5 ms·

Call me naive, but my read is the opposite. It's impressive to me that we have systems which can interpret plain english instructions with a progressively highe

by derac 10mo ago

Call me naive, but my read is the opposite. It's impressive to me that we have systems which can interpret plain english instructions with a progressively higher degree of reliability. Also, that such a simple mechanism for extending memory (if you believe it's an apt analogy) is possible. That seems closer to AGI to me, though maybe it is a stopgap to better generality/"intelligence" in the model. I'm not sure English is a bad way to outline what the system should do. It has tradeoffs. I'm not sure library functions are a 1:1 analogy either. Or if they are, you might grant me that it's possible to write a few english sentences that would expand into a massive amount of code. It's very difficult to measure progress on these models in a way that anyone can trust, moreso when you involve "agent" code around the model.

adastra22 10mo ago
I’ve posted this before, but here goes: we achieved AGI in either 2017 or 2022 (take your pick) with the transformer architecture and the achievement of scaled-up NLP in ChatGPT. What is AGI? Artificial. General. Intelligence. Applying domain independent intelligence to solve problems expressed in fully general natural language. It’s more than a pedantic point though. What people expect from AGI is the transformative capabilities that emerge from removing the human from the ideation-creation loop. How do you do that? By systematizing the knowledge work process and providing deterministic structure to agentic processes. Which is exactly what these developments are doing.
- aaronblohowiak 10mo ago
  We have achieved AGI no more than we have achieved human flight.
  - kelchm 10mo ago
    Are you really making the argument that human flight hasn’t been effectively achieved at this point? I actually kind of love this comparison — it demonstrates the point that just like “human flight”, “true AGI” isn’t a single point in time, it’s a many-decade (multi-century?) process of refinement and evolution. Scholars a millennia from now will be debating about when each of these were actually “truly” achieved.
    - mbreese 10mo ago
      I’ve never heard it described this way: AGI as similar to human flight. I think it’s subtle and clever - my two most favorite properties. To me, we have both achieved and not human flight. Can humans themselves fly? No. Can people fly in planes across continents. Yes. But, does it really matter if it counts as “human flight” if we can get from point A to point B faster? You’re right - this is an argument that will last ages. It’s a great turn of phrase to describe AGI.
      - aaronblohowiak 10mo ago
        Thank you! I’m bored of “moving goalposts” arguments as I think “looks different than we expected” is the _ordinary_ way revolutions happen.
  - adastra22 10mo ago
    Yes, I agree! Thank you for that apt comparison.
- bluefirebrand 10mo ago
  > we achieved AGI in either 2017 or 2022 Even if this is true, which I disagree with, it simply creates a new bar: AGCI. Artificial Generally Correct Intelligence Because Right now it is more like Randomly correct
  - decremental 10mo ago
    [dead]
  - doug_durham 10mo ago
    Kind of like humans.
    - freeone3000 10mo ago
      The reason we made systems on computers is so they would not be falliable like humans would be.
      - derac 10mo ago
        No it isn't, it's because they are useful tools for doing a lot of calculations quickly.
        bluefirebrand 10mo ago
        accurate calculations, quickly If they did calculations as sloppily as AI currently produces information, they would not be as useful
        adastra22 10mo ago
        A stochastically correct oracle just requires a little more care units use, that’s all.
  - micromacrofoot 10mo ago
    to be fair we accept imperfection as some natural trait of life, to err, human
- colechristensen 10mo ago
  >What is AGI? Artificial. General. Intelligence. Here's the thing, I get it, and it's easy to argue for this and difficult to argue against it. BUT It's not intelligent. It just is not. It's tremendously useful and I'd forgive someone for thinking the intelligence is real, but it's not. Perhaps it's just a poor choice of words. What a LOT of people really mean would go along the lines more like Synthetic Intelligence. That is, however difficult it might be to define, REAL intelligence that was made, not born. Transformer and Diffusion models aren't intelligent, they're just very well trained statistical models. We actually (metaphorically) have a million monkeys at a million typewriters for a million years creating Shakespeare. My efforts manipulating LLMs into doing what I want is pretty darn convincing that I'm cajoling a statistical model and not interacting with an intelligence. A lot of people won't be convinced that there's a difference, it's hard to do when I'm saying it might not be possible to have a definition of "intelligence" that is satisfactory and testable.
  - adastra22 10mo ago
