3 ms·

A 0% error rate is unrealistic in humans as well. Impossible really.

azinman2 2y ago
But the mistakes made will be different, and historically you’d have a source to consider. Here there is just one global source telling you to add glue to your pizza to make the cheese stick.
- ein0p 2y ago
  There are models that can provide references if you’d like
  - malfist 2y ago
    And they'll happily make up those references as well
    - ein0p 2y ago
      Nope, that’s not how it works. Those references aren’t generated in such systems, they are retrieved. They might not provide references to all the sources, of course, same as humans.
      - azinman2 2y ago
        Exactly. Right now if I google something (ai overview aside), I’m linked to a source. That source may or may not include its sources, but its provenance tells me a lot. If I’m reading info linked off Mayo Clinic, their reputation compels the information to be judged of high quality. If they start putting in a bunch of garbage, their reputation gets shot and will cause me to look elsewhere. With LLMs there is no such choice, and it will spew everything from high to low quality (to dangerously wrong) info.
        ein0p 2y ago
        LLMs can provide references. There’s no limitation on that. Even GPT4 includes references sometimes when it deems them beneficial.
        azinman2 2y ago
        An LLM itself cannot provide references with any integrity. They’re autogressive probabilistic models. They’ll happily make something up, and you can even try and train a reference with it, but as the article states this is very very far from a guarantee. What you can do is a kind of RAG situation where you have some existing database that you include into the prompt to ground it, but that’s not inherent into the model itself.
        ein0p 2y ago
        Sure, but it can semantically query a vector DB on the side and use the results to generate a "grounded" response.
        azinman2 2y ago
        “LLMs can provide references. There’s no limitation on that.” That’s not really an LLM providing references, but a separate db as an extra step providing the references.
        ein0p 2y ago
        All outputs of an LLM are generated by LLM. LLMs today can and do use external data sources. Applied to humans, what you're saying is like saying that it's not humans who provide references because they copy the bibtex for them from Arxiv.
        azinman2 2y ago
        But if you’re using an external data source and putting it into the context then it’s the external data source that’s providing the reference — the LLM is just asked to regurgitate it. The large language model, pretrained on trillions of tokens of text, is unable to provide those references. If I take llama3, for example, and ask it to provide a reference.. it will just make something up. Sometimes these things happen to exist, often times they don’t. And that’s the fundamental problem - they hallucinate. This is well understood.
      - leereeves 2y ago
        Those aren't sources. They aren't where the model got the information (that info isn't stored in an LLM). Rather, such AI systems have two parts: an LLM that writes whatever, and a separate system that searches for links related to what the LLM wrote.
deleted 2y ago
[deleted]
rgbrgb 2y ago
It’s encouraging that we can get domains like aviation to 5 or 6 9s of reliability with human scale systemization. To me, that implies we can get there with AI systems at least in certain domains.
- seadan83 2y ago
  Could you elaborate on why that implication might be true? I can think of a counter-point: Humans can deal with 'error-cases' pretty readily. The effort of humans to get those last few 9's might be a linear effort. For example, one extra checklist might be the thing that was needed, or simply adding a whole extra human to avoid a problem. OTOH, computers to get that last 0.001% correct might need magnitudes more effort to get right. The effort of humans vs computers does not scale at the same rate. Why therefore should we think that something that humans can do with 2x effort would not require 2000x better AI? There are certainly cases where the inverse would be true. Where human effort to get better reliability would be more than what is needed for a computer (monitoring and control systems are good examples; eg: radar operators, nuclear power plants). Though, in cases where computer effort scales better than human effort - it's very likely those efforts have been automated already. That high level of reliability in aviation is likely thanks in part due to automation of tasks that computers are good at.
  - moffkalast 2y ago
    > 2000x better AI? Even if it does, that puts AI ahead in what, 22 years with 2x improvements every 2 years? The simple problem with us humans, we haven't notably improved in inteligence for the last 100.000 years and we'll be beat eventually. It's not even a question barring some end of the world event, we already know it's completely possible because we are living proof that 20 W can be at least this smart. And it's really just that the upfront R&D is expensive, silicon is cheap. Humans are ultra expensive all round constantly, so the longer you use the result, the more that initial seemingly ludicrous cost amortizes to near zero.
    - seadan83 2y ago
      If I can paraphrase, you believe that humans are "intelligence level" 100, and AI is essentially somewhere at like 10 or 20, and is doubling every 2 years. First, is AI actually improving 2x every 2 years? Can you link to some studies that show the benchmarks? AFAIK OpenAI with ChatGPT was something of an 8 or 10 year project. It being released seemingly "out of nowhere" really biases the perception that things are happening faster than they actually are. Second, is human intelligence and AI even in the same domain? How many dimensions of intelligence are there? Out of those dimensions, which ones have AI at a zero so far? Which of those dimensions are even entirely impossible for AI? If the answer is "AI" can be just as smart as humans in every way, and we still don't even understand that much about human intelligence and cognition, let alone that of other animals... I'm skeptical the answer is yes. (Thinking about sciences view of actual intelligence, animals, and for a long time the thought was animals are biological automotons, I think shows that we don't even understand intelligence, let alone how to build AGI). Next, even if the intelligence raise is single dimension and is actually the same sport & playing field, what is to say that the exponential growth you describe will be consistent? Could it not be the case that the 1000x to 1001x improvement might be just as hard as all of the first 1000x improvement? What is to say the complexity increase is not also exponential, or even a combinatoric growth? > 20 W can be at least this smart. 20 W? I'm not familiar with it.

