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Artificial Intelligence (AI) Guide

Artificial Intelligence - AI Timeline

Key events and milestones in the evolution of artificial intelligence are as follows:

1950 Alan Turing (famous for breaking the Nazi's ENIGMA code during WWII) published a landmark paper, Computing Machinery and Intelligence. In the paper, he speculates about the possibility of creating machines that think. Turing created a test known as the Turing test, aimed at determining whether a computer can think like a human being. The Turing test stands out as the first contribution to the philosophy of artificial intelligence. (Lidströmer & Ashrafian, 2022)
1956 The concept of AI was coined for the first time at the Dartmouth Conference by John McCarthy. (Lidströmer & Ashrafian, 2022)
1960 The first AI robot was implemented into the General Motors assembly line, and the first chatbot, ELIZA, was invented. (Lidströmer & Ashrafian, 2022)
1997 IBM's Deep Blue beats then world chess champion Garry Kasparov, in a chess match (and rematch). (Lidströmer & Ashrafian, 2022)
2011 IBM Watson beats champions Ken Jennings and Brad Rutter at Jeopardy!  (Lidströmer & Ashrafian, 2022)
2015 Baidu's Minwa supercomputer uses a special kind of deep neural network called a convolutional neural network to identify and categorize images with a higher rate of accuracy than the average human.  (IBM 2023)
2016 DeepMind's AlphaGo program, powered by a deep neural network, beats Lee Sodol, the world champion Go player, in a five-game match. (IBM 2023)
2023 A rise in large language models, or LLMs, such as ChatGPT, create an enormous change in performance of AI and its potential to drive enterprise value. With these new generative AI practices, deep-learning models can be pre-trained on vast amounts of raw, unlabeled data. (IBM 2023)

A common question is how AI could have existed for over half a century and suddenly now appears as a “sudden” hype and attracting all attention? The main reasons for the present demand for AI are (Lidströmer & Ashrafian, 2022):

  • Improved computational power is available to train deep learning models which makes it possible to broadly and globally implement AI.
  • Data is now generated at an immeasurable pace.  AI is trained on large datasets, big data, to process it to provide insight from it and let businesses grow as a result.
  • Better algorithms which are more effective and based on the concept of neural networks, i.e., the deep learning architecture. All this enables quicker and more accurate computations.
  • Governments, venture capitalists, tech giants, and start-ups are now all focused on AI and pour in investments. For instance, companies in the FAANG group (Facebook, Apple, Amazon, Netflix, Google), Microsoft, and most car manufacturers – and a long list of major tech companies – are deeply investing in AI. The consensus among all mentioned instances is that AI is the way of the future.

For more details on the importance of the AI in medicine, please see the chapter “On the Importance of AIM,” by Dr Katarina Gospic, et. al. (Lidströmer & Ashrafian, 2022)

Disclaimer

The Artificial Intelligence (AI) Guide provides an introduction to this evolving field for faculty, fellows, residents, postdocs, students, and staff. Due to the rapid advancement of this emerging technology, information in the Guide may become outdated at times. 

For information on Artificial Intelligence (AI) Data Security and Privacy, see Artificial Intelligence (AI) Data Security and Privacy - Information Resources (utsouthwestern.net), VPN/On Campus access only.  NOTE:  this Guide supplements but does not supersede information provided by UT Southwestern or University of Texas policies and guidelines.