Свобода выбора: миф или реальность?

[Диалектик]

я не имею возражений против предложенной вами классификации:
...
Эта схема описывает наблюдаемы антропологией факты.

Именно поэтому я и предлагал Микротону строить свою логику на данных фактах, а не брать их из головы.
Вне зависимости от дальнейшего содержания, спасибо Yuki, за серьезный подход к делу. А то я уже начал думать, что только мне, хочется думать.  :lol:
Что касается остальных ваших мыслей, то с вашим заключением

Эта потребность создается интенсивным воздействием ОБЩЕСТВА на индивида в момент формирования его мышления, но не возможна без врожденной возможности  и способности .

полностью согласен. Об остальном еще подумаю.

[Микротон]

Попробуйте.

Сознание является не столько продуктом развития природы, сколько продуктом общественной жизни человека, общественного труда предыдущих поколений людей. Оно является существенной частью деятельности человека, посредством которой создается человеческая природа и не может быть принята вне этой природы.
Если в неорганической природе отражение есть пассивный, мертвый физико-химический, механический акт без обобщения и проникновения в сущность обобщаемого явления, то отражение в форме сознания есть, по моему мнению - познание высокоорганизованной материей самой себя, проникновение в сущность, закон развития природы, предметов и явлений объективного мира.

[KWAKS]

химического воздействия может случиться вообще без какого-либо физического воздействия

«Физическое» здесь употреблено в узком смысле. Читай — «механическое».

Уважаемый Shlyapa !
Даже в самом узком смысле -
без «механического» перемещения атомов
химического воздействия НЕ может случиться вообще ...

А Вы не поленитесь попробовать -
НЕ по законам математической логики !
Потом : сравним полученные результаты ! ! !

Я и не ленюсь, пытаясь вывести сознание и прочие воли из специфики человеческого общества. ...

Новое Диалектик-цкое "открытие" :
"сознание и прочие воли из специфики человеческого общества" -
НЕ подчиняюЦЦа законам формальной логики ! ! !

И опять - при-имайте мои пз-др-льения,ув. Диалектик !

[Shlyapa]

И что ты называешь принципом, а что способом. Дай определения.

Принцип - толкать.
Способ - толкать одному или вдвоем.
Разницу видишь?

 Вижу.
Толкать одному или вдвоём — количественная хактеристика.

А вот в какое место предмета упираться, толкая, во что упираться самому, упираться руками в толкаемый предмет, а ногами в неподвижную опору или наоборот и т.д., и т.п. — вот это способ.

[Shlyapa]

KWAKS, не пытайся выглядеть глупее, чем ты уже выглядишь.

[Gillette]

Это не вся правда, а полуправда. На прежних принципах строятся только те компьютеры, которые предназначены для "широкого использования". То есть те, за которыми сидите и Вы и я. Компьютеры же нового поколения (те, что еще не продаются, а только разрабатываются), отличаются от своих предшественников именно принципиально.(я говорю о создании аналогвых компьютеров)

Создании аналоговых компютеров? Их уже давно создали, академик. И заменили цифровыми пока вы тут с Диалектиком спорили. Сеичас идет реч о всего лиш очен узком применении аналоговых процессов в компютировании. Вы наверно толкуете о тщетных попытках смоделироват мозг? Вот популярная статеика для зеленых "компютерщиков":


Why Can't a Computer Be More Like a Brain?
Andrew Watson
Computer scientists may tout their machines' abilities to perform millions or billions of operations a second, but neuroscientist Christof Koch of the California Institute of Technology (Caltech) in Pasadena thinks they need a lesson in humility. "Any creature vastly outperforms any machine today," he says. "Not in logical thought, but in sensing the environment, smelling, seeing, moving about." The lesson, as Caltech's Carver Mead recognized in the early 1980s, is that biological systems are fantastically efficient at certain types of computation. Inspired by Mead's vision, some computer scientists are taking this lesson to heart--and they're using it to try to build a new kind of computer.

In an effort they call neuromorphics, researchers are capturing in silicon what Ralph Etienne-Cummings of Southern Illinois University (SIU) in Carbondale likes to call the "essence" of biological subsystems. Neuromorphic engineers, adds Koch, are "essentially adapting those features, those algorithms, those tricks that the nervous system came up with through the last 600 million years."

Those tricks include neurons' ability to change their behavior based on experience and to work as tiny, autonomous computers in their own right, performing operations that might take thousands of transistors in a conventional computer. By mimicking these abilities in silicon, says Koch, neuromorphic engineers are producing "systems [that] are able to do many useful tasks that have so far proved impossible." Research teams worldwide are using conventional Very Large Scale Integrated (VLSI) circuit technology to build these new kinds of chips, trying to reproduce elements of sensing and sense processing from the animal kingdom.

