The computer has evolved from the great hulking beast that once took up three rooms to the now portable versions that can be taken anywhere. The abilities that the computers have are also highly advanced in comparison. The early machines did little more than help humans to solve complicated math problems and store large amounts of information. Today computers play movies and control complicated machinery and systems to help avoid human mistakes.



Advancing what a computer can accomplish is the main area of study in the industry. People all over the world know the day is coming when computers can think and react like a human does, which they fear will render the human race obsolete. Most researchers are not a subscriber to this theory because computers will always lack most of the basic human emotions and not reactionary as the homo-sapien species does. This does little to console the masses as Hollywood has forever taken us to the dark place where computers rule the Earth and decide that the humans are not worth saving. Most of the experts in the field seriously doubt that it will ever come to that place in time but there are some concerns that they voice about the concept none the less.



Artificial intelligence is the term that is commonly used for computers that can think. This is actually a true term when you think about it. A computer is a completely artificial machine, made up of parts developed for a specific purpose. If the machine is given any kind of intelligence it must come from man himself, because the computer lacks the capability to perform such a task on its own. With this in mind, the researchers for artificial intelligence are working on a way to make the computers of the future more human like in nature. This is done by way of intelligence chips that are built into the computer system which teaches the machine how to learn on its own through outside sources and not having to be prompted to do so by man.



There have been some developments in the field over the years but there are still many questions that need to be answered. For one thing, computers lack the function to act outside of the logic stage. Computers will act upon their programming from the most logical point of view and not take any other factors into consideration. This is why such things occur that make computers seem like they are not human at all.



An experiment by a large company to build a computer that would defeat grand master chess champions took place some years ago. The company believed that they had created the perfect chess playing machine and put it to the test. In the end the grand master would win simply because he was able to think outside of the box rather than based on the statistics that the computer was playing with. This showed the world that we are still light years away from actual thinking machines that will replace the human race. The computer needs to come full circle from its current state and earn the marks that it can think with emotion and not just logic.

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कोम्पुताशनल टूल्स फॉर अर्तिफिसिअल Intelligence

Like a good gambler, Daphne Koller, a researcher at Stanford whose work has led to advances in artificial intelligence, sees the world as a web of probabilities.

There is, however, nothing uncertain about her impact.

A mathematical theoretician, she has made contributions in areas like robotics and biology. Her biggest accomplishment — and at age 39, she is expected to make more — is creating a set of computational tools for artificial intelligence that can be used by scientists and engineers to do things like predict traffic jams, improve machine vision and understand the way cancer spreads.

Ms. Koller’s work, building on an 18th-century theorem about probability, has already had an important commercial impact, and her colleagues say that will grow in the coming decade. Her techniques have been used to improve computer vision systems and in understanding natural language, and in the future they are expected to lead to an improved generation of Web search.

“She’s on the bleeding edge of the leading edge,” said Gary Bradski, a machine vision researcher at Willow Garage, a robotics start-up firm in Menlo Park, Calif.

Ms. Koller was honored last week with a new computer sciences award sponsored by the Association for Computing Machinery and the Infosys Foundation, the philanthropic arm of the Indian computer services firm Infosys.

The award to Ms. Koller, with a prize of $150,000, is viewed by scientists and industry executives as validating her research, which has helped transform artificial intelligence from science fiction and speculation into an engineering discipline that is creating an array of intelligent machines and systems. It is not the first such recognition; in 2004, Ms. Koller received a $500,000 MacArthur Fellowship.

Ms. Koller is part of a revival of interest in artificial intelligence. After three decades of disappointments, artificial intelligence researchers are making progress. Recent developments made possible spam filters, Microsoft’s new ClearFlow traffic maps and the driverless robotic cars that Stanford teams have built for competitions sponsored by the Defense Advanced Research Projects Agency.

Since arriving at Stanford as a professor in 1995, Ms. Koller has led a group of researchers who have reinvented the discipline of artificial intelligence. Pioneered during the 1960s, the field was originally dominated by efforts to build reasoning systems from logic and rules. Judea Pearl, a computer scientist at the University of California, Los Angeles, had a decade earlier advanced statistical techniques that relied on repeated measurements of real-world phenomena.

