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>> In my view, instead of trying to bring back the zombie of symbolic programming for AI, the authors’ time would be better spent on contributing novel, highly

>> In my view, instead of trying to bring back the zombie of symbolic programming for AI, the authors’ time would be better spent on contributing novel, highly complex neural architectures. It's misinformed to characterise Symbolic AI as a "zombie". Research in symbolic AI has continued even since the days of the last AI winter. It does not enjoy of the extravagant publicity given to statistical machine learning research but that doesn't really say anything. Symbolic systems are well-known to have capabilities missing from neural networks, particularly reasoning and knowledge representation. There are many AI fields on which symbolic systems are still dominant, for example search, planning, SAT solving, automated theorem proving, etc. And of course, one of the most successful machine learning approaches of all times, decision tree learning, is a symbolic machine learning approach that learns propositional logic theories, rather than statistical models. Finally, and lest we forget: one of the most publicised successes of neural networks reserach, the AlphaGo family of systems, is a neuro-symbolic system that combines a symbolic adversarial search algorithm, Monte-Carlo Tree Search, with a neural network-learned evaluation function.

9wzYQbTYsAIc 5y ago
> Symbolic systems are well-known to have capabilities missing from neural networks, particularly reasoning and knowledge representation. I believe that the point is still being missed that: if you have a complex enough ANN architecture, you can obtain reasoning and knowledge representation. > It's misinformed to characterise Symbolic AI as a "zombie". I disagree. I (currently) agree with Searle’s position that any AI, symbolic or otherwise, would be a p-zombie if it didn’t originate from a biological brain. > the AlphaGo family of systems, is a neuro-symbolic system that combines a symbolic adversarial search algorithm, Monte-Carlo Tree Search, with a neural network-learned evaluation function. Please keep in mind that evaluation functions are symbolically programmed, themselves. The difference between Symbolic AI and Statistical AI is that the programmer uses symbols to describe symbols with GOFAI but uses symbols to describe stimuli with Statistical AI. GOFAI symbols are just ANN engrams. That is my point, here. Anything symbolic could be represented in an ANN. Intelligence is due to Parallel Distributed Processing, using semiotic symbolism (neuro-glial programming) in the neural architecture.
- YeGoblynQueenne 5y ago
  Well, the original artificial neuron, the Pitts and McCullock neuron, was a propositional logic circuit, so clearly neural networks do not need to be "statistical", as in function approximators trained with gradient descent. I don't understand what you mean by "GOFAI systems are just ANN engrams", though. Which GOFAI systems, of what kind? All of them? "GOFAI" or, perhaps more clearly, symbolic AI, is a broad church. I don't understand "ANN engrams" anyway. >> I believe that the point is still being missed that: if you have a complex enough ANN architecture, you can obtain reasoning and representation. Who knows. This is only a conjecture, currently. I really didn't read your original comment as talking about philosophical zombies. Is that what you really meant, or is your new comment a play on words? Please excuse my confusion.
  - 9wzYQbTYsAIc 5y ago
    > I really didn't read your original comment as talking about philosophical zombies. Is that what you really meant, or is your new comment a play on words? Please excuse my confusion. No worries - p-zombie is definitely what I meant by “zombie”. > I don't understand what you mean by "GOFAI systems are just ANN engrams" > I don't understand "ANN engrams" anyway. So, in psychology, there was and is a tradition of understanding where in the brain any given thing exists. For example, what specific neurons or groups of neurons are responsible for individual memories, thoughts, concepts, etc. They call that location the “engram”. https://en.wikipedia.org/wiki/Engram_%28neuropsychology%29 https://en.wikipedia.org/wiki/Engram_%28neuropsychology%29 What I’m trying to say is that with a complex enough neural architecture, one would be able to conduct the same engram investigations on an ANN. > so clearly neural networks do not need to be "statistical" Eh, I’m not an expert (just a bachelor), but I generally agree. Some (all?) artificial neurons may need to be statistical functions over a sample space. However, some (many?) artificial glia may not need to be statistical functions over a sample space. An artificial glia’s sample space might simply be the artificial neurons, themselves, rather than the direct stimuli, if an artificial glia were to be programmed using statistical functions. https://en.m.wikipedia.org/wiki/Glia https://en.m.wikipedia.org/wiki/Glia > Who knows. This is only a conjecture, currently. Indeed :/ it’s something that I’ve been thinking about for a while, but I’m a busy software development professional, not a professional academic. I’ve got to run some errands for the day, but I would be stoked to continue this conversation. Perhaps we can build up some sort of testable conjecture.
9wzYQbTYsAIc 5y ago
> Symbolic systems are well-known to have capabilities missing from neural networks, particularly reasoning and knowledge representation. If I were to posit an initial draft operational definition for “artificial glial network”, then I would use exactly what you describe as a “symbolic system”: Artificial Glial Networks (AGN) have capabilities missing from Artificial Neural Networks (ANN), particularly reasoning and knowledge representation. I would also go on about: In addition, AGN have the capability to define the structure of any associated ANN (“ANN Modification”), create new associated ANN (“ANN Generation”), and modify associations between any ANN (“ANN-ANN Associations”) in the AGN’s aggregate of associated ANN’s (“AGN-ANN Associations”). Furthermore, AGN explicitly are constrained such that no AGN can create, modify, or associate with any other AGN (“AGN Isolation”). That’s just some thoughts off the top of my head though. Eventually I’ll get around to sifting through some research and elaborating, refining, etc.

