4 ms·

Adopting this perspective would improve the quality of efforts around this technology. Instead of thinking of it as somehow creating an "intelligence", seeing i

by lsy 2y ago

Adopting this perspective would improve the quality of efforts around this technology. Instead of thinking of it as somehow creating an "intelligence", seeing it as a complex lens on the training data that is controlled by the prompt helps you understand that the output isn't generated by the model, but by people. And various existing pieces of human effort are brought into focus and collimated by nudging the lens in different directions with a "prompt". The user then gives those pieces meaning and determines whether the result is useful or not. This makes certain things more clear: notions of "truth" are not in play beyond statistical happenstance, certain efforts to make outputs uniform are more trouble than they're worth, and valuable use cases are strongly correlated with the ability and convenience of the user to confirm the usefulness of the result.

kridsdale1 2y ago
I see the models as oracular seeing-stones like a wizard might use. Ponder the orb! Probe its secrets! Holographically, all our text is encoded in there. If you know how to query.
- eMPee584 2y ago
  oh so wonderous times ahead may they converge towards peace and prosperity for the whole galaxy
  - 01HNNWZ0MV43FF 2y ago
    Verily winds converge with force
xpe 2y ago
Using various metaphors carefully and fluidly is key. No one is sufficient. Not this one, nor any other. I say: go back to basics. One good foundational point is dispelling confusion and conflation around "intelligence". So many people have woefully narrow and unexamined notions of "intelligence". It wouldn't be unfair to say many people have broken definitions. Broken because they just aren't good enough to make meaningful progress in a modern world where many kinds of agents display many different kinds of intelligence. Such broken definitions are often too specific; too arbitrary; too rooted in binary thinking. Many of our current language patterns are liabilities. Not to mention corporate and organizational cultures where hazy definitions slide around and few people will admit that they don't really know what others mean by the term. Sometimes it feels like a big charade where no one wants to hurt anyone's feelings nor appear uninformed. And so it goes, some kind of elaborate mystical ritual where the confused participants lead each other further into madness. With this in mind, I find tremendous value in Stuart Russell's definition of intelligence: the ability of an agent to solve some task. An agent is anything that makes a decision: a human, an animal, a system of any kind. This definition intentionally leaves out any notion of (a) humans; (b) consciousness; (c) some arbitrary quality line. This usage cuts through so much bullsh*t. I highly recommend finding a way to shift conversations towards it wherever possible. This isn't easy in my experience. We have so much baggage and crufty thinking, even we're able to put aside our baser instincts. One might say that Russell's definition just "kicks the can down the road". I don't think so. It encourages people to define their metrics a bit more clearly -- hopefully out loud or on paper -- for a particular context. It is one step closer to clarifying things. One step in the right direction -- to stop pretending like we all know each other means -- and instead actually pose an answerable question. Now, what about "general" intelligence you say? Well, one step at a time. Wait until a group of people have demonstrated some ability to find some kind of consensus on particular tasks. It is hard work to socialize these ideas. Defining general intelligence in meaningful ways is really hard and contentious. It often becomes a lightning rod for all number of other disagreements. As one example, look at the shitstorm around various sociological attempts to measure the general aspects of intelligence in humans. Without attempting to summarize it in any detail, there has been a huge dumpster fire involving: poor statistical understanding, shoddy research, tone-deaf communication, willful misinterpretation, accusations of racism, and so on. There are pockets of truth in there, but even trying finding the core nuggets of useful truth something makes everything radioactive, depending on the context. A typical person in modern culture is usually unable to calmly make sense of these issues, and who can blame them? Statistical understanding doesn't grow on trees. The same goes for understanding machine learning theory.
- dredmorbius 2y ago