Called the Bayesian approach, it centers on a formula for updating the probabilities of events based on repeated observations. The Bayes rule, named for the 18th-century mathematician Thomas Bayes, describes how to transform a current assumption about an event into a revised, more accurate assumption after observing further evidence.

Ms. Koller has led research that has greatly increased the scope of existing Bayesian-related software. “When I started in the mid- to late 1980s, there was a sense that numbers didn’t belong in A.I.,” she said in a recent interview. “People didn’t think in numbers, so why should computers use numbers?”

Ms. Koller is beginning to apply her algorithms more generally to help scientists discern patterns in vast collections of data.

“The world is noisy and messy,” Ms. Koller said. “You need to deal with the noise and uncertainty.”

That philosophy has led her to do research in game theory and artificial intelligence, and more recently in molecular biology.

Her tools led to a new type of cancer gene map based on examining the behavior of a large number of genes that are active in a variety of tumors. From the research, scientists were able to develop a new explanation of how breast tumors spread into bone.

One potentially promising area to apply Ms. Koller’s theoretical work will be the emerging field of information extraction, which could be applied to Web searches. Web pages would be read by software systems that could organize the information and effectively understand unstructured text.

“Daphne is one of the most passionate researchers in the A.I. community,” said Eric Horvitz, a Microsoft researcher and president of the Association for the Advancement of Artificial Intelligence. “After being immersed for a few years with the computational challenges of decoding regulatory genomics, she confided her excitement to me, saying something like, ‘I think I’ve become a biologist — I mean a real biologist — and it’s fabulous.’ ”

To that end, Ms. Koller is spending a sabbatical doing research with biologists at the University of California, San Francisco. Because biology is increasingly computational, her expertise is vital in gaining deeper understanding of cellular processes.

Ms. Koller grew up in an academic family in Israel, the daughter of a botanist and an English professor. While her father spent a year at Stanford in 1981 when she was 12, she began programming on a Radio Shack PC that she shared with another student.

When her family returned to Israel the next year, she told her father, the botanist, that she was bored with high school and wanted to pursue something more stimulating in college. After half a year, she persuaded him to let her enter Hebrew University, where she studied computer science and mathematics.

By 17, she was teaching a database course at the university. The next year she received her master’s degree and then joined the Israeli Army before coming to the United States to study for a Ph.D. at Stanford.

She didn’t spend her time looking at a computer monitor. “I find it distressing that the view of the field is that you sit in your office by yourself surrounded by old pizza boxes and cans of Coke, hacking away at the bowels of the Windows operating system,” she said. “I spend most of my time thinking about things like how does a cell work or how do we understand images in the world around us?”

In recent years, many of her graduate students have gone to work at Google. However she tries to persuade undergraduates to stay in academia and not rush off to become software engineers at start-up companies.

She acknowledges that the allure of Silicon Valley riches can be seductive. “My husband still berates me for not having jumped on the Google bandwagon at the beginning,” she said. Still, she insists she does not regret her decision to stay in academia. “I like the freedom to explore the things I care about,” she said.

artificial intelligence

(AI)

Main

the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. Since the development of the digital computer in the 1940s, it has been demonstrated that computers can be programmed to carry out very complex tasks—as, for example, discovering proofs for mathematical theorems or playing chess—with great proficiency. Still, despite continuing advances in computer processing speed and memory capacity, there are as yet no programs that can match human flexibility over wider domains or in tasks requiring much everyday knowledge. On the other hand, some programs have attained the performance levels of human experts and professionals in performing certain specific tasks, so that artificial intelligence in this limited sense is found in applications as diverse as medical diagnosis, computer search engines, and voice or handwriting recognition.

अर्तिफिसिअल intelligence

Artificial intelligence (AI) is a branch of computer science and engineering that deals with intelligent behavior, learning, and adaptation in machines. Research in AI is concerned with producing machines to automate tasks requiring intelligent behavior. Examples include control, planning and scheduling, the ability to answer diagnostic and consumer questions, handwriting, speech, and facial recognition. As such, it has become an engineering discipline, focused on providing solutions to real life problems, software applications, traditional strategy games like computer chess and other video games.

