Artificial Intelligence is not the same as it looks today, it took many years to bring the AI to this shape as it sees today, and many researchers, scientists, and mathematicians play vital roles in developing AI to the position where it is now.
AI utilizes many
previous scientific inventions not identified here because AI as a science has
only existed since the middle of the 20th century. The information below
provides examples of key milestones and major trends in AI.
Learn more about the basics of AI in our comprehensive guide: Introduction to AI.
1931:
In 1931, an Austrian
mathematician, Kurt Gödel, demonstrated that in first-order predicate logic,
every true statement can be derived. When it comes to higher-order logic, on
the other hand, there are true statements that can not be derived.
1937:
In 1937, Alan Turing, an English Mathematician and Computer scientist, pointed out the limits of
intelligent machines with the halting problem.
1943:
In 1943, McCulloch and
Pitts wrote a paper in scientific history, titled ‘A Logical Calculus of Ideas
Immanent in Nervous Activity,’ that models neural networks and makes the
connection to propositional logic.
1950:
In 1950, Alan Turing again defined machine intelligence with the Turing test and wrote about
learning machines and genetic algorithms.
1951:
In 1951, Marvin Minsky, an American Computer Scientist, developed a neural network machine and
demonstrated it with 3000 vacuum tubes, which simulated 40 neurons.
1955:
In 1955, Arthur Samuel, an American Computer Scientist, developed a computer program for a game checker
that plays better than its developer.
1956:
In 1956, McCarthy
organized a conference at Dartmouth College, at which the name
Artificial Intelligence was first introduced.
In the same year,
Newell and Simon of Carnegie Mellon University presented the Logic Theorist, which was the first symbol-processing computer program.
1958:
In 1958, McCarthy
first invented the high-level language LISP at MIT (Massachusetts Institute of
Technology). He writes a program that is capable of modifying itself.
1959:
In 1959, an American
Computer Scientist, David Gelernter, built the Geometry Theorem Prover, which
automatically proves geometric theorems by using established geometric axioms,
definitions, and previously proven theorems.
1961:
The General Problem
Solver (GPS) was developed by Allen Newell and Herbert A. Simon,n in 1961, was
designed to simulate human problem-solving processes and thought, making it a
foundational program in the field of Artificial Intelligence.
1963:
In 1963, Professor
John McCarthy founded the AI Lab at Stanford University.
1969:
Minsky and Papert show
in their book Perceptrons that the perceptron, a very simple neural network,
can only represent linear functions.
1972:
In 1972, Alain
Colmerauer, a French scientist, invented the logic programming language PROLOG.
Also in 1972, British
physician de Dombal developed an expert system for the diagnosis of severe
abdominal pain.
1976:
An expert system, MYCIN, was developed by Shortliffe and Buchanan. MYSIN was used for the diagnosis of
infectious diseases.
1981:
Japan’s Ministry of
International Trade and Administration (MITI), in cooperation with eight
leading computer companies, launched a research project, project called the
‘Fifth Generation Project’. The goal of the project was to build a powerful
PROLOG machine.
1982:
In 1982, the expert
system R1 was developed. It was used for configuring computers, saving Digital
Equipment Corporation 400 million dollars per year.
1986:
1986 was a pivotal
year; the field of AI experienced a significant resurgence, which was referred to
as the ‘Renaissance of neural networks’. It was an expert system that brought
advancement in deep learning and the availability of powerful hardware like
GPUs, enabling them to tackle complex problems in various fields like computer
vision, natural language processing, and drug discovery.
1990:
Pearl and Cheeseman
played important roles in the period 1990, they integrated artificial
intelligence with probability theory with Bayesian networks. Multi-agent
systems became popular in this period.
1992:
TD-Gammon showcased
the power of temporal difference (TD) learning by demonstrating the advantages
of reinforcement learning.
1993:
In 1993, autonomous
robots called Robocup were developed. It was an initiative to build
soccer-playing robots.
1995:
In 1995, an American
statistician, Vladimir Vapnik, developed a support vector machine from
statistical learning theory. That machine is very important today.
1997:
In 1997, IBM developed
a chess computer called Deep Blue. Which then defeated the chess world champion, Gary Kasparov.
In 1997, the first
international RoboCup competition took place in Japan.
2003:
The robots that
performed in RoboCup demonstrate impressively what AI and robotics are capable
of.
2006:
Service robotics has
become a major artificial intelligence research area.
2009:
In 2009, Google
developed the first self-driving car that drove on the California freeway.
2010:
In 2010, Autonomous
robots began to improve their behaviors through self-learning.
2011:
In 2011, IBM developed
a machine called ‘Watson’ that beat two human champions on the television game
show called ‘Jeopardy!’. Watson understands natural language and can answer
difficult questions very quickly.
2015:
2015 was the most
important year in the evolution of artificial intelligence:
- Daimler developed the first autonomous
truck on the Autobahn.
- Google self-driving cars have driven over
one million miles and operate within cities.
- Deep learning enables very good image
classification.
- Paintings in the style of the old masters
can be automatically generated using deep learning. AI becomes creative.
2016:
In 2016, the Go program
AlphaGo, which was developed by Google DDeepMinddefeatedt the European champion with
5:0 in January and Korean Lee Sedol, one of the world’s best Go players, with
4:1 in March. Deep learning techniques applied to pattern recognition, as well
as reinforcement learning and Monte Carlo tree search, led to this success.
The AI revolution:
Around 2010, after about twenty-five years of research on artificial neural networks, the researchers began to reap the rewards of this research effort. The deep learning networks are quite capable of learning how to classify images, with very high accuracy in some cases. Since image classification is pivotal for all classes of smart robots, this was the kick-off point for the AI revolution that led to the development of smart self-driving cars and service robots.
The future of AI is
technological advancements, with more money flowing into and changing social
perception of AI algorithms and related hardware, which will shape the future of AI.
The area ripe for empowerment and development in AI is machine learning.
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