    “Intelligence” has technical meaning, as it must if we want to have any clarity in discussions about it. It basically boils down to being able to exploit structure in a problem or problem domain to efficiently solve problems. The “G” and AGI just means that it is unconstrained by problem domain, but the “intelligence” remains the same: problem solving. Can ChatGPT solve problems? It is trivial to see that it can. Ask it to sort a list of numbers, or debug a piece of segfaulting code. You and I both know that it can do that, without being explicitly trained or modified to handle that problem, other than the prompt/context (which itself natural language that can express any problem, hence generality). What you are sneaking into this discussion is the notion of human-equivalence. Is GPT smarter than you? Or smarter than some average human? I don’t think the answer to this is as clear-cut. I’ve been using LLMs on my work daily for a year now, and I have seen incredible moments of brilliance as well as boneheaded failure. There are academic papers being released where AIs are being credited with key insights. So they are definitely not limited to remixing their training set. The problem with the “AI are just statistical predictors, not real intelligence” argument is what happens when you turn it around and analyze your own neurons. You will find that to the best of our models, you are also just a statistical prediction machine. Different architecture, but not fundamentally different in class from an LLM. And indeed, a lot of psychological mistakes and biases start making sense when you analyze them from the perspective of a human being like an LLM. But again, you need to define “real intelligence” because no, it is not at all obvious what that phrase means when you use it. The technical definitions of intelligence that have been used in the past, have been met by LLMs and other AI architectures.
AdieuToLogic 10mo ago
> I'm not sure English is a bad way to outline what the system should do. It isn't, as these are how stakeholders convey needs to those charged with satisfying same (a.k.a. "requirements"). Where expectations become unrealistic is believing language models can somehow "understand" those outlines as if a human expert were doing so in order to produce an equivalent work product. Language models can produce nondeterministic results based on the statistical model derived from their training data set(s), with varying degrees of relevance as determined by persons interpreting the generated content. They do not understand "what the system should do."
- idopmstuff 10mo ago
  This is just semantics. You can say they don't understand, but I'm sitting here with Nano Banana Pro creating infographics, and it's doing as good of a job as my human designer does with the same kinds of instructions. Does it matter if that's understanding or not?
  - AdieuToLogic 10mo ago
    > This is just semantics. Precisely my point: semantics: the branch of linguistics and logic concerned with meaning. > You can say they don't understand, but I'm sitting here with Nano Banana Pro creating infographics, and it's doing as good of a job as my human designer does with the same kinds of instructions. Does it matter if that's understanding or not? Understanding, when used in its unqualified form, implies people possessing same. As such, it is a metaphysical property unique to people and defined wholly therein. Excel "understands" well-formed spreadsheets by performing specified calculations. But who defines those spreadsheets? And who determines the result to be "right?" Nano Banana Pro "understands" instructions to generate images. But who defines those instructions? And who determines the result to be "right?" "They" do not understand. You do.
    - bonoboTP 10mo ago
      "This is just semantics" is a set phrase in English and it means that the issue being discussed is merely about definitions of words, and not about the substance (the object level). And generally the point is that it does not matter whether we call what they do "understanding" or not. It will have the same kind of consequences in the end, economic and otherwise. This is basically the number one hangup that people have about AI systems, all the way back since Turing's time. The consequences will come from AI's ability to produce certain types of artifacts and perform certain types of transformations of bits. That's all we need for all the scifi stuff to happen. Turing realized this very quickly, and his famous Turing test is exactly about making this point. It's not an engineering kind of test. It's a thought experiment trying to prove that it does not matter whether it's just "simulated understanding". A simulated cake is useless, I can't eat it. But simulated understanding can have real world effects of the exact same sort as real understanding.
deleted 10mo ago
[deleted]
raincole 10mo ago
I 100% agree. I don't know what the GP is on. Being able to write instructions in a .md file is "further away from AGI"? Like... what? It's just a little quality of life feature. How and why is it related to AGI? Top HN comments sometime read like a random generator: return random_criticism_of_ai_companies() + " " + unrelated_trivia_fact() Why are people treating everything OpenAI does as an evidence of anti- AGI? It's like saying if you don't mortgage your house to all-in AAPL, you "don't really believe Apple has a future." Even OpenAI does believe there is X% chance AGI will be achieved, it doesn't mean they should stop literally everything else they're doing.

