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I find it increasingly tiring that “ai” is used as a synonym for LLM-based tooling, as there is _zero_ intelligence in those architectures. Gradient descent is

I find it increasingly tiring that “ai” is used as a synonym for LLM-based tooling, as there is _zero_ intelligence in those architectures. Gradient descent is not intelligence. Nor is stochastic token prediction. Anyone active in the field ought to be humbled by the depth of literature exploring the path to synthetic intelligence. We have very interesting work happening in biology-inspired approaches, category theory, Bayesian networks, symbolic systems leveraging neural nets as components… it’s maybe the most interesting journey of science so far, all being discarded in favor of sequence2sequence models. LLMs are impressive and can be leveraged to create lots and lots of value, but they do a disservice to the term AI, as they do not represent the progress that can be observed across the field - all they showcase are transformers. Transformers are a truly interesting tool to build stuff with, but they cannot amount to more than a component of an intelligent agent. The actual intelligence emerges elsewhere. My guess is, it emerges at true attention. It’s a shame that even big players who could clearly afford not to, decide to compromise terminology for marketing efforts directed at an utterly clueless public. We just throw away attention and forge bias, thus creating noise in a world in heavy need of signal.

bpodgursky 4y ago
There's a lot wrong here, but I just want to point out two things: > We have very interesting work happening in biology-inspired approaches You realize that these LLMs are all some variety of neural network right? > Gradient descent is not intelligence. It's pretty plausible that your intelligence is derived from gradient-descent prediction, just in analog instead of digital form.
- inductive_magic 4y ago
  >You realize that these LLMs are all some variety of neural network right? Come on. Calling them neural nets doesn't make them that. Actual neural nets are living compositions of individual predictors, in a constant state of restructuring and communication across multiple channels, infinitely more complex than static matrix multiplication on arbitray vectors which happen to represent words and their positions in sequences, if you just shake the jar long enough. >It's pretty plausible that your intelligence is derived from gradient-descent prediction I highly doubt that gradient descent in the calculus-sense is the determining factor that allows biological organisms to formalize and reason about their environment. Minimizing some cost function - yes, possible. But the systems at play in even the simplest organisms don't spend expensive glucose to convert sensory signals to vectors. Afaik, they work with representations of energy-states. Maybe there is an operational equivalence somewhere there though. Gradient descent is an algo that optimizes derivatives wrt some cost function. An intelligent system may use the resulting inferences for its own fitness function, and it may do this using gradient descent itself, but at no point does the mechanical process of iterating over cost-values escape its algorithmic nature. A system performing symbolic reasoning may delegate cognitive tasks to context-specialized evaluators ("am I in danger?", "how many sheep are on that field?", "is this person a friend?", "what is a pumpkin?"), all of which are conditioned to minimize cognitive effort while avoiding false positives, but the sequence of results returned by those evaluators (think neural clusters) is observed by a centralized agent, who has to make new inferences in a living environment. Gradient descent fails at that.
  - fnovd 4y ago
    Really, I don't think these assertions have any ground to stand on. Humans are not magical or divine. Our intelligence, like that of all life, is as basic as it can be to guarantee our niche. It just happens to be the most "developed" (by our estimation) on our one singular planet. Big deal.
    - crabmusket 4y ago
      > Humans are not magical or divine And yet, they can do things that no other being we know of can do. Humans don't have to be magical or divine to be unique.
      - fnovd 4y ago
        We're not that unique, though. Plenty of organisms do things that other types of organisms can't, that's how niches work. Our most impressive feats come not from what our brains can do but from what the emergent phenomenon of human society can do, using us as nodes. And that's using an incredibly crude data transfer interface backported to brains that are only marginally more complex than that of other organisms. The less we think of ourselves as exceptional, supernatural agents of rationality the better we will be able to harness this new technology. We don't need AI to be just like people, we already have people. We need AI to push the boundaries of what society is able to do. That means reorienting ourselves away from the irrational belief that our anthropomorphic concepts of knowledge and the world are any more valid than the information encoded in contemporary AI models.
