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Those two branches despise each other with intense passion. I never actually made this connection before, but having spent far too much time listening to both

Those two branches despise each other with intense passion. I never actually made this connection before, but having spent far too much time listening to both camps, this observation does ring true. Another observation I had is that there is an orthogonal axis to the safety/ethics dichotomy that follows a more scholastic approach, namely neuro/symbolism. These two branches correspond, roughly, to the debate between machine learning and mechanized reasoning (a.k.a. good old fashioned AI). Vastly oversimplifying, the neural branch believes reasoning comes after learning and that by choosing the right inductive biases (e.g., maximum entropy, minimum description length), then training everything up using gradient descent, reasoning will emerge. The symbolists place a heavier emphasis on model interpretability and believe learning emerges from a logical substrate, albeit at a much higher level of abstraction. Like safety/ethics "research", neural/symbolic folks are constantly bickering about first principles, but unlike their colleagues in the S/E camp (which is at best philosophy and at worst fanfiction), N/S debates are resolved by actually building things like language models, chess bots, programming languages, type theories and model checkers. Both are valid debates to have, but N/S is more technically grounded while S/E is a bit like LARPing.

deleted 4y ago
[deleted]
xvilka 4y ago
N/S dichotomy is fake, a lot of organizations these days explore hybrid approaches, involving both NN or other probabilistic models, plus symbolic rules of some kind.
xyzzy123 4y ago
I feel the difference is that to a first approximation, ai alignment folks tend to be grey tribe and ai ethics people tend to be blue or rainbow tribe. The schism isn't a technical disagreement about ai, it's a disagreement between subcultures about what values are important.
- Dylan16807 4y ago
  Am I supposed to know these terms?
  - xyzzy123 4y ago
    Sorry, yes that wasn't clear. Scott Alexander coined most of them in "I can tolerate anything except the outgroup" [1] Red & blue are exactly what a U.S reader would think they are. "Grey" is an attempt by Scott to define something else, loosely hacker-ish / rationalist (you can tell who he thinks the cool people are). "Rainbow tribe" I just made up, but I think you can guess what I mean now based on context. Alignment tends to be "rationalist panic" and ethics is a more traditional "moral panic". I believe both perspectives are valuable. [1] https://web.archive.org/web/20200219044501/https://slatestarcodex.com/2014/09/30/i-can-tolerate-anything-except-the-outgroup/ https://web.archive.org/web/20200219044501/https://slatestar...
    - ben_w 4y ago
      > Red & blue are exactly what a U.S reader would think they are. As a non-American, only the fact you wrote "American" makes me think democrat-republican; but for that I would've thought of the similarly named TV tropes entry: https://tvtropes.org/pmwiki/pmwiki.php/Main/BlueAndOrangeMorality https://tvtropes.org/pmwiki/pmwiki.php/Main/BlueAndOrangeMor... > "Rainbow tribe" I just made up, but I think you can guess what I mean now based on context. 50-50 this is either a LGBT+ reference or a reference to all the other color schemes used by various political parties around the world: https://en.wikipedia.org/wiki/Political_colour https://en.wikipedia.org/wiki/Political_colour
    - thrown123098 4y ago
      Odly enough at the wedding of a friend you could tell his friends were from the grey tribe because they all wore monochrome and his wife's friends from the rainbow tribe because they all wore rainbows. There was no mixture between the two groups.
    - chaps 4y ago
      This is still wildly unclear.
Xcelerate 4y ago
> The symbolists place a heavier emphasis on model interpretability and believe learning emerges from a logical substrate, albeit at a much higher level of abstraction. These people still exist? Personally, I don’t think gradient descent is the way to AGI either (I think it’s efficient algorithmic search over the space of all computable programs), but I haven’t heard anything from the symbols crowd since the early 2010s.
- YeGoblynQueenne 4y ago
  >> These people still exist? Of course, I just finished reviewing a few papers from ICLP (the International Conference of Logic Programming) 2023. This year there was a substantial machine learning element, most of it in the form of Inductive Logic Programming (i.e. logic programming for learning; ordinary logic programming is for reasoning). But also a few neuro-symbolic ones. This September I was in the second IJCLR (International Joint Conference of Learning and Reasoning) where I helped run the Neuro-Symbolic part of the conference. Like the first year we had people from IBM, MIT, Stanford, etc etc (to clarify, my work is not in NeuroSymbolic AI, but I was asked to help). Then in January there was the IBM Neuro-Symoblic workshop, again getting together people from academia and industry. Yeah, there's interest in combining symbolic and statistical machine learning. Just two data points about why (well because, why not, but): a) Machine Learning really started out as symbolic machine learning, back in the '90s when people realised Expert Systems need too many rules to write by hand. A textbook to read about that era of Machine Learning is Tom Mitchell's "Machine Learning" (1997). The work the public at large knows as "machine learning" today was at the time mostly being published under the "Pattern Recognition" rubrik. b) To the early pioneers in AI having two camps, of "statistical" and "symbolic" AI, or "connectionist" and "logic-based" AI, just wouldn't make any sense at all. Consider Claude Shannon. Shannon was at the Dartmouth workshop were "Artificial Intelligence" was coined, in 1956. Shannon invented both logic gates (in his Master's thesis... what the fuck did I do in my Master's thesis?) and statistical language processing ("A Mathematical Theory of Communication"; where he also invented Information Theory; btw). Or, take the first artificial neuron: the Pitts and McCulloch neuron, first described in 1943, by er, by Pitts and McCulloch, as luck would have it. The Pitts & McCulloch neuron was a self-programming logic gate, a propositional logic circuit. Or, of course, take Turing. Turing described Turing Machines in the language of the first order predicate calculus, a.k.a. First Order Logic (mainly because he was following from Gödel's work) and also described "the child machine", a computer that would learn, like a child. To be honest, I don't really understand when or why the split happened, between "learning" and "reasoning". Anyone who knows how to fill in the blanks, you're welcome. But it's clear to me that having one without the other is just dumb. If not downright impossible.
fnordpiglet 4y ago
The issues is the safety folks keep making the trolly logic run over the ethics folks, and the ethics folks keep pointing out an ethical safety AI person would prioritize sacrificing themselves.
RickHull 4y ago
Wordcels vs shape rotators
- bmc7505 4y ago
  Not quite, but close. True words are formidable things and some shapes are just whirlygigs. It's more like, are you building or just performing?

