[joe_the_user]

Nice post,

Here's a different way to think about the situation with current AI/deep-learning; if the current upsurge of methodologies was getting close to general AI, it would be getting closer and closer to a hammer that really did let you treat everything as a nail. IE, it would be general purpose.

But I think I can say we're not seeing that even though deep learning seems to be continually expanding the domains that it can operate on. How is that? This Open AI is very eye-opening; "We’re releasing an analysis showing that since 2012, the amount of compute used in the largest AI training runs has been increasing exponentially with a 3.5 month-doubling time (by comparison, Moore’s Law had an 18-month doubling period)." Essentially, as a rather brute-force-y method, we have shown we can expand deep learning's impact to a larger and large domain but not at all in the fashion of human learning tricks (where the new isn't that much harder than the old trick).

Maybe, in this process, a better algorithm that adjusts to new situations without increased costs will surface. But until then it seems new and old methods will need to coexist.

https://blog.openai.com/ai-and-compute/?

[halflings]

This doesn't sound related to the post, no? The post doesn't argue for general AI that learns like humans do, it only discusses the merits of AI as a whole vs hard-coded SQL queries and heuristics.