- author = "Holger H. Hoos",
- title = "Automated Artificial Intelligence (AutoAI)",
- institution = "ADA Research Group, Leiden Institute of Advanced Computer Science (LIACS), Universiteit Leiden",
- year = "2018",
- number = "TR-2018-1",
- address = "The Netherlands",
- month = "24 " # dec,
- keywords = "genetic algorithms, genetic programming",
-
URL = "
https://ada.liacs.nl/auto-ai/vision-paper.pdf",
- size = "12 pages",
-
abstract = "While there has been research on artificial
intelligence (AI) for at least 50 years, we are now
standing on the threshold of an AI revolution, a
transformational change whose effects may surpass that
of the industrial revolution in the first half of the
19th century. There are multiple reasons why AI is
rapidly gaining traction now. Firstly, much of our
infrastructure is already controlled by computers; so
deploying AI systems is technologically quite
straightforward. Secondly, in many situations, there is
now easy access to large amounts of data, which can be
used as a basis for customising AI systems using
machine learning. Thirdly, due to tremendous
improvements not only in computer hardware, but also in
AI algorithms, advanced AI systems can now be deployed
broadly and at low cost.
As a result, AI systems are poised to fundamentally change the way we live and work. AI is quickly becoming a major driver of innovation, growth and competitiveness, and is bound to play a crucial role in addressing the challenges we face individually and as societies. However, high-quality AI systems require considerable expertise to build, maintain and operate. For the foreseeable future, AI expertise will be a limiting factor in the broad deployment of AI systems, and, unless managed very carefully, this will lead to uneven access and increasing inequality. It is also likely to cause the wide-spread use of low-quality AI systems, developed without the proper expertise.
Here, we propose to address this problem using AI methods, specifically, automated algorithm design, machine learning and optimisation techniques, to help build and deploy the next generation of AI systems. This gives rise to an approach we refer to as automated artificial intelligence (AutoAI). Ultimately, research on AutoAI aims to make it possible for people who benefit from AI to develop, deploy and maintain AI systems that are performant, robust and predictable, without requiring deep and highly specialised AI expertise. AutoAI will thus dramatically broaden access to high-quality AI systems.",
- notes = "Mention of GP, Genetic Improvement, SBSE",
