Artificial general intelligence

YLEM Journal Vol 26, No. 10 Sep-Oct 2006

A brief exploration of artificial general intelligence and the "singularity," in which I address the problems artificial intelligence theories have had with explaining distributed cognition, consciousness, and various other things, as mediated through the memories of a class assignment writing a program that would respond to random input with natural language responses. I'd call this an inductive commentary on the lacunae of historical AI approaches more than it is a serious exploration of applications of current AI issues. Were I to rewrite it today, I would probably address the differences between a corpus and statistical probabilities of word patterns (n-grams) and the world of superhuman metonymy, as opposed to the kind of communicative creativity that CHAT and Cognitive Linguistics address. I might have wanted to spend more time on supervised learning, unsupervised learning, transfer learning, and so on, and most certainly GPT-3. I might also state more explicitly the difference between my personal effort of generative grammar, and mind in society, rather than working quite so hard to amuse. However, that would have required far more words than I would have been allowed for this little piece, and I'm not qualified to write it, anyway. For a deeper exploration of the research issues, see Kaptelinin and Nardi, Activity Theory in HCI (2012), and George Lakoff, Women, Fire, and Dangerous Things (1987), and Raymond W. Gibbs, Jr., Embodiment and Social Cognition (2005). For a more informed exploration of what my computer science professor was trying to have us think about, see Norvig, Paradigms of Artificial Intelligence Programming (1992), or perhaps Russell and Norvig, Artificial Intelligence: A Modern Approach. And for a recent discussion of some of the interesting fallout of GPT-3, see: https://dailynous.com/2020/07/30/philosophers-gpt-3/

It is a branch of computer science which deals with making computers behave as humans. It refers to " intelligence " of machinery to understand environment. The term " artificial intelligence was first coined by John McCarthy in a conference held in M.I.T in the year 1956.he defined it as the science and engineering of making intelligent agents. AI is a field that studies the synthesis and analysis of computational agents which acts intelligently. Now computational agents are the machines which perform some calculations and algorithms to perform a particular task successfully. There are several programming languages which are known as A.I languages because they are exclusively used for A.I applications. The two most common are LISP and prolog When it is said that the machine is " intelligent " ? • It does according to circumstances and goals: it performs its tasks such that the work given by the programmer/user is completed successfully. • Flexible: it should be able to change its actions which suit the environment to complete the task successfully. • Learn from the experiences: as humans learn from their mistakes, the machine should be able to record the mistakes which it performed in completing its task and should never repeat in the future. It should learn to perform the computations which can yield a success in its respective task given. • Make appropriate choices: it should be able to make correct decisions which lead to complete the task. Scientific goal of A.I The main goal of artificial intelligence is to understand the principles that make intelligent behavior possible in natural or artificial systems. They are mainly used to study the mechanism of neurons and the process of thinking.

2022

I believe that AGI (Artificial General Intelligence), unlike current AI models must operate with meanings / knowledge. This is exactly what distinguishes it from neural network based AI. Any successful AI implementations (playing chess, self-driving, face recognition, etc.) in no way operate with knowledge about the objects being processed and do not recognize their meanings / cognitive structure. This is not necessary for them, they demonstrate good results based on pre-training. But for AGI, which imitates human thinking, the ability to operate with knowledge is crucial. Numerous attempts to define the concept of “meaning” have one very significant drawback - all such definitions are not rigorous and formalized, therefore they cannot be programmed. The procedure of searching for meaning / knowledge should use a formalized determination of its existence and possible forms of its perception, usually multimodal. For the practical implementation of AGI, it is necessary to develop such “ready-to-code” formalized definitions of the cognitive concepts of “meaning”, “knowledge”, “intelligence” and others related to them. This article attempts to formalize the definitions of such concepts.

Intelligence can be defined as an overall mental capacity for thinking, critical thinking, and learning. In view of its overall nature, knowledge coordinates psychological capacities, for example, perception, attention, memory, language, or planning. Human intelligence revolves around adjusting to the climate utilizing a mix of a few intellectual cycles. The field of Artificial Intelligence centers on planning machines that can copy human conduct. In this way, right now, the simple capacity to copy human conduct is considered as Artificial Intelligence. Artificial Intelligence (AI) is the capacity of a PC program or a machine to think and learn.

Artificial Intelligence: Critical Concepts, 2000

Introduction to Part 1 of Volume 4 of the four-volume reference work Artificial Intelligence: Critical Concepts. Although the preceding articles have represented changes in the concept of artificial intelligence, the papers in these three sections explicitly address the question: what is artificial intelligence? - either directly, in section 1, or by way of trying to clarify one of the constituent concepts (artificiality or intelligence), in sections 2 and 3 that follow.

What is Artificial Intelligence ? , 2023

In our Age of Information, the new catchword, Artificial Intelligence (AI) gets established more and more. Till now I heard in this context the term Digital Age or fitting for our children the combination Digital Generation. So – how to understand this catchword? This term Artificial Intelligence (AI) is a very comprehensive one. Journalists or public speakers use it actually very often, because it includes all what sounds up to date and interesting new - in life and work. Most of us know: Information Technology (IT), and Industrial Production by robots are involved. It’s fascinating but not yet exact understandable. This term is not new; it was created about 1950 in USA but it’s content gets just now in center of actual IT-world. Scientific thinking people need and create more staying and definitive terms. They want understand connections to the original reasons. Therefore, I try in the following text a discussion of some headwords in this connection. We need to know, what AI is.

The Frustrating Quest to Define AGI, 2024

AI researchers' have strived for decades, for better or worse, to match or exceed human intelligence. Recent advances in Large Language Models (LLMs) "chatbots" have resulted in a blizzard of commentaries and opinions about whether Artificial General Intelligence (AGI), as it is called, can be a reality; and if so, when. This paper attempts at clarifying these questions by reviewing the various approaches, identifying their validity, and proposing alternative thinking. A recent conversation thread between Gary Marcus and Ray Kurzweil, wherein Kurzweil claims AGI by 2029 but inexplicably excludes poetry, was a welcome catalyst for this analysis. But to be clear, this paper is NOT describing a technical pathway to AGI, nor a deep dive into benchmarks (benchmarks have been improving, beaten by successive generations of Models,-even if contrived and sometimes cheated on.