deleted 4y ago

[deleted]

xvilka 4y ago

N/S dichotomy is fake, a lot of organizations these days explore hybrid approaches, involving both NN or other probabilistic models, plus symbolic rules of some kind.

xyzzy123 4y ago

I feel the difference is that to a first approximation, ai alignment folks tend to be grey tribe and ai ethics people tend to be blue or rainbow tribe. The schism isn't a technical disagreement about ai, it's a disagreement between subcultures about what values are important.

Dylan16807 4y ago
Am I supposed to know these terms?
- xyzzy123 4y ago
  Sorry, yes that wasn't clear. Scott Alexander coined most of them in "I can tolerate anything except the outgroup" [1] Red & blue are exactly what a U.S reader would think they are. "Grey" is an attempt by Scott to define something else, loosely hacker-ish / rationalist (you can tell who he thinks the cool people are). "Rainbow tribe" I just made up, but I think you can guess what I mean now based on context. Alignment tends to be "rationalist panic" and ethics is a more traditional "moral panic". I believe both perspectives are valuable. [1] https://web.archive.org/web/20200219044501/https://slatestarcodex.com/2014/09/30/i-can-tolerate-anything-except-the-outgroup/ https://web.archive.org/web/20200219044501/https://slatestar...
  - ben_w 4y ago
    > Red & blue are exactly what a U.S reader would think they are. As a non-American, only the fact you wrote "American" makes me think democrat-republican; but for that I would've thought of the similarly named TV tropes entry: https://tvtropes.org/pmwiki/pmwiki.php/Main/BlueAndOrangeMorality https://tvtropes.org/pmwiki/pmwiki.php/Main/BlueAndOrangeMor... > "Rainbow tribe" I just made up, but I think you can guess what I mean now based on context. 50-50 this is either a LGBT+ reference or a reference to all the other color schemes used by various political parties around the world: https://en.wikipedia.org/wiki/Political_colour https://en.wikipedia.org/wiki/Political_colour
  - thrown123098 4y ago
    Odly enough at the wedding of a friend you could tell his friends were from the grey tribe because they all wore monochrome and his wife's friends from the rainbow tribe because they all wore rainbows. There was no mixture between the two groups.
  - chaps 4y ago
    This is still wildly unclear.