  Submitter here. I came across the Gopnik piece after hearing her discuss it on a recent episode of the Complexity podcast from the Santa Fe Institute (SFI). The series begins here: <https://www.santafe.edu/culture/podcasts/ep-1-what-is-intelligence https://www.santafe.edu/culture/podcasts/ep-1-what-is-intell...>. As I recall that episode doesn't directly tackle what intelligence is, though numerous others from the Complexity back catalogue do, as does an episode from another podcast in the New Books Network (NBN). Two specific approaches stand out. In the NBN episode, a discussion of the Turing Test makes specific and detailed note of how that test side-steps the question of what intelligence is entirely by focusing on what it does, and specifically whether an artificial agent can convince a human interlocutor that it is intelligent, through text-based interactions. I find this particular approach (focusing on outputs and appearances rather than inner states and motivations) generally useful, and not only for artificial behaviours. To a great extent, for example, I find what a person, organisation, or institution does far more accessible and generally useful than why it does that. This isn't to say that ends (results/actions) are more significant than means (causes/motivations/intent), but they are accessible and determinable with far less ambiguity or presumption. Knowing causes or motivations is useful for its predictive value, but given even a small sampling of behaviours and instances, it's generally possible to posit or infer these to a useful degree without deep introspection. Another approach, taken in multiple Complexity episodes as well as writings and discussions elsewhere, former SFI president David Krakauer posits that intelligence is search, and specifically search through a pattern space for a solution or approach to some given problem. (See especially "Ingenious: David Krakauer", Nautilus 16 April 2015 <https://nautil.us/ingenious-david-krakauer-235383/ https://nautil.us/ingenious-david-krakauer-235383/>.) I've put some thinking into an ontology of technological mechanisms, where one of those is information, consisting generally of input (sensing, parsing), storage/retrieval, output, and logic. Intelligence falls under logic, and I'd argue involves comparisons on current and prior experience (e.g., sensing and storage/retrieval), as well as applying rules, algorithms, inferences, and the like (all forms of logic, broadly). "Intelligence" then is a form of logic where logic is generally processing (as opposed to input/output/storage) of information. At what stage a human-like or general intelligence emerges is of course somewhat nebulous. To quote a long-standing US National Parks Service observation, there's a considerable overlap between the smartest bears, and stupidest humans, when it comes to storing and/or raiding food and garbage. In the AI field, we've seen specific problems, applications, domains, or however you'd choose to call them fall into the class of those in which artificial search (or artificial intelligence, though "search" may be more accurate in the sense of "search through problem space to a useful solution) routinely bests humans, including checkers (trivial), chess (challenging), go (even more so), and now creative endeavours such as image, music, and text generation. (A professional classical musician friend recently told me directly that at least some of the AI compositions they're encountering are not only good but show what can only be described as strong musical content and coherence as compared to the classical tradition. I'd think that the standards for popular music with its general simplicity would be far less challenging, their assessment in this case strikes me as notable.) I'll also note I'm not especially enthusiastic about AI's potential. The field has seen many periods of apparent rapid progress followed by very long, often decades-long, "winters". Recent progress, say, 2023 onward, has been spectacular, but also seems to be somewhat stalling out and showing profound limits. That isn't to say that new approaches might come up with greater capabilities, cheaper approaches, or both. China's DeepSeek, and the story of human intelligence including the shrinking of the braincase over recent evolution despite greater apparent intelligence suggests that efficiency gains may well be the path forward, perhaps utilising something akin to Chomsky's "universal grammar" or grammar hierarchy, or notions of parsing and grouping patterns within the human brain, whether through genetic inheritance, direct experience, or education, might be ways of drastically reducing size and analysis requirements of training corpora. I think it's Krakauer again (this time in a Complexity episode) who notes that total training set humans require to acquire basic linguistic skills by, say, age 5, is roughly 5 MB of data. This is phenomenally less than current LLM AI models require, and strongly suggests far greater possible efficiencies. Another factor, discussed in the recent Complexity series, is that humans of course not only from reading texts, but from observing and interacting with our environment. That's something AI presently does relatively little of, as I understand it, though certain domains (e.g., autonomous vehicles) may be applying this method. I'm not following progress on this at all presently, though I suspect I should.
K0balt 2y ago
I think this is relevant and adjacent at least: https://open.substack.com/pub/ctsmyth/p/the-generative-ai-revolution-is-just?r=5b7kus&utm_medium=ios https://open.substack.com/pub/ctsmyth/p/the-generative-ai-re...

