Fifty Years of AI: From Symbols to Embodiment - and Back

There are many stories to tell about the first fifty years of AI. One story is about AI as one of the big forces of innovation in information technology. It is now forgotten that initially computers were just viewed as calculating machines. AI has moved that boundary, by projecting visions on what might be possible, and by building technologies to realise them. Another story is about the applications of AI. Knowledge systems were still a rarity in the late seventies but are now everywhere, delivered through the web. Knowledge systems routinely deal with financial and legal problem solving, diagnosis and maintenance of power plants and transportation networks, symbolic mathematics, scheduling, etc. The innovative aspects of search engines like Google are almost entirely based on the information extraction, data mining, semantic networks and machine learning techniques pioneered in AI. Popular games like SimCity are straightforward applications of multi-agent systems. Sophisticated language processing capacities are now routinely embedded in text processing systems like Microsoft's Word. Tens of millions of people use AI technology every day, often without knowing it or without wondering how these information systems can do all these things. In this essay I will focus however on another story: AI as a contributor to the scientific study of mind.

[1]  W. Daniel Hillis,et al.  The connection machine , 1985 .

[2]  John Maynard Smith,et al.  The Concept of Information in Biology , 2000, Philosophy of Science.

[3]  Susan Gauch.  The Knowledge Level in Expert Systems: Conversations and Commentary , 1997, Inf. Process. Manag..

[4]  J. Elman.  Distributed Representations, Simple Recurrent Networks, And Grammatical Structure , 1991 .

[5]  M. Pickering,et al.  Toward a mechanistic psychology of dialogue , 2004, Behavioral and Brain Sciences.

[6]  Rolf Pfeifer,et al.  How the Body Shapes the Way We Think: A New View of Intelligence (Bradford Books) , 2006 .

[7]  Susan Bowsfield.  The Symbolic Species: The Co-Evolution of Language and the Brain , 2004 .

[8]  Douglas B. Lenat,et al.  CYC: a large-scale investment in knowledge infrastructure , 1995, CACM.

[9]  Barbara Messing,et al.  An Introduction to MultiAgent Systems , 2002, Künstliche Intell..

[10]  Luc Steels,et al.  The artificial life route to artificial intelligence : building embodied , 1995 .

[11]  Rolf Pfeifer,et al.  Understanding intelligence , 2020, Inequality by Design.

[12]  Terry Winograd,et al.  Understanding natural language , 1974 .

[13]  Luc Steels,et al.  Semiotic Dynamics for Embodied Agents , 2006, IEEE Intelligent Systems.

[14]  John McCarthy,et al.  A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, August 31, 1955 , 2006, AI Mag..

[15]  L. Steels.  Evolving grounded communication for robots , 2003, Trends in Cognitive Sciences.

[16]  H. Dreyfus.  What Computers Can't Do: The Limits of Artificial Intelligence , 1978 .

[17]  Michael Wooldridge,et al.  Introduction to multiagent systems , 2001 .

[18]  Wolfram Burgard,et al.  Probabilistic Robotics (Intelligent Robotics and Autonomous Agents) , 2005 .

[19]  R. Axelrod.  Agent-based Modeling as a Bridge Between Disciplines , 2006 .

[20]  S. Strogatz.  Exploring complex networks , 2001, Nature.

[21]  T. Deacon.  The Symbolic Species: The Co-evolution of Language and the Brain , 1998 .

[22]  Rolf Pfeifer,et al.  How the body shapes the way we think - a new view on intelligence , 2006 .