"Silicon retinas"--eyes on a chip that sense a scene or pattern and interpret it--are among the best developed products of this approach. These systems implement in silicon the kinds of neural circuits that control vision in biological systems, enabling them to perceive such features of a scene as brightness contrasts and the polarization, or orientation, of light. "We can now go out there and see the world with eyes that we did not have before," says Andreas Andreou of Johns Hopkins University. Neuromorphic engineers are also developing brainlike hardware to detect drugs and explosives, to generate music, and to allow vehicles to drive themselves, to mention just a few ongoing efforts.

Conventional digital computers speak only in ones and zeros, their millions of transistors linked into vast arrays of logic gates that require huge numbers of switchings to perform the most modest tasks. Even the simplest operation inside a computer, such as multiplication, requires at least 10,000 switchings. All this happens serially, in a strict sequence controlled by a systemwide clock that synchronizes the activities of every component. Even so-called parallel supercomputers are, in reality, modest collections of smaller, ordinary computers joined together.

The brain, on the other hand, is totally different. As far as anybody knows, there is no systemwide clock in the brain: A neuron simply signals its neighbors when it is ready. What's more, "the individual components are very, very slow in a brain," says Koch. Yet the brain can perform 1016 operations per second, while consuming less power than an electric light bulb. To do the same amount of computation using a conventional digital chip would consume the output of an entire power station.

This stunning speed and efficiency results in part from "the massive, massive parallelism" of the brain's hundred million neurons, all working at the same time, says Koch. It also reflects the style of computing that goes on in neurons: analog computing, an approach that computer scientists have traditionally shunned. The digital computers of today solve a problem by imposing a computational recipe, or algorithm, on general purpose hardware. So a PC can, in principle and with enough time, solve any problem that a supercomputer can. Analog computers, by contrast, embody a specific computational problem in the actual physics of the hardware. Where digital computers traffic in ones and zeros, analog computers use continuously varying quantities. For example, a set of rods, springs, and weights can be turned into an analog computer--a mechanical model--to rapidly evaluate a bridge design.

Similarly, brains use electrical signals and varying membrane properties, instead of stretchy springs and weights, to do their job. Like the springs in the bridge design, which can stretch to many different lengths, a neuron has maybe a hundred internal electrical levels, giving it far more information content than the binary on-off of a digital switch. And instead of treating all inputs alike, as a digital circuit does, neurons can give added weight to pulses coming from certain favorite neighbors.

What's more, synapses--the junctions where one neuron receives input from another--act as little memory elements, aware of their previous inputs. "The circuitry you compute with is also the circuitry that remembers, in a sense," says Koch. That property dramatically reduces the need for data to be swapped during a computation, increasing efficiency. And like any analog computer, neurons are much faster than their digital counterparts.

Even though the transistors in standard integrated circuits are restricted to flipping between "on" and "off" states, they are capable of mimicking some of this behavior. Simple circuits already exist in which transistors are used in analog mode--not as switches, but as amplifiers that can operate at many different voltages--to perform operations such as multiplication, division, and subtraction.

Neuromorphic engineering aims to go much further, by transforming microcircuitry into an analog computing medium resembling neural tissue. "If we use that [circuitry] in the peculiar way we do, we can generate physical processes that are similar to neurons," says Rodney Douglas, who heads the Institute of Neuroinformatics in Zurich, Switzerland.

Computer scientists have already tried to mimic some aspects of the brain in so-called neural nets, networks of "processors" linked by "synapses"--connections that strengthen or weaken depending on activity, enabling the net to learn from experience. But these neural nets generally aren't real physical devices--instead, they are simulated ones, running as software on conventional computers. What's more, their neurons are, with few exceptions (see sidebar), generally much simplified versions of the real thing. Neuromorphics, on the other hand, is an effort to capture some of the richness of actual neurons in hardware--transistors, capacitors, and resistors, all fabricated onto silicon chips--in what is called analog VLSI, or simply AVLSI.

Besides allowing transistors to operate at many different voltage levels, neuromorphic engineers are designing them to serve as both calculation and memory elements. Work by Lance Glasser at the Massachusetts Institute of Technology, and by Mead and his team at Caltech, has led to the design of a new type of transistor, the floating gate transistor, which can reliably store analog information as electrical charge, enabling it to keep track of previous signals fed to it. These new transistors should open the way to building systems that can learn from experience, as neural-net software does, but more efficiently, because the hardware itself is learning.

