Until a language model can develop a generalized solution to a real-world phenomena, it's not even close to AGI. The current iteration of ML algorithms are useful, yes, but not intelligent.
Github Copilot solved my business problem by itself just as I would've done. Is that real-world enough and the solution generalized enough?
No, it isn't. Co-Pilot is unable to provide a rationalisation for the generated code and is incapable of assessing its security or performance properties.
It's a very advanced auto-complete that just happens to have been specialised on auto-completing code.
> It's a very advanced auto-complete that just happens to have been specialised on auto-completing code.
It's not just an autocomplete. It's an intelligent autocomplete. As long as you need to generate text, it's practically generally intelligent.
I really wonder what will happen when somebody runs a text generator like Copilot in a loop with a simulated work-memory and longterm-memory and realtime I/O interface.
It also cannot generate comprehensive unit tests - it can can generate unit tests. The definition of "comprehensive" is way too subjective.
> Is that not good enough for you..?
No, it indeed isn't, but maybe that's because I actually have at least some idea of what happens behind the scenes and how the system works. From the implementation details, I can tell you for a fact that none of what you described can be done by the system in the general case - and especially not correctly. It's hit and miss depending on the input and that's basically a design limitation.