Ways to think about machine learning — Benedict Evans

In new AI hype frenzy, tech is applying the label to everything now

Today’s AI promoters are trying to have it both ways: They insist that AI is crossing a profound boundary into untrodden territory with unfathomable risks. But they also define AI so broadly as to include almost any large-scale, statistically-driven computer program.

Under this definition, everything from the Google search engine to the iPhone’s face-recognition unlocking tool to the Facebook newsfeed algorithm is already “AI-driven” — and has been for years.

To have “true AI,” we need much more than ChatGPT - Big Think

LLMs have never experienced anything. They are just programs that have ingested unimaginable amounts of text. LLMs might do a great job at describing the sensation of being drunk, but this is only because they have read a lot of descriptions of being drunk. They have not, and cannot, experience it themselves. They have no purpose other than to produce the best response to the prompt you give them.

This doesn’t mean they aren’t impressive (they are) or that they can’t be useful (they are). And I truly believe we are at a watershed moment in technology. But let’s not confuse these genuine achievements with “true AI.”

The Technium: Dreams are the Default for Intelligence

I feel like there’s a connection here between what Kevin Kelly is describing and what I wrote about guessing (though I think he might be conflating consciousness with intelligence).

This, by the way, is also true of immersive “virtual reality” environments. Instead of trying to accurately recreate real-world places like meeting rooms, we should be leaning into the hallucinatory power of a technology that can generate dream-like situations where the pleasure comes from relinquishing control.

Artificial Intelligence: Threat or Menace? - Charlie’s Diary

I am not a believer in the AI singularity — the rapture of the nerds — that is, in the possibility of building a brain-in-a-box that will self-improve its own capabilities until it outstrips our ability to keep up. What CS professor and fellow SF author Vernor Vinge described as “the last invention humans will ever need to make”. But I do think we’re going to keep building more and more complicated, systems that are opaque rather than transparent, and that launder our unspoken prejudices and encode them in our social environment. As our widely-deployed neural processors get more powerful, the decisions they take will become harder and harder to question or oppose. And that’s the real threat of AI — not killer robots, but “computer says no” without recourse to appeal.

Norbert Wiener’s Human Use of Human Beings is more relevant than ever.

What would Wiener think of the current human use of human beings? He would be amazed by the power of computers and the internet. He would be happy that the early neural nets in which he played a role have spawned powerful deep-learning systems that exhibit the perceptual ability he demanded of them—although he might not be impressed that one of the most prominent examples of such computerized Gestalt is the ability to recognize photos of kittens on the World Wide Web.