Artificial intelligence: a paper symposium

2019

5 1.0 CONCEPT OF ARTIFICIAL INTELLIGENCE "Artificial intelligence" is a synthetic term whichdue to its suggestive potentialhas caused many misunderstandings and false expectations. Its origin can be traced back to the year 1956. This year was important in many aspects. For example, the book "Automata Studies" came out, compiling now famous articles in the field of cybernetics (Shannon & McCarthy, 1956). Since the first appearance of the words "artificial intelligence", usually associated with John McCarthy's 1956 Dartmouth Summer Research Project, interest in the topic and research into the development of intelligent machines has seen several ups and downs. Artificial intelligence (AI) is an important technology that supports daily social life and economic activities. It contributes greatly to the sustainable growth of Japan's economy and solves various social problems (Lu, Li, Chen, Kim, Serikawa, 2018). Artificial intelligence refers to the ability of a computer or a computer-enabled robotic system to process information and produce outcomes in a manner similar to the thought process of humans in learning, decision making and solving problems (Intelligence, 2017). By extension, the goal of AI systems is to develop systems capable of tacking complex problems in ways similar to human logic and reasoning.

2003

Artificial intelligence is a relatively new scientific and technological field which studies the nature of intelligence by using computers to produce intelligent behaviour. Initially, the main goal was a purely scientific one, understanding human intelligence, and this remains the aim of cognitive scientists. Unfortunately, such an ambitious and fascinating goal is not only far from being achieved but has yet to be satisfactorily approached. Fortunately, however, artificial intelligence also has an engineering goal: building systems that are useful to people even if the intelligence of such systems has no relation whatsoever with human intelligence, and therefore being able to build them does not necessarily provide any insight into the nature of human intelligence. This engineering goal has become the predominant one among artificial intelligence researchers and has produced impressive results, ranging from knowledge-based systems to autonomous robots, that have been applied to many different domains. Furthermore, artificial intelligence products and services today represent an annual market of tens of billions of dollars worldwide. This article summarizes the main contributions to the field of artificial intelligence made at the IIIA-CSIC (Artificial Intelligence Research Institute of the Spanish Scientific Research Council) over the last five years. Resum La intel•ligència artificial (IA) és un camp científic i tecnològic relativament nou dedicat a l'estudi de la intel•ligència mitjançant l'ús d'ordinadors com a eines per produir comportament intel•ligent. Inicialment, l'objectiu era essencialment científic: assolir una millor comprensió de la intel•ligència humana. Aquest objectiu ha estat, i encara és, el dels investigadors en ciència cognitiva. Dissortadament, aquest fascinant però ambiciós objectiu és encara molt lluny de ser assolit i ni tan sols podem dir que ens hi haguem acostat significativament. Afortunadament, però, la IA també persegueix un objectiu més aplicat: construir sistemes que ens resultin útils encara que la intel•ligència artificial de què estiguin dotats no tingui res a veure amb la intel•ligència humana i, per tant, aquests sistemes no ens proporcionarien necessàriament informació útil sobre la naturalesa de la intel•ligència humana. Aquest objectiu, que s'emmarca més aviat dins de l'àmbit de l'enginyeria, és actualment el que predomina entre els investigadors en IA i ja ha donat resultats impresionants, tan teòrics com aplicats, en moltíssims dominis d'aplicació. A més, avui dia, els productes i les aplicacions al voltant de la IA representen un mercat anual de desenes de milers de milions de dòlars. Aquest article resumeix les principals contribucions a la IA fetes pels investigadors de l'Institut d'Investigació en Intel•ligència Artificial del Consell Superior d'Investigacions Científiques durant els darrers cinc anys.

Many problems in AI study can be traced back to the confusion of different research goals. In this paper, five typical ways to define AI are clarified, analyzed, and compared. It is argued that though they are all legitimate research goals, they lead the research to very different directions, and most of them have trouble to give AI a proper identity. Finally, a working definition of AI is proposed, which has important advantages over the alternatives.

