Conceptions of Artificial Intelligence and Singularity

Abstract

In the current discussions about "artificial intelligence" (AI) and "singularity", both labels are used with several very different senses, and the confusion among these senses is the root of many disagreements. Similarly, although "artificial general intelligence" (AGI) has become a widely used term in the related discussions, many people are not really familiar with this research, including its aim and status. We analyze these notions, and introduce the results of our own AGI research. Our main conclusions are that: (1) it is possible to build a computer system that follows the same laws of thought and shows similar properties as the human mind, but, since such an AGI will have neither a human body nor human experience, it will not behave exactly like a human, nor will it be "smarter than a human" on all tasks; and (2) since the development of an AGI requires a reasonably good understanding of the general mechanism of intelligence, the system's behaviors will still be understandable and predictable in principle. Therefore, the success of AGI will not necessarily lead to a singularity beyond which the future becomes completely incomprehensible and uncontrollable.Information 2018, 9, 79 2 of 15 the features that the leading article claimed to be impossible for AI. In the conclusion, we agree with the authors of the leading article [1] that the recent achievements of deep learning are still far from showing that the related techniques can give us AGI or singularity; however, we believe AGI can be achieved via paths outside the vision of mainstream AI researchers, as well as that of its critics. This conception of AGI is fundamentally different from that of the current mainstream conception of AI. As for "singularity", we consider it an ill-conceived notion, as it is based on an improper conception of intelligence. Notions Distinguished and ClarifiedLet us first analyze what people mean when talking about "AI" and "Singularity". Both notions have no widely accepted definitions, although there are common usages. Different Types of AIIn its broadest sense, AI is the attempt "to make a computer work like a human mind". Although it sounds plain, this description demands an AI to be similar (or even identical) to the human mind in certain aspects. On the other hand, because a computer is not a biological organism, nor does it live a human life, it cannot be expected to be similar to the human mind in all details. The latter is rarely mentioned but implicitly assumed, as it is self-evident. Consequently, by focusing on different aspects of the human mind, different paradigms of AI have been proposed and followed, with different objectives, desiderata, assumptions, road-maps, and applicabilities. They are each valid but distinct paradigms of scientific research [2].In the current discussion, there are at least three senses of "AI" involved:

On Defining Artificial Intelligence

“…Therefore, it is worth the effort to give this topic a treatment it deserves. This paper is a summary of my previous opinions and arguments on this topic (Wang, 1994(Wang, , 1995(Wang, , 2006a(Wang, , 2008(Wang, , 2012Wang, Liu, and Dougherty, 2018), with additional discussions to give it a more systematic and comprehensive treatment. In the following, I will start at the meta-level by discussing definitions in general, then move to the specific case of defining intelligence and AI.…”

“…I do not consider any of these concepts well-conceived, so will not analyze them in this paper. My arguments against them can be found in (Wang, Liu, and Dougherty, 2018).…”

Artificial Intelligence and the Limitations of Information

“…It has already caused reputational damage in the case of simple, narrow AI [17,18]. In [52], the authors address this issue when they ask the question: "why not give AGI human experience"? They then show how human experience is difficult to achieve.…”

“…Thus, we cannot expect linear progress. This is discussed in [52] in which the authors include the following quote from [56] "The learning of meta-level knowledge and skills cannot be properly handled by the existing machine learning techniques, which are designed for object-level tasks". Perhaps AGI will need the ability to learn about component patterns themselves-when a new component pattern is needed the AGI will need to recognize it and evolve a new one; but even if this is feasible, where will the data come from?…”

Pareidolic and Uncomplex Technological Singularity

“…Instead, what it is important for this paper is the conclusion of one of the papers from this special issue that even id "it is possible to build a computer system that follows the same laws of thought and shows similar properties as the human mind," "such an AGI will have neither a human body nor human experience, it will not behave exactly like a human, nor will it be "smarter than a human" on all tasks" [93]. A similar conclusion related to something other than full human-like evolution of AGI is underlined by other researchers, too [81].…”