by lsy 2y ago

kridsdale1 2y ago

I see the models as oracular seeing-stones like a wizard might use. Ponder the orb! Probe its secrets! Holographically, all our text is encoded in there. If you know how to query.

eMPee584 2y ago
oh so wonderous times ahead may they converge towards peace and prosperity for the whole galaxy
- 01HNNWZ0MV43FF 2y ago
  Verily winds converge with force

xpe 2y ago

Using various metaphors carefully and fluidly is key. No one is sufficient. Not this one, nor any other. I say: go back to basics. One good foundational point is dispelling confusion and conflation around "intelligence". So many people have woefully narrow and unexamined notions of "intelligence". It wouldn't be unfair to say many people have broken definitions. Broken because they just aren't good enough to make meaningful progress in a modern world where many kinds of agents display many different kinds of intelligence. Such broken definitions are often too specific; too arbitrary; too rooted in binary thinking. Many of our current language patterns are liabilities. Not to mention corporate and organizational cultures where hazy definitions slide around and few people will admit that they don't really know what others mean by the term. Sometimes it feels like a big charade where no one wants to hurt anyone's feelings nor appear uninformed. And so it goes, some kind of elaborate mystical ritual where the confused participants lead each other further into madness. With this in mind, I find tremendous value in Stuart Russell's definition of intelligence: the ability of an agent to solve some task. An agent is anything that makes a decision: a human, an animal, a system of any kind. This definition intentionally leaves out any notion of (a) humans; (b) consciousness; (c) some arbitrary quality line. This usage cuts through so much bullsh*t. I highly recommend finding a way to shift conversations towards it wherever possible. This isn't easy in my experience. We have so much baggage and crufty thinking, even we're able to put aside our baser instincts. One might say that Russell's definition just "kicks the can down the road". I don't think so. It encourages people to define their metrics a bit more clearly -- hopefully out loud or on paper -- for a particular context. It is one step closer to clarifying things. One step in the right direction -- to stop pretending like we all know each other means -- and instead actually pose an answerable question. Now, what about "general" intelligence you say? Well, one step at a time. Wait until a group of people have demonstrated some ability to find some kind of consensus on particular tasks. It is hard work to socialize these ideas. Defining general intelligence in meaningful ways is really hard and contentious. It often becomes a lightning rod for all number of other disagreements. As one example, look at the shitstorm around various sociological attempts to measure the general aspects of intelligence in humans. Without attempting to summarize it in any detail, there has been a huge dumpster fire involving: poor statistical understanding, shoddy research, tone-deaf communication, willful misinterpretation, accusations of racism, and so on. There are pockets of truth in there, but even trying finding the core nuggets of useful truth something makes everything radioactive, depending on the context. A typical person in modern culture is usually unable to calmly make sense of these issues, and who can blame them? Statistical understanding doesn't grow on trees. The same goes for understanding machine learning theory.

