4 ms·

> Fully Correct, Out of Thin Air I think if you're an expert in an area, this effect is easier to see through. You know where the github repo is, where the lib

> Fully Correct, Out of Thin Air I think if you're an expert in an area, this effect is easier to see through. You know where the github repo is, where the library example is, which ebooks there area -- etc. and you're mostly at-ease not using them and just writing the solution yourself. These systems are not "fully correct" and not "out of thin area". They are trained on everything ever digitised, including the entire internet. They, in effect, find similar historical cases to your query and merge them. In many cases, for specific enough queries, the text is verbatim from an original source. This is less revolutionary than the spreadsheet; it's less than google search. It's a speed boost to what was always the most wrote element to what we do. Yes, that often took us the longest -- and so some might be afraid that's what labour is -- but it isnt. We never "added value" to products via what may be automated. Value is always a matter of the desire of the buyer of the products of our labour (vs. the supply) -- and making those products for those buyers was always what they wanted. This will be clear to everyone pretty quickly, as with all tech, it's "magic" on the first encounter -- until the limitations are exposed. I actually work in an area where what took 3mo last year, I can now do in maybe 3 days due to ChatGPT. But when it comes to providing my customers with that content, the value was always in how I provided it and what it did for them. I think this makes my skills more valuable, not less. Since the quality of products will be even more stratified by experts who can quickly assemble what the customer needs from non-experts who have to fight through AI dialogue to get something generic.

mchaver 3y ago
I agree. LLMs are very impressive, but it isn't helpful to think of them of magic. LLMs are a great tool to explore and remix the body of human knowledge on the internet (limited to what it has been trained on). The user needs to keep in mind that it can give plenty of false information. To make good use of it, the user needs to be able to verify if the returned information is useful, makes sense, compare with first hand sources, etc. In the hands of expert that is really powerful. In the hands of a layman (on the subject in question), they can generate a lot of crap and misunderstand what it is saying. It is similar to the idea that Democracy can be a great tool, but it needs an educated and participatory populous or it may generate a lot of headaches.
- byby 3y ago
  > I agree. LLMs are very impressive, but it isn't helpful to think of them of magic. LLMs are a great tool to explore and remix the body of human knowledge on the internet (limited to what it has been trained on). Of course you shouldn't think of it as magic. But, the experts self admit they don't fully understand how LLMs can produce such output. It's definitely emergent behavior. We've built something we don't understand, and although it's not magic, it's one of the closest things to it that can exist. Think about it. What is the closest thing in reality to magic? Literally, building something we can't understand is it. It's one thing to think of something as magic, it's another thing to try to simplify a highly complex concept into a box. When elon musk got his rockets to space why were people so floored by decades old technology that he simply made cheaper? But when someone makes AI that can literally do almost anything you ask it to everyone just suddenly says it's a simple stochastic parrot that can't do much? I think it's obvious. It's because a rocket can't replace your job or your identity. If part of your skillset and identity is "master programmer" and suddenly there's a machine that can do better than you, the easiest thing to stop that machine is to first deny reality.
  - mjburgess 3y ago
    > the experts self admit they don't fully understand how LLMs can produce such output Well I take myself to be an expert in this area, and I think it's fairly obvious how they work. Many of these so-called "Experts" are sitting on the boards of commercial companies with vested interests in presenting this technology as revolutionary. Indeed, much of what has been said recently in the media is little more than political and economic power plays disguised as philosophical musings. A statistical AI system is a function `answer = f(question; weights)`. The `answer` obtains apparent "emergent" properties such as "suitability for basic reasoning tasks" when used by human operators. But the function does not actually have those properties. It's a trick -- the weights are summaries of unimaginable number of similar cases, and the function is little more than "sample from those cases and merge". Properties of the output of this function obtain trivially in the way that all statistical functions generate increasingly useful output: by having increasingly relevant weights. If you model linear data with just y = ax then as soon as you shift to "y = ax + b" you'll see the "emergent property" that the output is now sensitive to a background bias, b. Emergence is an ontological phenomenon concerning how `f` would be reaslised by a physical system. In this case any physical system implementing `f` shows no such emergence. Rather the output of `f` has a "shift in utility" as the properties of the data its training on, as summarised by the weights, "shifts in utilty". In other words, if you train a statistical system on everything ever written by billions of people over decades, then you will in fact see "domains of applicability" increases, just as much as when you shift from a y=ax model to a y=ax+b. To make this as simple as I can: statistical AI is just a funnel. ChatGPT is a slightly better funnel, but moreso, it's had the ocean pass through it. Much of its apparent properties are illusary, and much of the press around it puts in cases where it appears to work and claims "look it works!". This is pseudoscience -- if you want to test a hypothesis of ChatGPT, find all the cases where it doesnt work -- and you will find that in the cases where it does there was some "statistical shortcut" taken
byby 3y ago
>I think if you're an expert in an area, Experts in the area, including Hinton, the father of modern AI, self admit they don't fully understand what's going on but they think that LLMs know what they are talking about. >These systems are not "fully correct" and not "out of thin area". They are trained on everything ever digitised, including the entire internet. They, in effect, find similar historical cases to your query and merge them. In many cases, for specific enough queries, the text is verbatim from an original source. I never said the systems are fully correct. I said that for my specific example the answer is fully correct and out of thin air. No such question and answer pair exists on the internet. Find it and prove me wrong. >This will be clear to everyone pretty quickly, as with all tech, it's "magic" on the first encounter -- until the limitations are exposed. Except many experts are saying the exact opposite of what you're saying. I'm just parroting the experts.. >I actually work in an area where what took 3mo last year, I can now do in maybe 3 days due to ChatGPT. But when it comes to providing my customers with that content, the value was always in how I provided it and what it did for them. So if they knew you were just copying and pasting their queries to chatgpt would they still care about the "how"? I doubt it.

