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Definitely not my intention to forgot or denigrate the past. Obviously all this exists due to deep learning and prior architectures. What I have been running in

Definitely not my intention to forgot or denigrate the past. Obviously all this exists due to deep learning and prior architectures. What I have been running into is many people and companies are interpreting this as "just more of the same" for prior ML waves, when really this is an entirely new capability set. To the (bad) analogy on cars versus planes - both have wheels and can drive on the ground, but planes open up an entirely new dimension / capability set that can transform transportation, logistics, defense and other areas that cars were important, but different enough in.

simonw 3y ago
I'm finding the comparison to the previous wave of ML absolutely fascinating. I tinkered with ML for a few years, and it took a LOT of work to get anything useful out of it at all. Now with LLMs I can literally type out a problem in English and there's a reasonably good chance I'll get a useful result! It really does feel like an entirely new set of capabilities to me.
- andy99 3y ago
  That's the big difference in this round. Before you had to have the ML expertise and the expertise to understand the implication of say a MNIST classifier example. Now anyone can "get" it because you're prompting and getting inference back in English. Underneath the fundamentals aren't all that different though, it has the same novelty factor and the same limitations apply.
  - simonw 3y ago
    I think the fundamentals are radically different, just due to the ease of applying this stuff. I used to be able to train and deploy a ML model to help solve a problem... if I put aside a full week to get that done. Now I tinker with LLMs five minutes at a time, or maybe for a full hour if I have something harder - and get useful results. I use them on a daily basis.
    - npsomaratna 3y ago
      Exactly. LLMs are useful out of the box—and that enables tons of use-cases that just weren't economical (or even possible) with older models.
hulitu 3y ago
The only "entirely new dimension / capability set" that i saw at this new AI hype is its use in propaganda. Microsoft is not capable of basic testing of its software so i don't see what AI will bring to the table besides more bugs.

simonw 3y ago

I'm finding the comparison to the previous wave of ML absolutely fascinating. I tinkered with ML for a few years, and it took a LOT of work to get anything useful out of it at all. Now with LLMs I can literally type out a problem in English and there's a reasonably good chance I'll get a useful result! It really does feel like an entirely new set of capabilities to me.

andy99 3y ago
That's the big difference in this round. Before you had to have the ML expertise and the expertise to understand the implication of say a MNIST classifier example. Now anyone can "get" it because you're prompting and getting inference back in English. Underneath the fundamentals aren't all that different though, it has the same novelty factor and the same limitations apply.
- simonw 3y ago
  I think the fundamentals are radically different, just due to the ease of applying this stuff. I used to be able to train and deploy a ML model to help solve a problem... if I put aside a full week to get that done. Now I tinker with LLMs five minutes at a time, or maybe for a full hour if I have something harder - and get useful results. I use them on a daily basis.
  - npsomaratna 3y ago
    Exactly. LLMs are useful out of the box—and that enables tons of use-cases that just weren't economical (or even possible) with older models.

hulitu 3y ago

The only "entirely new dimension / capability set" that i saw at this new AI hype is its use in propaganda. Microsoft is not capable of basic testing of its software so i don't see what AI will bring to the table besides more bugs.