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I'd almost say anyone not doing that is being foolish. The goal of the service is to answer complex queries correctly, not to have a pure LLM that can do it al

I'd almost say anyone not doing that is being foolish. The goal of the service is to answer complex queries correctly, not to have a pure LLM that can do it all. I think some engineers feel that if they are leaning on an old school classically programed tool to assist the LLM, it's somehow cheating or impure.

uoaei 3y ago
Of course. But we must acknowledge that many have blinders on, assuming that scale is all you need to beat statistical errors.
- sigmoid10 3y ago
  Well, these people are not wrong per se. Scale is what drove what we have today and as hardware improves, the models will too. It's just that in the very short term it turns out to be faster to just code around some of these issues on the backend of an API rather than increase the compute you spend on the model itself.
  - uoaei 3y ago
    Monkey sees moon. Monkey climbs tree. "See? Monkey is closer to moon than before. To reach moon, monkey just needs taller tree." How long before monkey finds tall enough tree to reach moon?
    - sigmoid10 3y ago
      We're rapidly approaching the compute capacity of the human brain in individual server racks. This "moon" is neither unreachable nor is there any doubt that we will cross the threshold soon.
      - uoaei 3y ago
        I find it incredibly hard to believe we stumbled upon an efficient architecture that requires nothing but more compute not 10 years after the AI winter thawed. That's incredibly optimistic to the point of blind hope. What is your background and what makes you think we've somehow already figured everything out?
        sigmoid10 3y ago
        I have been working on architectures in this field for almost a decade now and I've seen firshand how things have changed. It might seem hard to believe if you have been to university ~10 years ago and only know the state of deep learning from the early revolutions back then, but we are in a totally different era now. With the transformer, we now have a true general-purpose, efficiently scalable, end-to-end differentiable algorithm. Meaning you can apply it to any task as long as you convert it to the right embedding space, you can train gigantic models that compress huge amounts of information using enormous datasets and you can still use good-ol' gradient descent to optimize it (which is kind of sad since we still haven't found a better way of training models, but hey it works).
        asadotzler 3y ago
        and yet SamA says it's actual trillions of dollars in entirely new compute capacity to reach the next level. hmmm. to believe him or you... so hard to decide.
        sigmoid10 3y ago
        SamA is a business bro who tries to hoard investor capital. He has to say these things to collect more VCs. If you want to learn about the tech, listen to what the actual techies at openai have to say. This stuff is no secret.
        uoaei 3y ago
        > Meaning you can apply it to any task as long as you convert it to the right embedding space This glosses over a massive issue which is that not everything can be efficiently represented as a vector space via embeddings. So your claim of "general purpose" rings hollow. Not to mention that there is no feedback mechanism for the supposed "knowledge" advocates claim Transformer-based models have, so things like metacognition are literally impossible with this architecture. As it stands LLM outputs are isomorphic to psychotic stream-of-consciousness babble. You've managed to find a tall tree, but from your response it seems like you haven't yet gotten to considering rockets.
ignoramous 3y ago
> I'd almost say anyone not doing that is being foolish The problem is, such tricks are sold as if there's superior built-in multi-modal reasoning and intelligence instead of taped up heuristics, exacerbating the already amped up hype cycle in the vacuum left behind by web3.
- brokencode 3y ago
  Why is this a trick or somehow inferior to getting the AI model to be able to do it natively? Most humans also can’t reliably do complex arithmetic without the use of something like a calculator. And that’s no trick. We’ve built the modern world with such tools. Why should we fault AI for doing what we do? To me, training the AI use a calculator is not just a trick for hype, it’s exciting progress.
  - lanstin 3y ago
    It would be exciting if the LLM knew it needed a calculator for certain things and went out and got it. If the human supervisors are pre-screening the input and massaging what the LLM is doing that is a sign we don't understand LLMs enough to engineer them precisely and can't count on them to be aware of their own limitations, which would seem to be a useful part of general intelligence.
    - Spivak 3y ago
      It can if you let it, that's the whole premise of LangChain style reasoning and it works well enough. My dumb little personal chatbot knows it can access a Python REPL to carry out calculations and it does.
    - CamperBob2 3y ago
      It would be exciting if the LLM knew it needed a calculator for certain things and went out and got it Isn't that what it does, when it writes a Python program to compute the answer to the user's question?
  - bufferoverflow 3y ago
bufferoverflow 3y ago
> The goal of the service is to answer complex queries correctly, not to have a pure LLM that can do it all. No, that's the actual end goal. We want a NN that does everything, trained end-to-end.
- ben_w 3y ago
  I'm unclear if you're saying that as a user who wants that feature, or an AI developer (for Anthropic or other) who is trying to achieve that goal?
- coffeebeqn 3y ago
  Like a AGI? I think we’ll put up with hacks for some more time still. Unless the model gets really really good at generalizing and then it’s probably close to human level already
- netghost 3y ago
  "We" contains more than just one perspective though. As someone applying LLMs to a set of problems in a production application, I just want a tool that solves the problem. Today, that tool is an LLM, tomorrow it could be anything. If there are ~hacks~ elegant techniques that can get me the results I need faster, cheaper, or more accurately, I absolutely will use those until there's a better alternative.
Agentlien 3y ago
I took an artificial neutral network class at the university back in 2009. On the exam we were asked to design a (hardware) system to solve a certain complex problem, then present it to the professor. The professor was actually a biologist specialised in neurology who had veered off into ANN without understanding electronics nor programming. I recognised that the problem, while being beyond what an ANN could do at the time, could be split into two parts each of which was a classic ANN task. For communication between the two I described a very simple electronic circuit - just a few logic gates. When presenting the design, the professor questioned why this component was not also a neutral network. Thinking it was a trick question, I happily answered that solving it that way would be stupid since this component was so simple and building and training another network to approximate such a simple logical function is just a waste of time and money. He got really upset, saying that is how he would have done it. He ended up giving me a lower score than expected saying I technically had everything right but he didn't like my attitude.
- xerox13ster 3y ago
  You built a digital corpus callosum in hardware and a Neurobiologist, Neural Net professor got mad. Why are people like this?
imtringued 3y ago
It's not cheating or impure. It's a path that is not pointing towards AGI. Heterogenous architectures are seen with contempt nowadays. Everyone told us that it is better to have a single huge model, than to specialize anything at all.

