bochen2 Posted October 17, 2008 Report Posted October 17, 2008 Frequently Asked Questions LINKY The cerebral cortex, the convoluted "grey matter" that makes up 80% of the human brain, is responsible for our ability to remember, think, reflect, empathize, communicate, adapt to new situations and plan for the future. The cortex first appeared in mammals, and it has a fundamentally simple repetitive structure that is the same across all mammalian species. In the cortex, neurons are organized into basic functional units, cylindrical volumes 0.5 mm wide by 2 mm high, each containing about 10,000 neurons that are connected in an intricate but consistent way. These units operate much like microcircuits in a computer. This microcircuit, known as the neocortical column (NCC), is repeated millions of times across the cortex. The difference between the brain of a mouse and the brain of a human is basically just volume - humans have many more neocortical columns and thus neurons than mice. "Ten years ago, starting at the Max-Planck and continuing at the Weizmann Institute for Science, I began using the new approach of infrared-DIC microscopy to do multi-neuron patch-clamp recordings. This allowed my lab to essentially map out, in a highly quantitative manner, the main elements and synaptic pathways making up the neocortical column (NCC)." The human neocortex has many millions of NCCs. For this reason we would need first an accurate replica of the NCC and then we will simplify the NCC before we begin duplications. The other approach is to covert the software NCC into a hardware version - a chip, a blue gene on a chip - and then make as many copies as one wants. The number of neurons various markedly in the Neocortex with values between 10-100 Billion in the human brain to millions in small animals. At this stage the important issue is how to build one column. This column has 10-100'000 neurons depending on the species and particular neocortical region, and there are millions of columns. We have estimated that we may approach real-time simulations of a NCC with 10'000 morphologically complex neurons interconnected with 10x8 synapses on a 8-12'000 processor Blue Gene/L machine. To simulate a human brain with around millions of NCCs will probably require more than proportionately more processing power. That should give an idea how much computing power will need to increase before we can simulate the human brain at the cellular level in real-time. Simulating the human brain at the molecular level is unlikely with current computing systems. The cellular level is a form of phenomenological model of the underlying molecular processes - a simplification - so it does capture many key processes, but molecular interactions are of course very complex and they keep neurons on a growth trajectory (real neurons are never biochemically stable), whereas in the simulations, neurons will tend to go back to a resting position when not activated. A very important reason for going to the molecular level is to link gene activity with electrical activity. Ultimately, that is what makes neurons become and work as neurons - an interaction between nature and nuture. The NCC is very stereotypical from mouse to man and across brain regions. The rat template that we are building will provide a starting point for incorporating the small variations in different brain regions and different species to allow us to create columns from different brain regions and species. We will of course be examining the computational power of the NCC. In particular, we will explore the ability of the NCC to act as a Liquid Computer (a form of analog computer that handles continuous data streams). This could be used for dynamic vision and scene segmentation, real-time auditory processing, as well as sensory-motor integration for robotics. Another special ability of the neocortex is the ability to anticipate the future based on current data (the birth of cognition) and so we will examine the ability of the NCC to make intelligent predictions on complex data. We will also examine other forms of computing that can be used - perhaps hybrid digital-analog computing... The most important feature of the brain that makes it different from computers is that it is constantly changing. If the resistors and capacitors in a computer started changing, then it would immediately malfunction, whereas in the brain such equivalent properties change constantly on the time scales of milliseconds to years. The brain is more like a dynamically morphing computer. We are still far from understanding the rules that govern the brain's genetically and environmentally driven self-organization in response to external stimulus. Seed: Out of the Blue LINKYThe skeptics, for the most part, have been proven wrong. It took less than two years for the Blue Brain supercomputer to accurately simulate a neocortical column, which is a tiny slice of brain containing approximately 10,000 neurons, with about 30 million synaptic connections between them. "The column has been built and it runs," Markram says. "Now we just have to scale it up." Blue Brain scientists are confident that, at some point in the next few years, they will be able to start simulating an entire brain. "If we build this brain right, it will do