Artificial Intelligence(AI) is quite wide-ranging branch of computer science which involves building smart machines capable of performing tasks which otherwise requires human intelligence. AI is interdisciplinary as it can be applied to many fields and there does exists some of it’s application in almost every other science field.
Specific advancements in Machine Learning have enabled AI to do more complex tasks and it’s capability is improving day by day, moving onto doing quite complex tasks as well.
How AI Works?
It all started in 20th century when Alan Turing put forward his paper “Computing Machinery And Intelligence(1950)” which establishes a benchmark for intelligence of computing systems called Turing Test.
At it’s core, AI is the branch of computer science that aims to answer Turing’s question in the affirmative. It is the endeavour to replicate or simulate human intelligence in machines. The expansive goal of artificial intelligence has given rise to many questions and debates. So much so, that no singular definition of the field is universally accepted.
The following is kind of benchmark set by different researchers about a machine being intelligence: –
- Thinking like a human
- Acting like a human
- Thinking rationally
Can Machines Think? – Alan Turing, 1950
History of AI
The idea about Artificial Intelligence ways back to the Ancient Greece, where great people like Aristotle was developing Syllogism and using it to understand human intelligence ultimately. While the roots are long, the major developments in area of AI happened just in the last century. The following are important events in the development of AI: –
|Time||Development in AI|
|1943||Warren McCullough and Walter Pitts publish “A Logical Calculus of Ideas Immanent in Nervous Activity.” The paper proposed the first mathematic model for building a neural network.|
|1949||In his book The Organization of Behavior: A Neuropsychological Theory, Donald Hebb proposes the theory that neural pathways are created from experiences and that connections between neurons become stronger the more frequently they’re used. Hebbian learning continues to be an important model in AI.|
|1950||1. Alan Turing publishes “Computing Machinery and Intelligence, proposing what is now known as the Turing Test, a method for determining if a machine is intelligent. |
2. Harvard undergraduates Marvin Minsky and Dean Edmonds build SNARC, the first neural network computer.
3. Claude Shannon publishes the paper “Programming a Computer for Playing Chess.“
4. Isaac Asimov publishes the “Three Laws of Robotics.”
|1952||Arthur Samuel develops a self-learning program to play checkers.|
|1954||The Georgetown-IBM machine translation experiment automatically translates 60 carefully selected Russian sentences into English.|
|1956||1. The phrase artificial intelligence is coined at the “Dartmouth Summer Research Project on Artificial Intelligence.” Led by John McCarthy, the conference, which defined the scope and goals of AI, is widely considered to be the birth of artificial intelligence as we know it today.|
2. Allen Newell and Herbert Simon demonstrate Logic Theorist (LT), the first reasoning program.
|1958||John McCarthy develops the AI programming language Lisp and publishes the paper “Programs with Common Sense.” The paper proposed the hypothetical Advice Taker, a complete AI system with the ability to learn from experience as effectively as humans do.|
|1958||1. Allen Newell, Herbert Simon and J.C. Shaw develop the General Problem Solver (GPS), a program designed to imitate human problem-solving.|
2. Herbert Gelernter develops the Geometry Theorem Prover program.
3. Arthur Samuel coins the term machine learning while at IBM.
4. John McCarthy and Marvin Minsky found the MIT Artificial Intelligence Project.
|1963||John McCarthy starts the AI Lab at Stanford.|
|Time||Development in AI|
|1966||The Automatic Language Processing Advisory Committee (ALPAC) report by the U.S. government details the lack of progress in machine translations research, a major Cold War initiative with the promise of automatic and instantaneous translation of Russian. The ALPAC report leads to the cancellation of all government-funded MT projects.|
|1969||The first successful expert systems are developed in DENDRAL, a XX program, and MYCIN, designed to diagnose blood infections, are created at Stanford.|
|1972||The logic programming language PROLOG is created.|
|1973||The “Lighthill Report,” detailing the disappointments in AI research, is released by the British government and leads to severe cuts in funding for artificial intelligence projects.|
|1974-1980||Frustration with the progress of AI development leads to major DARPA cutbacks in academic grants. Combined with the earlier ALPAC report and the previous year’s “Lighthill Report,” artificial intelligence funding dries up and research stalls. This period is known as the “First AI Winter.”|
|1980||Digital Equipment Corporations develops R1 (also known as XCON), the first successful commercial expert system. Designed to configure orders for new computer systems, R1 kicks off an investment boom in expert systems that will last for much of the decade, effectively ending the first “AI Winter.”|
|1982||Japan’s Ministry of International Trade and Industry launches the ambitious Fifth Generation Computer Systems project. The goal of FGCS is to develop supercomputer-like performance and a platform for AI development.|
|1983||In response to Japan’s FGCS, the U.S. government launches the Strategic Computing Initiative to provide DARPA funded research in advanced computing and artificial intelligence.|
|1985||Companies are spending more than a billion dollars a year on expert systems and an entire industry known as the Lisp machine market springs up to support them. Companies like Symbolics and Lisp Machines Inc. build specialized computers to run on the AI programming language Lisp.|
|1987-1993||As computing technology improved, cheaper alternatives emerged and the Lisp machine market collapsed in 1987, ushering in the “Second AI Winter.” During this period, expert systems proved too expensive to maintain and update, eventually falling out of favor.|
|1991||U.S. forces deploy DART, an automated logistics planning and scheduling tool, during the Gulf War.|
|1997||IBM’s Deep Blue beats world chess champion Gary Kasparov|
|2005||STANLEY, a self-driving car, wins the DARPA Grand Challenge.|
|2008||Google makes breakthroughs in speech recognition.|
|2011||IBM’s Watson trounces the competition on Jeopardy!.|
|2012||Andrew Ng, founder of the Google Brain Deep Learning project, feeds a neural network using deep learning algorithms 10 million YouTube videos as a training set. The neural network learned to recognize a cat without being told what a cat is, ushering in breakthrough era for neural networks and deep learning funding.|
|2014||Google makes first self-driving car to pass a state driving test.|
|2016||Google DeepMind’s AlphaGo defeats world champion Go player Lee Sedol. The complexity of the ancient Chinese game was seen as a major hurdle to clear in AI.|
Use Cases Of AI
There are many companies which are using AI for making their products either better or offering AI as a service to their users: –
- Grammerly – Uses AI for text analysis like spelling checking etc
- Tempus – Uses AI to analyze the Medical and Clinical data
- Data Robot – DataRobot provides data scientists with a platform for building and deploying machine learning models. The software helps companies solve challenges by finding the best predictive model for their data.
- Narrative Stories – This company translate data into stories by highlighting the most relevant and interesting information in data.
- Alphasense – It’s an AI-powered search engine designed to help investment firms, banks and Fortune 500 companies find important information within transcripts, filings, news and research. The technology uses artificial intelligence to expand keyword searches for relevant content.
- Neurala – It’s developing “The Neurala Brain,” a deep learning neural network software that makes devices like cameras, phones and drones smarter and easier to use.