No. AI is about making machines that are intelligent. Period. It's not about solving problems. You can solve problems with simple mechanical contrivances, such as the 60s-era matchbox tictactoe machine. The point of AI is to create machines that achieve cognitition. Otherwise machines are just an executor of algorithms.
The definition of cognition does not require language. It encompasses language. A fish has cognition, but no language. You simply don't want to accept that intelligence has never been achieved by AI, and we are no closer to building an AI today than we ever were. All that science has discovered so far is how NOT to make an AI.
It is said that as soon as a problem is solved, it's no longer AI.
That's a misstatement of the so-called "AI Effect", coined by Pamela McCorduck: "It's part of the history of the field of artificial intelligence that every time somebody figured out how to make a computer do something—play good checkers, solve simple but relatively informal problems—there was chorus of critics to say, 'that's not thinking'."
The thing is, it's not an "effect", like the "CSI Effect", which refers to the overestimation of science. It's a fact, as in no machine has ever achieved thinking at any level. As any AI researcher will admit when pressed.
As for defining cognition, I'm surprised you can't look up word defintions yourself, but I'm happy to do it for you. From The Psychological Dictionary: "Cognition is a term referring to the mental processes involved in gaining knowledge and comprehension. These processes include thinking, knowing, remembering, judging, and problem-solving. These are higher-level functions of the brain and encompass language, imagination, perception, and planning."
Which no machine has ever done. Machines compute, record, compare, collect data, and choose among a set of computable solutions, but only when programmed to do so by mankind. They do not think, know, remember, judge, or problem-solve.
On the heels of Google's "AI" that the WSJ claims got "testy" comes this claim of a "machine learning" system that can identify suicidal tendencies. Once again, BOGUS! The claim that machines learn anything is bogus to begin with, as to date no machine has ever done anything other than record information, as in so-called maze-learning programs. Learning is a cognitive process, and until we ourselves know how it works in humans (which we don't), we can never program a machine to learn anything.
But the real proof that this is bogus is that in order to "learn" to identify suicidal thoughts, even we humans would have to be given evidence that a given text actually came from someone as a result of their suicidal thinking. Which nobody can do, as this would require clairevoiance. Even the best psychiatric researchers can't know what someone was thinking when they composed a particular text.
AI is being dramatically overstated once more. What AI researcher has the guts to call them out on this?
I think you need to go back and revisit the history of speech recognition. It's not done using AI. It's done using brute force statistics. Chomsky's ideal of a universal grammar turned out to be a dead end -- language is too complex to get semantic meaning from grammar alone. In fact, AI still cannot consistently extract semantic meaning from everyday English sentences.
The breakthrough with speech recognition was to abandon AI and instead gather a huge corpus of actual sentences. These are tabulated, and with parallel processing can be quickly scanned to determine the likelihood of any particular word following another, regardless of grammatical relationship. Transcription is the process of mapping phonemes to words and querying the language corpus to find statistically likely matches. You don’t have to solve the Chomskyan problem of how language and meaning are structured. You just brute force it mathematically.
But you can't brute force what mosquito's do, because those task require true cognition. Learning is just one of the cognitive processes mosquitoes perform, and I earlier enumerated many more. It's true that a computer can perform some of these tasks computationally, one at a time, but it can't integrate the solutions simultaneously to achieve a goal. That requires cognition, and AI has never achieved cognition at any level.
Let's put it this way: a mosquito brain provides all the neurological equipment to enable the bug to sense air currents, process visual data and perform aerobatic flight maneuvers to evade attempted execution via swatting, execute sophisticated navigation algorithms, smell and classify pheromones for preferred host animals, identify infrared signatures of near-surface blood vessels, deploy a penetrating blood extraction needle to the appropriate depth, acquire a meal without bursting, then fly away after accommodating its new weight and balance condition to a safe place. Oh, and Mosquitos clearly learn about their environment and can locate familiar places. Later it will perform all the intricacies of mating and laying eggs in water, a totally different environment.