mchaver 3y ago

I agree. LLMs are very impressive, but it isn't helpful to think of them of magic. LLMs are a great tool to explore and remix the body of human knowledge on the internet (limited to what it has been trained on). The user needs to keep in mind that it can give plenty of false information. To make good use of it, the user needs to be able to verify if the returned information is useful, makes sense, compare with first hand sources, etc. In the hands of expert that is really powerful. In the hands of a layman (on the subject in question), they can generate a lot of crap and misunderstand what it is saying. It is similar to the idea that Democracy can be a great tool, but it needs an educated and participatory populous or it may generate a lot of headaches.

byby 3y ago
> I agree. LLMs are very impressive, but it isn't helpful to think of them of magic. LLMs are a great tool to explore and remix the body of human knowledge on the internet (limited to what it has been trained on). Of course you shouldn't think of it as magic. But, the experts self admit they don't fully understand how LLMs can produce such output. It's definitely emergent behavior. We've built something we don't understand, and although it's not magic, it's one of the closest things to it that can exist. Think about it. What is the closest thing in reality to magic? Literally, building something we can't understand is it. It's one thing to think of something as magic, it's another thing to try to simplify a highly complex concept into a box. When elon musk got his rockets to space why were people so floored by decades old technology that he simply made cheaper? But when someone makes AI that can literally do almost anything you ask it to everyone just suddenly says it's a simple stochastic parrot that can't do much? I think it's obvious. It's because a rocket can't replace your job or your identity. If part of your skillset and identity is "master programmer" and suddenly there's a machine that can do better than you, the easiest thing to stop that machine is to first deny reality.
- mjburgess 3y ago
  > the experts self admit they don't fully understand how LLMs can produce such output Well I take myself to be an expert in this area, and I think it's fairly obvious how they work. Many of these so-called "Experts" are sitting on the boards of commercial companies with vested interests in presenting this technology as revolutionary. Indeed, much of what has been said recently in the media is little more than political and economic power plays disguised as philosophical musings. A statistical AI system is a function `answer = f(question; weights)`. The `answer` obtains apparent "emergent" properties such as "suitability for basic reasoning tasks" when used by human operators. But the function does not actually have those properties. It's a trick -- the weights are summaries of unimaginable number of similar cases, and the function is little more than "sample from those cases and merge". Properties of the output of this function obtain trivially in the way that all statistical functions generate increasingly useful output: by having increasingly relevant weights. If you model linear data with just y = ax then as soon as you shift to "y = ax + b" you'll see the "emergent property" that the output is now sensitive to a background bias, b. Emergence is an ontological phenomenon concerning how `f` would be reaslised by a physical system. In this case any physical system implementing `f` shows no such emergence. Rather the output of `f` has a "shift in utility" as the properties of the data its training on, as summarised by the weights, "shifts in utilty". In other words, if you train a statistical system on everything ever written by billions of people over decades, then you will in fact see "domains of applicability" increases, just as much as when you shift from a y=ax model to a y=ax+b. To make this as simple as I can: statistical AI is just a funnel. ChatGPT is a slightly better funnel, but moreso, it's had the ocean pass through it. Much of its apparent properties are illusary, and much of the press around it puts in cases where it appears to work and claims "look it works!". This is pseudoscience -- if you want to test a hypothesis of ChatGPT, find all the cases where it doesnt work -- and you will find that in the cases where it does there was some "statistical shortcut" taken

byby 3y ago

>I think if you're an expert in an area, Experts in the area, including Hinton, the father of modern AI, self admit they don't fully understand what's going on but they think that LLMs know what they are talking about. >These systems are not "fully correct" and not "out of thin area". They are trained on everything ever digitised, including the entire internet. They, in effect, find similar historical cases to your query and merge them. In many cases, for specific enough queries, the text is verbatim from an original source. I never said the systems are fully correct. I said that for my specific example the answer is fully correct and out of thin air. No such question and answer pair exists on the internet. Find it and prove me wrong. >This will be clear to everyone pretty quickly, as with all tech, it's "magic" on the first encounter -- until the limitations are exposed. Except many experts are saying the exact opposite of what you're saying. I'm just parroting the experts.. >I actually work in an area where what took 3mo last year, I can now do in maybe 3 days due to ChatGPT. But when it comes to providing my customers with that content, the value was always in how I provided it and what it did for them. So if they knew you were just copying and pasting their queries to chatgpt would they still care about the "how"? I doubt it.