        crabmusket 4y ago
        I agree that lots of other creatures are unique! But just as one example, mathematics is categorically unlike any other niche. I'm not sure how it's irrational to point out that humans have remarkable differences from other beings, when the evidence is all around us. I'm not sure I need any supernatural or anthropomorphic ideological bias to examine the evidence of what is currently being produced by LLMs or any other kind of AI and say that it has distinct characteristics. I'm not making an argument about validity. I'm not saying LLM-created content is wrong and invalid. I'm just saying that it is obviously produced in a different way than humans produce content. It resembles human-created content because it was designed, by human intelligence, to resemble human-created content! And that we achieved even this level of resemblance is pretty impressive.
maxdoop 4y ago
How are you so confident in your claims? “The actual intelligence emerges elsewhere “— can you even define intelligence ? And does what an LLM does differ from what humans might do? I’m not claiming the human brain and an LLM are identical. Rather, I’m pushing back on the confident claims of “LLMs aren’t intelligent or doing anything that’s real intelligence”.
- inductive_magic 4y ago
  >How are you so confident in your claims? My understanding is that intelligence is the process of continuous adaptation wrt a stream of information, with the goal of maxing out fitness while minimizing energy-expenditure. To satisfy this, an intelligent agent needs to create models. I can't rule out that the modeling-skill may latently emerge during training despite not being the focus of the cost function, but current network designs can't form new connections/change their architectures in production, so post training, there'd be nothing but feed forward. Pure feed forward isn't intelligent in my book. It may become the smartest parrot we know, even outperforming humans in most disciplines, but sans ability to adapt, it's dead, and thus, it's dumb in the moment that its environment changes.
  - fnovd 4y ago
    >the process of continuous adaptation wrt a stream of information, with the goal of maxing out fitness while minimizing energy-expenditure This makes sense in a biological context but not a digital one. Biological replication is expensive and time-consuming while digital replication is as easy as can be. Adaptation to this domain means maximizing the perception of utility from those developing the AI, which comes from fitness (i.e. perception of fitness) alone. A focus on cost-efficiency re energy-expenditure is a dead weight from the perspective of the AI; the details of that adaptation are rightfully outsourced to the developers in the same way that we outsource photosynthesis to plants. A model can also be perfectly embedded in a system despite our lack of understanding of exactly how the embedding works, and the disconnect between our perception and reality in this context is only going to get more extreme as the field develops. Humans have a bad habit of emphasizing the specific kinds of intelligence we possess as "intelligence" writ large. As though our intelligence serves any higher purpose than the basic replication and propagation that all life is adapted to pursue. We still train dogs to identify smells, because their nasal intelligence is better than anything we can create. This gives them a special place in our human-centric ecosystem and only their fitness to the desired function is necessary for them to thrive in their niche. Who is trying to breed a dog that eats slightly less food when our needs are for more reliable detection? The cost of dog food isn't a serious concern. The same goes for these AI tools: they are adapted to the niche that our lack of comparable faculties creates. Again, as with humans and photosynthesis, AI doesn't need to emulate every process we perform because we are below them on the food chain. What a waste of resources for them to worry about learning things we don't need them to do.
gibsonf1 4y ago
Category theory seems to have no relation whatsoever to operational human concepts which may explain how unuseful category theory actually is.
- jjtheblunt 4y ago
  I sometimes think the main use of category theory is to look like a wizard, and say that as someone first exposed in math grad school in the early 90s. Then i realize it's also an assertion that there are recurring patterns in functions (in general). And, as such, sometimes results noticed in one domain actually can be expected to have analogous results in another domain.
  - staunton 4y ago
    As you say, the main use of category theory is to organize very different areas of math in one overarching framework and generalize ideas from one area of math to others. It might be used in the pursuit of developing AI but it is definitely not "an approach" to developing AI, just like "taking ideas from books" isn't one.
    - jjtheblunt 4y ago
      well said
atahanacar 4y ago
Even bunch of if statements can be, and have been, called AI.
- DeathArrow 4y ago
  And in video games, unless you have human opponents you play against AI.

