7 ms·

To each their own. Personally I will start feeling the AGI as soon as we move from chatting about benchmark results to learn that some lab just announced the di

by pera 14d ago

To each their own. Personally I will start feeling the AGI as soon as we move from chatting about benchmark results to learn that some lab just announced the discovery of tens of novel treatments for rare diseases. Maybe I'm too boring but it seems quite pointless to have this same prediction game every time a new model is released.

azan_ 14d ago
I think treatment is not good benchmark - it requires lots of waiting and lots of regulatory work. The better benchmark - in my opinion- would be math discovery.
- indoorfish 14d ago
  That is an absolutely terrible benchmark. Inference over a bounded search space is not a good measure of what "intelligence" actually is. part of the reason they are using math and not something actually challenging like long distance interstate trucking is because it's so much simpler and easier than what make intelligence intelligent.
  - dash2 14d ago
    Just seems very weird to call getting Fields-medal-level results "inference over a bounded search space" and "not actually challenging".
    - naishoya 14d ago
      Plagiarizing on a massive scale to generate works which appear to be Fields-medal-level results is not the same thing as inventing new conceptualizations in mathematics. No matter how bodly they write the headlines, what has happened in mathematics using Large Language Models is very much "inference over a bounded search space" even if those bounds are immense. For a comparison of true creation of novel conceptualization in mathematics is submit the works of Martin Hairer, one of which is Introduction to Regularity Structures, [https://arxiv.org/pdf/1401.3014 https://arxiv.org/pdf/1401.3014] None of the so called, "novel math discoveries" by any LLM is as enlightening and expands the state of the art in math like any of his writings.
      - dash2 13d ago
        If they’re getting results which mathematicians have been trying to do for decades, then I think the “plagiarism” is extremely socially valuable. If they aren’t valuable results, then why were mathematicians being paid to solve them? I don’t buy this “the journey was the insights we got along the way” stuff that disgruntled mathematicians are selling.
        naishoya 13d ago
        It is irrelevant whether in general public understands the value of the insights, and the lesson that mathematics coursework should have made more clear for everyone is that understanding the process that is required to get an 'answer' is where the entire value of a mathematics education exists. The 'cheat code' approach to math results, a result which no one understands, and which no one can teach has no real value. The system of payment for publications in order to support math discovery is simply the narrow 'commercial system' applied to supporting foundational science in the absence of a broader civilization level appreciation for the mathematical arts. Looking to history, from the late renaissance through the early 20th century the support for mathematical discovery was more generally understood and supported by institutional level organizations and more generally understood to be important for the progress of scientific progress by the private and public wealth . This system enabled the development of topology, numerical analysis, complexity, set and group theories. The lapse in this level of support that did not give mathematicians the same protection from front line deployment in WWI brought that era to nearly a close. Reading about 'Nicholas Bourbaki' might lend some deeper appreciation of the effects of the losses from that shift in collective appreciation of foundational math. The idea that these LLM's are getting results that mathematicians haven't produced demonstrates the shallow understanding of math in modern times, due in part to the limited accessibility of so much