's answer - telling me: 'Well Daddy, it's basic maths if 1+1 = 2 then of course 2+2 = 4'.

The simple pattern matching of an 8 year old's brain underlines a fundamental truth of AI - that all intelligence is essentially 'guessing' (or a very accurate version of guessing based on probabilistic pattern-matching).