4 ms·

> one of the biggest scientific puzzles of our time > a hidden mathematical pattern in language that large language models somehow come to exploit... the fact

> one of the biggest scientific puzzles of our time > a hidden mathematical pattern in language that large language models somehow come to exploit... the fact that these things model language is probably one of the biggest discoveries in history Is it? Excuse my ignorance. If we were talking about simple Markov chains, aren't these just stationary distributions?

psyklic 3y ago
Even if LLMs are thought of as Markov chains, there are still substantial unsolved scientific questions. For a given prompt/"state", an LLM essentially computes the next-state probabilities. This is done by compressing language and storing particular patterns/distributions. To date, we still don't understand what statistics are stored by LLMs. Or even what stats might be necessary to produce natural-sounding language. (A canonical Markov chain only works with n-gram statistics, but we know these are insufficient.) IMO figuring this out to a human-level understanding could be a major breakthrough in science. It would reveal a deeper structure to language. It may even help understand how our brains process language; we'll at least know one plausible algorithm. Prior to LLMs, many scholars assumed language was unique to the brain and could only poorly be "computed".
yetihehe 3y ago
> Is it? Excuse my ignorance. If we were talking about simple Markov chains, aren't these just stationary distributions? "- You're just a robot, you can't write a poem - Can you?" It seems that GPT3.5 is already a much better writer than I am, and I'm still much better than a Markov chain, so LLM's are verifiably better.
- dmz73 3y ago
  (Most) LLMs have been trained on more data than any human will ever be able to read, let alone process. Then they have been tweaked to produce human like output (most of the time). They can just as easily be tweaked to produce incomprehensible rubbish. This means that they can generate convincing human like output for any possible combination of data they were trained on and this output will usually be as good or better than that of average modern human that has consumed and comprehended tiny amount of data (in comparison). But even the dumbest of humans can be assumed to understand what they know where LLMs are just giant repository of numbers. LLMs have no capacity to understand what these numbers mean or what their output represents, that requires a human like intelligence. "AI" term has been grossly overloaded (just a couple of decades ago, a complex if/then/else statement was touted as "AI") and as we gain better and better understanding of information science, we assign that term to more sophisticated information systems we build but these systems are still far below of any threshold we would accept as sign of intelligence from another human being. Better term for these system would be Complicated Parrots rather than Artificial Intelligence. Intelligence requires understanding and understanding, I believe, requires sentience.
  - gorlilla 3y ago
    I find that most people have an incredibly hard time understanding the scale of an LLM's lexicon. I think that scale is always a struggle because it directly challenges our ability to comprehend beyond our own personal/tangible reference frame.

psyklic 3y ago

Even if LLMs are thought of as Markov chains, there are still substantial unsolved scientific questions. For a given prompt/"state", an LLM essentially computes the next-state probabilities. This is done by compressing language and storing particular patterns/distributions. To date, we still don't understand what statistics are stored by LLMs. Or even what stats might be necessary to produce natural-sounding language. (A canonical Markov chain only works with n-gram statistics, but we know these are insufficient.) IMO figuring this out to a human-level understanding could be a major breakthrough in science. It would reveal a deeper structure to language. It may even help understand how our brains process language; we'll at least know one plausible algorithm. Prior to LLMs, many scholars assumed language was unique to the brain and could only poorly be "computed".

yetihehe 3y ago

> Is it? Excuse my ignorance. If we were talking about simple Markov chains, aren't these just stationary distributions? "- You're just a robot, you can't write a poem - Can you?" It seems that GPT3.5 is already a much better writer than I am, and I'm still much better than a Markov chain, so LLM's are verifiably better.

dmz73 3y ago
(Most) LLMs have been trained on more data than any human will ever be able to read, let alone process. Then they have been tweaked to produce human like output (most of the time). They can just as easily be tweaked to produce incomprehensible rubbish. This means that they can generate convincing human like output for any possible combination of data they were trained on and this output will usually be as good or better than that of average modern human that has consumed and comprehended tiny amount of data (in comparison). But even the dumbest of humans can be assumed to understand what they know where LLMs are just giant repository of numbers. LLMs have no capacity to understand what these numbers mean or what their output represents, that requires a human like intelligence. "AI" term has been grossly overloaded (just a couple of decades ago, a complex if/then/else statement was touted as "AI") and as we gain better and better understanding of information science, we assign that term to more sophisticated information systems we build but these systems are still far below of any threshold we would accept as sign of intelligence from another human being. Better term for these system would be Complicated Parrots rather than Artificial Intelligence. Intelligence requires understanding and understanding, I believe, requires sentience.
- gorlilla 3y ago
  I find that most people have an incredibly hard time understanding the scale of an LLM's lexicon. I think that scale is always a struggle because it directly challenges our ability to comprehend beyond our own personal/tangible reference frame.