Xcelerate 4y ago

> The symbolists place a heavier emphasis on model interpretability and believe learning emerges from a logical substrate, albeit at a much higher level of abstraction. These people still exist? Personally, I don’t think gradient descent is the way to AGI either (I think it’s efficient algorithmic search over the space of all computable programs), but I haven’t heard anything from the symbols crowd since the early 2010s.

YeGoblynQueenne 4y ago
>> These people still exist? Of course, I just finished reviewing a few papers from ICLP (the International Conference of Logic Programming) 2023. This year there was a substantial machine learning element, most of it in the form of Inductive Logic Programming (i.e. logic programming for learning; ordinary logic programming is for reasoning). But also a few neuro-symbolic ones. This September I was in the second IJCLR (International Joint Conference of Learning and Reasoning) where I helped run the Neuro-Symbolic part of the conference. Like the first year we had people from IBM, MIT, Stanford, etc etc (to clarify, my work is not in NeuroSymbolic AI, but I was asked to help). Then in January there was the IBM Neuro-Symoblic workshop, again getting together people from academia and industry. Yeah, there's interest in combining symbolic and statistical machine learning. Just two data points about why (well because, why not, but): a) Machine Learning really started out as symbolic machine learning, back in the '90s when people realised Expert Systems need too many rules to write by hand. A textbook to read about that era of Machine Learning is Tom Mitchell's "Machine Learning" (1997). The work the public at large knows as "machine learning" today was at the time mostly being published under the "Pattern Recognition" rubrik. b) To the early pioneers in AI having two camps, of "statistical" and "symbolic" AI, or "connectionist" and "logic-based" AI, just wouldn't make any sense at all. Consider Claude Shannon. Shannon was at the Dartmouth workshop were "Artificial Intelligence" was coined, in 1956. Shannon invented both logic gates (in his Master's thesis... what the fuck did I do in my Master's thesis?) and statistical language processing ("A Mathematical Theory of Communication"; where he also invented Information Theory; btw). Or, take the first artificial neuron: the Pitts and McCulloch neuron, first described in 1943, by er, by Pitts and McCulloch, as luck would have it. The Pitts & McCulloch neuron was a self-programming logic gate, a propositional logic circuit. Or, of course, take Turing. Turing described Turing Machines in the language of the first order predicate calculus, a.k.a. First Order Logic (mainly because he was following from Gödel's work) and also described "the child machine", a computer that would learn, like a child. To be honest, I don't really understand when or why the split happened, between "learning" and "reasoning". Anyone who knows how to fill in the blanks, you're welcome. But it's clear to me that having one without the other is just dumb. If not downright impossible.

fnordpiglet 4y ago

The issues is the safety folks keep making the trolly logic run over the ethics folks, and the ethics folks keep pointing out an ethical safety AI person would prioritize sacrificing themselves.

RickHull 4y ago

Wordcels vs shape rotators

bmc7505 4y ago
Not quite, but close. True words are formidable things and some shapes are just whirlygigs. It's more like, are you building or just performing?