5 ms·

- loaded premise (that AI community thinks causal reasoning, whatever form that may take, is the silver bullet) - Failure to distinguish b/w narrow and human-l

- loaded premise (that AI community thinks causal reasoning, whatever form that may take, is the silver bullet) - Failure to distinguish b/w narrow and human-level AI - Zero mention of attention/transformer models - Zero mention of BERT, GPT, let alone GPT-3 Note-to-self: ignore and file away in the gary-marcus box.

mellosouls 6y ago
Ignoring the lack-of-any-progress-towards-AGI elephant in the room is presumably the main reason the AI hype train happily speeds on its pointless route throwing out it's cute tricks and tools with no actual destination in sight. Best not to ignore.
- SpicyLemonZest 6y ago
  I would definitely argue there's been substantial progress towards AGI. We've been seeing increasingly impressive AI results with increasingly more general architectures. Which criteria do you have for progress towards AGI that aren't being met?
  - dumbfoundded 6y ago
    I don't entirely disagree that we're progressing towards AGI. For me, the most disappointing part of machine learning is complete lack of systems that can extrapolate.
  - AnimalMuppet 6y ago
    The problem is that AI doesn't actually understand anything. We don't know what understanding is, but we know that an AI doesn't. But because we don't know what understanding is, we don't know how to make an AI do it. We're seeing AIs that can kind of hide their lack of any kind of understanding by means of more and more sophisticated behavior. But unless understanding = "not understanding, but in a really sophisticated way", that road isn't going to get us to AGI, ever. Personally, my bet is that understanding != "sophisticated non-understanding". I can't prove it, of course, because I don't know what understanding is either... [Edit: I suppose the other alternative is that understanding is real, but AGI doesn't actually require understanding. That seems improbable to me, but it is at least theoretically possible.]
    - bryanrasmussen 6y ago
      perhaps understanding = sophisticated non-understanding + (being unaware that one does not understand || having a belief that one understands)
    - HenryKissinger 6y ago
      I know what understanding is...
    - visarga 6y ago
      We have all been surprised a few times in the last years by AI advancements, not even experts could predict AlphaGo or GPT. Why are you so sure you won't be surprised again, this time by human level understanding? The missing piece for real understanding is embodiment - we can act on our environment while GPT-3 like models can only see a fixed training set. This means an AI could construct its own hypothesis and test it if it were embodied, but can't do that otherwise. Understanding comes from playing 'the same game' with us: sharing the same environment and having aligned rewards. Imagine if a scientist were kept locked up all her life and only had access to a video feed showing the world. Would she gain anything by coming out of the cave and actually seeing and interacting with the world?
      - deleted 6y ago
        [deleted]
    - SpicyLemonZest 6y ago
      My concern is, if we don't know what understanding is, how do we know it's not becoming a god of the gaps? Playing Go and writing poetry were canonical examples of things AI couldn't do without true understanding, right up until we wrote AIs that could do them.
      - wahern 6y ago
        Go was never thought of in that way. Search Google books for "Go chess artificial intelligence computer" and use the 20th century time filter. It was only believed that Go was impervious to the brute-force search strategies employed by the most successful chess programs. The alternative was never "true understanding", but rather believed to be the application of knowledge engineering, expert systems, etc, none of which could be equivocated with "true understanding". Regarding poetry, the counter argument is that a modern neural network can't generate poetry any more than a Xerox machine can generate poetry. IOW, it can only replicate styles, not invent new ones. Though, that's often good enough as far as the vast majority of readers are concerned.
variaga 6y ago
Yeah, was not impressed. Skipping over the (opinionated) survey of recent AI techniques (and their failure modes) and philosophic theories of cognition that is the bulk of the paper, the only bit that claims to be novel (pp 29-31) is an argument I will summarize as follows: 1) any computational AI can be represented as a finite state machine or FSM (author calls it a finite state automata. Same thing.) 2) when said computational AI performs an "act of cognition" it (as a FSM) will iterate through a defined series of states, based on a defined series of inputs 3) It is possible to build a simpler FSM composed of a counter and a lookup table that would take the same series of inputs + the counter as an input, and produce the same state/output as the original computational AI 4) since the response to stimulus is identical, the 2 finite state machines are equivalent. 5) if the state machines are equivalent, they must be equivalently conscious 6) the counter+look-up table is obviously not conscious ("reducto ad absurdum") 7) from (6) and (5) no computational AI can be conscious To me, this argument fails in the following manner; the only way to actually construct the "simpler" finite state machine in step (3) above is to actually let the computational AI react with the world first and record its combinations of input and state. There is no way to predict what series of states an arbitrary FSM will go through in response to a particular series of inputs without actually running it. That would be equivalent to solving the halting problem. Any program can be encoded as an FSM. If you could predict the state sequence of such an FSM, you could tell whether the FSM would enter the 'halt' state. IMO this is analogous to arguing that: 1) the animatronic band at Chuck E. Cheese could be programmed to play identical music to that which has been (previously) performed by a human band (and recorded in perfect detail). 2) because they produce identical outputs the 2 bands are equivalent 3) if they are equivalent, they must equally be said to create original music 4) the animatronic band obviously doesn't create original music 5) from (3) and (4) no band can create original music He also elides any discussion of whether or not actual human intelligence manages to avoid the failure modes he uses to conclude that neural networks are not intelligent - e.g. he mentions adversarial examples fooling visual classifier networks without mentioning that "optical illusions" exist and people will reliably misperceive certain images in certain ways too. I actually agree that neural nets as they currently exist are aggressively stupid, but the author concludes way too much. TL;DR, author starts from a premise that there is something uniquely special about human consciousness that machines can't duplicate, and reaches the conclusion that there is something uniquely special about human consciousness that machines can't duplicate.

mellosouls 6y ago

Ignoring the lack-of-any-progress-towards-AGI elephant in the room is presumably the main reason the AI hype train happily speeds on its pointless route throwing out it's cute tricks and tools with no actual destination in sight. Best not to ignore.