uoaei 3y ago

Of course. But we must acknowledge that many have blinders on, assuming that scale is all you need to beat statistical errors.

sigmoid10 3y ago
Well, these people are not wrong per se. Scale is what drove what we have today and as hardware improves, the models will too. It's just that in the very short term it turns out to be faster to just code around some of these issues on the backend of an API rather than increase the compute you spend on the model itself.
- uoaei 3y ago
  Monkey sees moon. Monkey climbs tree. "See? Monkey is closer to moon than before. To reach moon, monkey just needs taller tree." How long before monkey finds tall enough tree to reach moon?
  - sigmoid10 3y ago
    We're rapidly approaching the compute capacity of the human brain in individual server racks. This "moon" is neither unreachable nor is there any doubt that we will cross the threshold soon.
    - uoaei 3y ago
      I find it incredibly hard to believe we stumbled upon an efficient architecture that requires nothing but more compute not 10 years after the AI winter thawed. That's incredibly optimistic to the point of blind hope. What is your background and what makes you think we've somehow already figured everything out?
      - sigmoid10 3y ago
        I have been working on architectures in this field for almost a decade now and I've seen firshand how things have changed. It might seem hard to believe if you have been to university ~10 years ago and only know the state of deep learning from the early revolutions back then, but we are in a totally different era now. With the transformer, we now have a true general-purpose, efficiently scalable, end-to-end differentiable algorithm. Meaning you can apply it to any task as long as you convert it to the right embedding space, you can train gigantic models that compress huge amounts of information using enormous datasets and you can still use good-ol' gradient descent to optimize it (which is kind of sad since we still haven't found a better way of training models, but hey it works).
        asadotzler 3y ago
        and yet SamA says it's actual trillions of dollars in entirely new compute capacity to reach the next level. hmmm. to believe him or you... so hard to decide.
        sigmoid10 3y ago
        SamA is a business bro who tries to hoard investor capital. He has to say these things to collect more VCs. If you want to learn about the tech, listen to what the actual techies at openai have to say. This stuff is no secret.
        uoaei 3y ago
        > Meaning you can apply it to any task as long as you convert it to the right embedding space This glosses over a massive issue which is that not everything can be efficiently represented as a vector space via embeddings. So your claim of "general purpose" rings hollow. Not to mention that there is no feedback mechanism for the supposed "knowledge" advocates claim Transformer-based models have, so things like metacognition are literally impossible with this architecture. As it stands LLM outputs are isomorphic to psychotic stream-of-consciousness babble. You've managed to find a tall tree, but from your response it seems like you haven't yet gotten to considering rockets.

