Artificial Intelligence and Human Intelligence——On Human-Computer Competition from the Five-Level Theory of Cognitive Science

It is generally accepted that the human mind and cognition can be viewed at five levels; nerves, psychology, language, thinking and culture. Artificial intelligence(AI) simulates human intelligence at all five levels of human cognition, however, AI has yet to outperform human intelligence, although it is making progress. Presently artificial intelligence lags far behind human intelligence in higher-order cognition, namely, the cognitive levels of language, thinking and culture. In fact, artificial intelligence and human intelligence fall into very different intelligence categories. Machine learning is no more than a simulation of human cognitive ability and therefore should not be overestimated. There is no need for us to feel scared even panic about it. Put forward by John R. Searle, the"Chinese Room"argument, a famous AI model and standard, is not yet out of date. According to this argument, a digital computer will never acquire human intelligence. Given that, no artificial intelligence will outperform human intelligence in the foreseeable future.

Is the Genie of Artificial Intelligence Technology Out of the Bottle and Control?(A Short Review)

In recent years,AI(artificial intelligence)has made considerable strides,transforming a number of industries and facets of daily life.However,as AI develops more,worries about its potential dangers and unforeseen repercussions have surfaced.This article investigates the claim that AI technology has broken free from human control and is now unstoppable.We look at how AI is developing right now,what it means for society,and what steps are being taken to reduce the risks that come with it.We seek to highlight the need for responsible development and implementation of this game-changing technology by examining the opportunities and challenges that AI presents.

The Prime Sequence: Demonstrably Highly Organized While Also Opaque and Incomputable-With Remarks on Riemann’s Hypothesis, Partition, Goldbach’s Conjecture, Euclid on Primes, Euclid’s Fifth Postulate, Wilson’s Theorem along with Lagrange’s Proof of It and Pascal’s Triangle, and Rational Human Intelligence

The main design of this paper is to determine once and for all the true nature and status of the sequence of the prime numbers, or primes—that is, 2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, and so on. The main conclusion revolves entirely around two points. First, on the one hand, it is shown that the prime sequence exhibits an extremely high level of organization. But second, on the other hand, it is also shown that the clearly detectable organization of the primes is ultimately beyond human comprehension. This conclusion runs radically counter and opposite—in regard to both points—to what may well be the default view held widely, if not universally, in current theoretical mathematics about the prime sequence, namely the following. First, on the one hand, the prime sequence is deemed by all appearance to be entirely random, not organized at all. Second, on the other hand, all hope has not been abandoned that the sequence may perhaps at some point be grasped by human cognition, even if no progress at all has been made in this regard. Current mathematical research seems to be entirely predicated on keeping this hope alive. In the present paper, it is proposed that there is no reason to hope, as it were. According to this point of view, theoretical mathematics needs to take a drastic 180-degree turn. The manner of demonstration that will be used is direct and empirical. Two key observations are adduced showing, 1), how the prime sequence is highly organized and, 2), how this organization transcends human intelligence because it plays out in the dimension of infinity and in relation to π. The present paper is part of a larger project whose design it is to present a complete and final mathematical and physical theory of rational human intelligence. Nothing seems more self-evident than that rational human intelligence is subject to absolute limitations. The brain is a material and physically finite tool. Everyone will therefore readily agree that, as far as reasoning is concerned, there are things that the brain can do and things that it cannot do. The search is therefore for the line that separates the two, or the limits beyond which rational human intelligence cannot go. It is proposed that the structure of the prime sequence lies beyond those limits. The contemplation of the prime sequence teaches us something deeply fundamental about the human condition. It is part of the quest to Know Thyself.

The Physical Laws and Mathematical Axioms of the Brain’s OS and the Traditional Fundamental Laws of Thought of Logic and Philosophy

This article presents four (4) additions to a book on the brain’s OS published by SciRP in 2015 [1]. It is a kind of appendix to the book. Some familiarity with the earlier book is presupposed. The book itself proposes a complete physical and mathematical blueprint of the brain’s OS. A first addition to the book (see Chapters 5 to 10 below) concerns the relation between the afore-mentioned blueprint and the more than 2000-year-old so-called fundamental laws of thought of logic and philosophy, which came to be viewed as being three (3) in number, namely the laws of 1) Identity, 2) Contradiction, and 3) the Excluded Middle. The blueprint and the laws cannot both be the final foundation of the brain’s OS. The design of the present paper is to interpret the laws in strictly mathematical terms in light of the blueprint. This addition constitutes the bulk of the present article. Chapters 5 to 8 set the stage. Chapters 9 and 10 present a detailed mathematical analysis of the laws. A second addition to the book (Chapter 11) concerns the distinction between the laws and the axioms of the brain’s OS. Laws are part of physics. Axioms are part of mathematics. Since the theory of the brain’s OS involves both physics and mathematics, it exhibits both laws and axioms. A third addition (Chapter 12) to the book involves an additional flavor of digitality in the brain’s OS. In the book, there are five (5). But brain chemistry requires a sixth. It will be called Existence Digitality. A fourth addition (Chapter 13) concerns reflections on the role of imagination in theories of physics in light of the ignorance of deeper causes. Chapters 1 to 4 present preliminary matter, for the most part a brief survey of general concepts derived from what is in the book [1]. Some historical notes are gathered at the end in Chapter 14.