Nobody knows how the 100K-neuron mosquito brain does that. We have further developed no AI that can perform even mosquito-grade cognitive functions. My whole point is that the AI community gives the public the impression that we are much further along in achievement than even the mosquito brain, yet today's AI can't remotely do what a mosquito brain does. .
So I am confident in my statement that we have no idea whatsoever how a human brain works.
Your functional mapping does not describe how anything works, only the physical locations where various events occur coincident with some stimulus. It is certainly a predicate to developing theories of operation for later research into brain processing. But my original assertion stands: we have no idea how it works.
As for NMR vs MRI, the difference is in test targe. In current use, NMR typically describes the physical resonance phenomenon itself, or when referring to the measurements of the nuclear induction signal in physics or chemical laboratories. The prefix "nuclear" is dropped when referring to imaging or spectroscopic techniques for humans or animals. Since we're talking about both computers and biological subjects, both terms apply.
I mention thermal imaging only as an example. There are many clever imaging techniques, including the electrical induction technique you mention. But imaging does not tell us how the brain works. We don't yet know how any single cognitive operation of the bran operates.
Your description of imaging is true for functional mapping. However it is not true for reverse engineering. You're missing fine-grained data over time. In both neurons and computers state changes happen millions of times per second. For example, in a neural field of a million neurons, many firings occur multiple times at un-synchronized intervals The best thermal imaging can capture only a handful of state changes per second. Thus it is missing the vast majority of fine-grained activity over time, which is essential to reverse engineering the processes occurring (as opposed to merely mapping function).
Other biological imaging techniques, such as NMR and MRI, are geared to plotting fine detail, not short time intervals. In your computer thermal example, there is no faster non-intrusive imaging technique, e.g., no NMR, etc. The next step is intrusive logic tracing, which requires direct connection to individual chips in the processor, or individual gates in a chip. We have no equivalent for those techniques in biology.
Thus extending biology's imaging processes to a computer is invalid.
If you're going to redefine learning, then you can make anything learn. So that avenue is pointless. "My pencil just learned how to delete data it printed!"
Ironically, the AI community in essence did this very thing when it divided AI as "weak" and "strong" variants. They did this as the result of decades of failed milestones and underestimation of the problem. But the key word is "intelligence". Before weak AI, that word referred to versatile cognition and self-awareness at least in the order of mammals. Now "intelligence" has been diluted to refer even to simple state machines. In the meantime, software that could qualify as strong AI is nowhere in sight.
Why put on the restriction "in such a way that it could hypothetically be performed by a computer"? That's a tautology, just as if I were to say "Define flying in such a way that it could be hypothetically performed by a pig."
In any event, the origin of this thread is the assertion that a computer is operating "the same way the human brain works", so you can't exclude the human brain as a standard of reference.
The thing is, a rat can do a great many more things than run a maze. But the neural network just runs the maze, and it doesn't do it with the flexibility and multi-ability that the rat does it with. AI has to be much more versatile than a one trick pony, and we don't even have one trick ponies. An implicit assumption of neural networks is that increased complexity will somehow magically produce increased capability: more neurons equals more skills. But there is zero evidence for this optimism.
I challenge you to name one thing a computer has learned. Computers can store information, and they can process it using weighted decision-making. But they have never, ever "learned" anything. Researchers are anthropomorphizing when they say a program "learns". Computers never learned to play chess; they were programmed to do that. Programming is not teaching, and a computer running a program has not been "taught". Some programs can alter themselves in specific, pre-programmed ways, but that is not learning.
Google acquired DeepMind Technologies last year and announced that they have devised a "Neural Turing Machine" that learns. But the NTM contains no neurons, so the name is highly misleading. According to Google, they chose this name because they were "inspired" by neurons. Not surprisingly, Google had to admit that they took similar license with the use of the verb "learn." What they really meant is that the NTM's programming mimicked the results of prior neural network simulations (which also do not learn), only faster.
