3 ms·

> Just last week i asked for a script to do image segmentation with a basic UI and claude just generated that for me in under 1 Minute. Thing is we just see th

> Just last week i asked for a script to do image segmentation with a basic UI and claude just generated that for me in under 1 Minute. Thing is we just see that it's copy pasting stack overflow, but now in a fancy way so this is sounding like "I asked Google for a nearby restaurant and it found it in like 500ms, my C64 couldn't do that". It sounds impressive (and it is) because it sounds like "it learned about navigating in the real world and it can now solve everything related to that" but what it actually solved is "fancy lookup in a GIS database". It's useful, damn sure it is, but once the novelty wears off you start seeing it for what it is instead of what you imagine it is. Edit: to drive the point home. > claude just generated that What you think happened is AI is "thinking" and building a ontology over which it reasoned and came to the logical conclusion that this script was the right output. What actually happened is your input correlates to this output according to the trillion examples it saw. There is no ontology. There is no reasoning. There is nothing. Of course this is still impressive and useful as hell, but the novelty will wear off in time. The limitations are obvious by this point.

Flamentono2 1y ago
I'm following LLMs, AI/ML for a few years now and not just on a high level. There is not a single system out there today which can do what claude can do. I stil see it for what it is: A technology i can communicate/use with natural language and get a very diverse of tasks done. From writing/generating code, to svgs, to emails, translation etc. etc. etc. Its a paradigma shift for the whole world literaly. We finally have a system which encodes not just basic things but high level concepts. And we humans are doing often enough something very similiar. And what limitations are obvious? Tell me? We have not reached any real ceiling yet. We are limited by GPU capacity or how many architectural experiments a researcher can run. We have plenty of work to do to cleanup the data set we use and have. We need to build more infrastructure, better software support etc. We have not even reached the phase were we all have local AI/ML chips build in. We don't even know yet how a system will act if everyone of us has access to very fast inferencing like you already get with groq.
- whyowhy3484939 1y ago
  > We finally have a system which encodes not just basic things but high level concepts That's the thing I'm trying to convey: it's in fact not encoding anything you'll recognize and if it is, it's certainly not "concepts" as you understand them. Not saying it cannot correlate text that includes what you call "high level concepts" or do what you imagine to be useful work in that general direction. Again not making claims it's not useful, just saying that it becomes kind of meh once you factor in all costs and not just the hypothetical imaginary future productivity gains. AKA building literal nuclear reactors to do something that basically amounts to filling in React templates or whatever BS needs doing. If it was reasoning it could start with a small set of bootstrap data and infer/deduce the rest from experience. It cannot. We are not even close as in there is not even theory to get us there forget about the engineering. It's not a subtle issue. We need to throw literally all data we have at it to get it to acceptable levels. At some point you have to retrace some steps and think over some decisions, but I guess I'm a skeptic. In short it's a correlation engine which, again, is very useful and will go ways to improve our lives somewhat - I hope - but I'm not holding my breath for anything more. A lot of correlation does not causation make. No reasoning can take place until you establish ontology, causality and the whole shebang.
  - Flamentono2 1y ago
    I do understand it but i also think that the current LLMs are the first step to it. GPT-3 started proper investment into this topic, there was not enough research done in this direction and now it is. People like Yann LeCun already analyse different approaches/architecture but they still use the infrastructure of LLMs (ML/GPUs) and potentially the data. I never said that LLM is the breaktrhough in consesnes. But you can also ask LLM strategies for thinking. It can tell you a lot of things. We will see if a LLM will be a fundamental part of AGI or not but GPU/ML will probably be. I also think that the compression mechanism through LLM lead to concepts through optimization. You can see from the antropic paper, that an LLM doesn't work in normal language space but in a high dimensional one and then 'expresses' the output in a language you like. We also see that real multi modal models are better in a lot of tasks due to a lot more context available through them. Estimating what someone said due to context. The necessary infrastructure and power requirement is something i accept too. We can assume, i do, that further progress in a lot of topics will require this type of compute and it also solves our data bottleneck: normal CPU architecture is limited by memory databus. Also in comparision to a lot of other companies, if the richest companies in the world invest in nuclear, i think this is a lot better than any other companies. They have a lot higher margins and knowledge. co2 is a market separator for them too. I also expect this amount of compute to be the base for fixing real issues we all face like cancer or optimizing cancer or any other sickness detection. We need to make medicin a lot cheaper and if someone in africa can do a cheap x ray and send it to the cloud to get any feedback, that would / could help a lot of people. Doing complex and massive protein analysis or mRna research in virtual space, also requires GPUs. All of this happened in a timespan of only a few years. I have not seen anything progressing as fast as AI/ML currenly does and as unfortunate it is, this needs compute. Even my small inhouse image recognition fine tuning explodes when you do a handful parameter optimizations but the quality is a lot better than what we had before. And enabling people to have real natural language UI is HUGE. It makes so much more accessable. Not just for people with a disability. Things like 'do a eli5 on topic x'. "explain to me this concept" etc. I would have loved that when i tried to be successful in the university math curiculum. All of that is already crazy and still is. But in parallel what Nvidia and others currently do with ML and Robotics is also something which requires all of that compute. And the progress is again breath taking. The current flood of basic robots standing and walking around is due to ML.
skydhash 1y ago
Yeah. It’s just fancier techniques than linear regression. Just like the latter takes a set of numbers and produces another set, LLMs takes words and produces another set of words. The actual techniques are the breakthrough. The result are fun to play with and may be useful in some occasions, but we don’t have to put them on a pedestal.
holoduke 1y ago
You have the wrong idea of how an LLM works. Its more like an model that iteratively finds associating / relevant blocks. The reasoning are the iterative steps it takes.

