|
|
|
|
|
by WanderPanda
818 days ago
|
|
I’ve been using Github copilot daily for two years and ChatGPT for 1 year now. And I think the tide lifts all the boats. I’ve seen a (perceived) 2-3x productivity increase. I think these tools slightly favor people in front of the learning curve of a particular field. I’ve been dabbling in all sorts of things so if you’re a focused expert (who doesn’t need to explore but just exploit) you probably get less than a 2x boost from using LLMs. I can see LLMs eating into the expert regime IF they get another 5-10x better. But even in that case human (expert) knowledge will be required to know what is possible and hence what to ask (kind of like reward function design in reinforcement learning) |
|
A key requirement is the AGI will need the autonomy, like a human expert, to collect data and perform experiments it needs; but it seems several companies are set on doing precisely that.
My advice and personal strategy is to broaden one's scope beyond pure cognitive tasks.
"if you value intelligence above all other human qualities, you’re gonna have a bad time" -- Ilya Sutskever, OpenAI's Chief Scientist, Oct 7, 2023.
-----
Exchanges in the link below seem informative:
"I don't know about chess, but in the similar game Go, the very best centaur teams were at a similar or maybe even slightly higher level than engines until recently. This was due to cheese strategies, details of the rulesets and better extrapolation of intermediate results. However, this changed a few years ago, when engines learned many of the tricks that the human could contribute. Since then, I believe pure engines are stronger in all practical applications.
Source: am national champion in centaur Go and worked on modern Go engines" " -- mafuy on May 18, 2021
https://news.ycombinator.com/item?id=27189283