direct, theory-laden, causal contact and pro-active engagement with their environments... eg., taking the stick out of the water.
"data" (even from an animal's pov) is basically, by nature, ambiguous. Measurement is an event in the world (eg., light hitting the eye) which isn't somehow unambiguously informative. To over come this problem, basically, animals move stuff.
The adaptable motor cortex of the most intelligent animals, therefore, isnt something to be tacked-on to "intelligence", it's the precondition for it.
Glibly: tools before thoughts. Reason needs content to operate on, the content of our thoughts is built via our (sensory-)motor systems.
The idea that we need ever more theoretically powerful models of reasoning here is a misdirection -- it misses that the heart of everything we know arrives via some form of repeated experiment.
Every more automated "pure reasoning" either via statistics or symbolically, is always just a means of juicing the data we provide the system. Useful as a technology, but not as a genuine learning system. It will never have the means of resolving the many ambiguities within data itself.
In the case, for example, of NLP -- the structure of 1 trillion documents will never enable a machine to answer the question "what do you like about what i'm wearing?" -- because (1) the machine isnt here with me; (2) i am asking for its personal judgement, not a summary of a trillion documents; and (3) that summary of those trillion documents has to be unique, but the question has no "right answer".
Whilst computer science has the driving seat over what "intelligence" is, we will forever be stuck with this incredibly diminished view of our own capacities and the size of the technological challenge. The goal isnt to sift through everything we have already done and "take a mean", the goal is to produce a system which could have done "everything we have done" without us.
"data" (even from an animal's pov) is basically, by nature, ambiguous. Measurement is an event in the world (eg., light hitting the eye) which isn't somehow unambiguously informative. To over come this problem, basically, animals move stuff.
The adaptable motor cortex of the most intelligent animals, therefore, isnt something to be tacked-on to "intelligence", it's the precondition for it.
Glibly: tools before thoughts. Reason needs content to operate on, the content of our thoughts is built via our (sensory-)motor systems.
The idea that we need ever more theoretically powerful models of reasoning here is a misdirection -- it misses that the heart of everything we know arrives via some form of repeated experiment.
Every more automated "pure reasoning" either via statistics or symbolically, is always just a means of juicing the data we provide the system. Useful as a technology, but not as a genuine learning system. It will never have the means of resolving the many ambiguities within data itself.
In the case, for example, of NLP -- the structure of 1 trillion documents will never enable a machine to answer the question "what do you like about what i'm wearing?" -- because (1) the machine isnt here with me; (2) i am asking for its personal judgement, not a summary of a trillion documents; and (3) that summary of those trillion documents has to be unique, but the question has no "right answer".
Whilst computer science has the driving seat over what "intelligence" is, we will forever be stuck with this incredibly diminished view of our own capacities and the size of the technological challenge. The goal isnt to sift through everything we have already done and "take a mean", the goal is to produce a system which could have done "everything we have done" without us.