But the mistakes made will be different, and historically you’d have a source to consider. Here there is just one global source telling you to add glue to your pizza to make the cheese stick.

ein0p 2y ago
There are models that can provide references if you’d like
- malfist 2y ago
  And they'll happily make up those references as well
  - ein0p 2y ago
    Nope, that’s not how it works. Those references aren’t generated in such systems, they are retrieved. They might not provide references to all the sources, of course, same as humans.
    - azinman2 2y ago
      Exactly. Right now if I google something (ai overview aside), I’m linked to a source. That source may or may not include its sources, but its provenance tells me a lot. If I’m reading info linked off Mayo Clinic, their reputation compels the information to be judged of high quality. If they start putting in a bunch of garbage, their reputation gets shot and will cause me to look elsewhere. With LLMs there is no such choice, and it will spew everything from high to low quality (to dangerously wrong) info.
      - ein0p 2y ago
        LLMs can provide references. There’s no limitation on that. Even GPT4 includes references sometimes when it deems them beneficial.
        azinman2 2y ago
        An LLM itself cannot provide references with any integrity. They’re autogressive probabilistic models. They’ll happily make something up, and you can even try and train a reference with it, but as the article states this is very very far from a guarantee. What you can do is a kind of RAG situation where you have some existing database that you include into the prompt to ground it, but that’s not inherent into the model itself.
        ein0p 2y ago
        Sure, but it can semantically query a vector DB on the side and use the results to generate a "grounded" response.
        azinman2 2y ago
        “LLMs can provide references. There’s no limitation on that.” That’s not really an LLM providing references, but a separate db as an extra step providing the references.
        ein0p 2y ago
        All outputs of an LLM are generated by LLM. LLMs today can and do use external data sources. Applied to humans, what you're saying is like saying that it's not humans who provide references because they copy the bibtex for them from Arxiv.
        azinman2 2y ago
        But if you’re using an external data source and putting it into the context then it’s the external data source that’s providing the reference — the LLM is just asked to regurgitate it. The large language model, pretrained on trillions of tokens of text, is unable to provide those references. If I take llama3, for example, and ask it to provide a reference.. it will just make something up. Sometimes these things happen to exist, often times they don’t. And that’s the fundamental problem - they hallucinate. This is well understood.
    - leereeves 2y ago
      Those aren't sources. They aren't where the model got the information (that info isn't stored in an LLM). Rather, such AI systems have two parts: an LLM that writes whatever, and a separate system that searches for links related to what the LLM wrote.

deleted 2y ago

[deleted]

rgbrgb 2y ago

It’s encouraging that we can get domains like aviation to 5 or 6 9s of reliability with human scale systemization. To me, that implies we can get there with AI systems at least in certain domains.

seadan83 2y ago
Could you elaborate on why that implication might be true? I can think of a counter-point: Humans can deal with 'error-cases' pretty readily. The effort of humans to get those last few 9's might be a linear effort. For example, one extra checklist might be the thing that was needed, or simply adding a whole extra human to avoid a problem. OTOH, computers to get that last 0.001% correct might need magnitudes more effort to get right. The effort of humans vs computers does not scale at the same rate. Why therefore should we think that something that humans can do with 2x effort would not require 2000x better AI? There are certainly cases where the inverse would be true. Where human effort to get better reliability would be more than what is needed for a computer (monitoring and control systems are good examples; eg: radar operators, nuclear power plants). Though, in cases where computer effort scales better than human effort - it's very likely those efforts have been automated already. That high level of reliability in aviation is likely thanks in part due to automation of tasks that computers are good at.
- moffkalast 2y ago
  > 2000x better AI? Even if it does, that puts AI ahead in what, 22 years with 2x improvements every 2 years? The simple problem with us humans, we haven't notably improved in inteligence for the last 100.000 years and we'll be beat eventually. It's not even a question barring some end of the world event, we already know it's completely possible because we are living proof that 20 W can be at least this smart. And it's really just that the upfront R&D is expensive, silicon is cheap. Humans are ultra expensive all round constantly, so the longer you use the result, the more that initial seemingly ludicrous cost amortizes to near zero.
  - seadan83 2y ago
    If I can paraphrase, you believe that humans are "intelligence level" 100, and AI is essentially somewhere at like 10 or 20, and is doubling every 2 years. First, is AI actually improving 2x every 2 years? Can you link to some studies that show the benchmarks? AFAIK OpenAI with ChatGPT was something of an 8 or 10 year project. It being released seemingly "out of nowhere" really biases the perception that things are happening faster than they actually are. Second, is human intelligence and AI even in the same domain? How many dimensions of intelligence are there? Out of those dimensions, which ones have AI at a zero so far? Which of those dimensions are even entirely impossible for AI? If the answer is "AI" can be just as smart as humans in every way, and we still don't even understand that much about human intelligence and cognition, let alone that of other animals... I'm skeptical the answer is yes. (Thinking about sciences view of actual intelligence, animals, and for a long time the thought was animals are biological automotons, I think shows that we don't even understand intelligence, let alone how to build AGI). Next, even if the intelligence raise is single dimension and is actually the same sport & playing field, what is to say that the exponential growth you describe will be consistent? Could it not be the case that the 1000x to 1001x improvement might be just as hard as all of the first 1000x improvement? What is to say the complexity increase is not also exponential, or even a combinatoric growth? > 20 W can be at least this smart. 20 W? I'm not familiar with it.