If this level of misdirection were used in any other branch of science, it would be called academic fraud.
Youngatheart,
You just said "...and understands us..." That is the crux of the matter. We don't even know how _we_ accomplish understanding, let alone how to create software that does.
Well said. Your description of yourself as a dualist, with regard to intelligence, is congruent with Maxwell's theory of the dual forces of electro-magnetism. Maxwell predicted radio waves, but it wasn't until Marconi created a new apparatus that detected them that science accepted the theory as proven. In just this way a modern cognitive researcher could predict an external source of order and information essential to intelligence but not detectable with today's technology. Some future scientist might well invent the apparatus to detect this information source. That it has many of the same properties philosophers attribute to the soul would not be surprising. Nor would it be unscientific.
It might even turn out that this suspected source for intelligence is the same information source for biological morphology. That would be in keeping with the essential attribute of science to seek the minimum set of processes to explain observed phenomenon. A Unified Life Theory, as it were.
A true scientist would not rule out an external force that could be termed a soul if it could be tested and measured. It would not be supernatural in that case, but part of nature. The term supernatural is a man-made descriptor for any phenomenon outside our current knowledge. Until Marconi discovered radio waves, the idea of transmitting information at a distance was considered supernatural. In reality, that misconception was just ignorance.
Genomics, like cognition, is another discipline that may have to admit to an information repository other than the one we found in DNA. Because the encoding for a vast amount of biological information -- such as the structure of organs, systems, and process sequences -- does not appear to exist in the genome. Call it epigenetics. Call it a bio field. It's still the antithesis of the self-contained genome. Neurophysiology should at least be as open to external information sources as genomics is.
You're taking the expression "no idea" too literally, and that's not really an argument. If I say "I have no idea how to drive a car," I obviously don't mean that I literally don't have a single idea, it means that I cannot functionally perform that task.
Regarding your other point, in this discussion, human brains are exactly what is at issue. The WSJ said the paper they cited illustrates how Google scientists are "teaching computers to mimic some of the ways a human brain works."
My point in this area would be: does our knowledge allow us to generate desired outcomes in novel subjects with any level of certainty?
For instance: we know with great certainty that you can stimulate the optic nerve and cause the subject to "see things" (and also: not see things that are really there).
On the other hand, with respect to cognition, can we do anything that simulates (reconstructs) a biological cognition system?
Can we learn a maze the way a rat does? I think so. Neural nets with reward and punishment inputs can perform approximately the same.
Similar outcomes prove nothing. Neural nets do not "learn" a maze the way a rat does, and in fact there is no evidence that learning, in the sense of brain cognition, occurs in neural nets at all. What they do is record a maze using a matrix of differential equations modeling how we think neurons work. Science has not demonstrated that those models are correct, and getting the same results as rats doesn't prove they are correct. We can also record a maze with a digital shift register and some input gates, but that doesn't mean that's how rats learn a maze. Moreover, if you put a cat in the maze, rats can adapt. Neural nets do not, because the goal for a neural net be must be encoded in advance.
With our understanding of even these simple cognitive tasks essentially at ground zero, we have no right to claim AI has made any progress at all toward true cognition. Everything done to date could be a dead end.
Improv,
You are the one asserting that we know how the brain works. Knowing "what some parts do" is not the same as knowing how the brain works, i.e. how it performs cognitive tasks.
As the asserter, you need to provide the proof, not I. Name calling is the refuge of the debater who has no actual argument. I'm still open to an example of one cognitive function science can explain. Absent that, at a minimum we have no idea at all how far along we are toward AI. Without describing how cognition is done, we can't program an AI to do it.
Alas, no. See what you did there? The same thing the WSJ did. You inflated a tiny bit of research about a portion of the locust's visual system into "reverse engineered". In a nutshell, the paper you cite only posited a theory, based on some observations, for a possible neuronal substrate influencing excitation and inhibition in the visual field. The researchers then incorporated a mathematica model of that substrate into the control structure of a small mobile robot, which subsequently avoided collisions with objects. That's not cognition. Or a reverse-engineered visual system.
