What is artificial intelligence| How A.I used | History of A.I

              ARTIFICIAL  INTELLIGENCE 

What is Artificial Intelligence?

Artificial intelligence (AI) is wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. AI is an interdisciplinary science with multiple approaches, but advancements in machine learning and deep learning are creating a paradigm shift in virtually every sector of the tech industry. 

HOW DOES ARTIFICIAL INTELLIGENCE WORK?

Can machines think? — Alan Turing, 1950 

Not exactly 10 years in the wake of breaking the Nazi encryption machine Enigma and aiding the Allied Forces win World War II, mathematician Alan Turing changed history a second time with a straightforward inquiry: "Can machines think?" 

Turing's paper "Registering Machinery and Intelligence" (1950), and it's resulting Turing Test, set up the major objective and vision of man-made brainpower. 

what is fake intelligenceAt it's center, AI is the part of software engineering that expects to address Turing's inquiry in the confirmed. It is the undertaking to reproduce or mimic human knowledge in machines. 

The far reaching objective of man-made consciousness has offered ascend to numerous inquiries and discussions. To such an extent, that no solitary meaning of the field is all around acknowledged. 

The significant impediment in characterizing AI as just "building machines that are shrewd" is that it doesn't really clarify what man-made brainpower is? What makes a machine astute? 

In their earth shattering course reading Artificial Intelligence: A Modern Approach, writers Stuart Russell and Peter Norvig approach the inquiry by binding together their work around the topic of shrewd specialists in machines. In light of this, AI is "the investigation of specialists that get percepts from the climate and perform activities." (Russel and Norvig viii) 

Norvig and Russell proceed to investigate four unique methodologies that have verifiably characterized the field of AI: 

Thinking humanly 

Thinking soundly 

Acting humanly 

Acting reasonably 

The initial two thoughts concern manners of thinking and thinking, while the others manage conduct. Norvig and Russell center especially around normal specialists that demonstration to accomplish the best result, noticing "all the abilities required for the Turing Test likewise permit a specialist to act reasonably." (Russel and Norvig 4). 

Patrick Winston, the Ford teacher of man-made reasoning and software engineering at MIT, characterizes AI as "calculations empowered by requirements, uncovered by portrayals that help models focused at circles that tie thinking, discernment and activity together."

While these definitions may appear to be theoretical to the normal individual, they help center the field as a region of software engineering and give a plan to mixing machines and projects with AI and different subsets of man-made reasoning. 

While tending to a group at the Japan AI Experience in 2017, DataRobot CEO Jeremy Achin started his discourse by offering the accompanying meaning of how AI is utilized today: 

"Computer based intelligence is a PC framework ready to perform assignments that customarily require human insight... A significant number of these man-made brainpower frameworks are controlled by AI, some of them are fueled by profound learning and some of them are controlled by exceptionally exhausting things like principles."

HOW IS AI USED?

Man-made brainpower by and large falls under two general classifications: 

Thin AI: Sometimes alluded to as "Feeble AI," this sort of man-made brainpower works inside a restricted setting and is a recreation of human knowledge. Slender AI is regularly centered around playing out a solitary assignment very well and keeping in mind that these machines may appear to be insightful, they are working under definitely a greater number of imperatives and impediments than even the most essential human knowledge. 

Counterfeit General Intelligence (AGI): AGI, here and there alluded to as "Solid AI," is the sort of computerized reasoning we find in the motion pictures, similar to the robots from Westworld or Data from Star Trek: The Next Generation. AGI is a machine with general insight and, similar as an individual, it can apply that knowledge to tackle any issue.

HISTORY OF AI

 

Savvy robots and fake creatures previously showed up in the antiquated Greek fantasies of Antiquity. Aristotle's improvement of the logic and it's utilization of deductive thinking was a critical second in humanity's mission to comprehend its own knowledge. While the roots are long and profound, the historical backdrop of man-made reasoning as we consider it today traverses not exactly a century. Coming up next is a brief glance at the absolute most significant occasions in AI. 

1943 

Warren McCullough and Walter Pitts distribute "A Logical Calculus of Ideas Immanent in Nervous Activity." The paper proposed the first mathematic model for building a neural organization. 

1949 

In his book The Organization of Behavior: A Neuropsychological Theory, Donald Hebb proposes the hypothesis that neural pathways are made from encounters and that associations between neurons become more grounded the more often they're utilized. Hebbian learning keeps on being a significant model in AI. 

1950 

Alan Turing distributes "Registering Machinery and Intelligence, proposing what is currently known as the Turing Test, a technique for deciding whether a machine is shrewd. 

Harvard students Marvin Minsky and Dean Edmonds assemble SNARC, the primary neural organization PC. 

Claude Shannon distributes the paper "Programming a Computer for Playing Chess." 