of the prior writings of the entire history in mathematics, whether that be due to few surviving copies of some arcane work in a private library collection, or due to a modern fee for access paywall. One example of this condition can be shown with a small excerpt from a work that I am currently composing: "In 1805, while computing the orbits of the newly discovered asteroids Ceres, Pallas, and Juno from limited observational data, Gauss developed an efficient method for evaluating trigonometric interpolations by recursively decomposing large sums into smaller ones before recombining the results. Because of a steadfast adherence to Gauss' own personal motto, "Pauca sed matura" (Few, but ripe), Gauss never formally published this specific algorithm nor the conclusions of investigations which also laid the foundations of non-Euclidean geometry. These methods remained hidden in his notes under a manuscript titled Theoria Interpolationis Methodo Nova Tractata which was published in 1866, 11 years after his death, and the Fast Fourier Transform-equivalent approach within it remained largely unnoticed until the twentieth century, when James Cooley and John Tukey independently rediscovered the same computational strategy. His discovery was seventeen years before Joseph Fourier published the original Fourier Transform in his 1822 results on harmonic analysis." That is to say; Tukey and Cooley were unaware when they discovered FFT that the knowledge had lay hidden in an obscure work for centuries. It should be understood that these 'novel' LLM discoveries are simply the models traversal of the huge corpus of all the maths publications in the training set, collecting and rearranging these techniques into synthetic 'results'. They are attention getting, but they are not new, and the proofs are insufficient to the task of improving the utility of mathematics for humanity. The 'disgruntled mathematicians' aren't selling anything. They are informing civilization as a whole that having a cheat sheet to the math test only cheats yourself in the end, the same point that math teachers have been making since grade-school. Anyone who doesn't internalize that truth will always need someone else to do the math for them. To paraphrase Curtis Jackson, ""If you don't know the numbers, you don't know your business."
- ogogmad 14d ago
  Claude Fable recently proved the existence of complex structures over S^6 (6-sphere). If I had to guess, I think LLMs will be inventing highly original new mathematics within the next year. I think it will be approached as an optimisation problem, targeting how quickly LLMs can solve classes of maths problems as a function of the definitions they need to conjure up to do so.
- pera 14d ago
  My point was that we won't care about benchmarks anymore because we would see an obvious and completely unprecedent increase in productivity (and I believe it will likely come from the same people who will develope such machine). The reason most of the conversations are focused on benchmarks is because we are still in the age of weak AI.
doctoboggan 14d ago
Wouldn’t that be ASI? I.e. surpassing humans by outputting novel treatments at a far greater rate than normal humans?
- pera 14d ago
  You don't need super-intelligence to produce at a greater rate when your tasks are parallelizable.
kaashif 14d ago
AGI would produce novel treatments for diseases at rates equivalent to what a human can do today. Which is to say, not that fast.
- dinfinity 14d ago
  Whether AI is AGI does not depend on the speed at which it operates/thinks. Clearly all the theoretical work done by AGI will be done orders of magnitude quicker than humans can do it. It is an open question to what extent practical experimentation/work will be a bottleneck for the theoretical work. It stands to reason that it is improbable that it will be the bottleneck for 100% of the speed of treatment development.
jrflo 14d ago
You are describing superintelligence (ASI) not general intelligence (AGI)

