diff betwn artificial intelligence and neural networks?
82031
Artificial Intellegence is a broad term that describes many programming techniques that mimic human thought. Things like expert systems, Alpha-Beta trees, and semantic networks.\n\nNeural Networks are formed from perceptrons that mimic a human neuron. So some inputs are fed to a perceptron and the perceptron weights them. If the output exceeds a threshold, the perceptron fires. The weights are the programming and there are ways to "learn" these weights from experience (for example, Hebb's learning rule). So neural networks can learn to read optical characters, for example, or play tic tac toe.\n\nOf course, AI can do the same thing, but AI techniques tend to be applied to more conventional programming techniques (how do we chain through rules, how do we examine the most number of game moves ahead). NN "learn" from training and experience how to get desired behaviors.
566997
There is a lot of information here, plus a large bibliography:\n\nhttp://www.dsi.unifi.it/ANNxDAR/
1114478
I think you just need to go through this Wikipedia Page..\nIts really well maintained & with thorough explaination of various concepts..\nCheck: http://en.wikipedia.org/wiki/Portal:Artificial_intelligence
55663
That depends on your definition of intelligence.\n\nThis is just the way I see it, machine learning is a machine learning, by training or whatever process, how to execute a process rather than it being preprogrammed. An example of this is the adaptive programming in the engine control computers of cars that have been around for almost 20 years.\n\nArtificial intelligence would, in my opinion, not only would it learn what to do, but also decide when to do what it wants to do. Intelligence, at least partly, is the ability to make decisions without instruction and programming.\n\nI know that this is vague, but I am not sure if that is what you were looking for. Hope this helps.
888477
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
70389
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.
158087
There is no such thing. Artificial intelligence hasn't progressed enough to reliably mimic natural language understanding, much less understand and solve word problems. Looks like you're stuck doing your own homework.
582832
As a theory in the philosophy of mind, artificial intelligence (or AI) is the view that human cognitive mental states can be duplicated in computing machinery. Accordingly, an intelligent system is nothing but an information processing system. Discussions of AI commonly draw a distinction between weak and strong AI. Weak AI holds that suitably programmed machines can simulate human cognition. Strong AI, by contrast, maintains that suitably programmed machines are capable of cognitive mental states. The weak claim is unproblematic, since a machine which merely simulates human cognition need not have conscious mental states. It is the strong claim, though, that has generated the most discussion, since this does entail that a computer can have cognitive mental states. In addition to the weak/strong distinction, it is also helpful to distinguish between other related notions. First, cognitive simulation is when a device such as a computer\nsimply has the same the same input and output as a human. Second, cognitive replication occurs when the same internal causal relations are involved in a computational device as compared with a human brain. Third, cognitive emulation occurs when a computational device has the same causal relations and is made of the same stuff as a human brain. This condition clearly precludes silicon-based computing machines from emulating human cognition. Proponents of weak AI commit themselves only to the first condition, namely cognitive simulation. Proponents of strong AI, by contrast, commit themselves to the second condition, namely cognitive replication, but not the third condition. Proponents of strong AI are split between two camps: (a) classical computationalists, and (b) connectionists. According to classical computationalism, computer intelligence involves central processing units operating on symbolic representations. That is, information in the form of symbols is processed serially (one datum after another) through a central processing unit. Daniel Dennett, a key proponent of classical computationalism, holds to a top-down progressive decomposition of mental activity. That is, more complex systems break down into more simple ones, which end in binary on-off switches. There is no homunculi, or tiny person inside a cognitive system which does the thinking. Several criticisms have been launched against the classical computationalist position. First, Dennett's theory, in particular, shows only that digital computers do not have homunculi. It is less clear that human cognition can be broken down into such subsystems. Second, there is no evidence for saying that cognition is computational in its structure, rather than saying that it is like computation. Since we do not find computational systems in the natural world, it is more safe to presume that human thinking is only like computational processes.\nThird, human cognition seems to involves a global understanding of one's environment, and this is not so of computational processes. Given these problems, critics contend that human thinking seems to be functionally different than digital or serial programming.
557740
whatever your area of interest(s) read up on them and learn stuff u didnt know before. thinkin bout stuff u kno dont improve neuron links that r there already - make some new ones. learn somethin new, take up a new hobbie
966099
It's way too early to ask this, so use your fictional imagination.  The application is likely to be extremely dense memory.  More benefit would come from using it for computations for which it is suited.  To build a von Neuman architecture machine, you would need to do a tremendous amount of work just to be as good as today's traditional technology.  I don't think it's particularly suited to neural net computation, and that technology is already short of applications.  NMRI is extremely unlikely, since it enormously increases the cost without any obvious benefit.  The technology is far too immature to answer the other questions.
616364
CS deals more with software and programming.\n\nCE includes hardware and architecture
691417
I hope you mean Neural Net.  It is a computer simulation (program) that mimics the brain activity in its simplest form.\n\nIt is used to learn by example.  That is if we want a computer program to learn to detect printed alphabets, then we feed the neural net (program) examples of alphabets (different alphabets, sizes, a bit changed once a while etc) and the true value.\n\nBy processing the error between the guess by the network and the actual value, it learns, like we learn to ride a bicycle.
585315
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.
671217
in terms of memory capacity and storage, the human brain outperforms even a supercomputer.\n\nhowever, in terms of memory retrieval and information processing, the computer outperforms the human brain...
172575
Well, using neural networks, the program can be trained to predict certain outcomes.\n\nSay you have a bunch of random data (the more random the better) that you think could build a connection to a specified outcome.  You can feed this data through neural network where it'll learn the data and any trends throughout.  Then you can program it to give certain results.\n\nAnother example:\n\nYour an ad agency and you want to target only four types of people.  Well obviously there are more than four categories of people to target, but with neural networks you can come up with four pretty good ads that'll cover all the people.  So you take data of people at Central Park.  You notice that people who wear white shirts usually eat hot dogs on friday, but only if the temperature is above 70 F.  So now you have for data points: color of shirt, what the eat, when they eat it, and on what kind of day.  Now do this same thing for 100's or 1000's of people and feed that data into the neural network you program.  The program if trained enough can come up with some conclusions about what those people would like for our advertisement.\n\nThis is a very simplified analysis but you can kind of get the point:\n\nLook at someones heredity, sertain genomes, and train the neural network with that data and then possibly figure out some pretty neat conclusions from that.
396535
Knowledge and intelligence are two wholly different things, but one usually follows the other. Intelligence is the ability to rationally solve problems. Knowledge is just knowing a lot of stuff, like an encyclopedia.\n\nOh, and I hope that Fruitcake! is joking.
88006
Neural Networks have the capability of learning from a just a few training samples. Solution space for neural network based reasoning is not restricted to linearity. i.e. a neural network automatically forms a complex formula(non-linear) fitting the inputs and the previous well known results. \nSolving non-linear systems using mathematical models is very complex and sometimes impossible. That's why we go for NN, Fuzzy, Genetic algorithms.