IEEE Potentials, 2000

Few human endeavors can be viewed both as extremely successful and unsuccessful at the same time. This is typically the case when goals have not been well defined or have been shifting in time. This has certainly been true of Artificial Intelligence (AI). The nature of intelligence has been the object of much thought and speculation throughout the history of philosophy. It is in the nature of philosophy that real headway is sometimes made only when appropriate tools become available. For instance, the nature and behavior of physical objects was a major topic of philosophy. That is until the experimental method and the advent of calculus allowed for the development of Physics. Similarly the computer, coupled with the ability to program (at least in principle) any function, appeared to be the tool that could tackle the notion of intelligence. To suit the tool, the problem of the "nature" of intelligence was soon sidestepped in favor of this notion: If a probing conversation with a computer could not be distinguished from a conversation with a human, then "artificial" intelligence had been achieved. This notion became known as the "Turing test", after the mathematician Alan Turing who proposed it in 1950. This challenge quickly attracted the best computer scientists in a worldwide search for techniques and principles of what soon became known as the field of Artificial Intelligence. The early efforts focused on creating "general problem solvers" like, for instance, the Soar system (Newell, Laird and Rosenbloom) which attempted to solve problems by breaking them down into sub-goals. Conceptually rich and interesting, these early efforts gave rise to a large portion of the field's framework. Key to artificial intelligence, rather than the "number crunching" typical of computers until then, was viewed as the ability to manipulate symbols and make logical inferences. To facilitate these tasks, "AI languages" such as LISP and Prolog were invented and used widely in the field. That this quest never strayed far from rigorous mathematical underpinnings was both its strength and its limitation. Its strength was to open a new fertile area of computer science. Its limitation was that "real world" problems tended to be too complex for the limitations imposed by mathematical rigor and the constraints of logic and symbol manipulation. Therefore, much effort continued to be focused on "toy problems." One idea that emerged and enabled some success with real world problems was the notion that "most" intelligence really resided in knowledge. A phrase attributed to Feigenbaum, one of the pioneers, was "knowledge is the power." With this premise, the problem is shifted from "how do we solve problems" to "how do we represent knowledge." A good knowledge representation scheme could allow one to draw conclusions from given premises. Such schemes took forms such as rules, frames and scripts. It allowed the building of what became known as "expert systems" or "knowledge based systems" (KBS). These types of systems could indeed help in real world problems (the author led a project for the first expert system to aid astronauts in performing some scientific experiments. It was called PI-in-a-Box). The technology that ensued from expert systems gave rise to the first instance of an "Al industry." Consulting "Knowledge Engineers" and products (Shells) could take some of the drudgery out of building these types of systems. The enthusiasm of this time, however, masked an important shift that had been made by this technology: "Real world" solutions were obtained by keeping the system's focus extremely narrow and limited in scope. These systems were, and, to a large extent, remain extremely "fragile." That is, unexpected inputs or straying from the scope of the system could easily result in unexpected and erroneous results. The most difficult aspects of intelligence to incorporate appeared to be understanding a) one's limits of knowledge and b) the, unfortunately, elusive "common sense." The very usefulness and continuing success of these types of systems has also brought to light the fundamental limitation of the behaviorist model of intelligence. This model has difficulty coping with the fact that intelligence seems to reside in the ability to achieve one's expertise and to use it appropriately more than, or certainly in addition to, the expertise itself. Again, this realization shouldn't take away from the continuing improvements and successes in these types of systems. Model Based Reasoning has emerged as a powerful approach to diagnosis, and planning and scheduling systems have had much success as well. The point is that AI, now increasingly called "Symbolic AI," has produced a new branch of computer science. Along with it, powerful tools have been created for knowledge representation, symbol manipulation, searching and optimization. AI is alive and well. However, many opine that its picture of intelligence is too fragmented to represent a satisfactory model of cognition.

RAW TRANSLATION OF THE INTRODUCTION TO THE ISSUE OF THE Zeitschrift für Medienwissenschaft 21, 2019, ed. by Christoph Ernst, Irina Kaldrack, Jens Schröter and Andreas Sudmann on „Künstliche Intelligenzen“

1982

: The ability and compulsion to know are as characteristic of our human nature as are our physical posture and our languages. Knowledge and intelligence, as scientific concepts, are used to describe how an organism's experience appears to mediate its behavior. This report discusses the relation between artificial intelligence (AI) research in computer science and the approaches of other disciplines that study the nature of intelligence, cognition, and mind. The state of AI after 25 years of work in the field is reviewed, as are the views of its practitioners about its relation to cognate disciplines. The report concludes with a discussion of some possible effects on our scientific work of emerging commercial applications of AI technology, that is, machines that can know and can take part in human cognitive activities.

International Journal of Advanced Computer Science and Applications

Artificial Intelligence was embraced as an idea of simulating unique abilities of humans, such as thinking, selfimprovement, and expressing their feelings using different languages. The idea of "Programs with Common Sense" was the main and central goal of Classical AI; it was, mainly built around an internal, updatable cognitive model of the world. But, now almost all the proposed models and approaches lacked reasoning and cognitive models and have been transferred to be more data driven. In this paper, different approaches and techniques of AI are reviewed, specifying how these approaches strayed from the main goal of Classical AI, and emphasizing how to return to its main objective. Additionally, most of the terms and concepts used in this field such as Machine Learning, Neural Networks and Deep Learning are highlighted. Moreover, the relations among these terms are determined, trying to remove mysterious and ambiguities around them. The transition from the Classical AI to Neuro-Symbolic AI and the need for new Cognitive-based models are also explained and discussed.

1982