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What is Artificial Intelligence? And its Uses in 2020

What is Artificial Intelligence?

Artificial intelligence (AI) is wide-going part of software engineering worried about structure brilliant machines fit for performing undertakings that ordinarily require human intelligence. Computer based intelligence is an interdisciplinary science with different methodologies, yet headways in AI and profound learning are making a change in outlook in for all intents and purposes each division of the tech business.

 

Artificial intelligence machine learning matlab ai2020 ai robot future ai

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 helping the Allied Forces win World War II, mathematician Alan Turing changed history a second time with a basic inquiry: “Can machines think?”

Turing’s paper “Registering Machinery and Intelligence” (1950), and it’s resulting Turing Test, set up the key objective and vision of artificial intelligence.

what is artificial intelligenceAt it’s center, AI is the part of software engineering that intends to respond to Turing’s inquiry in the certifiable. It is the undertaking to recreate or reproduce human intelligence in machines.

The sweeping objective of artificial intelligence has offered ascend to numerous inquiries and discussions. To such an extent, that no solitary meaning of the field is generally acknowledged.

The significant impediment in characterizing AI as just “building machines that are astute” is that it doesn’t really clarify what artificial intelligence is? What makes a machine shrewd?

In their historic course book Artificial Intelligence: A Modern Approach, writers Stuart Russell and Peter Norvig approach the inquiry by binding together their work around the subject of canny specialists in machines. Considering this, AI is “the investigation of operators that get percepts from the earth and perform activities.” (Russel and Norvig viii)

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

  • Thinking humanly
  • Thinking objectively
  • Acting humanly
  • Acting judiciously

The initial two thoughts concern manners of thinking and thinking, while the others manage conduct. Norvig and Russell center especially around objective specialists that demonstration to accomplish the best result, noticing “all the abilities required for the Turing Test additionally permit an operator to act soundly.” (Russel and Norvig 4).

Patrick Winston, the Ford teacher of artificial intelligence and software engineering at MIT, characterizes AI as “calculations empowered by limitations, uncovered by portrayals that help models focused at circles that tie thinking, recognition and activity together.”

 

While these definitions may appear to be unique to the normal individual, they help center the field as a territory of software engineering and give a plan to mixing machines and projects with AI and different subsets of artificial intelligence.

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:

“Simulated intelligence is a PC framework ready to perform errands that customarily require human intelligence… Huge numbers of these artificial intelligence frameworks are fueled by AI, some of them are controlled by profound learning and some of them are controlled by exceptionally exhausting things like principles.”

 

HOW IS AI USED?

Artificial intelligence generally falls under two broad categories:

  • Narrow AI: Sometimes referred to as “Weak AI,” this kind of artificial intelligence operates within a limited context and is a simulation of human intelligence. Narrow AI is often focused on performing a single task extremely well and while these machines may seem intelligent, they are operating under far more constraints and limitations than even the most basic human intelligence.
  • Artificial General Intelligence (AGI): AGI, sometimes referred to as “Strong AI,” is the kind of artificial intelligence we see in the movies, like the robots from Westworld or Data from Star Trek: The Next Generation. AGI is a machine with general intelligence and, much like a human being, it can apply that intelligence to solve any problem.

ARTIFICIAL INTELLIGENCE EXAMPLES

  • Smart assistants (like Siri and Alexa)
  • Disease mapping and prediction tools
  • Manufacturing and drone robots
  • Optimized, personalized healthcare treatment recommendations
  • Conversational bots for marketing and customer service
  • Robo-advisors for stock trading
  • Spam filters on email
  • Social media monitoring tools for dangerous content or false news
  • Song or TV show recommendations from Spotify and Netflix

Narrow Artificial Intelligence

Narrow AI is all around us and is easily the most successful realization of artificial intelligence to date. With its focus on performing specific tasks, Narrow AI has experienced numerous breakthroughs in the last decade that have had “significant societal benefits and have contributed to the economic vitality of the nation,” according to “Preparing for the Future of Artificial Intelligence,” a 2016 report released by the Obama Administration.

A few examples of Narrow AI include:

  • Google search
  • Image recognition software
  • Siri, Alexa and other personal assistants
  • Self-driving cars
  • IBM’s Watson

Machine Learning & Deep Learning

Much of Narrow AI is powered by breakthroughs in machine learning and deep learning. Understanding the difference between artificial intelligence, machine learning and deep learning can be confusing. Venture capitalist Frank Chen provides a good overview of how to distinguish between them, noting:

“Artificial intelligence is a set of algorithms and intelligence to try to mimic human intelligence. Machine learning is one of them, and deep learning is one of those machine learning techniques.”