everything," Markram says. I ask him if that includes selfconsciousness: Is it really possible to put a ghost into a machine? "When I say everything, I mean everything," he says, and a mischievous smile spreads across his face. It didn't take long before the model reacted. After only a few electrical jolts, the artificial neural circuit began to act just like a real neural circuit. Clusters of connected neurons began to fire in close synchrony: the cells were wiring themselves together. Different cell types obeyed their genetic instructions. The scientists could see the cellular looms flash and then fade as the cells wove themselves into meaningful patterns. Dendrites reached out to each other, like branches looking for light. "This all happened on its own," Markram says. "It was entirely spontaneous." For the Blue Brain team, it was a thrilling breakthrough. After years of hard work, they were finally able to watch their make-believe brain develop, synapse by synapse. The microchips were turning themselves into a mind. In fact, the model is so successful that its biggest restrictions are now technological. "We have already shown that the model can scale up," Markram says. "What is holding us back now are the computers." The numbers speak for themselves. Markram estimates that in order to accurately simulate the trillion synapses in the human brain, you'd need to be able to process about 500 petabytes of data (peta being a million billion, or 10 to the fifteenth power). That's about 200 times more information than is stored on all of Google's servers. (Given current technology, a machine capable of such power would be the size of several football fields.) Energy consumption is another huge problem. The human brain requires about 25 watts of electricity to operate. Markram estimates that simulating the brain on a supercomputer with existing microchips would generate an annual electrical bill of about $3 billion . But if computing speeds continue to develop at their current exponential pace, and energy efficiency improves, Markram believes that he'll be able to model a complete human brain on a single machine in ten years or less. "There is nothing inherently mysterious about the mind or anything it makes," Markram says. "Consciousness is just a massive amount of information being exchanged by trillions of brain cells. If you can precisely model that information, then I don't know why you wouldn't be able to generate a conscious mind." At moments like this, Markram takes on the deflating air of a magician exposing his own magic tricks. He seems to relish the idea of "debunking consciousness," showing that it's no more metaphysical than any other property of the mind. Consciousness is a binary code; the self is a loop of electricity. A ghost will emerge from the machine once the machine is built right. And yet, Markram is candid about the possibility of failure. He knows that he has no idea what will happen once the Blue Brain is scaled up. "I think it will be just as interesting, perhaps even more interesting, if we can't create a conscious computer," Markram says. "Then the question will be: 'What are we missing? Why is this not enough?'" ====================================================================== Rat Brain Robot - Cyborg YouTube - Rat Brain Robot http://www.youtube.com/watch?v=1QPiF4-iu6g [/url] THE nerve center of a conventional robot is a microprocessor of silicon and metal. But for a robot under development at Georgia Tech, commands are relayed by 2,000 or so cells from a rat's brain. A group led by a university researcher has created a part mechanical, part biological robot that operates on the basis of the neural activity of rat brain cells grown in a dish. The neural signals are analyzed by a computer that looks for patterns emitted by the brain cells and then translates those patterns into robotic movement. If the neurons fire a certain way, for example, the robot's right wheel rotates once. The leader of the group, Steve M. Potter, a professor in the Laboratory for Neuroengineering at Georgia Tech, calls his creation a Hybrot, short for hybrid robot.''It's very much a symbiosis,'' he said, ''a digital computer and a living neural network working together.'' ''These changes could be analogues of what happens in learning,'' Dr. Wolpaw said. ''You are dealing with neurons, the same tissue as in a brain,'' although in a different setting and with different circuitry. ''Some things presumably are in common, for example, the neuron's capacity for plasticity,'' he said. ====================================================================== The Cognitive-Theoretic Model of the Universe:A New Kind of Reality Theory http://www.ctmu.net/ IMPLICATIONS OF A FUNDAMENTAL CONSCIOUSNESS IMPLICATIONS OF A FUNDAMENTAL CONSCIOUSNESS By Copthorne Macdonald Facing Up to the Problem of ConsciousnessFacing Up to the Problem of Consciousness Can Matter be Explained in Termsof Consciousness?RapidShare: Easy Filehosting Let’s begin with the key question posed in Vol. 1, No. 2 of this Journal: ‘Why, in principle, should a neuronal system of any degree of complexity give rise to the phenomenal experience of consciousness?’ My answer has two parts: If we start with radical physicalist assumptions about the nature of reality, then there is no reason why, ‘in principle’, a neuronal system should give rise to conscious experience. If, however, we start with the assumption that primal reality is as described in this paper, then the arising of conscious experience is not astonishing at all. It’s just what we might expect. If reality is this second way, then the role of the neuronal system is not to mysteriously create awareness and mind from alien substance. Rather, it is to organize a pre-existing propensity for awareness into useful, functional awareness, and provide for its modulation by useful information. Again we face the utility issue. If awareness is as primal and ubiquitous as energy, then it will be present in every system. But whether or not it plays a functional role will depend on how a particular system has been configured, and the nature of its connections to the world outside it. It is quite possible that during pre-biological evolution (the cosmological and geological phases of evolution) awareness played no functional role. It is clear, however, that at some point during biological evolution, awareness was harnessed and put to work. If, today, we humans were given the task of designing systems that have useful mental characteristics, we quite literally would not know where to begin. Yet evolution — with its slow, plodding, chance-and-necessity genius — did a magnificent job of it. As I see it, this was possible because the medium on which the cosmic algorithms have been operating is a mental-physical medium, not just a physical one. Early in the evolution of living things, organisms exhibited sensitivity to their environments, and some were able to respond to environmental changes. Many types of plants align themselves to maximize their exposure to light. Paramecia move away from irritating stimuli. These behaviors appear to most scientists to arise from totally physical, reflex-like mechanisms. They feel that subjectivity and mind play no functional role, and this may well be true. It may even be true of amphibians. In the classic Lettvin, J. et al. (1965) study of ‘What the frog’s eye tells the frog’s brain’, the frog’s eye is reported to have different types of retinal sensors that give rise, quite automatically it seems, to a limited set of stereotyped behaviors. If a small dark object passes across the frog’s field of vision (a fly, perhaps?), the frog’s tongue reflexively darts out. If the overall light level suddenly drops (the shadow of a hawk, perhaps?) the frog reflexively jumps off the lily pad. Whether or not these organisms have a functional consciousness may become clear when we finally understand how human mentality works at the neural level. In this view, the conscious field is the great simplifier. The creation of a mental ‘workspace’ allows large amounts of relevant and irrelevant information to be brought together in one subjective arena. Selective attention then allows that mass of data to be rapidly surveyed in serial fashion. Neuronal correlates of items attended to are checked computationally for relevance. Different kinds of relevance cause different kinds of data-processing outputs to appear in the mind: mental images; thoughts; and feelings of pleasure, pain, fear, anger, hate, or tenderness, for example. These newly-arisen qualia are themselves then available for possible selection by attention, and if selected, their neuronal correlates would be used as input data for further processing. At the end of all the processing, the ultimate behavioral decision is frequently accompanied by a mental correlate of its own — a YES feeling, a NO feeling, fear, anger, etc. It is undeniable that some organisms are subjects of experience. But the question of how it is that these systems are subjects of experience is perplexing. Why is it that when our cognitive systems engage in visual and auditory information-processing, we have visual or auditory experience: the quality of deep blue, the sensation of middle C? How can we explain why there is something it is like to entertain a mental image, or to experience an emotion? It is widely agreed that experience arises from a physical basis, but we have no good explanation of why and how it so arises. Why should physical processing give rise to a rich inner life at all? It seems objectively unreasonable that it should, and yet it does. If any problem qualifies as the problem of consciousness, it is this one. In this central sense of "consciousness", an organism is conscious if there is something it is like to be that organism, and a mental state is conscious if there is something it is like to be in that state. Sometimes terms such as "phenomenal consciousness" and "qualia" are also used here, but I find it more natural to speak of "conscious experience" or simply "experience". Another useful way to avoid confusion (used by e.g. Newell 1990, Chalmers 1996) is to reserve the term "consciousness" for the phenomena of experience, using the less loaded term "awareness" for the more straightforward phenomena described earlier. If such a convention were widely adopted, communication would be much easier; as things stand, those who talk about "consciousness" are frequently talking past each other. The ambiguity of the term "consciousness" is often exploited by both