by derac 10mo ago

adastra22 10mo ago

I’ve posted this before, but here goes: we achieved AGI in either 2017 or 2022 (take your pick) with the transformer architecture and the achievement of scaled-up NLP in ChatGPT. What is AGI? Artificial. General. Intelligence. Applying domain independent intelligence to solve problems expressed in fully general natural language. It’s more than a pedantic point though. What people expect from AGI is the transformative capabilities that emerge from removing the human from the ideation-creation loop. How do you do that? By systematizing the knowledge work process and providing deterministic structure to agentic processes. Which is exactly what these developments are doing.

aaronblohowiak 10mo ago
We have achieved AGI no more than we have achieved human flight.
- kelchm 10mo ago
  Are you really making the argument that human flight hasn’t been effectively achieved at this point? I actually kind of love this comparison — it demonstrates the point that just like “human flight”, “true AGI” isn’t a single point in time, it’s a many-decade (multi-century?) process of refinement and evolution. Scholars a millennia from now will be debating about when each of these were actually “truly” achieved.
  - mbreese 10mo ago
    I’ve never heard it described this way: AGI as similar to human flight. I think it’s subtle and clever - my two most favorite properties. To me, we have both achieved and not human flight. Can humans themselves fly? No. Can people fly in planes across continents. Yes. But, does it really matter if it counts as “human flight” if we can get from point A to point B faster? You’re right - this is an argument that will last ages. It’s a great turn of phrase to describe AGI.
    - aaronblohowiak 10mo ago
      Thank you! I’m bored of “moving goalposts” arguments as I think “looks different than we expected” is the _ordinary_ way revolutions happen.
- adastra22 10mo ago
  Yes, I agree! Thank you for that apt comparison.
bluefirebrand 10mo ago
> we achieved AGI in either 2017 or 2022 Even if this is true, which I disagree with, it simply creates a new bar: AGCI. Artificial Generally Correct Intelligence Because Right now it is more like Randomly correct
- decremental 10mo ago
  [dead]
- doug_durham 10mo ago
  Kind of like humans.
  - freeone3000 10mo ago
    The reason we made systems on computers is so they would not be falliable like humans would be.
    - derac 10mo ago
      No it isn't, it's because they are useful tools for doing a lot of calculations quickly.
      - bluefirebrand 10mo ago
        accurate calculations, quickly If they did calculations as sloppily as AI currently produces information, they would not be as useful
        adastra22 10mo ago
        A stochastically correct oracle just requires a little more care units use, that’s all.
- micromacrofoot 10mo ago
  to be fair we accept imperfection as some natural trait of life, to err, human
colechristensen 10mo ago
>What is AGI? Artificial. General. Intelligence. Here's the thing, I get it, and it's easy to argue for this and difficult to argue against it. BUT It's not intelligent. It just is not. It's tremendously useful and I'd forgive someone for thinking the intelligence is real, but it's not. Perhaps it's just a poor choice of words. What a LOT of people really mean would go along the lines more like Synthetic Intelligence. That is, however difficult it might be to define, REAL intelligence that was made, not born. Transformer and Diffusion models aren't intelligent, they're just very well trained statistical models. We actually (metaphorically) have a million monkeys at a million typewriters for a million years creating Shakespeare. My efforts manipulating LLMs into doing what I want is pretty darn convincing that I'm cajoling a statistical model and not interacting with an intelligence. A lot of people won't be convinced that there's a difference, it's hard to do when I'm saying it might not be possible to have a definition of "intelligence" that is satisfactory and testable.
- adastra22 10mo ago
  “Intelligence” has technical meaning, as it must if we want to have any clarity in discussions about it. It basically boils down to being able to exploit structure in a problem or problem domain to efficiently solve problems. The “G” and AGI just means that it is unconstrained by problem domain, but the “intelligence” remains the same: problem solving. Can ChatGPT solve problems? It is trivial to see that it can. Ask it to sort a list of numbers, or debug a piece of segfaulting code. You and I both know that it can do that, without being explicitly trained or modified to handle that problem, other than the prompt/context (which itself natural language that can express any problem, hence generality). What you are sneaking into this discussion is the notion of human-equivalence. Is GPT smarter than you? Or smarter than some average human? I don’t think the answer to this is as clear-cut. I’ve been using LLMs on my work daily for a year now, and I have seen incredible moments of brilliance as well as boneheaded failure. There are academic papers being released where AIs are being credited with key insights. So they are definitely not limited to remixing their training set. The problem with the “AI are just statistical predictors, not real intelligence” argument is what happens when you turn it around and analyze your own neurons. You will find that to the best of our models, you are also just a statistical prediction machine. Different architecture, but not fundamentally different in class from an LLM. And indeed, a lot of psychological mistakes and biases start making sense when you analyze them from the perspective of a human being like an LLM. But again, you need to define “real intelligence” because no, it is not at all obvious what that phrase means when you use it. The technical definitions of intelligence that have been used in the past, have been met by LLMs and other AI architectures.