Simply put, machine learning feeds a computer data and uses statistical techniques to help it “learn” how to get progressively better at a task, without having been specifically programmed for that task, eliminating the need for millions of lines of written code. Machine learning consists of both supervised learning (using labeled data sets) and unsupervised learning (using unlabeled data sets).

Deep learning is a type of machine learning that runs inputs through a biologically-inspired neural network architecture. The neural networks contain a number of hidden layers through which the data is processed, allowing the machine to go “deep” in its learning, making connections and weighting input for the best results.

Artificial General Intelligence

The creation of a machine with human-level intelligence that can be applied to any task is the Holy Grail for many AI researchers, but the quest for AGI has been fraught with difficulty.

The search for a “universal algorithm for learning and acting in any environment,” (Russel and Norvig 27) isn’t new, but time hasn’t eased the difficulty of essentially creating a machine with a full set of cognitive abilities.

AGI has long been the muse of dystopian science fiction, in which super-intelligent robots overrun humanity, but experts agree it’s not something we need to worry about anytime soon.

HISTORY OF AI

Shrewd robots and artificial creatures originally showed up in the antiquated Greek fantasies of Antiquity. Aristotle’s improvement of the logic and it’s utilization of deductive thinking was a key second in humankind’s journey to comprehend its own intelligence. While the roots are long and profound, the historical backdrop of artificial intelligence 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 system.

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 as often as possible they’re utilized. Hebbian learning keeps on being a significant model in AI.

1950

  • Alan Turing distributes “Figuring Machinery and Intelligence, proposing what is presently known as the Turing Test, a technique for deciding whether a machine is astute.
  • Harvard students Marvin Minsky and Dean Edmonds manufacture SNARC, the main neural system 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 explore naturally deciphers 60 deliberately chose Russian sentences into English.

1956

  • The expression artificial intelligence 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 generally viewed as the introduction of artificial intelligence as we probably am aware it today.
  • Allen Newell and Herbert Simon show Logic Theorist (LT), the principal 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 as a matter of 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 emulate 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 momentary interpretation of Russian. The ALPAC report prompts the crossing out of all administration financed MT ventures.

1969

  • The principal effective 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 mistake in AI research, is delivered by the British government and prompts extreme cuts in subsidizing for artificial intelligence ventures.

1974-1980

  • Disappointment with the advancement of AI improvement prompts significant DARPA reductions in scholarly awards. Joined with the before ALPAC report and the earlier year’s “Lighthill Report,” artificial intelligence financing evaporates and research slows down. This period is known as the “Main AI Winter.”

1980

  • Computerized Equipment Corporations creates R1 (otherwise called XCON), the principal effective plug master framework. Intended to design orders for new PC frameworks, R1 commences a speculation blast in master frameworks that will keep going for a significant part of the decade, adequately finishing the main “simulated intelligence Winter.”

1982

  • Japan’s Ministry of International Trade and Industry dispatches the goal-oriented Fifth Generation Computer Systems venture. The objective of FGCS is to create supercomputer-like execution and a stage for AI improvement.

1983

  • Because of Japan’s FGCS, the U.S. government dispatches the Strategic Computing Initiative to give DARPA financed research in cutting edge processing and artificial intelligence.

1985

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

1987-1993

  • As registering innovation improved, less expensive options developed and the Lisp machine advertise crumbled in 1987, introducing the “Second AI Winter.” During this period, master frameworks demonstrated too costly to even think about maintaining and update, in the end becoming undesirable.
  • Japan ends the FGCS venture in 1992, refering to disappointment in meeting the goal-oriented objectives sketched out 10 years sooner.
  • DARPA closes the Strategic Computing Initiative in 1993 in the wake of spending almost $1 billion and missing the mark concerning desires.

1991

  • U.S. powers send DART, a computerized coordinations arranging and booking instrument, 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 independent robots as dynamic Boston’s “Large Dog” and iRobot’s “PackBot.”

2008

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

2011

  • IBM’s Watson wallops the opposition on Jeopardy!.

2012

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

2014

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

2016

  • Google DeepMind’s AlphaGo routs best on the planet Go player Lee Sedol. The intricacy of the old Chinese game was viewed as a significant obstacle to clear in AI.

 

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