Flamentono2 1y ago

I'm following LLMs, AI/ML for a few years now and not just on a high level. There is not a single system out there today which can do what claude can do. I stil see it for what it is: A technology i can communicate/use with natural language and get a very diverse of tasks done. From writing/generating code, to svgs, to emails, translation etc. etc. etc. Its a paradigma shift for the whole world literaly. We finally have a system which encodes not just basic things but high level concepts. And we humans are doing often enough something very similiar. And what limitations are obvious? Tell me? We have not reached any real ceiling yet. We are limited by GPU capacity or how many architectural experiments a researcher can run. We have plenty of work to do to cleanup the data set we use and have. We need to build more infrastructure, better software support etc. We have not even reached the phase were we all have local AI/ML chips build in. We don't even know yet how a system will act if everyone of us has access to very fast inferencing like you already get with groq.

whyowhy3484939 1y ago
> We finally have a system which encodes not just basic things but high level concepts That's the thing I'm trying to convey: it's in fact not encoding anything you'll recognize and if it is, it's certainly not "concepts" as you understand them. Not saying it cannot correlate text that includes what you call "high level concepts" or do what you imagine to be useful work in that general direction. Again not making claims it's not useful, just saying that it becomes kind of meh once you factor in all costs and not just the hypothetical imaginary future productivity gains. AKA building literal nuclear reactors to do something that basically amounts to filling in React templates or whatever BS needs doing. If it was reasoning it could start with a small set of bootstrap data and infer/deduce the rest from experience. It cannot. We are not even close as in there is not even theory to get us there forget about the engineering. It's not a subtle issue. We need to throw literally all data we have at it to get it to acceptable levels. At some point you have to retrace some steps and think over some decisions, but I guess I'm a skeptic. In short it's a correlation engine which, again, is very useful and will go ways to improve our lives somewhat - I hope - but I'm not holding my breath for anything more. A lot of correlation does not causation make. No reasoning can take place until you establish ontology, causality and the whole shebang.
- Flamentono2 1y ago
  I do understand it but i also think that the current LLMs are the first step to it. GPT-3 started proper investment into this topic, there was not enough research done in this direction and now it is. People like Yann LeCun already analyse different approaches/architecture but they still use the infrastructure of LLMs (ML/GPUs) and potentially the data. I never said that LLM is the breaktrhough in consesnes. But you can also ask LLM strategies for thinking. It can tell you a lot of things. We will see if a LLM will be a fundamental part of AGI or not but GPU/ML will probably be. I also think that the compression mechanism through LLM lead to concepts through optimization. You can see from the antropic paper, that an LLM doesn't work in normal language space but in a high dimensional one and then 'expresses' the output in a language you like. We also see that real multi modal models are better in a lot of tasks due to a lot more context available through them. Estimating what someone said due to context. The necessary infrastructure and power requirement is something i accept too. We can assume, i do, that further progress in a lot of topics will require this type of compute and it also solves our data bottleneck: normal CPU architecture is limited by memory databus. Also in comparision to a lot of other companies, if the richest companies in the world invest in nuclear, i think this is a lot better than any other companies. They have a lot higher margins and knowledge. co2 is a market separator for them too. I also expect this amount of compute to be the base for fixing real issues we all face like cancer or optimizing cancer or any other sickness detection. We need to make medicin a lot cheaper and if someone in africa can do a cheap x ray and send it to the cloud to get any feedback, that would / could help a lot of people. Doing complex and massive protein analysis or mRna research in virtual space, also requires GPUs. All of this happened in a timespan of only a few years. I have not seen anything progressing as fast as AI/ML currenly does and as unfortunate it is, this needs compute. Even my small inhouse image recognition fine tuning explodes when you do a handful parameter optimizations but the quality is a lot better than what we had before. And enabling people to have real natural language UI is HUGE. It makes so much more accessable. Not just for people with a disability. Things like 'do a eli5 on topic x'. "explain to me this concept" etc. I would have loved that when i tried to be successful in the university math curiculum. All of that is already crazy and still is. But in parallel what Nvidia and others currently do with ML and Robotics is also something which requires all of that compute. And the progress is again breath taking. The current flood of basic robots standing and walking around is due to ML.

skydhash 1y ago

Yeah. It’s just fancier techniques than linear regression. Just like the latter takes a set of numbers and produces another set, LLMs takes words and produces another set of words. The actual techniques are the breakthrough. The result are fun to play with and may be useful in some occasions, but we don’t have to put them on a pedestal.

holoduke 1y ago

You have the wrong idea of how an LLM works. Its more like an model that iteratively finds associating / relevant blocks. The reasoning are the iterative steps it takes.