Artificial intelligence is a field that attempts to provide machines with human-like thinking.
In 1950s first artificial intelligence laboratories were established at Carnegie-Mellon University, and MIT. Early successes created a sense of optimism and false hopes that some kind of grand unified theory of mind would soon emerge and make general AI possible.
In 1982 following the recommendations of technology foresight exercises, Japan's Ministry of International Trade and Industry initiated the Fifth Generation Computer Systems project to develop massively parallel computers that would take computing and AI to a new level.
The United States responded with a DARPA-led project that involved large corporations, such as Kodak and Motorola.
But despite some significant results, the grand promises failed to materialise and the public started to see AI as failing to live up to its potential. This culminated in the "AI winter" of the 1990s, when the term AI itself fell out of favour, funding decreased and the interest in the field temporarily dropped.
Researchers concentrated on more focused goals, such as machine learning, robotics, and computer vision, though research in pure AI continued at reduced levels.
Historically there were two main approaches to AI:
- classical approach (designing the AI), based on symbolic reasoning - a mathematical approach in which ideas and concepts are represented by symbols such as words, phrases or sentences, which are then processed according to the rules of logic.
- a connectionist approach (letting AI develop), based on artificial neural networks, which imitate the way neurons work, and genetic algorithms, which imitate inheritance and fitness to evolve better solutions to a problem with every generation.
Symbolic reasoning have been successfully used in expert systems and other fields. Neural nets are used in many areas, from computer games to DNA sequencing. But both approaches have severe limitations. A human brain is neither a large inference system, nor a huge homogenous neural net, but rather a collection of specialised modules. The best way to mimic the way humans think appears to be specifically programming a computer to perform individual functions (speech recognition, reconstruction of 3D environments, many domain-specific functions) and then combining them together.
- genetics, evolution
- Bayesian probabily inferencing
- combinations - i.e.: "evolved (genetic) neural networks that influence probability distributions of formal expert systems"
By breaking up AI research into more specific problems, such as computer vision, speech recognition and automatic planning, which had more clearly definable goals, scientists managed to create a critical mass of work aimed at solving these individual problems.
Some of the fields, where technology has matured and enabled practical applications, are:
- speech recognition
- computer vision
- text analysis
- robot control
Some examples of real-world systems based on artificial intelligence are:
- Intelligence Distribution Agent (IDA), developed for the U.S. Navy, helps assign sailors new jobs at the end of their tours of duty by negotiating with them via email.
- systems that trade stocks and commodities without human intervention
- banking software for approving bank loans and detecting credit card fraud (developed by Fair Isaac Corp.)
- search engines such as Brain Boost (or even Google)
Cyc is a 22 year old project based on symbolic reasoning with the aim of amassing general knowledge and acquiring common sense. Online access to Cyc will be opened in mid-2005. The volume of knowledge it has accumulated makes it able to learn new things by itself. Cyc will converse with Internet users and acquire new knowledge from them.
Open Mind and mindpixel are similar projects.
These projects are unlikely to directly lead to the creation of AI, but can be helpful when teaching the artificial intelligence about English language and the human-world domain.
In the next 10 years technologies in narrow fields such as speech recognition will continue to improve and will reach human levels. In 10 years AI will be able to communicate with humans in unstructured English using text or voice, navigate (not perfectly) in an unprepared environment and will have some rudimentary common sense (and domain-specific intelligence).
There will be an increasing number of practical applications based on digitally recreated aspects human intelligence, such as cognition, perception, rehearsal learning, or learning by repetitive practice.
- first AI laboratory
- chess champion
- speech recognition
- autonomous humanoid robots
- Turing test passed
- Why Artificial General Intelligence may be near - this article describes what kind of work leading in this direction is being being done and what can be done in the future.
- American Association for Artificial Intelligence — one of the best and largest sites on AI.
- Whatever happened to machines that think?, Justin Mullins, 23 April 2005 - Clever computers are everywhere. But can you honestly call any machine intelligent in a meaningful sense of the word?
- Spring comes to AI winter, by Heather Havenstein, February 14, 2005 - A thousand applications bloom in medicine, customer service, education and manufacturing.
- AI wiki - for general AI study