philosophers and scientists writing on the subject. It is common to see a paper on consciousness begin with an invocation of the mystery of consciousness, noting the strange intangibility and ineffability of subjectivity, and worrying that so far we have no theory of the phenomenon. Here, the topic is clearly the hard problem - the problem of experience. In the second half of the paper, the tone becomes more optimistic, and the author's own theory of consciousness is outlined. Upon examination, this theory turns out to be a theory of one of the more straightforward phenomena - of reportability, of introspective access, or whatever. At the close, the author declares that consciousness has turned out to be tractable after all, but the reader is left feeling like the victim of a bait-and-switch. The hard problem remains untouched. ====================================================================== So in a nutshell there is nothing "special" about consciousness, awareness and qualia. When technology reaches a point that it becomes computationally and physically feasible to create emulations and replicas of human brain structure and mimic conscious activity then it will be done ... Machines can be truly "sentient" and self aware (strong AI) not because of some emergent property of complexity or of the mistaken notion of "epiphenomenon convergence" but because of the top down reality that consciousness is as fundamental and basic as space and time itself. It we take consciousness (micro/macro qualia) as nondual and in conjunction to space-time matter and energy then the "hard problem of consciousness" and the "reductionists' paradox all resolve itself quite well... Pattern is the essence of information, which is the essence of identity, and awareness shines as microqualia rubs through the substrate of this film mathematically encoded structure resulting in the physical universe, the mental realm and the abstract platonic existences... All pattern has microlevel qualia "conciousness". Rocks have low level basic bacteria like concisenesses. Even abstract mathematical patterns such as the circle or mandelbrot set has a "essence" of its own... Human brains achieve critical mass complexity and reach a high level embodied qualia macro conciousness ... There is no distinction whether such critical mass complexity is biological or silicon or even purely abstract platonic (math/code/abstract...) for at the core of essence it is all one expression and one experience and the triune worlds (mental, physical, platonic) are seen as ONE as it converges to the highest pinnacle of reality. If we can build the machine right, the ghost will appear. But in reality the physical world is the machine that is in the ghost. And we are simply now trying to create the ghost in the machine that is the machine in the ghost... Full circle... P.S. Some current examples and inspiritations of AI: FACADE - InteractiveStory.net 3D simulation and evolution | Framsticks - 3D simulation and evolution | FramsticksLoebner Prize - Loebner Prize for Artificial Intelligence Game/AI Game/AI Top 10 Most Influential AI GamesTop 10 Most Influential AI Games — AiGameDev.com Emotionally Driven Natural Language Generation for Personality RichCharacters in Interactive Gameshttp://www.cc.gatech.edu/faculty/ashwin/papers/er-07-09.pdf Embodied Agents in GamesEmbodied Agents in Games — AiGameDev.com Female robots:YouTube - AKIBA ROBOT FESTIVAL 2006: Actroid Female Robot http://www.youtube.com/watch?v=WbFFs4DHWysYouTube - Eva the female robot http://www.youtube.com/watch?v=RuxFJcG9SEoYouTube - Actroid DER2 fembot - Face expressions http://www.youtube.com/watch?v=4sjV_lxSVQo Quote
nkt Posted October 18, 2008 Report Posted October 18, 2008 And yet, this year, no AI did well enough to fool 30% of the Turing Test judges. So close, and yet so far... Quote
bochen2 Posted October 18, 2008 Author Report Posted October 18, 2008 And yet, this year, no AI did well enough to fool 30% of the Turing Test judges. So close, and yet so far... Well that's the thing, I wouldn't consider what you are referring to as "AI" in the first place. The ELIZA program , the Turing Test (or at least the version that most of the community are most familiar with) and even case in point demonstrations like "FACADE" are not true "AI" at all. Language is ambiguous. Language is vague, language unlike mathematics is inherently imprecise but language also has the benefit of being the platform for a workspace of "consciousness" as the great simplifier of things. Language (English or otherwise) is the cumulative result of thousand of years of change and is directly morphed by society and the people,events and changes within it. In order for language to work (and the whole purpose of language itself...) there must be consciousness and 'true understanding' in the first place. This is not something you can 'script' it must be built from the bottom up ... Language naturally evolved after human beings become complexed enough to merit the existence and the use of this tool called language. So to build a perfect "language" bot but have not the brains behind it to back it up is futile and ridiculously impossible attempt.. So to attempt to build a "texting" robot in the attempts to FOOL humans 30% or 50% or whatever