by pera 14d ago

azan_ 14d ago

I think treatment is not good benchmark - it requires lots of waiting and lots of regulatory work. The better benchmark - in my opinion- would be math discovery.

indoorfish 14d ago
That is an absolutely terrible benchmark. Inference over a bounded search space is not a good measure of what "intelligence" actually is. part of the reason they are using math and not something actually challenging like long distance interstate trucking is because it's so much simpler and easier than what make intelligence intelligent.
- dash2 14d ago
  Just seems very weird to call getting Fields-medal-level results "inference over a bounded search space" and "not actually challenging".
  - naishoya 14d ago
    Plagiarizing on a massive scale to generate works which appear to be Fields-medal-level results is not the same thing as inventing new conceptualizations in mathematics. No matter how bodly they write the headlines, what has happened in mathematics using Large Language Models is very much "inference over a bounded search space" even if those bounds are immense. For a comparison of true creation of novel conceptualization in mathematics is submit the works of Martin Hairer, one of which is Introduction to Regularity Structures, [https://arxiv.org/pdf/1401.3014 https://arxiv.org/pdf/1401.3014] None of the so called, "novel math discoveries" by any LLM is as enlightening and expands the state of the art in math like any of his writings.
    - dash2 13d ago
      If they’re getting results which mathematicians have been trying to do for decades, then I think the “plagiarism” is extremely socially valuable. If they aren’t valuable results, then why were mathematicians being paid to solve them? I don’t buy this “the journey was the insights we got along the way” stuff that disgruntled mathematicians are selling.
      - naishoya 13d ago
        It is irrelevant whether in general public understands the value of the insights, and the lesson that mathematics coursework should have made more clear for everyone is that understanding the process that is required to get an 'answer' is where the entire value of a mathematics education exists. The 'cheat code' approach to math results, a result which no one understands, and which no one can teach has no real value. The system of payment for publications in order to support math discovery is simply the narrow 'commercial system' applied to supporting foundational science in the absence of a broader civilization level appreciation for the mathematical arts. Looking to history, from the late renaissance through the early 20th century the support for mathematical discovery was more generally understood and supported by institutional level organizations and more generally understood to be important for the progress of scientific progress by the private and public wealth . This system enabled the development of topology, numerical analysis, complexity, set and group theories. The lapse in this level of support that did not give mathematicians the same protection from front line deployment in WWI brought that era to nearly a close. Reading about 'Nicholas Bourbaki' might lend some deeper appreciation of the effects of the losses from that shift in collective appreciation of foundational math. The idea that these LLM's are getting results that mathematicians haven't produced demonstrates the shallow understanding of math in modern times, due in part to the limited accessibility of so much of the prior writings of the entire history in mathematics, whether that be due to few surviving copies of some arcane work in a private library collection, or due to a modern fee for access paywall. One example of this condition can be shown with a small excerpt from a work that I am currently composing: "In 1805, while computing the orbits of the newly discovered asteroids Ceres, Pallas, and Juno from limited observational data, Gauss developed an efficient method for evaluating trigonometric interpolations by recursively decomposing large sums into smaller ones before recombining the results. Because of a steadfast adherence to Gauss' own personal motto, "Pauca sed matura" (Few, but ripe), Gauss never formally published this specific algorithm nor the conclusions of investigations which also laid the foundations of non-Euclidean geometry. These methods remained hidden in his notes under a manuscript titled Theoria Interpolationis Methodo Nova Tractata which was published in 1866, 11 years after his death, and the Fast Fourier Transform-equivalent approach within it remained largely unnoticed until the twentieth century, when James Cooley and John Tukey independently rediscovered the same computational strategy. His discovery was seventeen years before Joseph Fourier published the original Fourier Transform in his 1822 results on harmonic analysis." That is to say; Tukey and Cooley were unaware when they discovered FFT that the knowledge had lay hidden in an obscure work for centuries. It should be understood that these 'novel' LLM discoveries are simply the models traversal of the huge corpus of all the maths publications in the training set, collecting and rearranging these techniques into synthetic 'results'. They are attention getting, but they are not new, and the proofs are insufficient to the task of improving the utility of mathematics for humanity. The 'disgruntled mathematicians' aren't selling anything. They are informing civilization as a whole that having a cheat sheet to the math test only cheats yourself in the end, the same point that math teachers have been making since grade-school. Anyone who doesn't internalize that truth will always need someone else to do the math for them. To paraphrase Curtis Jackson, ""If you don't know the numbers, you don't know your business."
ogogmad 14d ago
Claude Fable recently proved the existence of complex structures over S^6 (6-sphere). If I had to guess, I think LLMs will be inventing highly original new mathematics within the next year. I think it will be approached as an optimisation problem, targeting how quickly LLMs can solve classes of maths problems as a function of the definitions they need to conjure up to do so.
pera 14d ago
My point was that we won't care about benchmarks anymore because we would see an obvious and completely unprecedent increase in productivity (and I believe it will likely come from the same people who will develope such machine). The reason most of the conversations are focused on benchmarks is because we are still in the age of weak AI.

doctoboggan 14d ago

Wouldn’t that be ASI? I.e. surpassing humans by outputting novel treatments at a far greater rate than normal humans?

pera 14d ago
You don't need super-intelligence to produce at a greater rate when your tasks are parallelizable.

kaashif 14d ago

AGI would produce novel treatments for diseases at rates equivalent to what a human can do today. Which is to say, not that fast.

dinfinity 14d ago
Whether AI is AGI does not depend on the speed at which it operates/thinks. Clearly all the theoretical work done by AGI will be done orders of magnitude quicker than humans can do it. It is an open question to what extent practical experimentation/work will be a bottleneck for the theoretical work. It stands to reason that it is improbable that it will be the bottleneck for 100% of the speed of treatment development.

jrflo 14d ago

You are describing superintelligence (ASI) not general intelligence (AGI)