SpicyLemonZest 6y ago
I would definitely argue there's been substantial progress towards AGI. We've been seeing increasingly impressive AI results with increasingly more general architectures. Which criteria do you have for progress towards AGI that aren't being met?
- dumbfoundded 6y ago
  I don't entirely disagree that we're progressing towards AGI. For me, the most disappointing part of machine learning is complete lack of systems that can extrapolate.
- AnimalMuppet 6y ago
  The problem is that AI doesn't actually understand anything. We don't know what understanding is, but we know that an AI doesn't. But because we don't know what understanding is, we don't know how to make an AI do it. We're seeing AIs that can kind of hide their lack of any kind of understanding by means of more and more sophisticated behavior. But unless understanding = "not understanding, but in a really sophisticated way", that road isn't going to get us to AGI, ever. Personally, my bet is that understanding != "sophisticated non-understanding". I can't prove it, of course, because I don't know what understanding is either... [Edit: I suppose the other alternative is that understanding is real, but AGI doesn't actually require understanding. That seems improbable to me, but it is at least theoretically possible.]
  - bryanrasmussen 6y ago
    perhaps understanding = sophisticated non-understanding + (being unaware that one does not understand || having a belief that one understands)
  - HenryKissinger 6y ago
    I know what understanding is...
  - visarga 6y ago
    We have all been surprised a few times in the last years by AI advancements, not even experts could predict AlphaGo or GPT. Why are you so sure you won't be surprised again, this time by human level understanding? The missing piece for real understanding is embodiment - we can act on our environment while GPT-3 like models can only see a fixed training set. This means an AI could construct its own hypothesis and test it if it were embodied, but can't do that otherwise. Understanding comes from playing 'the same game' with us: sharing the same environment and having aligned rewards. Imagine if a scientist were kept locked up all her life and only had access to a video feed showing the world. Would she gain anything by coming out of the cave and actually seeing and interacting with the world?
    - deleted 6y ago
      [deleted]
  - SpicyLemonZest 6y ago
    My concern is, if we don't know what understanding is, how do we know it's not becoming a god of the gaps? Playing Go and writing poetry were canonical examples of things AI couldn't do without true understanding, right up until we wrote AIs that could do them.
    - wahern 6y ago
      Go was never thought of in that way. Search Google books for "Go chess artificial intelligence computer" and use the 20th century time filter. It was only believed that Go was impervious to the brute-force search strategies employed by the most successful chess programs. The alternative was never "true understanding", but rather believed to be the application of knowledge engineering, expert systems, etc, none of which could be equivocated with "true understanding". Regarding poetry, the counter argument is that a modern neural network can't generate poetry any more than a Xerox machine can generate poetry. IOW, it can only replicate styles, not invent new ones. Though, that's often good enough as far as the vast majority of readers are concerned.

variaga 6y ago

Yeah, was not impressed. Skipping over the (opinionated) survey of recent AI techniques (and their failure modes) and philosophic theories of cognition that is the bulk of the paper, the only bit that claims to be novel (pp 29-31) is an argument I will summarize as follows: 1) any computational AI can be represented as a finite state machine or FSM (author calls it a finite state automata. Same thing.) 2) when said computational AI performs an "act of cognition" it (as a FSM) will iterate through a defined series of states, based on a defined series of inputs 3) It is possible to build a simpler FSM composed of a counter and a lookup table that would take the same series of inputs + the counter as an input, and produce the same state/output as the original computational AI 4) since the response to stimulus is identical, the 2 finite state machines are equivalent. 5) if the state machines are equivalent, they must be equivalently conscious 6) the counter+look-up table is obviously not conscious ("reducto ad absurdum") 7) from (6) and (5) no computational AI can be conscious To me, this argument fails in the following manner; the only way to actually construct the "simpler" finite state machine in step (3) above is to actually let the computational AI react with the world first and record its combinations of input and state. There is no way to predict what series of states an arbitrary FSM will go through in response to a particular series of inputs without actually running it. That would be equivalent to solving the halting problem. Any program can be encoded as an FSM. If you could predict the state sequence of such an FSM, you could tell whether the FSM would enter the 'halt' state. IMO this is analogous to arguing that: 1) the animatronic band at Chuck E. Cheese could be programmed to play identical music to that which has been (previously) performed by a human band (and recorded in perfect detail). 2) because they produce identical outputs the 2 bands are equivalent 3) if they are equivalent, they must equally be said to create original music 4) the animatronic band obviously doesn't create original music 5) from (3) and (4) no band can create original music He also elides any discussion of whether or not actual human intelligence manages to avoid the failure modes he uses to conclude that neural networks are not intelligent - e.g. he mentions adversarial examples fooling visual classifier networks without mentioning that "optical illusions" exist and people will reliably misperceive certain images in certain ways too. I actually agree that neural nets as they currently exist are aggressively stupid, but the author concludes way too much. TL;DR, author starts from a premise that there is something uniquely special about human consciousness that machines can't duplicate, and reaches the conclusion that there is something uniquely special about human consciousness that machines can't duplicate.