1 History

20200223 Artificial Intelligence (AI) has been variously defined over time. It often incorporates some latest technology that needs a marketing boost! So it’s useful to go back to the origins from a workshop in 1956 at Dartmouth College in the USA funded by the Rockefeller Foundation. The workshop agreed on the term artificial intelligence and proposed that it encapsulated:

an attempt to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. … For the present purpose the artificial intelligence problem is taken to be that of making a machine behave in ways that would be called intelligent if a human were so behaving.

That’s quite broad but we note that Machine Learning is a key element, as is language, and alluding to knowledge representation as abstractions and concepts, with a purpose of problem solving. Learning of course is also core to the human experience of intelligence.

Data Science, which as a term has gained currency since 2014, is a profession oriented endeavour of using both Machine Learning and Artificial Intelligence technology (plus Statistics) to deliver new insights and knowledge from data. It is the translational endeavour of taking the fundamental research developments and making them available and applicable to the world.

Since those early days of Artificial Intelligence we have seen four “revolutions” or surges in AI. The 1950’s saw reasoning as search with developments in natural language, micro worlds, and neural networks all featured during this time. The 1980’s saw a surge in expert systems, knowledge representation, and back propagation. The 1990’s saw a focus on agents with Deep Blue, intelligent agents and the emergence of Data Mining. The 2010’s saw the emergence of massive data and compute, with data science and deep learning made possible by the coupling of accessible massive compute on the cloud and the centralised collection of massive amounts of personal data. The Future will see a resurgence of interest in causal reasoning, knowledge graphs (frames), accessible AI, and federated data privacy.

The current era continues to set the foundations for computing machines that will one day demonstrate artificial intelligence, though that is still some way off. We see the foundations emerging from the research laboratories in universities and in industry. It is a goal of the MLHub that we be able to share in the results of these developments, simply, within a framework that is easy for people to build the models to share and a platform that makes it easy for anyone to explore and utilise the technology.

Your donation will support ongoing development and give you access to the PDF version of this book. Desktop Survival Guides include Data Science, GNU/Linux, and MLHub. Books available on Amazon include Data Mining with Rattle and Essentials of Data Science. Popular open source software includes rattle, wajig, and mlhub. Hosted by Togaware, a pioneer of free and open source software since 1984.
Copyright © 1995-2021 Graham.Williams@togaware.com Creative Commons Attribution-ShareAlike 4.0.