Isaac Asimov distributes the "Three Laws of Robotics." 

1952 

Arthur Samuel builds up a self-learning system to play checkers. 

1954 

The Georgetown-IBM machine interpretation try consequently deciphers 60 deliberately chose Russian sentences into English. 

1956 

The expression man-made consciousness is begat at the "Dartmouth Summer Research Project on Artificial Intelligence." Led by John McCarthy, the meeting, which characterized the extension and objectives of AI, is broadly viewed as the introduction of man-made reasoning as far as we might be concerned today. 

Allen Newell and Herbert Simon show Logic Theorist (LT), the main thinking program. 

1958 

John McCarthy builds up the AI programming language Lisp and distributes the paper "Projects with Common Sense." The paper proposed the theoretical Advice Taker, a total AI framework with the capacity to gain for a fact as adequately as people do. 

1959 

Allen Newell, Herbert Simon and J.C. Shaw build up the General Problem Solver (GPS), a program intended to copy human critical thinking. 

Herbert Gelernter builds up the Geometry Theorem Prover program. 

Arthur Samuel coins the term AI while at IBM. 

John McCarthy and Marvin Minsky found the MIT Artificial Intelligence Project. 

1963 

John McCarthy begins the AI Lab at Stanford. 

1966 

The Automatic Language Processing Advisory Committee (ALPAC) report by the U.S. government subtleties the absence of progress in machine interpretations research, a significant Cold War activity with the guarantee of programmed and immediate interpretation of Russian. The ALPAC report prompts the retraction of all administration financed MT projects. 

1969 

The main fruitful master frameworks are created in DENDRAL, a XX program, and MYCIN, intended to analyze blood contaminations, are made at Stanford. 

1972 

The rationale programming language PROLOG is made. 

1973 

The "Lighthill Report," itemizing the failure in AI research, is delivered by the British government and prompts extreme cuts in subsidizing for man-made brainpower projects. 

1974-1980 

Dissatisfaction with the advancement of AI improvement prompts significant DARPA reductions in scholarly awards. Joined with the prior ALPAC report and the earlier year's "Lighthill Report," man-made reasoning subsidizing evaporates and research slows down. This period is known as the "Principal AI Winter." 

1980 

Computerized Equipment Corporations creates R1 (otherwise called XCON), the principal fruitful business master framework. Intended to design orders for new PC frameworks, R1 commences a speculation blast in master frameworks that will keep going for a large part of the decade, viably finishing the principal "man-made intelligence Winter." 

1982 

Japan's Ministry of International Trade and Industry dispatches the aggressive Fifth Generation Computer Systems project. The objective of FGCS is to create supercomputer-like execution and a stage for AI advancement. 

1983 

In light of Japan's FGCS, the U.S. government dispatches the Strategic Computing Initiative to give DARPA financed research in cutting edge processing and computerized reasoning. 

1985 

Organizations are spending in excess of a billion dollars per year on master frameworks and a whole industry known as the Lisp machine market jumps up to help them. Organizations like Symbolics and Lisp Machines Inc. assemble particular PCs to run on the AI programming language Lisp. 

1987-1993 

As figuring innovation improved, less expensive choices arose and the Lisp machine market fell in 1987, introducing the "Second AI Winter." During this period, master frameworks demonstrated too costly to even think about keeping up and update, ultimately becoming undesirable. 

Japan ends the FGCS project in 1992, refering to disappointment in gathering the goal-oriented objectives plot 10 years sooner. 

DARPA closes the Strategic Computing Initiative in 1993 subsequent to spending almost $1 billion and missing the mark concerning assumptions. 

1991 

U.S. powers send DART, a mechanized coordinations arranging and planning apparatus, during the Gulf War. 

1997 

IBM's Deep Blue beats world chess champion Gary Kasparov 

2005 

STANLEY, a self-driving vehicle, wins the DARPA Grand Challenge. 

The U.S. military starts putting resources into self-governing robots as dynamic Boston's "Huge Dog" and iRobot's "PackBot." 

2008 

Google makes advancements in discourse acknowledgment and presents the element in its iPhone application. 

2011 

IBM's Watson destroys the opposition on Jeopardy!. 

2012 

Andrew Ng, author of the Google Brain Deep Learning project, takes care of a neural organization utilizing profound learning calculations 10 million YouTube recordings as a preparation set. The neural organization figured out how to perceive a feline without being determined what a feline is, introducing advancement time for neural organizations and profound picking up financing. 

2014 

Google makes first self-driving vehicle to finish a state driving assessment. 

2016 

Google DeepMind's AlphaGo massacres title holder Go player Lee Sedol. The multifaceted nature of the old Chinese game was viewed as a significant obstacle to clear in AI.