benchmark amount of times is imho rather silly and dull... Even if we had a program that DID do that quite well, it STILL would not be categorized as "artificially intelligent" Language (written or verbal) is only one aspect of human intelligence. We cannot forget the expressiveness of body language and our facial gestures and moods and tones of voice that cannot be expressed through text alone. Language is an approximation of one of our means of self expression and not the ends to itself. The words of an author are not the author herself, the pictures and videos of an actress are a time-slice digital representation and approximation of the actress but not the actual actress herself. Humans are multifaceted ever changing "holographic" omni dimensional beings and language, art, math, all the disciplines and means to express and communicate are just a narrow and restricted avenue of expression.... So to build a "text" bot capable of matching on par with our "texting" abilities and call it AI is just plain sad because there is still 99.99% missing under the hood (so to speak) There is no "strong AI", there is no "ghost in the machine" there is no AI at all in cases like this... However what the blue brain project is attempting to do is something different altogether. If we can build an exact replica of the human brain from a top down functional perspective AND from a button up molecular resolution (simulating not only the connections and orientations and size and structure of the neurons but also the complexity and dynamics within each neuron itself (ie not just simple Point-Neurons)) then we can say we have achieved a great accomplishment. Then we can say there is the ghost in the machine, and if solve the "input/output" problem there would be no reason why this artificial mind could not be just as real when texting or chatting with other humans, and it would NOT be restricted to just one narrow avenue of expression either. Given robotic hands it could paint like an artist, given speak and hearing it could become an actress or engage in deep conversation, given mobility and dexterity it could learn to skate or drive a car or fly a plane just like any of the rest of us. No doubt the technology is just not there and may never be there. The sheer mathematical and computation requirements to duplicate a human brain is just to far out ... But the point is there is NOTHING (other than current technology) that would prevent us from creating a TRUE AI just as real as the "real thing" making it not one bit distinguishable from you or me.. I do believe once that can be done the "person" inside would be truly sentient in the strongest sense and it would be immoral and cruel to hurt the being. But perhaps instead of going about the roundabout way of using almost one CPU/PROCESSOR PER neuron to do the simulation the Blue Brain project should let the greatest ultimate computer (the universe itself | after all every computer is just an emulator if you think about it) the universe itself do the computing at the first level rather than build an emulator of an emulator... Modeling a neuron at the molecular level that has thousands of connections with other neurons in the region is no small task, but instead of using a whole processor per neuron they should come up with a way to use the laws of nature and not go against it. If they can artificially mass create in the laboratory nanobots that replicate the structure and behavior of neurons then there would be no need for the complex mathematical software and modeling, then if they can find a way to suspend the neurons in the precise manner the interactions would just take place itself and the calculations would be the universe and the laws of physics doing the "calculating" and not some bulky chunky obsolete silicon processor... If we can build an artificial brain it would use the same amount of power, produce the same amount of heat, and would be of similar size as well. To build one using conventional computing would take many football fields and the power consumption would be greater than many smaller nations... Quote
nkt Posted October 23, 2008 Report Posted October 23, 2008 Growing an artificial brain seems rather pointless to me. We already have millions of them spare and, in many cases, practically un-used. Plus, it really doesn't answer much. It's like growing a plant and making bread from the grain tells you nothing about how the DNA of the plant works. Moulding one out of silicon? Now that's more interesting. However, I'd suggest using something like an FPGA to model the neuron systems in hardware, if they are well understood. As such, perhaps a few hundred could be run in parallel on each FPGA, and then many PGAs could be interlinked to get parity with a small animal, then, adding more units and newer units and more power, you would build up to having a human equivalent brain model, and, (perhaps) hopefully have a functional brain with a mind in there. Of course, it wouldn't be much like a human. Being able to have perfect recall of everything, and thinking a thousand times faster (at least) than a human, combined with nearly instant look-up of everything via the web, you'd have something the world has never seen. Quote
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