My original post complained about the WSJ hyping Google's research (read the title). I read both Journal pieces and Google's published paper. I suspect Google is as much to blame for not correcting the Journal's misconceptions. But my overriding concern is that this AI inflation seems to be happening with more frequency, and the hype is getting exponentially more hyperbolic.
I use the term "cargo cult" because it's accurate. I'm reasonably well read in neurobiology and biochemistry, and participated in a fair amount of early neural network implementation. But the burden isn't on me to "know what I'm talking about". The burden is on anyone, including as you, claiming science knows anything about how the brain works. You're making the assertion, so you must provide the proof. I'm happy to consider any examples you have of how the cognitive function of your choice operates.
Fyngyrz,
by "some ideas" you mean "some theory". And in your case it's a theory with no research and no testable hypothesis. When I say "no idea" I mean literally we have no demonstrable understanding of any one single cognitive function of the brain. Any brain. We don't know how a gnat processes tactile information from its antennae, how a fly integrates spacial information while flying, or even how a planaria stores its memory of a maze. Human brains? We've got nothing.
Kola and Wishaw's text discusses the brain from two organizational perspectives, anatomical and behavioral. The authors never undertake to explain how the brain functions to produce the behaviors they describe. We thus know some of what the brain does, but nothing about how it does it. And the authors admit as much. Nobody knows how memories are stored, how vision is processed, how decisions are made. Science doesn't even know for sure that these functions occur inside the brain at all. There is, after all, the soul to contend with. That concept is no more outside the realm of science than were radio waves before Marconi discovered them.
Slashdot Mirror
No. AI is about making machines that are intelligent. Period. It's not about solving problems. You can solve problems with simple mechanical contrivances, such as the 60s-era matchbox tictactoe machine. The point of AI is to create machines that achieve cognitition. Otherwise machines are just an executor of algorithms.
The definition of cognition does not require language. It encompasses language. A fish has cognition, but no language. You simply don't want to accept that intelligence has never been achieved by AI, and we are no closer to building an AI today than we ever were. All that science has discovered so far is how NOT to make an AI.
It is said that as soon as a problem is solved, it's no longer AI.
That's a misstatement of the so-called "AI Effect", coined by Pamela McCorduck: "It's part of the history of the field of artificial intelligence that every time somebody figured out how to make a computer do something—play good checkers, solve simple but relatively informal problems—there was chorus of critics to say, 'that's not thinking'."
The thing is, it's not an "effect", like the "CSI Effect", which refers to the overestimation of science. It's a fact, as in no machine has ever achieved thinking at any level. As any AI researcher will admit when pressed.
As for defining cognition, I'm surprised you can't look up word defintions yourself, but I'm happy to do it for you. From The Psychological Dictionary: "Cognition is a term referring to the mental processes involved in gaining knowledge and comprehension. These processes include thinking, knowing, remembering, judging, and problem-solving. These are higher-level functions of the brain and encompass language, imagination, perception, and planning."
Which no machine has ever done. Machines compute, record, compare, collect data, and choose among a set of computable solutions, but only when programmed to do so by mankind. They do not think, know, remember, judge, or problem-solve.
On the heels of Google's "AI" that the WSJ claims got "testy" comes this claim of a "machine learning" system that can identify suicidal tendencies. Once again, BOGUS! The claim that machines learn anything is bogus to begin with, as to date no machine has ever done anything other than record information, as in so-called maze-learning programs. Learning is a cognitive process, and until we ourselves know how it works in humans (which we don't), we can never program a machine to learn anything.
But the real proof that this is bogus is that in order to "learn" to identify suicidal thoughts, even we humans would have to be given evidence that a given text actually came from someone as a result of their suicidal thinking. Which nobody can do, as this would require clairevoiance. Even the best psychiatric researchers can't know what someone was thinking when they composed a particular text.