AdieuToLogic 10mo ago

> I'm not sure English is a bad way to outline what the system should do. It isn't, as these are how stakeholders convey needs to those charged with satisfying same (a.k.a. "requirements"). Where expectations become unrealistic is believing language models can somehow "understand" those outlines as if a human expert were doing so in order to produce an equivalent work product. Language models can produce nondeterministic results based on the statistical model derived from their training data set(s), with varying degrees of relevance as determined by persons interpreting the generated content. They do not understand "what the system should do."

idopmstuff 10mo ago
This is just semantics. You can say they don't understand, but I'm sitting here with Nano Banana Pro creating infographics, and it's doing as good of a job as my human designer does with the same kinds of instructions. Does it matter if that's understanding or not?
- AdieuToLogic 10mo ago
  > This is just semantics. Precisely my point: semantics: the branch of linguistics and logic concerned with meaning. > You can say they don't understand, but I'm sitting here with Nano Banana Pro creating infographics, and it's doing as good of a job as my human designer does with the same kinds of instructions. Does it matter if that's understanding or not? Understanding, when used in its unqualified form, implies people possessing same. As such, it is a metaphysical property unique to people and defined wholly therein. Excel "understands" well-formed spreadsheets by performing specified calculations. But who defines those spreadsheets? And who determines the result to be "right?" Nano Banana Pro "understands" instructions to generate images. But who defines those instructions? And who determines the result to be "right?" "They" do not understand. You do.
  - bonoboTP 10mo ago
    "This is just semantics" is a set phrase in English and it means that the issue being discussed is merely about definitions of words, and not about the substance (the object level). And generally the point is that it does not matter whether we call what they do "understanding" or not. It will have the same kind of consequences in the end, economic and otherwise. This is basically the number one hangup that people have about AI systems, all the way back since Turing's time. The consequences will come from AI's ability to produce certain types of artifacts and perform certain types of transformations of bits. That's all we need for all the scifi stuff to happen. Turing realized this very quickly, and his famous Turing test is exactly about making this point. It's not an engineering kind of test. It's a thought experiment trying to prove that it does not matter whether it's just "simulated understanding". A simulated cake is useless, I can't eat it. But simulated understanding can have real world effects of the exact same sort as real understanding.

deleted 10mo ago

[deleted]

raincole 10mo ago

I 100% agree. I don't know what the GP is on. Being able to write instructions in a .md file is "further away from AGI"? Like... what? It's just a little quality of life feature. How and why is it related to AGI? Top HN comments sometime read like a random generator: return random_criticism_of_ai_companies() + " " + unrelated_trivia_fact() Why are people treating everything OpenAI does as an evidence of anti- AGI? It's like saying if you don't mortgage your house to all-in AAPL, you "don't really believe Apple has a future." Even OpenAI does believe there is X% chance AGI will be achieved, it doesn't mean they should stop literally everything else they're doing.