alt Hacker NewsI like the networking perspective, but the ML perspective is such a loose analogy that it's hard to even judge. I mean, we've known forever softening constraints allows you to reach solutions otherwise unreachable, for one? There's a gulf of difference between succeeding at something deterministic by allowing failure vs. good pattern matching by optimizing over a rough landscape of examples.
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nh23423fefe • yesterday at 5:08 PM
I'm not seeing how describing measures over possibility space as allowing for mistakes.
Seems like content reverse engineered from title.
xg15 • yesterday at 6:42 PM
Yeah, I didn't find his initial take very convincing, but he lost me at the followup:
> For most cases I don't think having explainability is worth the trade offs in capability. That'll be a good topic for a future post.
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> I like the networking perspective, but the ML perspective is such a loose analogy that it's hard to even judge.
Right. ML doesn't have to work well because it's used in situations where the cost of the errors falls on someone other than the service provider. Hallucinations require a business model where their cost is an externality, like pollution.
With an objective goal, such as tests or a spec or driving without hitting anything, to check the results, it's possible to do better, of course.
The Internet only works because fiber optic bandwidth is cheap. As someone who was working on congestion in the early days, I could see that congestion in the middle of the network had no known solution. If congestion could be pushed out to the edges, there were strategies, but there were no good solutions in the middle. And, in fact, the whole Internet would sometimes go into congestion collapse in the early 1990s, with the big peering points at MAE-EAST and MAE-WEST losing well over half of the packets. What saved the Internet was cheap long-haul bandwidth and big hardware-supported switches. This kept congestion at the fringes.