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I can't say for sure that robots will ever be able to do everything a human can do but it is possible. People say that robots can not preform emotion. That is false. Robots can easily be happy sad angry or annoyed that is if we program it to do so. For instance, if you program a robot to not be good at somthing like opening a bottle and you program him to do the body language of a physical human and use the speech of the physical human when he does not do what you tell him to do right it is personifying anger because when humans are angry they tend to use there hands and mouth more than anything else. I think that there will always however be somthing a human can do that a robot can't...

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Current research should be focused upon emulating a certain function performed by us humans rather than trying to create an entire human robot. This strategy should be promoted as the matter at hand, the engineering of the brain, is a complex one. Copying the brain entirely the way it is might not be impossible, but it is time consuming for sure. For quicker results the folowing startegy should be used: 1. Select the problem to be tackled. 2. Evaluate the feasibilty of using AI in place of NI for the particular problem. 3. If AI selected, narrow down on the specific functions that need to be emulated. 4. Reverse engineer accordingly. 5. Implement results through a pilot project. 6. Explore expansion opportunities for the researched techniques. The strategy can be used to create a library of AI tools that can be integrated through time to handle more intricate tasks.

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Current research should be focused upon emulating a certain function performed by us humans rather than trying to create an entire human robot. This strategy should be promoted as the matter at hand, the engineering of the brain, is a complex one. Copying the brain entirely the way it is might not be impossible, but it is time consuming for sure. For quicker results the folowing startegy should be used: 1. Select the problem to be tackled. 2. Evaluate the feasibilty of using AI in place of NI for the particular problem. 3. If AI selected, narrow down on the specific functions that need to be emulated. 4. Reverse engineer accordingly. 5. Implement results through a pilot project. 6. Explore expansion opportunities for the researched techniques. The strategy can be used to create a library of AI tools that can be integrated through time to handle more intricate tasks.

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There is precious little available to practice neurosurgical techniques outside of a real patient in a no-risk setting. Simulation-based education for healthcare could greatly benefit from advances in artificial intelligence in addition to virtual reality.

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Intelligence per se can be better understood by attempting to re-create intelligent beings. At an extreme I envision that intelligence could supercede current cognitive activities to embrace mystical qualities. I "define" what we call intelligence today as - The ability to work efficiently in areas that have complex, incomplete and ambiguous patterns. To enable intelligence we need a system that is both sub-symbolic (neurons based) and symbolic (higher level of processing, more analytical). We need a system that will easily integrate processing sensory signals with the processing of experiences and creative thinking. To do this I believe that we have to first identify what I call the Most Primitive Conceptual Entities of the world. I believe that somehow these are implanted in us human beings at birth. We are then capable of developing these Primitives into the conceptual primitives for a particular domain (like mechanical engineering or medicine) and subsequently learn the more advanced topics in those areas. In a similar manner we have to first identify these Most Primitive Conceptual Entities and then represent them 'adequately' in the system and then provide the right learning mechanisms to build cognitive structures for a domain, from them. This work would help in identifying the reasons for cognitive impairment in children and provide the option of implanting, nurturing the growth of the required primitives that will eventually remove the impairments.

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This challenge is very closely related to one of the UKCRC's Computing Research grand challenges summarised here: http://www.ukcrc.org.uk/g rand_challenges One of those grand challenges is particularly close, namely GC5: "Architecture of Brain and Mind: Integrating high level cognitive processes with brain mechanisms and functions in a working robot". It is described here: http://www.cs.bham.ac.uk/ research/projects/cogaff/ gc/ I have elaborated on the importance of the more general study of biological information-processing (not only in brains, e.g. development of an embryo uses chemical/molecuar information processing) as a major engineering challenge here: http://www.cs.bham.ac.uk/ research/projects/cogaff/ misc/synthetic-biology.html Aaron Sloman http://www.cs.bham.ac.uk/ ~axs/

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first of all, I would like to thank Yu Chou for his wonderful remark. and to my opinion, Human brain is the most complex architecture in existence. But understanding it and implement the understanding to design newer intelligent machine can really contribute. Like in the field of AI and machine learning. we cannot totally imitate it, but we must try as close as possible to get better approximation and data interpretation. And I personally feel very interested in this subject .