AI is being dramatically overstated once more. What AI researcher has the guts to call them out on this?
I think you need to go back and revisit the history of speech recognition. It's not done using AI. It's done using brute force statistics. Chomsky's ideal of a universal grammar turned out to be a dead end -- language is too complex to get semantic meaning from grammar alone. In fact, AI still cannot consistently extract semantic meaning from everyday English sentences.
The breakthrough with speech recognition was to abandon AI and instead gather a huge corpus of actual sentences. These are tabulated, and with parallel processing can be quickly scanned to determine the likelihood of any particular word following another, regardless of grammatical relationship. Transcription is the process of mapping phonemes to words and querying the language corpus to find statistically likely matches. You don’t have to solve the Chomskyan problem of how language and meaning are structured. You just brute force it mathematically.
But you can't brute force what mosquito's do, because those task require true cognition. Learning is just one of the cognitive processes mosquitoes perform, and I earlier enumerated many more. It's true that a computer can perform some of these tasks computationally, one at a time, but it can't integrate the solutions simultaneously to achieve a goal. That requires cognition, and AI has never achieved cognition at any level.
Let's put it this way: a mosquito brain provides all the neurological equipment to enable the bug to sense air currents, process visual data and perform aerobatic flight maneuvers to evade attempted execution via swatting, execute sophisticated navigation algorithms, smell and classify pheromones for preferred host animals, identify infrared signatures of near-surface blood vessels, deploy a penetrating blood extraction needle to the appropriate depth, acquire a meal without bursting, then fly away after accommodating its new weight and balance condition to a safe place. Oh, and Mosquitos clearly learn about their environment and can locate familiar places. Later it will perform all the intricacies of mating and laying eggs in water, a totally different environment.
Nobody knows how the 100K-neuron mosquito brain does that. We have further developed no AI that can perform even mosquito-grade cognitive functions. My whole point is that the AI community gives the public the impression that we are much further along in achievement than even the mosquito brain, yet today's AI can't remotely do what a mosquito brain does. .
So I am confident in my statement that we have no idea whatsoever how a human brain works.
Improv,
Your functional mapping does not describe how anything works, only the physical locations where various events occur coincident with some stimulus. It is certainly a predicate to developing theories of operation for later research into brain processing. But my original assertion stands: we have no idea how it works.
As for NMR vs MRI, the difference is in test targe. In current use, NMR typically describes the physical resonance phenomenon itself, or when referring to the measurements of the nuclear induction signal in physics or chemical laboratories. The prefix "nuclear" is dropped when referring to imaging or spectroscopic techniques for humans or animals. Since we're talking about both computers and biological subjects, both terms apply.
I mention thermal imaging only as an example. There are many clever imaging techniques, including the electrical induction technique you mention. But imaging does not tell us how the brain works. We don't yet know how any single cognitive operation of the bran operates.
Improv,
Your description of imaging is true for functional mapping. However it is not true for reverse engineering. You're missing fine-grained data over time. In both neurons and computers state changes happen millions of times per second. For example, in a neural field of a million neurons, many firings occur multiple times at un-synchronized intervals The best thermal imaging can capture only a handful of state changes per second. Thus it is missing the vast majority of fine-grained activity over time, which is essential to reverse engineering the processes occurring (as opposed to merely mapping function).
Other biological imaging techniques, such as NMR and MRI, are geared to plotting fine detail, not short time intervals. In your computer thermal example, there is no faster non-intrusive imaging technique, e.g., no NMR, etc. The next step is intrusive logic tracing, which requires direct connection to individual chips in the processor, or individual gates in a chip. We have no equivalent for those techniques in biology.
Thus extending biology's imaging processes to a computer is invalid.
If you're going to redefine learning, then you can make anything learn. So that avenue is pointless. "My pencil just learned how to delete data it printed!"
