AI and human society

This paper considers the impact of the AI R&D programme on human society and the individual human being on the assumption that a full realisation of the engineering objective of AI, namely, construction of human-level, domain-independent intelligent entities, is possible. Our assumption is essentially identical tothe maximum progress scenario of the Office of Technology Assessment, US Congress.Specifically, the first section introduces some of the significant issues on the relational nexus among work, education and the human-machine boundary. In particular, based on a Russellian conception of rationality I briefly argue that we need to change our related conceptions of work, employment and free time, through a new human-centred education. On the human-machine boundary problem, I make a couple of tentative suggestions and put forward some crucial open questions.Section two discusses the impact of the emerging machine intelligence on human nature both as modification of its self-image, keeping human nature itself unchanged, and its potential for altering human nature itself. I briefly argue that: (i) in a certain context, the question of the supremacy or uniqueness of human intelligence loses much, if not all, of its weight; and (ii) appearance of Robot-X species would immortalise the human spirit.     

Programming by example: The human face of AI

It is argued that “human-centredness” will be an important characteristic of systems that learn tasks from human users, as the difficulties in inductive inference rule out learning without human assistance. The aim of “programming by example” is to create systems that learn how to perform tasks from their human users by being shown examples of what is to be done. Just as the user creates a learning environment for the system, so the system provides a teaching opportunity for the user, and emphasis is placed as much on facilitating successful teaching as on incorporating techniques of machine learning. If systems can “learn” repetitive tasks, their users will have the power to decide for themselves which parts of their jobs should be automated, and teach the system how to do them — reducing their dependence on intermediaries such as system designers and programmers. This paper presents principles for programming by example derived from experience in creating four prototype learners: for technical drawing, text editing, office tasks, and robot assembly. A teaching metaphor (a) enables the user to demonstrate a task by performing it manually, (b) helps to explain the learner's limited capabilities in terms of a persona, and (c) allows users to attribute intentionality. Tasks are represented procedurally, and augmented with constraints. Suitable mechanisms for attention focusing are necessary in order to control inductive search. Hidden features of a task should be made explicit so that the learner need not embark on the huge search entailed by hypothesizing missing steps.     

AI and similarity

As AI moves into the second half of its first century, we certainly have much to cheer about. For AI to become truly robust, we must further our understanding of similarity-driven reasoning, analogy, learning, and explanation. In this article, the author presents some suggested research directions.     

Professionalism and AI

The time has come for all those working in AI to take the issue of professionalism seriously. Professional standards will be difficult to establish in AI. However, there will be pressure from various directions to produce a code or codes which will demonstrate that work is being done responsibly. Such codes will be largely worthless unless they are produced by people actually working at the sharp end of AI.     

AI holds the keys

Artificial intelligence holds the keys to different things to different researchers. Feigenbaum, one of the forefathers of AI, has many opinions and perceptions of AI and has seen where AI has come from and where it is going. Feigenbaum made his name in expert systems. He invented the first expert system in 1967, an AI program that determined the molecular structure of chemical compounds. The author discusses Feigenbaum's achievements.     

Constraints and AI planning

Tackling real-world planning problems often requires considering various types of constraints, which can range from simple numerical comparators to complex resources. This article provides an overview of techniques to deal with such constraints by expressing planning within general constraint-solving frameworks. Our goal here is to explore the interplay of constraints and planning, highlighting the differences between propositional satisfiability (SAT), integer programming (IP), and constraint programming (CP), and discuss their potential in expressing and solving AI planning problems.     

AI Bridges and dreams

This paper is a modified version of my acceptance lecture for the 1986 SPL-Insight Award. It turned into something of a personal credo — describing my view ofthe nature of AIthe potential social benefit of applied AIthe importance of basic AI researchthe role of logic and the methodology of rational constructionthe interplay of applied and basic AI research, andthe importance of funding basic AI.These points are knitted together by an analogy between AI and structural engineering: in particular, between building expert systems and building bridges.     

AI Bridges and Dreams

This paper is a modified version of my acceptance lecture for the 1986 SPL-Insight Award. It turned into something of a personal credo -describing my view of

AI paradigms

The aim of this paper is comparative analysis of most important AI paradigms. An AI paradigm is defined as the pair composed by a concept of intelligence and a methodology in which intelligent computer systems are developed and operated. Three paradigms, the behaviourist paradigm, the agent paradigm, and the artificial life paradigm are discussed.