Branchs of Artificial Intelligence

Branches of AI

Q. What are the branches of AI?

A. Here's a list, but some branches are surely missing, because no-one has identified them yet. Some of these may be regarded as concepts or topics rather than full branches.

logical AI
What a program knows about the world in general the facts of the specific situation in which it must act, and its goals are all represented by sentences of some mathematical logical language. The program decides what to do by inferring that certain actions are appropriate for achieving its goals. The first article proposing this was [McC59]. [McC89] is a more recent summary. [McC96b] lists some of the concepts involved in logical aI. [Sha97] is an important text.

search
AI programs often examine large numbers of possibilities, e.g. moves in a chess game or inferences by a theorem proving program. Discoveries are continually made about how to do this more efficiently in various domains.

pattern recognition
When a program makes observations of some kind, it is often programmed to compare what it sees with a pattern. For example, a vision program may try to match a pattern of eyes and a nose in a scene in order to find a face. More complex patterns, e.g. in a natural language text, in a chess position, or in the history of some event are also studied. These more complex patterns require quite different methods than do the simple patterns that have been studied the most.

representation
Facts about the world have to be represented in some way. Usually languages of mathematical logic are used.

inference
From some facts, others can be inferred. Mathematical logical deduction is adequate for some purposes, but new methods of non-monotonic inference have been added to logic since the 1970s. The simplest kind of non-monotonic reasoning is default reasoning in which a conclusion is to be inferred by default, but the conclusion can be withdrawn if there is evidence to the contrary. For example, when we hear of a bird, we man infer that it can fly, but this conclusion can be reversed when we hear that it is a penguin. It is the possibility that a conclusion may have to be withdrawn that constitutes the non-monotonic character of the reasoning. Ordinary logical reasoning is monotonic in that the set of conclusions that can the drawn from a set of premises is a monotonic increasing function of the premises. Circumscription is another form of non-monotonic reasoning.

common sense knowledge and reasoning
This is the area in which AI is farthest from human-level, in spite of the fact that it has been an active research area since the 1950s. While there has been considerable progress, e.g. in developing systems of non-monotonic reasoning and theories of action, yet more new ideas are needed. The Cyc system contains a large but spotty collection of common sense facts.

learning from experience
Programs do that. The approaches to AI based on connectionism and neural nets specialize in that. There is also learning of laws expressed in logic. [Mit97] is a comprehensive undergraduate text on machine learning. Programs can only learn what facts or behaviors their formalisms can represent, and unfortunately learning systems are almost all based on very limited abilities to represent information.

planning
Planning programs start with general facts about the world (especially facts about the effects of actions), facts about the particular situation and a statement of a goal. From these, they generate a strategy for achieving the goal. In the most common cases, the strategy is just a sequence of actions.

epistemology
This is a study of the kinds of knowledge that are required for solving problems in the world.

ontology
Ontology is the study of the kinds of things that exist. In AI, the programs and sentences deal with various kinds of objects, and we study what these kinds are and what their basic properties are. Emphasis on ontology begins in the 1990s.

heuristics
A heuristic is a way of trying to discover something or an idea imbedded in a program. The term is used variously in AI. Heuristic functions are used in some approaches to search to measure how far a node in a search tree seems to be from a goal. Heuristic predicates that compare two nodes in a search tree to see if one is better than the other, i.e. constitutes an advance toward the goal, may be more useful. [My opinion].

genetic programming
Genetic programming is a technique for getting programs to solve a task by mating random Lisp programs and selecting fittest in millions of generations. It is being developed by John Koza's group and here's a tutorial.

AI definition c/o Brittanica

the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. Since the development of the digital computer in the 1940s, it has been demonstrated that computers can be programmed to carry out very complex tasks—as, for example, discovering proofs for mathematical theorems or playing chess—with great proficiency. Still, despite continuing advances in computer processing speed and memory capacity, there are as yet no programs that can match human flexibility over wider domains or in tasks requiring much everyday knowledge. On the other hand, some programs have attained the performance levels of human experts and professionals in performing certain specific tasks, so that artificial intelligence in this limited sense is found in applications as diverse as medical diagnosis, computer search engines, and voice or handwriting recognition.

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