Ironically, the AI community in essence did this very thing when it divided AI as "weak" and "strong" variants. They did this as the result of decades of failed milestones and underestimation of the problem. But the key word is "intelligence". Before weak AI, that word referred to versatile cognition and self-awareness at least in the order of mammals. Now "intelligence" has been diluted to refer even to simple state machines. In the meantime, software that could qualify as strong AI is nowhere in sight.
serviscope,
Why put on the restriction "in such a way that it could hypothetically be performed by a computer"? That's a tautology, just as if I were to say "Define flying in such a way that it could be hypothetically performed by a pig."
In any event, the origin of this thread is the assertion that a computer is operating "the same way the human brain works", so you can't exclude the human brain as a standard of reference.
If you saw dinosaur footprints appearing out of thin air, you might be prompted to look for invisible dinosaurs.
The thing is, a rat can do a great many more things than run a maze. But the neural network just runs the maze, and it doesn't do it with the flexibility and multi-ability that the rat does it with. AI has to be much more versatile than a one trick pony, and we don't even have one trick ponies. An implicit assumption of neural networks is that increased complexity will somehow magically produce increased capability: more neurons equals more skills. But there is zero evidence for this optimism.
Tmosley,
I challenge you to name one thing a computer has learned. Computers can store information, and they can process it using weighted decision-making. But they have never, ever "learned" anything. Researchers are anthropomorphizing when they say a program "learns". Computers never learned to play chess; they were programmed to do that. Programming is not teaching, and a computer running a program has not been "taught". Some programs can alter themselves in specific, pre-programmed ways, but that is not learning.
Google acquired DeepMind Technologies last year and announced that they have devised a "Neural Turing Machine" that learns. But the NTM contains no neurons, so the name is highly misleading. According to Google, they chose this name because they were "inspired" by neurons. Not surprisingly, Google had to admit that they took similar license with the use of the verb "learn." What they really meant is that the NTM's programming mimicked the results of prior neural network simulations (which also do not learn), only faster.
If this level of misdirection were used in any other branch of science, it would be called academic fraud.
Youngatheart, You just said "...and understands us..." That is the crux of the matter. We don't even know how _we_ accomplish understanding, let alone how to create software that does.
Gweihir,
Well said. Your description of yourself as a dualist, with regard to intelligence, is congruent with Maxwell's theory of the dual forces of electro-magnetism. Maxwell predicted radio waves, but it wasn't until Marconi created a new apparatus that detected them that science accepted the theory as proven. In just this way a modern cognitive researcher could predict an external source of order and information essential to intelligence but not detectable with today's technology. Some future scientist might well invent the apparatus to detect this information source. That it has many of the same properties philosophers attribute to the soul would not be surprising. Nor would it be unscientific.
It might even turn out that this suspected source for intelligence is the same information source for biological morphology. That would be in keeping with the essential attribute of science to seek the minimum set of processes to explain observed phenomenon. A Unified Life Theory, as it were.
A true scientist would not rule out an external force that could be termed a soul if it could be tested and measured. It would not be supernatural in that case, but part of nature. The term supernatural is a man-made descriptor for any phenomenon outside our current knowledge. Until Marconi discovered radio waves, the idea of transmitting information at a distance was considered supernatural. In reality, that misconception was just ignorance.
Genomics, like cognition, is another discipline that may have to admit to an information repository other than the one we found in DNA. Because the encoding for a vast amount of biological information -- such as the structure of organs, systems, and process sequences -- does not appear to exist in the genome. Call it epigenetics. Call it a bio field. It's still the antithesis of the self-contained genome. Neurophysiology should at least be as open to external information sources as genomics is.
Anonycow,
On the contrary, cargo cults are a well documented phenomenon, in particular the cargo cults of World War II:
https://en.m.wikipedia.org/wik...
Fyngyrz,
You're taking the expression "no idea" too literally, and that's not really an argument. If I say "I have no idea how to drive a car," I obviously don't mean that I literally don't have a single idea, it means that I cannot functionally perform that task.