ignoramous 3y ago

> I'd almost say anyone not doing that is being foolish The problem is, such tricks are sold as if there's superior built-in multi-modal reasoning and intelligence instead of taped up heuristics, exacerbating the already amped up hype cycle in the vacuum left behind by web3.

brokencode 3y ago
Why is this a trick or somehow inferior to getting the AI model to be able to do it natively? Most humans also can’t reliably do complex arithmetic without the use of something like a calculator. And that’s no trick. We’ve built the modern world with such tools. Why should we fault AI for doing what we do? To me, training the AI use a calculator is not just a trick for hype, it’s exciting progress.
- lanstin 3y ago
  It would be exciting if the LLM knew it needed a calculator for certain things and went out and got it. If the human supervisors are pre-screening the input and massaging what the LLM is doing that is a sign we don't understand LLMs enough to engineer them precisely and can't count on them to be aware of their own limitations, which would seem to be a useful part of general intelligence.
  - Spivak 3y ago
    It can if you let it, that's the whole premise of LangChain style reasoning and it works well enough. My dumb little personal chatbot knows it can access a Python REPL to carry out calculations and it does.
  - CamperBob2 3y ago
    It would be exciting if the LLM knew it needed a calculator for certain things and went out and got it Isn't that what it does, when it writes a Python program to compute the answer to the user's question?
- bufferoverflow 3y ago

bufferoverflow 3y ago

> The goal of the service is to answer complex queries correctly, not to have a pure LLM that can do it all. No, that's the actual end goal. We want a NN that does everything, trained end-to-end.

ben_w 3y ago
I'm unclear if you're saying that as a user who wants that feature, or an AI developer (for Anthropic or other) who is trying to achieve that goal?
coffeebeqn 3y ago
Like a AGI? I think we’ll put up with hacks for some more time still. Unless the model gets really really good at generalizing and then it’s probably close to human level already
netghost 3y ago
"We" contains more than just one perspective though. As someone applying LLMs to a set of problems in a production application, I just want a tool that solves the problem. Today, that tool is an LLM, tomorrow it could be anything. If there are ~hacks~ elegant techniques that can get me the results I need faster, cheaper, or more accurately, I absolutely will use those until there's a better alternative.

Agentlien 3y ago

I took an artificial neutral network class at the university back in 2009. On the exam we were asked to design a (hardware) system to solve a certain complex problem, then present it to the professor. The professor was actually a biologist specialised in neurology who had veered off into ANN without understanding electronics nor programming. I recognised that the problem, while being beyond what an ANN could do at the time, could be split into two parts each of which was a classic ANN task. For communication between the two I described a very simple electronic circuit - just a few logic gates. When presenting the design, the professor questioned why this component was not also a neutral network. Thinking it was a trick question, I happily answered that solving it that way would be stupid since this component was so simple and building and training another network to approximate such a simple logical function is just a waste of time and money. He got really upset, saying that is how he would have done it. He ended up giving me a lower score than expected saying I technically had everything right but he didn't like my attitude.

xerox13ster 3y ago
You built a digital corpus callosum in hardware and a Neurobiologist, Neural Net professor got mad. Why are people like this?

imtringued 3y ago

It's not cheating or impure. It's a path that is not pointing towards AGI. Heterogenous architectures are seen with contempt nowadays. Everyone told us that it is better to have a single huge model, than to specialize anything at all.