Despite executive warnings about AI replacing workers, only 75 job cuts were explicitly tied to AI implementation out of 286,679 layoffs this year. The real story is more complex.
I remember the times when people used to say, well, let’s talk when a computer beats a human in chess. After Deep Blue defeated Kasparov, everyone started saying, oh, it’s all nonsense, just a set of algorithms. The wheel of ‘betrayal-victory’… )
The issue here is that human intelligence and computer intelligence work completely different and things that are easy for one are hard for the other.
Because of that, measures of intelligence don’t really work across humans and computers and it’s really easy to misjudge which milestones are meaningful and which aren’t.
For example, it’s super hard for a human to perform 100 additions within a second, and a human who could do that would be perceived as absolutely super human. But for a computer that’s ridiculously easy. While on the other hand there are things a child can do that were impossible for computers just a few years ago (e.g. reckognizing a bird).
For humans, playing high-level chess is really hard, so we arbitrarily chose it as a measure of intelligence: “Only very intelligent people can beat Kasparov”. So we figured that a computer being able to do that task must be intelligent too. Turns out that chess greatly benefits from large memory and fast-but-simple calculations, two things computers are really, really good at and humans are not.
And it turns out that, contrary to what many people believed, chess doesn’t actually require any generally intelligent code at all. In fact, a more general approach (like LLMs) actually performs much, much worse at specific tasks like chess, as exemplified by some chess program for the Atari beating one LLM after another.
I remember the times when people used to say, well, let’s talk when a computer beats a human in chess. After Deep Blue defeated Kasparov, everyone started saying, oh, it’s all nonsense, just a set of algorithms. The wheel of ‘betrayal-victory’… )
The issue here is that human intelligence and computer intelligence work completely different and things that are easy for one are hard for the other.
Because of that, measures of intelligence don’t really work across humans and computers and it’s really easy to misjudge which milestones are meaningful and which aren’t.
For example, it’s super hard for a human to perform 100 additions within a second, and a human who could do that would be perceived as absolutely super human. But for a computer that’s ridiculously easy. While on the other hand there are things a child can do that were impossible for computers just a few years ago (e.g. reckognizing a bird).
(Relevant, if slightly outdated, XKCD: https://xkcd.com/1425/)
For humans, playing high-level chess is really hard, so we arbitrarily chose it as a measure of intelligence: “Only very intelligent people can beat Kasparov”. So we figured that a computer being able to do that task must be intelligent too. Turns out that chess greatly benefits from large memory and fast-but-simple calculations, two things computers are really, really good at and humans are not.
And it turns out that, contrary to what many people believed, chess doesn’t actually require any generally intelligent code at all. In fact, a more general approach (like LLMs) actually performs much, much worse at specific tasks like chess, as exemplified by some chess program for the Atari beating one LLM after another.
Good answer, thank you!