Regarding your other point, in this discussion, human brains are exactly what is at issue. The WSJ said the paper they cited illustrates how Google scientists are "teaching computers to mimic some of the ways a human brain works."
My point in this area would be: does our knowledge allow us to generate desired outcomes in novel subjects with any level of certainty?
For instance: we know with great certainty that you can stimulate the optic nerve and cause the subject to "see things" (and also: not see things that are really there).
On the other hand, with respect to cognition, can we do anything that simulates (reconstructs) a biological cognition system?
Can we learn a maze the way a rat does? I think so. Neural nets with reward and punishment inputs can perform approximately the same.
Similar outcomes prove nothing. Neural nets do not "learn" a maze the way a rat does, and in fact there is no evidence that learning, in the sense of brain cognition, occurs in neural nets at all. What they do is record a maze using a matrix of differential equations modeling how we think neurons work. Science has not demonstrated that those models are correct, and getting the same results as rats doesn't prove they are correct. We can also record a maze with a digital shift register and some input gates, but that doesn't mean that's how rats learn a maze. Moreover, if you put a cat in the maze, rats can adapt. Neural nets do not, because the goal for a neural net be must be encoded in advance.
With our understanding of even these simple cognitive tasks essentially at ground zero, we have no right to claim AI has made any progress at all toward true cognition. Everything done to date could be a dead end.
Improv, You are the one asserting that we know how the brain works. Knowing "what some parts do" is not the same as knowing how the brain works, i.e. how it performs cognitive tasks.
As the asserter, you need to provide the proof, not I. Name calling is the refuge of the debater who has no actual argument. I'm still open to an example of one cognitive function science can explain. Absent that, at a minimum we have no idea at all how far along we are toward AI. Without describing how cognition is done, we can't program an AI to do it.
Alas, no. See what you did there? The same thing the WSJ did. You inflated a tiny bit of research about a portion of the locust's visual system into "reverse engineered". In a nutshell, the paper you cite only posited a theory, based on some observations, for a possible neuronal substrate influencing excitation and inhibition in the visual field. The researchers then incorporated a mathematica model of that substrate into the control structure of a small mobile robot, which subsequently avoided collisions with objects. That's not cognition. Or a reverse-engineered visual system.
That's a motion sensor.
I'm sorry Dave. I'm afraid I can't do that. :)
My original post complained about the WSJ hyping Google's research (read the title). I read both Journal pieces and Google's published paper. I suspect Google is as much to blame for not correcting the Journal's misconceptions. But my overriding concern is that this AI inflation seems to be happening with more frequency, and the hype is getting exponentially more hyperbolic.
I use the term "cargo cult" because it's accurate. I'm reasonably well read in neurobiology and biochemistry, and participated in a fair amount of early neural network implementation. But the burden isn't on me to "know what I'm talking about". The burden is on anyone, including as you, claiming science knows anything about how the brain works. You're making the assertion, so you must provide the proof. I'm happy to consider any examples you have of how the cognitive function of your choice operates.
Fyngyrz, by "some ideas" you mean "some theory". And in your case it's a theory with no research and no testable hypothesis. When I say "no idea" I mean literally we have no demonstrable understanding of any one single cognitive function of the brain. Any brain. We don't know how a gnat processes tactile information from its antennae, how a fly integrates spacial information while flying, or even how a planaria stores its memory of a maze. Human brains? We've got nothing.
Improv,
Kola and Wishaw's text discusses the brain from two organizational perspectives, anatomical and behavioral. The authors never undertake to explain how the brain functions to produce the behaviors they describe. We thus know some of what the brain does, but nothing about how it does it. And the authors admit as much. Nobody knows how memories are stored, how vision is processed, how decisions are made. Science doesn't even know for sure that these functions occur inside the brain at all. There is, after all, the soul to contend with. That concept is no more outside the realm of science than were radio waves before Marconi discovered them.