A Note on Artificial Intelligence

2004

Donald Gillies is one of the pioneers in the philosophical analysis of artificial intelligence (AI). In his recent book, Gillies (1996) not only makes a new and rapidly developing field of science accessible to philosophers; he also introduces philosophical topics relevant to researchers in AI and thereby helps establish a dialogue between the two disciplines. His book clearly and convincingly demonstrates the fruitful interplay between AI and philosophy of science.

Science, engineering and technology have been moulding and placing an ever increasing pressure on society and in turn, on life styles. The inquisitive nature of man has led to the amazing development of computer. In just four decades the computer has changed its role from a mere data cruncher to decision aid. A reading of the Artificial Intelligence, 17 (1-3), January, 1991, Special Volume on Foundations of AI, has motivated me to transcribe some of my long persistent feelings in writing. Loose usage and blown up speculations may bring discredit to concepts. Not founded on the characteristic behaviour of computer and its numerical instability, I fear that the same thing has happened to computers and to the associated fields of study. The purpose of this autoethnographic article is to reflect on the lessons learnt from AI and search for a right perspective for research and practice.

The preceding parts of Thoughts... got numerous comments and thoughts, allowing us to approach the sources and expected growth of artificial intelligence from a different angle. Our starting point is always human creation, which is motivated by common sense, Power's quest for omnipotence, and the capitalist economy's limitlessness. According to current scientific knowledge, artificial intelligence is based on the anthropomorphic paradigm, which attempts to mimic human thought and behavior. Human intelligence is founded on the human brain, whereas artificial intelligence is founded on computers. People organize themselves into communities based on rules and morality. Common sense contradicts giving artificial intelligence such selforganizing abilities, and human aims do not even necessitate them. This study attempts to uncover the differences and possible convergence of the two intelligences based on the model's components. Meanwhile, always stressing man's supremacy and duty. The immoral, the destruction, is produced by man-truly by Power-even if it is done by intelligent machines. The model's brain and its functioning will now be discussed.

2018

The article showcases a few considerations with regard to the implications generated by artificial intelligence in universities and on the labour market, by leading to the appearance of new professions which entail new abilities. One possible paradigm change at university level is given by the Open Loop University Stanford model. The article also presents Romania’s status within the context generated by the development of artificial intelligence.

Interdisciplinary Science Reviews

Not many people seem to understand what it is to mimic human intelligence successfully irrespectivethat is, irrespective of internal states such as intentions. Successful mimicking will involve embedding in human societies. AI practitioners have concerned themselves with reproducing something that can mimic an individual human brain, failing to notice that if such a brain is to mimic human intelligence it will also have to mimic the process of becoming socialised in human society because crucial features of human intelligence are located in human societies. One notable example of this is natural language, which is continually changing and enormously flexible in ways that cannot be predicted or controlled by individuals. The fluent natural language speaker draws on society's language in the same way that a thermometer draws on the liquid in which it is embedded. The temperature that registers on the thermometer is a property of the liquid, not the thermometer, changing when the liquid warms or cools. In the same way the language spoken by a fluent individual is a property of the society in which he or she is embedded, not a property of the individual; like the thermometer, it changes when the society changes. This means that a Turing Test, based purely on linguistic performance, can be an excellent test of human-like intelligence in a machine. The nature of natural language explains some of the recent stunning successes of deep learning and explains its failures. I also try to explain why the science of AI is so poor at presenting and testing its claims. It aims at convincing the outside world of its success rather than engaging in the kind of assiduous selfcriticism engaged in by the physical sciences.

New Note(s): ****Fragment(s) On Artificial Intelligence will now comprise Volume 2 and Feudalism2.0 will now comprise Volume 1, due to the fact that the new order works better for flow and readability. While Volume 3 will remain the same. **** Fragment on Artificial Intelligence is now shorter, due to the fact that the section on Feudalism 2.0 shall comprise Volume 2 of the Android series, while, the death-drop theory of economic development and disintergration (DDT) will comprise Volume 3, the final Volume of the Android series. "We are all the target-victims in the telescopic gun lens of Artificial Intelligence and its algorithms." This is the only original work I have produced in the last year of 2023. A short fragment on Artificial Intelligence and its algorithms that I plan to build into a book on Artificial Intelligence. It is a critique of dialectical-materialism, i.e., its notion of dialectical reciprocity. In the sense that with Artificial Intelligence and its algorithms, there is no such thing as dialectical reciprocity, or mutual agreement, or mutual exchange in-between humans and A.I. technology. There is only the total absorption of humans by A.I. technology, via its mechanical processes of all-consuming data-collection. Finally, I ask the reader to refrain from downloading this original piece of work, since, it is a very rudimentary skeleton of a theory on technology, to be fully-outlined in a future book. I am being kind enough to show you my weird disjointed creative process. But that process is best demonstrated and encouraged, when it doesn't feel like it is being stripped of its free creative labor and free outputs. Thank you for your patience,.....

Artificial Intelligence: Critical Concepts, 2000

General Introduction to the four-volume reference work "Artificial Intelliegnce: Critical Concepts"

2022

This study, Thoughts Concerning Artificial Intelligence & Machine Learning Part II is a continuation of a study published under a similar title and aims to rethink our image of artificial intelligence by taking into account the latest results and technical possibilities. It is timely because receives support that has never been seen before and because the results highly affect our lives. This writing places special emphasis on the fact that artificial intelligence-despite any fantastic expectationsmust be a purposeful activity, which should only be to help man, supporting his work and life. Rather fast development has many gains for the applicants, but at the same time arise many questions about the effect on humankind. This paper states the necessity of continuous revision and shows some points for that. The high speed may cause fast changes and failures, etc. This paper also calls attention to these. In Hungary, the actuality and high demands against this field require nationwide integration. The recently formed and now government-supported AI Coalition with its program also serves this purpose. A workgroup in this Coalition turn its attention to the side effect of the new results of the Artificial Intelligence. The main attention is given to the effect of everyday life and behavior caused by the new prescriptions and regulations. The purpose is to call attention to such anomalies.

Monitoring of Public Opinion: Economic and Social Changes, 2021

The paper presents the working definition of AI. It claims that AI has to be examined in relation to AS. The paper argues that the human-machine-interdependence is a new reality of artificial sociality. It envisages AI research as a multidisciplinary and potentially a-disciplinary scientific activity. The questions the Theses raise: What should we be concerned about as artificial intelligence advances? Can AI technologies solve modern society's problems and bring human beings to a new level of community and well-being? Are there "no-AI areas" in society? prejudice. Do human biases and prejudices influence AI technologies? The paper’s essential assertion is that the challenges posed by AI technologies and AS should be addressed à propos three P’s of the capitalist society: private property, profit, price.

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.