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By far the most important goal in your list in my opinion. But the question one is really trying to answer is what is knowledge and how it is organized--not how to reverse engineer the brain. Certainly the brain is one way in which this is done. However, arguably the brain does this poorly--unable to handle more than 7-8 variables at a time, inaccurate memory recall, yet it is still much more usable than anything we can program up in silicon. I find the naming of the computer science field--artificial intelligence--amusing since any "bird-brain" (e.g. eagles) biological system can visualize and utilize sensory input much better than anything we can program up. Call that intelligence seems rather a hyperbole. Let's just say that there appears to be some very efficient algorithm done with very slow biological hardware with just a few tens of thousands of simple switches (the visual systems of birds) that we have no clues to. The visual task is low level and arguably devoid of any real "intelligence". Once the data is "recognized" by the visual system, then the real "intelligence" part of organizing the data into knowledge begins. Our problems here are several. The first of which is a lack of algorithms for simple tasks--going to more involved depth of simulating brains is not going to solve it. At bottom, no one will suggest that you can understand how computers calculate the weather by simulating the transistors of a CPU. Saying that the answer is to just simulate the hardware is an admission that we are clueless. For that matter, the airplane analogy is correct. It is only because we don't have the first clue about how to make an air foil that there is suggestions that making a bird would be easier and more direct. We need better ideas here--not necessarily more money to make bigger computers. But coming up with good algorithms is hard, asking NSF for more money to make a bigger computer is easier. Finally, perhaps before one goes on with this project, somebody actually defines knowledge and intelligence first. Most rely on circular logic or equating humans with intelligence (Turing Test for instance).

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I am not a programmer or engineer, but I have an intense interest in this field and i don't really know why. But here is my 2 cents on this matter: Asking questions seem to be a simple and fundamental part of being human, yet i have not read any articles saying that any computer has yet asked questions. So, can we program this ability to ask questions into computers?

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I do research in computer vision computer programming, which is one area of AI, and which utilizes findings from brain research. I currently have the opinion that part of consciousness is a fundamental characteristic of living matter that is not conducive to mathematics and computer implementation, and other aspects of the brain are more mechanical and conducive to mathematics, so that they are implementable in a computer. For example, the perception of a color like green seems to be a fundamental aspect of living matter. We all know what the color green looks like, but physics and mathematics can only describes the color green as a wave of certain nano meters. Does a photo diode "see" green as we see green instead of just sending out a voltage? If not, there does not appear to be a way to program a computer to see the color green as we see it. Image recognition may have aspects that can and cannot be programed or reduced to mathematics, although a greater part can probably be treated by mathematics, and thus programmable in a computer. Multi-celled organisms evolved relatively recently in Earth's history as oxygen was built up by photo-bacteria. The human brain while complex in terms of the number of nerve connections is probably to be relatively less complicated in terms of the number of brain structures, as it evolved in the recent history of the Earth. Many of the capabilities exhibited by the brain have been simulated by the computer like printed character recognition, speech recognition, simple decision making as well as less perfect simulations of face recognition, free writing recognition, music composition, and general image analysis.

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One great contribution to human civilization of deciphering the workings of the human brain is to figure out how to detect when the brain is lying and not telling the truth. A fool proof lie detection method will greatly promote world peace in the modern world in my opinion. There are great distrusts amongst people of the world, and they are building great arsenals including nuclear weapons in a great part due to this distrust. Almost every country say that they want peace, but few fully believe these sayings, as there are no ways to determine whether these saying are lies. While world peace is much more than just about controlling lying by countries, such understanding of the brain can potentially improve world peace.

