3 ms·

I think in five years, LLMs will be like expert systems: largely considered not really "AI" but sitting around in the back end of all sorts of random systems.

by gumby 3y ago

eladgil 3y ago
The bar for what is "AI" keeps moving. For example plane autopilots would be "AI" in the 1980s, the ability for a machine to win at chess, go, and other games etc.
- deleted 3y ago
  [deleted]
- calderknight 3y ago
  I don't think those things have ever been widely considered AI. It may have widely thought that those things would require AI, though.
  - beambot 3y ago
    Chess is featured in Peter Norvig's "Artificial Intelligence: A Modern Approach" dating back to the 1st edition (1995) and at least up until the 3rd edition (2009). Algorithms such as alpha-beta pruning were definitely considered AI at the time.
    - calderknight 3y ago
      Yeah AI has multiple denotations, those things have never been considered "real" AI tho
      - deleted 3y ago
        [deleted]
    - intelkishan 3y ago
      It's even mentioned in the recent 4th edition(2020).
  - robotresearcher 3y ago
    The MIT AI Group, including Marvin Minsky, were the mainstream of AI more than 50 years ago, and begat the MIT AI Lab. They and everyone else at the time called their work AI.
- TMWNN 3y ago
  As a non-expert in the field I was hesitant at the time to disagree with the legions of experts who last year denounced Blake Lemoine and his claims. I know enough to know, though, of the AI effect <https://en.wikipedia.org/wiki/AI_effect https://en.wikipedia.org/wiki/AI_effect>, a longstanding tradition/bad habit of advances being dismissed by those in the field itself as "not real AI". Anyone, expert or not, in 1950, 1960, or even 1970 who was told that before the turn of the century a computer would defeat the world chess champion would conclude that said feat must have come as part of a breakthrough in AGI. Same if told that by 2015 many people would have in their homes, and carry around in their pockets, devices that can respond to spoken queries on a variety of topics. To put another way, I was hesitant to be as self-assuredly certain about how to define consciousness, intelligence, and sentience—and what it takes for them to emerge—as the experts who denounced Lemoine. The recent GPT breakthroughs have made me more so. I found this recent Sabine Hossenfelder video interesting. <https://www.youtube.com/watch?v=cP5zGh2fui0 https://www.youtube.com/watch?v=cP5zGh2fui0>
  - ShamelessC 3y ago
    You should check out the WaPo article that originally published his concerns. He frequently makes many errors audibly with a reporter who is trying rather hard to see his point of view. I’m not trying to be rude, but he came off like kind of a sucker that would fall for a lot of scammer tactics. There were usually some form of strangeness such as him deciding when the content limit of the conversation began and ended. Further, he asks only leading questions, which would be fine if transformers didn’t specifically train to output the maximum likelihood text tokens from the distribution of their training set, which was internet text created by humans. He was frequently cited as an engineer but I don’t think he actually had a strong background in engineering but rather in philosophy.
  - dekhn 3y ago
    The term you are looking for with Lemoine is "credulous". (it's good to be a skeptic)
Animats 3y ago
> LLMs will be like expert systems No. LLMs actually work. Expert systems were a flop. I went through Stanford CS in the mid-1980s, just when it was becoming clear that expert systems were a flop. The faculty was in denial about that. It was sad to see. We're at the beginning of LLMs, and systems which use LLMs as components. This is the fun time for the technology. Ten years out, it will be boring, like Java. The next big thing is figuring out the best ways to couple LLMs to various sources of data, so they can find and use more information specifically relevant to the problem. And someone has to fix the hallucination problem. We badly need systems that know what they don't know.
- whimsicalism 3y ago
  > I went through Stanford CS in the mid-1980s, just when it was becoming clear that expert systems were a flop. The faculty was in denial about that. It was sad to see. It's interesting how this appears to be a recurring cycle - when I attended school, it appeared that the faculty were in denial about the death of probabilistic graphical models and advanced bayesian techniques in favor of simple linear algebra with unsupervised learning. Even when taught about deep ML, there was heavy emphasis on stuff like VAE which had fun bayesian interpretations.
- dragonwriter 3y ago
  > LLMs actually work. Expert systems were a flop. Expert systems actually work and, while we don’t normally even call them “expert systems” any more, are important in basically every domain – “business rules engines” are generalized expert system platforms, and are widely used in business process automation.) Despite how well they work, and early optimism resulting from that about how much further they’d be able to go, they ran into limits; it is not implausible that the same will turn out to be true for LLMs (or even transformer-based models more generally.) > We’re at the beginning of LLMs, and systems which use LLMs as components. This is the fun time for the technology. Ten years out, it will be boring, like Java. …and expert systems. (And, quite possibly, by then they will have revealed their fundamental, intractable llimitations, like expert systems.)
  - bloaf 3y ago