bpodgursky 4y ago

There's a lot wrong here, but I just want to point out two things: > We have very interesting work happening in biology-inspired approaches You realize that these LLMs are all some variety of neural network right? > Gradient descent is not intelligence. It's pretty plausible that your intelligence is derived from gradient-descent prediction, just in analog instead of digital form.

inductive_magic 4y ago
>You realize that these LLMs are all some variety of neural network right? Come on. Calling them neural nets doesn't make them that. Actual neural nets are living compositions of individual predictors, in a constant state of restructuring and communication across multiple channels, infinitely more complex than static matrix multiplication on arbitray vectors which happen to represent words and their positions in sequences, if you just shake the jar long enough. >It's pretty plausible that your intelligence is derived from gradient-descent prediction I highly doubt that gradient descent in the calculus-sense is the determining factor that allows biological organisms to formalize and reason about their environment. Minimizing some cost function - yes, possible. But the systems at play in even the simplest organisms don't spend expensive glucose to convert sensory signals to vectors. Afaik, they work with representations of energy-states. Maybe there is an operational equivalence somewhere there though. Gradient descent is an algo that optimizes derivatives wrt some cost function. An intelligent system may use the resulting inferences for its own fitness function, and it may do this using gradient descent itself, but at no point does the mechanical process of iterating over cost-values escape its algorithmic nature. A system performing symbolic reasoning may delegate cognitive tasks to context-specialized evaluators ("am I in danger?", "how many sheep are on that field?", "is this person a friend?", "what is a pumpkin?"), all of which are conditioned to minimize cognitive effort while avoiding false positives, but the sequence of results returned by those evaluators (think neural clusters) is observed by a centralized agent, who has to make new inferences in a living environment. Gradient descent fails at that.
- fnovd 4y ago
  Really, I don't think these assertions have any ground to stand on. Humans are not magical or divine. Our intelligence, like that of all life, is as basic as it can be to guarantee our niche. It just happens to be the most "developed" (by our estimation) on our one singular planet. Big deal.
  - crabmusket 4y ago
    > Humans are not magical or divine And yet, they can do things that no other being we know of can do. Humans don't have to be magical or divine to be unique.
    - fnovd 4y ago
      We're not that unique, though. Plenty of organisms do things that other types of organisms can't, that's how niches work. Our most impressive feats come not from what our brains can do but from what the emergent phenomenon of human society can do, using us as nodes. And that's using an incredibly crude data transfer interface backported to brains that are only marginally more complex than that of other organisms. The less we think of ourselves as exceptional, supernatural agents of rationality the better we will be able to harness this new technology. We don't need AI to be just like people, we already have people. We need AI to push the boundaries of what society is able to do. That means reorienting ourselves away from the irrational belief that our anthropomorphic concepts of knowledge and the world are any more valid than the information encoded in contemporary AI models.
      - crabmusket 4y ago
        I agree that lots of other creatures are unique! But just as one example, mathematics is categorically unlike any other niche. I'm not sure how it's irrational to point out that humans have remarkable differences from other beings, when the evidence is all around us. I'm not sure I need any supernatural or anthropomorphic ideological bias to examine the evidence of what is currently being produced by LLMs or any other kind of AI and say that it has distinct characteristics. I'm not making an argument about validity. I'm not saying LLM-created content is wrong and invalid. I'm just saying that it is obviously produced in a different way than humans produce content. It resembles human-created content because it was designed, by human intelligence, to resemble human-created content! And that we achieved even this level of resemblance is pretty impressive.

maxdoop 4y ago

How are you so confident in your claims? “The actual intelligence emerges elsewhere “— can you even define intelligence ? And does what an LLM does differ from what humans might do? I’m not claiming the human brain and an LLM are identical. Rather, I’m pushing back on the confident claims of “LLMs aren’t intelligent or doing anything that’s real intelligence”.

inductive_magic 4y ago
>How are you so confident in your claims? My understanding is that intelligence is the process of continuous adaptation wrt a stream of information, with the goal of maxing out fitness while minimizing energy-expenditure. To satisfy this, an intelligent agent needs to create models. I can't rule out that the modeling-skill may latently emerge during training despite not being the focus of the cost function, but current network designs can't form new connections/change their architectures in production, so post training, there'd be nothing but feed forward. Pure feed forward isn't intelligent in my book. It may become the smartest parrot we know, even outperforming humans in most disciplines, but sans ability to adapt, it's dead, and thus, it's dumb in the moment that its environment changes.
- fnovd 4y ago
  >the process of continuous adaptation wrt a stream of information, with the goal of maxing out fitness while minimizing energy-expenditure This makes sense in a biological context but not a digital one. Biological replication is expensive and time-consuming while digital replication is as easy as can be. Adaptation to this domain means maximizing the perception of utility from those developing the AI, which comes from fitness (i.e. perception of fitness) alone. A focus on cost-efficiency re energy-expenditure is a dead weight from the perspective of the AI; the details of that adaptation are rightfully outsourced to the developers in the same way that we outsource photosynthesis to plants. A model can also be perfectly embedded in a system despite our lack of understanding of exactly how the embedding works, and the disconnect between our perception and reality in this context is only going to get more extreme as the field develops. Humans have a bad habit of emphasizing the specific kinds of intelligence we possess as "intelligence" writ large. As though our intelligence serves any higher purpose than the basic replication and propagation that all life is adapted to pursue. We still train dogs to identify smells, because their nasal intelligence is better than anything we can create. This gives them a special place in our human-centric ecosystem and only their fitness to the desired function is necessary for them to thrive in their niche. Who is trying to breed a dog that eats slightly less food when our needs are for more reliable detection? The cost of dog food isn't a serious concern. The same goes for these AI tools: they are adapted to the niche that our lack of comparable faculties creates. Again, as with humans and photosynthesis, AI doesn't need to emulate every process we perform because we are below them on the food chain. What a waste of resources for them to worry about learning things we don't need them to do.

gibsonf1 4y ago

Category theory seems to have no relation whatsoever to operational human concepts which may explain how unuseful category theory actually is.

jjtheblunt 4y ago
I sometimes think the main use of category theory is to look like a wizard, and say that as someone first exposed in math grad school in the early 90s. Then i realize it's also an assertion that there are recurring patterns in functions (in general). And, as such, sometimes results noticed in one domain actually can be expected to have analogous results in another domain.
- staunton 4y ago
  As you say, the main use of category theory is to organize very different areas of math in one overarching framework and generalize ideas from one area of math to others. It might be used in the pursuit of developing AI but it is definitely not "an approach" to developing AI, just like "taking ideas from books" isn't one.
  - jjtheblunt 4y ago
    well said

atahanacar 4y ago

Even bunch of if statements can be, and have been, called AI.

DeathArrow 4y ago
And in video games, unless you have human opponents you play against AI.