ARTIFICIAL INTELLIGENCE HISTORY

History of AI

Artificial intelligence as a concept began to take off in the 1950s when computer scientist Alan Turing released the paper “Computing Machinery and Intelligence,” which questioned if machines could think and how one would test a machine’s intelligence. This paper set the stage for AI research and development, and was the first proposal of the Turing test, a method used to assess machine intelligence. The term “artificial intelligence” was coined in 1956 by computer scientist John McCartchy in an academic conference at Dartmouth College.
Following McCarthy’s conference and throughout the 1970s, interest in AI research grew from academic institutions and U.S. government funding. Innovations in computing allowed several AI foundations to be established during this time, including machine learning, neural networks and natural language processing. Despite its advances, AI technologies eventually became more difficult to scale than expected and declined in interest and funding, resulting in the first AI winter until the 1980s.
In the mid-1980s, AI interest reawakened as computers became more powerful, deep learning became popularized and AI-powered “expert systems” were introduced. However, due to the complication of new systems and an inability of existing technologies to keep up, the second AI winter occurred and lasted until the mid-1990s.
By the mid-2000s, innovations in processing power, big data and advanced deep learning techniques resolved AI’s previous roadblocks, allowing further AI breakthroughs. Modern AI technologies like virtual assistants, driverless cars and generative AI began entering the mainstream in the 2010s, making AI what it is today.
 

1940s

• (1942) Isaac Asimov publishes the Three Laws of Robotics, an idea commonly found in science fiction media about how artificial intelligence should not bring harm to humans.
• (1943) Warren McCullough and Walter Pitts publish the paper “A Logical Calculus of Ideas Immanent in Nervous Activity,” which proposes the first mathematical 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.

1950s

• (1950) Alan Turing publishes the paper “Computing Machinery and Intelligence,” proposing what is now known as the Turing Test, a method for determining if a machine is intelligent. 
• (1950) Harvard undergraduates Marvin Minsky and Dean Edmonds build SNARC, the first neural network computer.
• (1950) Claude Shannon publishes the paper “Programming a Computer for Playing Chess.”
• (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) The phrase “artificial intelligence” is coined at the Dartmouth Summer Research Project on Artificial Intelligence. Led by John McCarthy, the conference is widely considered to be the birthplace of AI.
• (1956) Allen Newell and Herbert Simon demonstrate Logic Theorist (LT), the first reasoning program. 
• (1958) John McCarthy develops the AI programming language Lisp and publishes “Programs with Common Sense,” a paper proposing the hypothetical Advice Taker, a complete AI system with the ability to learn from experience as effectively as humans.  
• (1959) Allen Newell, Herbert Simon and J.C. Shaw develop the General Problem Solver (GPS), a program designed to imitate human problem-solving. 
• (1959) Herbert Gelernter develops the Geometry Theorem Prover program.
• (1959) Arthur Samuel coins the term “machine learning” while at IBM.
• (1959) John McCarthy and Marvin Minsky found the MIT Artificial Intelligence Project.

1960s

• (1963) John McCarthy starts the AI Lab at Stanford.
• (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, DENDRAL and MYCIN, are created at Stanford.

1970s

• (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 AI 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, AI funding dries up and research stalls. This period is known as the “First AI Winter.”

1980s

• (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 AI. 
• (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.

1990s

• (1991) U.S. forces deploy DART, an automated logistics planning and scheduling tool, during the Gulf War.
• (1992) Japan terminates the FGCS project in 1992, citing failure in meeting the ambitious goals outlined a decade earlier.
• (1993) DARPA ends the Strategic Computing Initiative in 1993 after spending nearly $1 billion and falling far short of expectations. 
• (1997) IBM’s Deep Blue beats world chess champion Gary Kasparov.

2000s

• (2005) STANLEY, a self-driving car, wins the DARPA Grand Challenge.
• (2005) The U.S. military begins investing in autonomous robots like Boston Dynamics’ “Big Dog” and iRobot’s “PackBot.”
• (2008) Google makes breakthroughs in speech recognition and introduces the feature in its iPhone app.

2010s

• (2011) IBM’s Watson handily defeats the competition on Jeopardy!. 
• (2011) Apple releases Siri, an AI-powered virtual assistant through its iOS operating system. 
• (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 the breakthrough era for neural networks and deep learning funding.
• (2014) Google makes the first self-driving car to pass a state driving test. 
• (2014) Amazon’s Alexa, a virtual home smart device, is released.
• (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.
• (2016) The first “robot citizen,” a humanoid robot named Sophia, is created by Hanson Robotics and is capable of facial recognition, verbal communication and facial expression.
• (2018) Google releases natural language processing engine BERT, reducing barriers in translation and understanding by ML applications.
• (2018) Waymo launches its Waymo One service, allowing users throughout the Phoenix metropolitan area to request a pick-up from one of the company’s self-driving vehicles.

2020s

• (2020) Baidu releases its LinearFold AI algorithm to scientific and medical teams working to develop a vaccine during the early stages of the SARS-CoV-2 pandemic. The algorithm is able to predict the RNA sequence of the virus in just 27 seconds, 120 times faster than other methods.
• (2020) OpenAI releases natural language processing model GPT-3, which is able to produce text modeled after the way people speak and write. 
• (2021) The European Union Parliament proposes a regulatory framework that aims to ensure that AI systems deployed within the EU are “safe, transparent, traceable, non-discriminatory and environmentally friendly.”
• (2021) OpenAI builds on GPT-3 to develop DALL-E, which is able to create images from text prompts.
• (2022) The National Institute of Standards and Technology releases the first draft of its AI Risk Management Framework, voluntary U.S. guidance “to better manage risks to individuals, organizations, and society associated with artificial intelligence.”
• (2022) DeepMind unveils Gato, an AI system trained to perform hundreds of tasks, including playing Atari, captioning images and using a robotic arm to stack blocks.
• (2022) The White House introduces an AI Bill of Rights outlining principles for the responsible development and use of AI.
• (2022) OpenAI launches ChatGPT, a chatbot powered by a large language model that gains more than 100 million users in just a few months.
• (2023) Microsoft launches an AI-powered version of Bing, its search engine, built on the same technology that powers ChatGPT.
• (2023) Google announces Bard, a competing conversational AI.
• (2023) OpenAI Launches GPT-4, its most sophisticated language model yet.
• (2023) The Biden-Harris administration issues The Executive Order on Safe, Secure and Trustworthy AI, calling for safety testing, labeling of AI-generated content and increased efforts to create international standards for the development and use of AI. The order also stresses the importance of ensuring that artificial intelligence is not used to circumvent privacy protections, exacerbate discrimination or violate civil rights or the rights of consumers.