I think in five years, LLMs will be like expert systems: largely considered not really "AI" but sitting around in the back end of all sorts of random systems.

by gumby 3y ago

I think in five years, LLMs will be like expert systems: largely considered not really "AI" but sitting around in the back end of all sorts of random systems.

eladgil 3y ago

The bar for what is "AI" keeps moving. For example plane autopilots would be "AI" in the 1980s, the ability for a machine to win at chess, go, and other games etc.

deleted 3y ago
[deleted]
calderknight 3y ago
I don't think those things have ever been widely considered AI. It may have widely thought that those things would require AI, though.
- beambot 3y ago
  Chess is featured in Peter Norvig's "Artificial Intelligence: A Modern Approach" dating back to the 1st edition (1995) and at least up until the 3rd edition (2009). Algorithms such as alpha-beta pruning were definitely considered AI at the time.
  - calderknight 3y ago
    Yeah AI has multiple denotations, those things have never been considered "real" AI tho
    - deleted 3y ago
      [deleted]
  - intelkishan 3y ago
    It's even mentioned in the recent 4th edition(2020).
- robotresearcher 3y ago
  The MIT AI Group, including Marvin Minsky, were the mainstream of AI more than 50 years ago, and begat the MIT AI Lab. They and everyone else at the time called their work AI.
TMWNN 3y ago
As a non-expert in the field I was hesitant at the time to disagree with the legions of experts who last year denounced Blake Lemoine and his claims. I know enough to know, though, of the AI effect <https://en.wikipedia.org/wiki/AI_effect https://en.wikipedia.org/wiki/AI_effect>, a longstanding tradition/bad habit of advances being dismissed by those in the field itself as "not real AI". Anyone, expert or not, in 1950, 1960, or even 1970 who was told that before the turn of the century a computer would defeat the world chess champion would conclude that said feat must have come as part of a breakthrough in AGI. Same if told that by 2015 many people would have in their homes, and carry around in their pockets, devices that can respond to spoken queries on a variety of topics. To put another way, I was hesitant to be as self-assuredly certain about how to define consciousness, intelligence, and sentience—and what it takes for them to emerge—as the experts who denounced Lemoine. The recent GPT breakthroughs have made me more so. I found this recent Sabine Hossenfelder video interesting. <https://www.youtube.com/watch?v=cP5zGh2fui0 https://www.youtube.com/watch?v=cP5zGh2fui0>
- ShamelessC 3y ago
  You should check out the WaPo article that originally published his concerns. He frequently makes many errors audibly with a reporter who is trying rather hard to see his point of view. I’m not trying to be rude, but he came off like kind of a sucker that would fall for a lot of scammer tactics. There were usually some form of strangeness such as him deciding when the content limit of the conversation began and ended. Further, he asks only leading questions, which would be fine if transformers didn’t specifically train to output the maximum likelihood text tokens from the distribution of their training set, which was internet text created by humans. He was frequently cited as an engineer but I don’t think he actually had a strong background in engineering but rather in philosophy.
- dekhn 3y ago
  The term you are looking for with Lemoine is "credulous". (it's good to be a skeptic)

Animats 3y ago

> LLMs will be like expert systems No. LLMs actually work. Expert systems were a flop. I went through Stanford CS in the mid-1980s, just when it was becoming clear that expert systems were a flop. The faculty was in denial about that. It was sad to see. We're at the beginning of LLMs, and systems which use LLMs as components. This is the fun time for the technology. Ten years out, it will be boring, like Java. The next big thing is figuring out the best ways to couple LLMs to various sources of data, so they can find and use more information specifically relevant to the problem. And someone has to fix the hallucination problem. We badly need systems that know what they don't know.

whimsicalism 3y ago
> I went through Stanford CS in the mid-1980s, just when it was becoming clear that expert systems were a flop. The faculty was in denial about that. It was sad to see. It's interesting how this appears to be a recurring cycle - when I attended school, it appeared that the faculty were in denial about the death of probabilistic graphical models and advanced bayesian techniques in favor of simple linear algebra with unsupervised learning. Even when taught about deep ML, there was heavy emphasis on stuff like VAE which had fun bayesian interpretations.
dragonwriter 3y ago
> LLMs actually work. Expert systems were a flop. Expert systems actually work and, while we don’t normally even call them “expert systems” any more, are important in basically every domain – “business rules engines” are generalized expert system platforms, and are widely used in business process automation.) Despite how well they work, and early optimism resulting from that about how much further they’d be able to go, they ran into limits; it is not implausible that the same will turn out to be true for LLMs (or even transformer-based models more generally.) > We’re at the beginning of LLMs, and systems which use LLMs as components. This is the fun time for the technology. Ten years out, it will be boring, like Java. …and expert systems. (And, quite possibly, by then they will have revealed their fundamental, intractable llimitations, like expert systems.)
- bloaf 3y ago