dredmorbius 2y ago
Submitter here. I came across the Gopnik piece after hearing her discuss it on a recent episode of the Complexity podcast from the Santa Fe Institute (SFI). The series begins here: <https://www.santafe.edu/culture/podcasts/ep-1-what-is-intelligence https://www.santafe.edu/culture/podcasts/ep-1-what-is-intell...>. As I recall that episode doesn't directly tackle what intelligence is, though numerous others from the Complexity back catalogue do, as does an episode from another podcast in the New Books Network (NBN). Two specific approaches stand out. In the NBN episode, a discussion of the Turing Test makes specific and detailed note of how that test side-steps the question of what intelligence is entirely by focusing on what it does, and specifically whether an artificial agent can convince a human interlocutor that it is intelligent, through text-based interactions. I find this particular approach (focusing on outputs and appearances rather than inner states and motivations) generally useful, and not only for artificial behaviours. To a great extent, for example, I find what a person, organisation, or institution does far more accessible and generally useful than why it does that. This isn't to say that ends (results/actions) are more significant than means (causes/motivations/intent), but they are accessible and determinable with far less ambiguity or presumption. Knowing causes or motivations is useful for its predictive value, but given even a small sampling of behaviours and instances, it's generally possible to posit or infer these to a useful degree without deep introspection. Another approach, taken in multiple Complexity episodes as well as writings and discussions elsewhere, former SFI president David Krakauer posits that intelligence is search, and specifically search through a pattern space for a solution or approach to some given problem. (See especially "Ingenious: David Krakauer", Nautilus 16 April 2015 <https://nautil.us/ingenious-david-krakauer-235383/ https://nautil.us/ingenious-david-krakauer-235383/>.) I've put some thinking into an ontology of technological mechanisms, where one of those is information, consisting generally of input (sensing, parsing), storage/retrieval, output, and logic. Intelligence falls under logic, and I'd argue involves comparisons on current and prior experience (e.g., sensing and storage/retrieval), as well as applying rules, algorithms, inferences, and the like (all forms of logic, broadly). "Intelligence" then is a form of logic where logic is generally processing (as opposed to input/output/storage) of information. At what stage a human-like or general intelligence emerges is of course somewhat nebulous. To quote a long-standing US National Parks Service observation, there's a considerable overlap between the smartest bears, and stupidest humans, when it comes to storing and/or raiding food and garbage. In the AI field, we've seen specific problems, applications, domains, or however you'd choose to call them fall into the class of those in which artificial search (or artificial intelligence, though "search" may be more accurate in the sense of "search through problem space to a useful solution) routinely bests humans, including checkers (trivial), chess (challenging), go (even more so), and now creative endeavours such as image, music, and text generation. (A professional classical musician friend recently told me directly that at least some of the AI compositions they're encountering are not only good but show what can only be described as strong musical content and coherence as compared to the classical tradition. I'd think that the standards for popular music with its general simplicity would be far less challenging, their assessment in this case strikes me as notable.) I'll also note I'm not especially enthusiastic about AI's potential. The field has seen many periods of apparent rapid progress followed by very long, often decades-long, "winters". Recent progress, say, 2023 onward, has been spectacular, but also seems to be somewhat stalling out and showing profound limits. That isn't to say that new approaches might come up with greater capabilities, cheaper approaches, or both. China's DeepSeek, and the story of human intelligence including the shrinking of the braincase over recent evolution despite greater apparent intelligence suggests that efficiency gains may well be the path forward, perhaps utilising something akin to Chomsky's "universal grammar" or grammar hierarchy, or notions of parsing and grouping patterns within the human brain, whether through genetic inheritance, direct experience, or education, might be ways of drastically reducing size and analysis requirements of training corpora. I think it's Krakauer again (this time in a Complexity episode) who notes that total training set humans require to acquire basic linguistic skills by, say, age 5, is roughly 5 MB of data. This is phenomenally less than current LLM AI models require, and strongly suggests far greater possible efficiencies. Another factor, discussed in the recent Complexity series, is that humans of course not only from reading texts, but from observing and interacting with our environment. That's something AI presently does relatively little of, as I understand it, though certain domains (e.g., autonomous vehicles) may be applying this method. I'm not following progress on this at all presently, though I suspect I should.

K0balt 2y ago

I think this is relevant and adjacent at least: https://open.substack.com/pub/ctsmyth/p/the-generative-ai-revolution-is-just?r=5b7kus&utm_medium=ios https://open.substack.com/pub/ctsmyth/p/the-generative-ai-re...