Can neural network computers learn from experience, & if so, could they ever become what we would call 'smart'
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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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Smarter? Yes, I know there are, I have met many of them.\n\nWiser?  No.  Only experience gives you wisdom.
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Yes, theoretically its possible to make the computer think. This is called as AI (Artificial Intelligence). However the current technologies are good enough to make machines think within a narrow domain.\n\nYou tell the computer how a human would take a decision. The computer then analyses several such responses and decides the best course of action.
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Generally ANNs are considered to be more dynamic or adaptive to changing data in a system.  Where a logistic regression is more a statistical analysis network.  Actually logistic regression is a form of artificial network so you have the question a bit wrong.
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If it can't spell its own name correctly (ACQUIRED) I don't see how it will learn to spell computer...\n\nHowever if you are talking about an AI system, spelling is a rather simple task not requiring much in the way of knowledge.  A simple look up of valid words will do it...
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As a librarian, I highly recommend you go to Scirus, a comprehensive online medical science encyclopedia, and search for "neural coding."\n\nI did a preliminary search and found 7,157 professional sources.\n\nVisit Scirus online at: http://www.scirus.com/srsapp/
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A neural network is an interconnected group of biological neurons. In modern usage the term can also refer to artificial neural networks, which are constituted of artificial neurons. Thus the term 'Neural Network' specifies two distinct concepts:\n\n   1. A biological neural network is a plexus of connected or functionally related neurons in the peripheral nervous system or the central nervous system. In the field of neuroscience, it most often refers to a group of neurons from a nervous system that are suited for laboratory analysis.\n   2. Artificial neural networks were designed to model some properties of biological neural networks, though most of the applications are of technical nature as opposed to cognitive models.\n\nPlease see the corresponding articles for details on artificial neural networks or biological neural networks. This article focuses on the relationship between the two concepts.\n\nNeural networks are made of units that are often assumed to be simple in the sense that their state can be described by a single numbers, their "activation" values. Each unit generates an output signal based on its activation. Units are connected to each other very specifically, each connection having an individual "weight" (again described by a single number). Each unit sends its output value to all other units to which they have an outgoing connection. Through these connections, the output of one unit can influence the activations of other units. The unit receiving the connections calculates its activation by taking a weighted sum of the input signals (i.e. it multiplies each input signal with the weight that corresponds to that connection and adds these products). The output is determined by the activation function based on this activation (e.g. the unit generates output or "fires" if the activation is above a threshold value). Networks learn by changing the weights of the connections.
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Nah, I'd go back to what I was doing before. I was a Graphics Artist before I got into programming and unix system administration.\n\nI think the smart ones would be able to use their intelligence to retrain and get another good job. Something that takes curiosity and brains like an explorer or a scientist.\n\nThe ones that got into computers just because of the "big bucks" might be able to retrain. Perhaps something that takes some smarts like electrician or plumber.\n\nThen the ones who can't seem to find their butts with both hands (which is a good 75% of the folks I find on line) would go back to flipping burgers and asking "do you want fries with that?"
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A better, more direct man-machine interface between the brain and electronics.
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i thikn this page will help you with all your artificial intelligence questions\n\nhttp://en.wikipedia.org/wiki/Artificial_intelligence
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Less learned but perhaps smarter in the ways of the world?
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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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Not so much analyze as model. In other words, the ANN can be used to "fit" your data to a mathematical model through the process of "training" the ANN with your data. This would be useful for making predictions, etc. Also, you could run analyses on the predicted outputs from the ANN model, but I do not know if this would be of value to you in your work.
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Yes. It's called AI (Artificail Intelegence). i know a great site for it. Try http://www.pandorabots.com/pandora/talk?botid=f5d922d97e345aa1 for an AI chatbot, which you can have a conversation with.
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Machines can only be called intelligent when they learn new things without being programmed to. If from the basic program the machine takes in information and understands the relationship between the information. A computer is abit like the autistic person that can take in all the facts and builds up a knowledge base, but when it comes to expressing that knowledge it remains separate as in your example the computer would know the temperature, pressure etc but could not say if it was a good day or bad day, or what the temperature felt like at that time.
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Yes.  The human mind is just an incredibly complex computer which can manufacture it's own connections.  All we need to do is get the software and hardware sides of our current technology up to the level of our brains and beyond...  Easier said than done, I know.
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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.