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Greetings, I applaud your goal of "reverse engineering" the human brain. However, I doubt a complete working (hardware) prototype would yield much benefit. I'm a computer programmer (award winning) and have "programmed" many models and varieties of computing machines. It's (sometimes) painfully obvious that the "hardware" of the machine bears little upon the outward characteristics of it; quite the contrary in fact. It's the software, the programming that defines what it does, the "firmware" that defines the interface to the hardwares "how". The actual processor that does the work? It's essentially a "media". Virtually any algorithm can be executed on virtually any processor; again, processors are just media for algorithms. It's the algorithms that are the key to intelligence, algorithms that likely can be run on a variety of media, not just the human brain. And the algorithms running now, enabling you to read this, have been developed over hundreds of millions of years, steadily evolving, improving themselves generation after generation. However, all of our attempts at simulating intelligence have fallen woefully short. We don't even have a decent model of the human intellect. Machine Intelligence has proven as difficult to grasp as sunlight itself. How does one go about grabbing sunlight? Perhaps there are clues, clues in our own folklore and history. For instance, there's an old psychology saying which (I believe) provides some salient insight into "intelligence": It is: "All of our decisions are simply designed to please ourselves". Cute psychological device, or the First Rule of AI? I would suppose the latter. Think about it.

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The major issue here is the ultra-parallel nature of the cortex and the major submodules like the cerebellum, thalamus and spinal cord. Over millenia there has been biological engineering of subprocesses through evolution that we may not have the insight to reverse engineer.

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Fusion nuclear for all People

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I think there is an issue missing in the "What is needed to reverse engineer the brain?" section: intracellular processes. Learning and memory are hinted at in the above sections, but not discussed in this section. The key feature of neurons that makes them different than transistors is that they are plastic: they change their behavior over time. Although this may involve some rewiring, a large portion of this plasticity is due to changes in synaptic weight caused by intracellular processes. Some known work involves long term potentiation and depression via glutamatergic channels. Other work has shown that receptor activation can set off intracellular signalling cascades that may switch between bi- or tri-stable states. Finally, further work shows that receptor activation by neurotransmitters can initiate gene expression changes which result it differing amounts of intracellular and membrane-bound proteins. These three mechanisms may be the heart of "learning" and are not discussed at all. As for "How close can machines come to humans?", I believe it is more a question of "when" rather than "how close". Ray Kurzweil' book "The age of spiritual machines" predicts that computer power, if it continues to grow at the rate it is growing, will have the capacity to do as many calculations as the human brain by the year 2020 in a machine that costs $1000. The problem will then become, "how do we make this machine that is as computationally powerful as the brain act like a brain". I think that may take another century to figure it out completely, but in the meantime computers will become smarter and smarter. We will probably see individual systems being replicated before that time, and we have some of those in crude form today: speech recognition, optical character recognition (recognizing handwriting), feature discriminators (used by military to find camouflaged enemy artillery), etc.

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AI will be key in helping us design our next generations of toolsets as humanity is approaching its limits as to what it can design due to the scale, speeds and complexities involved at the frontier of nearly every science. Evolutionary algorithms, machine learning and computational theory will increasingly play key roles in nanotechnology, biological complexity, quantum physics and the many simulations and virtual worlds we will design in the coming centuries.

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After the machines have replaced all those labor-intensive works, they will come into the field of design and analysis aspect of works. But the fact that machines combined with higher and higher level of artificial intelligence should always be treated carefully.

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Defining "thinking" is the most important. Whether thinking is a deterministic or random process is a difficult question.Or it is indeed a single process with million or billion Monte-Carlo step? Moreover, it is not a 0 and 1 process. The ouput(s) may depend on the energy level or chemical composition of the input(s). I always suspect that even one can mimic a single brain cell, there shoud be a critical number of cells or connections before it works as a "thinking" brain.

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Why would you condemn a species to slavery before it even exists?