What is Artificial neural network?
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An artificial Neural Network is an artificial human brain, the attempt is as of yet unsuccessful
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yes, it's called communication.  Images can be conveyed through art or illustration.  Thoughts can be conveyed verbally.  Emotions through body language.  If a neurosurgeon tried cutting and pasting, he would have a big gooey mess on his hands, not to mention some very angry bereaved families.  Besides, neural networks only make sense in the context of the remainder of the brain's network system at large, so it would be like trying to do physical splicing with a floppy disk.  It wouldn't work.
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Your best bet is to go to your university library and look it up in the math and computer science section. There are books on this and if your library doesn't have them you can do an interlibrary loan to get them. To look extra smart, find an example in the literature and reference their work. Look up Peter Jurs' papers from Penn State University. They use genetic algorithms and neural networks to predict physical properties of molecules
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As a broad subfield of artificial intelligence, Machine learning is concerned with the development of algorithms and techniques, which allow computers to "learn". At a general level, there are two types of learning: inductive, and deductive. Inductive machine learning methods create computer programs by extracting rules and patterns out of massive data sets. It should be noted that although pattern identification is important to Machine Learning, without rule extraction a process falls more accurately in the field of data mining.\n\nMachine learning overlaps heavily with statistics, since both fields study the analysis of data, but unlike statistics, machine learning is concerned with the algorithmic complexity of computational implementations. Many inference problems turn out to be NP-hard or harder, so part of machine learning research is the development of tractable approximate inference algorithms.\n\nMachine learning has a wide spectrum of applications including search engines, medical diagnosis, bioinformatics and cheminformatics, detecting credit card fraud, stock market analysis, classifying DNA sequences, speech and handwriting recognition, object recognition in computer vision, game playing and robot locomotion.
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His CPU is a neural-net processor, it's a learning computer
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biological computers, the memory would grow and learn similar to our brains
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If your question is how can you use neural networks in a real-time application the answer is:\n\nYou "train" the neural network on statistically normal data and then you "feed" the real-time data to the neural network and then you have your neural network output.  Unless you have a small amount of data there is always some small yet possibly perceivable processing delay.
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"AI" is a word from movies and fiction books.\nUniversity labs are working on better computers and software to solve specific problems (e.g. weather prediction). \n\nReasons they want a computer to do this work is:\n\na computer is cheaper to maintain than a human being (salary, benefits, employer-side taxes, 401-K)\n\ncomputers can work 24/7/365.\n\ncomputers are much more accurate than humans in their work - no slips of hand or other accidents. \n\ncomputers can store much more information than human mind, and process it faster
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As opposed to artificial?
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Intelligence is defined by the ability to learn and reason. Artificial inteligence is basically a computer that can learn and reason without further input from a user. Typically, computers simply calculate and must be programmed to know how to calculate.
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Computers are lacking in their capability to learn. They tend to surpass humans in pure rationality issues, and eventually they'll be better at certain things humans are good at intuitively just by taking in enough information. However, the algorithms for a computer's intelligence requires everything to be objective. So there's no way for it to interpret something unless there's an algorithm there to determine how to interpret it. In short, the brain is self-programming. A computer does not really have that capability. To do such would require human beings knowing how to program computers that can program brains. And at that point, we might as well just program our own damned brains to be like that.\n\nBut in terms of raw processing speed, sure, computers will eventually surpass mankind. After all, if nothing else, they're likely using superior technology. All we have is a bunch of neurons and whatnot.
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Although our computing tools (such as our brain) are finite, but they can touch infinity through mathematical induction or recursion.\n\nFeeling finit ocuures for a neural network, when the data stored in it is going to overfow the nerons capacity.
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Yes, but depending on the complexity of your problem. If your data is linearly separable, then a simple regression analysis will do. Use neural networks (such as backpropagation) only when the problem is hard.
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Neural networks are mathematical models which basically learn from training data (so it does learn from experience in this sense) and apply it to test/evaluation data (to do classifications or predictions). But I will not consider it as smart, because it is 1) extensive parameter-tuning is needed by the human modeller and 2) my definition of smart is the ability to adapt to changing conditions, a neural network basically requires lots of iterations to form a model in the real world (slow to build and deploy).\n\nYes, two diverse neural networks can "teach" each other through ensemble learning - combining the results of both to form a meta-result. It has been shown that the more diverse (or different) the models are, the combination of their results will produce much better than individual ones.
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Artificial systems are good!  Or rather, neutral.  What makes them good or bad is their content.  Fight for freedom, but do it through the channels of these systems, unless they don't exist.  Without them, there is chaos, right?  Live your life as free as you can, but understand why the stuff is there.
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The brain is a highly parallel electrochemical computing and storage device.  Because of this, it can be (and is) quite "slow" compared with a linear device and not well suited for exact sequential calculations of any significant complexity, but it is extremely well-suited for large-scale pattern storage and recognition.  The massive parallelism also makes for a very fault-tolerant device, which is required for an organic construction where individual storage nodes can (and do) become inoperable over time, and the storage within individual nodes is not guaranteed to be permanent due to its electrochemical nature.\n\nMany computer scientists and engineers have been attempting to simulate the massive parallel computational ability of an organic brain for some time now.  Some topics for further research:  neuroscience, neural networks, parallel distributed computing, cognitive science, organic microcircuitry, and artificial intelligence.
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Yes.  The more you use a neuron network the stronger it becomes.  The increase is minuscule, but certainly large enough to measure.  Good luck in your quest for more knowledge. Ciao!\n-C