9wzYQbTYsAIc 5y ago

> Symbolic systems are well-known to have capabilities missing from neural networks, particularly reasoning and knowledge representation. I believe that the point is still being missed that: if you have a complex enough ANN architecture, you can obtain reasoning and knowledge representation. > It's misinformed to characterise Symbolic AI as a "zombie". I disagree. I (currently) agree with Searle’s position that any AI, symbolic or otherwise, would be a p-zombie if it didn’t originate from a biological brain. > the AlphaGo family of systems, is a neuro-symbolic system that combines a symbolic adversarial search algorithm, Monte-Carlo Tree Search, with a neural network-learned evaluation function. Please keep in mind that evaluation functions are symbolically programmed, themselves. The difference between Symbolic AI and Statistical AI is that the programmer uses symbols to describe symbols with GOFAI but uses symbols to describe stimuli with Statistical AI. GOFAI symbols are just ANN engrams. That is my point, here. Anything symbolic could be represented in an ANN. Intelligence is due to Parallel Distributed Processing, using semiotic symbolism (neuro-glial programming) in the neural architecture.

YeGoblynQueenne 5y ago
Well, the original artificial neuron, the Pitts and McCullock neuron, was a propositional logic circuit, so clearly neural networks do not need to be "statistical", as in function approximators trained with gradient descent. I don't understand what you mean by "GOFAI systems are just ANN engrams", though. Which GOFAI systems, of what kind? All of them? "GOFAI" or, perhaps more clearly, symbolic AI, is a broad church. I don't understand "ANN engrams" anyway. >> I believe that the point is still being missed that: if you have a complex enough ANN architecture, you can obtain reasoning and representation. Who knows. This is only a conjecture, currently. I really didn't read your original comment as talking about philosophical zombies. Is that what you really meant, or is your new comment a play on words? Please excuse my confusion.
- 9wzYQbTYsAIc 5y ago
  > I really didn't read your original comment as talking about philosophical zombies. Is that what you really meant, or is your new comment a play on words? Please excuse my confusion. No worries - p-zombie is definitely what I meant by “zombie”. > I don't understand what you mean by "GOFAI systems are just ANN engrams" > I don't understand "ANN engrams" anyway. So, in psychology, there was and is a tradition of understanding where in the brain any given thing exists. For example, what specific neurons or groups of neurons are responsible for individual memories, thoughts, concepts, etc. They call that location the “engram”. https://en.wikipedia.org/wiki/Engram_%28neuropsychology%29 https://en.wikipedia.org/wiki/Engram_%28neuropsychology%29 What I’m trying to say is that with a complex enough neural architecture, one would be able to conduct the same engram investigations on an ANN. > so clearly neural networks do not need to be "statistical" Eh, I’m not an expert (just a bachelor), but I generally agree. Some (all?) artificial neurons may need to be statistical functions over a sample space. However, some (many?) artificial glia may not need to be statistical functions over a sample space. An artificial glia’s sample space might simply be the artificial neurons, themselves, rather than the direct stimuli, if an artificial glia were to be programmed using statistical functions. https://en.m.wikipedia.org/wiki/Glia https://en.m.wikipedia.org/wiki/Glia > Who knows. This is only a conjecture, currently. Indeed :/ it’s something that I’ve been thinking about for a while, but I’m a busy software development professional, not a professional academic. I’ve got to run some errands for the day, but I would be stoked to continue this conversation. Perhaps we can build up some sort of testable conjecture.

9wzYQbTYsAIc 5y ago

> Symbolic systems are well-known to have capabilities missing from neural networks, particularly reasoning and knowledge representation. If I were to posit an initial draft operational definition for “artificial glial network”, then I would use exactly what you describe as a “symbolic system”: Artificial Glial Networks (AGN) have capabilities missing from Artificial Neural Networks (ANN), particularly reasoning and knowledge representation. I would also go on about: In addition, AGN have the capability to define the structure of any associated ANN (“ANN Modification”), create new associated ANN (“ANN Generation”), and modify associations between any ANN (“ANN-ANN Associations”) in the AGN’s aggregate of associated ANN’s (“AGN-ANN Associations”). Furthermore, AGN explicitly are constrained such that no AGN can create, modify, or associate with any other AGN (“AGN Isolation”). That’s just some thoughts off the top of my head though. Eventually I’ll get around to sifting through some research and elaborating, refining, etc.