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Comment We still know so little (Score 1) 191

And yet physics cannot explain consciousness - seems like we have quite a way to go. (Although quantum mechanics seems to tell us that consciousness and reality are somehow linked - seems like there might be quite a bit to explore there.) And we still do not understand our where our universe sits in the total scheme of things - are we in a black hole? And do we really think that there is no new physics in the range of size down to the Plank length? For those who think that we know a-lot about reality, I recommend the book "Doubt and Certainty", by George Sudarshan (https://en.wikipedia.org/wiki/E._C._George_Sudarshan) and Tony Rothman.

Comment I don't want it - and I am in IT (Score 5, Informative) 417

I know how software is made. I know how buggy and unreliable it is. In my car, I want things that are rock solid, or that at least fail gracefully. Also, I don't want distractions, like screens changing their content, or having to fiddle with a display while I am driving. I want fixed controls that are simple and display a single thing. Also, I don't want my car second-guessing what I want - there is nothing more annoying that the car deciding, "He pushed the window button to go down, but it is cold outside so he must only want it half way down" - I want my car to do exactly what I tell it: I don't want it to try to be "smart".

Comment Re:But don't equate coding with comp-sci (Score 1) 132

Yes, there is a-lot of programming involved today, but Watson is not just a search engine. So-called "deep learning" algorithms are really neural network simulations. They are programmed because most people don't have neural chips and so to create neural networks, people have to code then as a simulation. That's why IBM has now developed its "True North" chipset, with a million silicon neurons per chip. These are not programmed - they learn - and they run 1000 times faster (and with much less power) that equivalent simulations. Also, if you look at the code for deep learning neural network simulations, you will see that it is an implementation of neural network topologies (e.g., a cascaded restricted botzman network) and training algorithms (e.g., contrastive divergence) - you can't work on that code unless you understand those algorithms - the real work is in developing and fine-tuning the algorithms - not in the coding. Most of the people who work on that code are PhDs in AI - not programmers per se.

It is also true that there is a-lot of "glue code" to make these systems scale, but that is the type of code that I think will eventually be replaced by machine learning systems. But I don't have a crystal ball - we shall see!

Comment Re:But don't equate coding with comp-sci (Score 1) 132

Deep learning systems are not computers. They are neural networks. They are not programmed. If one runs such a system on a conventional computer, one is actually simulating the neural network - it will run 1000 times faster if you run it on a true neural network without a computer.

Comment Re:But don't equate coding with comp-sci (Score 1) 132

Actually, that is not true. Deep learning systems can learn to do things like weld. In the case of deep learning systems, the training involves having the system read a-lot of information. It "learns" very much the way that our brains learn. It is not like Prolog, which is a logic based system. Deep learning systems are networks connected by weighted paths, like the brain. Comparing these systems to a brain is premature - we don't yet understand the organization of the brain, but deep learning systems are neural networks like the brain is, and they can learn unstructured tasks by trial and error and by being shown - just as a welder would learn.

Comment Re:But don't equate coding with comp-sci (Score 1) 132

Yes, true, but those programmers are PhDs who have in-depth understanding of machine learning systems. And much of what they do is mathematics. Also, neural network "programs" are really just simulations of neural networks, e.g., "restricted boltzman networks" which are the key to current "deep learning" systems. If one uses an actual neural network (as in IBM's True North chip), there is no conventional programming.

Comment Re:But don't equate coding with comp-sci (Score 1) 132

Yes, I lived through CASE as well. I think you are right, this is not going to happen tomorrow. But I think it is coming. Watch the TED talk about deep learning - it is very enlightening about current prospects. I am anticipating that there will be further improvements - current learning systems cannot replace a programmer, but it seems to me that it does not have far to go, and there will be quite a lengthy period, I think, in which people will need to advise the systems and "operate them", the way that doctors operate the current Watson medical system. But it is anticipated that Watson will not need to be "operated" forever - that it will be able to act on its own. I guess we will have to wait to see what happens! :-/

Comment Re:But don't equate coding with comp-sci (Score 3, Interesting) 132

No, and perhaps I am wrong. But it is early days. Hinton's breakthrough in 2006 has opened up machine learning to a wide range of things that we thought impossible with those types of systems. Look at what is being done with IBM's Watson system - it has shrunk from a room to three pizza boxes and it is being used for medical diagnosis. Also look at their "True North" chips - these are not computers, but neural chips: each chip has a million neurons on it, and it can form connections to any other neuron on the system. This is early days, and we are still learning how to organize and train these systems. It is interesting that Hinton's breakthrough was in a training algorithm. I think that the handwriting is on the wall, but I could be wrong. But what I expect to see in 10 years is analysts working with machine learning systems to define requirements and the system takes it from there. Remember that systems like Watson are not programmed - they are trained, and they read the same things that you and I read (Watson has read all of wikipedia), and can listen and speak. They have already proven that they can do original research and have original insights that are beyond the reach of people due to the complexity.

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