Labs around the world are already exploiting AVLSI to build silicon noses, ears, and especially eyes. At the University of Adelaide in Australia, Alireza Moini and his team have built a succession of "bug-eye" chips, using a design abstracted from insect eyes. One variant senses motion by tracking regions of changing light intensity--an ability that could lead to collision sensors for cars. Andreou, together with Kwabena Boahen at Caltech, has built the most advanced silicon retina yet, with a resolution of 210 by 230 pixels. And Tamás Roska and his team at the Computer and Automation Institute in Budapest, Hungary, have produced a programmable "visual microprocessor" that can analyze a scene and swiftly pick out patterns for applications such as medical diagnosis.

In Zurich, Paul Verschure and Giacomo Indiveri are working on a silicon-retina-controlled robot that can follow a line across the floor of their laboratory. "We showed that we can reliably track an edge, independent of colors and textures of the surface," says Verschure. Their robot can stay on course for more than 100 meters--"the time we got fed up looking at the device doing the same thing."

Mimicking biology does have its disadvantages. Relying on continuously varying electrical states rather than the clearly defined ones and zeros of digital computing means that tiny variations in the components may cause them to respond differently to identical inputs. In silicon retinas, for example, pixels may vary in performance by as much as 20%. The brain can interpret sensory information reliably in spite of that kind of variability, thanks to the huge numbers of interconnections that allow it to smooth and correct data, says Koch.

Mimicking those dense interconnections is the field's other great challenge. "What the brain has that we do not have is connection technology," says Koch. Each cubic centimeter of the brain contains 100,000 cells and 2 kilometers of wiring, enabling each neuron to talk to 10,000 others. "We don't have that kind of technology right now." In the future, optical interconnections, relying on pulses of light that can crisscross freely, could solve the wiring problem. And nearly 10 years ago, the late Misha Mahowald at Zurich proposed a scheme to reduce the number of physical connections needed in a neuromorphic system. In her method, extended in a collaboration with Douglas, silicon neurons exchange addresses rather than actual pulses. A sender neuron communicates with its target over a common line linking many neurons, telling it to expect a signal from a particular address. The receiving neuron then recreates the signal, as if it had come over a dedicated line from the sender.

Even if they solve these problems, neuromorphic engineers are under no illusions about displacing conventional computing technology, which is unbeatable for number crunching. Ultimately, they hope to create neuromorphic sensors that can feed their readings of the world around them to digital electronics for subsequent processing, says Koch, who, for example, envisages cameras with a neuromorphic "seeing end" feeding a conventional data-processing unit. Says SUI's Etienne-Cummings: "If engineers can mimic the benefits of biological organisms while capitalizing on the speed of [digital] electronics, the resulting computational systems can be very powerful."



--------------------------------------------------------------------------------
Andrew Watson is a science writer in Norwich, U.K.

[KWAKS]

KWAKS, не пытайся выглядеть глупее, чем ты уже выглядишь.

Вам по силам "организовать" химическое воздействие -
без «механического» перемещения атомов ?

Примите мои глубочайшие "поздравления" Вам !

[Микротон]

sulting computational systems can be very powerful.

А что, ссылкой слабо было ограничится? Или приведенная эта простыня здесь Вам авторитетности добавила?

[Диалектик]

Микротону!

Сознание является не столько продуктом развития природы, сколько продуктом общественной жизни человека, общественного труда предыдущих поколений людей.
Верно, с той лишь поправкой, что так дело обстоит сегодня.
При становлении сознания, оба фактора играли важную роль. (Биология - перебирает, общество - закрепляет).
Оно является существенной частью деятельности человека, посредством которой создается человеческая природа и не может быть принята вне этой природы.
Если в неорганической природе отражение есть пассивный, мертвый физико-химический, механический акт без обобщения и проникновения в сущность обобщаемого явления, то отражение в форме сознания есть, по моему мнению - познание высокоорганизованной материей самой себя, проникновение в сущность, закон развития природы, предметов и явлений объективного мира.
И причина этому в том, что сознание есть отражение человеческой практики. Именно практика, первоначально, проникает в сущность, законы природы, потому как иначе чем, соблюдая эти законы, сообразуясь с ними, она действовать, не может. Практика – предметное сознание, сознание – идеальная практика.

С этими поправками, вы меня правильно поняли. Зачем же придурялись?

[Shlyapa]

KWAKS, словосочетание «уровень детализации» (рассмотрения, описания, модели и т.д.) тебе знакомо?

Впрочем, я уже уделил тебе слишком много внимания.