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Video Reverse Search
T-Bit Project
About TAPe
API
Team
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Demo
Ru
The almanac about new method of information processing
What's actually wrong with the concept of AI
Biology and artificial intelligence
Cognitive science: a beginning without an end
Holism and brain studies
Theory of Active Perception
Why perception is necessary for modeling human-like thinking
What's actually wrong with the concept of AI
Evolution of ideas underlying AI: Brief Description
Biology does not understand how the brain works
Why AI does need biology after all
How far artificial neurons are from the real ones
Creating something really similar to how the brain works
Cognitive science: a beginning without an end
Cognitive science has never produced anything practical
Consciousness is not amenable to science
No one knows what consciousness is, everyone keeps talking about it
A sudden idea — the quantum nature of consciousness
Orchestrated objective reduction: what it is and what for
Another theory of consciousness: the integrated information theory
Global workspace theory
Conscious and unconscious thinking. Questions to an academic
Questions for Theories of Consciousness
Ultimate ways to study consciousness without cutting into the brain
Albert Einstein suspected something
Why has psychoanalysis progressed more than science without scientific methods
Insights from intuition and deep observation are not exhausted and are as good as AI
There is no computation in the brain as we all know it. What kind is there?
Why it’s unreasonable to use word Learning in relation to AI
There is a different calculability: what Hilbert and Gödel discovered
Why the brain should be studied as a whole
TAPe models the workings of the mechanisms of perception
Language is a complete system, it’s how it should be studied
The principles by which the Language of Thought functions
The isomorphism of Chinese characters and TAPe
T-Bit: a unit of information 1000x of times more efficient
TAPe models the workings of the mechanisms of perception
TAPe represents an important link between biology, cognitive and other sciences that study the structure and functioning of the brain, and new computer solutions, including AI.
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Neurobiologists claim that photoreceptors send the signal to the neural retina. Inside the retina, there are four layers of nerve cells which process the visual signal. Then the output cells of the retina form the optic nerve with the help of their axons. About a million axons go further into the brain. Generally, from the point of view of the perception system, the eye is a part of the brain, not a separate independent organ.
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Data collected by neuroscientists suggest that the basic objects recognizable by the brain are a dot and a line. A line, a 12 degree turn, a total of 15 lines — that is 180 degrees. And these lines, according to neuroscientists, are innately recognised. There are some patterns, filters, properties within the human eye, or the visual system, that are imprinted in this way, and the human visual system tries to detect them in any object.
Let's consider AlexNet visualization of the filters for one of the initial convolutional networks, which has had a big impact on computer vision algorithms. Why are the filters the way they are (in shape, number, features, etc.)? One could say that it's just the result of random search by the AI. Or you could say that even the lines found by AI models are similar to those recreated by the human brain.
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But is there an understandable hypothesis of what happens to these lines and dots afterwards? Why are they exactly like that? Why is there this exact number of them, why couldn't there be 10 at 18 degrees for example? And what happens to these lines, or rather to the data that the brain receives from an object through the visual system?
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They are somehow — how? — recognised, some lines are detected in a particular object — but why exactly that many lines, not more or less? — and what happens to them next? How and why do they combine in the brain into this very object and not another? What laws are at work here?
The "mathematical" or, more precisely, computational framework of TAPe forms parts of a general system, which we call the Language of Thought system after Jerry Fodor. We see the Language of Thought as a mechanism more habitual to the brain's functioning than traditional mathematics or AI methods.
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Apparently, these innate primary elements, intuitively discovered by human genius, were the basis for the creation of visual images that are used even in natural human language. And the "mathematical" framework of the TAPe might be revealing the laws by which visual images are formed in the brain. "Mathematical" is put in quotes because TAPe does not involve the usual math — differentials, integrals or back propagation of error, as well as Bayes' theorem.
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Human vision is directly connected to the brain being the main provider of information about the world around us — this is a known fact of neurobiology. "'Innate' line detectors in the visual perceptual system are the primary elements found by evolution that are necessary and sufficient to recognise the most complex visual patterns."
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This argument, among other things, led to the conclusion that the picture "digitized" by the retina has about 1 million pixels. But the main question is how this information, be it in the form of pixels, is processed? However, according to TAPe, the very existence of "pixels" as elements of perception is highly questionable, because it is clearly an unnecessary, uninformative, energy-consuming "division" of reality into some patterns subsequently used by the brain. Pixels are an extreme simplification of biology for the needs of technology.