Posts Tagged ‘Artificial Intelligence’
Artificial Intelligence Overview
What is Artificial Intelligence?
Artificial Intelligence (AI) is the study and creation of computer systems that can perceive, reason and act. The primary aim of AI is to produce intelligent machines. The intelligence should be exhibited by thinking, making decisions, solving problems, more importantly by learning. AI is an interdisciplinary field that requires knowledge in computer science, linguistics, psychology, biology, philosopy and so on for serious research.
Strong Artificial Intelligence
It deals with creation of real intelligence artificially. Strong AI believes that machines can be made sentient or self-aware. There are two types of strong AI: Human-like AI, in which the computer program thinks and reasons to the level of human-being. Non-human-like AI, in which the computer program develops a non-human way of thinking and reasoning.
Weak Artificial Intelligence
Weak AI does not believe that creating human-level intelligence in machines is possible but AI techniques can be developed to solve many real-life problems.
AI and Nature
Nowadays AI techniques developed with the inspiration from nature is becoming popular. A new area of research what is known as Nature Inspired Computing is emerging. Biological inspired AI approaches such as neural networks and genetic algorithms are already in place.
Challenges
It is true that AI does not yet achieve its ultimate goal. Still AI systems could not defeat even a three year old child on many counts: ability to recognize and remember different objects, adapt to new situations, understand and generate human languages, and so on. The main problem is that we, still could not understand how human mind works, how we learn new things, especially how we learn languages and reproduce them properly.
Applications
There are many AI applications that we witness: Robotics, Machine translators, chatbots, voice recognizers to name a few. AI tehniques are used to solve many real life problems. Some kind of robots are helping to find land-mines, searching humans trapped in rubbles due to natural calamities.
Future of AI
AI is the best field for dreamers to play around. It must be evolved from the thought that making a human-machine is possible. Though many conclude that this is not possible, there is still a lot of research going on in this field to attain the final objective. There are inherent advantages of using computers as they do not get tired or loosing temper and are becoming faster and faster. Only time will say what will be the future of AI: will it attain human-level or above human-level intelligence or not.
References:
Stuart J. Russell, Artificial Intelligence: A Modern Approach (3rd Edition)
M. Tim Jones, Artificial Intelligence: A Systems Approach (Computer Science)
Patrick Henry Winston, Artificial Intelligence (3rd Edition)
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History Of Artificial Intelligence
History of Artificial Intelligence began when McCulloch and Walter Pitts proposed a model of artificial neurons in 1943. Significance of this work is that each neuron is characterised as being “on” or “off”. Switching to “on” occurred when significant number of neighbouring neurons stimulated. McCulloth and Pits showed that any computable function could be computed by network of connected neurons. In 1949, Donald Hebb modified the connection strength between neurons using a simple updating rule what is known as Hebbian learning even today. Marvin Minsky and Dean Edmonds built the first neural network computer called SNARC in 1951. This computer used 3000 vacuum tubes and a network of 40 neurons. Alan Turing introduced the infamous Turing test, machine learning, genetic algorithms, and reinforcement learning.
Artificial Intelligence was formally born in a workshop conducted by IBM at Dartmouth College in 1956. Mc Carthy coined the term Artificial Intelligence. It turns out to be the greatest milestone in the history of artificial intelligence. Newell, Shaw and Simon developed a reasoning program called Logic Theorist. It was meant for automatic theorem proving which led the development of Information Processing Language, the first list-processing language. Chomsky’s theory of generative grammar influenced Natural Language Processing. Rosenblatt invented perceptrons in 1958. John McCarthy developed LISP, an AI programming language.
Newell and Simson wrote General Problem Sover (GPS) in IPL. It imitated the way humans solve the problems. In 1976, they formulated physical symbol system and claimed that it is sufficient for general intelligent action. Herbert Gelernter developed Geometry Theorem Prover. A.L.Samuel developed checkers program between 1961 and 1965. J.A.Robinson introduced a inference method, resolution in 1965. In the same period DENDRAL, the first knowledge-based expert system was developed at Stanford University by J.Laderberg, Edward Feigenbaum and Carl Djerassi. DEDNDRAL was to infer molecular structure from the information provided by a mass spectrometer. Feigenbaum, Buchanan and Edward Shortlife developed an expert system called MYCIN to diagnose blood infections. MYCIN used 450 rules acquired from the information given by experts. MYCIN incorporated certainty factors, a calculas of uncertainty.
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