Empowering digital twins with eXtended reality collaborations

Background The advancements of Artificial Intelligence,Big Data Analytics,and the Internet of Things paved the path to the emergence and use of Digital Twins(DTs)as technologies to“twin”the life of a physical entity in different fields,ranging from industry to healthcare.At the same time,the advent of eXtended Reality(XR)in industrial and consumer electronics has provided novel paradigms that may be put to good use to visualize and interact with DTs.XR technologies can support human-to-human interactions for training and remote assistance and could transform DTs into collaborative intelligence tools.Methods We here present the Human Collaborative Intelligence empowered Digital Twin framework(HCLINT-DT)integrating human annotations(e.g.,textual and vocal)to allow the creation of an all-in-one-place resource to preserve such knowledge.This framework could be adopted in many fields,supporting users to learn how to carry out an unknown process or explore others’past experiences.Results The assessment of such a framework has involved implementing a DT supporting human annotations,reflected in both the physical world(Augmented Reality)and the virtual one(Virtual Reality).Con-clusions The outcomes of the interface design assessment confirm the interest in developing HCLINT-DT-based applications.Finally,we evaluated how the proposed framework could be translated into a manufacturing context.

Information Models for Forecasting Nonlinear Economic Dynamics in the Digital Era

The aim of this study was to develop an adequate mathematical model for long-term forecasting of technological progress and economic growth in the digital age (2020-2050). In addition, the task was to develop a model for forecast calculations of labor productivity in the symbiosis of “man + intelligent machine”, where an intelligent machine (IM) is understood as a computer or robot equipped with elements of artificial intelligence (AI), as well as in the digital economy as a whole. In the course of the study, it was shown that in order to implement its goals the Schumpeter-Kondratiev innovation and cycle theory on forming long waves (LW) of economic development influenced by a powerful cluster of economic technologies engendered by industrial revolutions is most appropriate for a long-term forecasting of technological progress and economic growth. The Solow neoclassical model of economic growth, synchronized with LW, gives the opportunity to forecast economic dynamics of technologically advanced countries with a greater precision up to 30 years, the time which correlates with the continuation of LW. In the information and digital age, the key role among the main factors of growth (capital, labour and technological progress) is played by the latter. The authors have developed an information model which allows for forecasting technological progress basing on growth rates of endogenous technological information in economics. The main regimes of producing technological information, corresponding to the eras of information and digital economies, are given in the article, as well as the Lagrangians that engender them. The model is verified on the example of the 5th information LW for the US economy (1982-2018) and it has had highly accurate approximation for both technological progress and economic growth. A number of new results were obtained using the developed information models for forecasting technological progress. The forecasting trajectory of economic growth of developed countries (on the example of the USA) on the upward stage of the 6th LW (2018-2042), engendered by the digital technologies of the 4th Industrial Revolution is given. It is also demonstrated that the symbiosis of human and intelligent machine (IM) is the driving force in the digital economy, where man plays the leading role organizing effective and efficient mutual work. Authors suggest a mathematical model for calculating labour productivity in the digital economy, where the symbiosis of “human + IM” is widely used. The calculations carried out with the help of the model show: 1) the symbiosis of “human + IM” from the very beginning lets to realize the possibilities of increasing work performance in the economy with the help of digital technologies;2) the largest labour productivity is achieved in the symbiosis of “human + IM”, where man labour prevails, and the lowest labour productivity is seen where the largest part of the work is performed by IM;3) developed countries may achieve labour productivity of 3% per year by the mid-2020s, which has all the chances to stay up to the 2040s.

辅助工程师设计的知识融合设计方法:卫星舱布局设计(英文)

As a complex engineering problem,the satellite module layout design (SMLD) is difficult to resolve by using conventional computation-based approaches. The challenges stem from three aspects:computational complexity,engineering complexity,and engineering practicability. Engineers often finish successful satellite designs by way of their plenty of experience and wisdom,lessons learnt from the past practices,as well as the assistance of the advanced computational techniques. Enlightened by the ripe patterns,th...

Demos of Passing Turing Test Successfully

Recently,a new kind of machine intelligence was born,called as UI(Ubit intelligence).The basic difference between UI and AI is encoding;UI is based on word encoding;but AI is based on character encoding.UI machine can learn from human,remember the characters,pronunciation,and meaning of a word like human.UI machine can think among the character,pronunciation,and meaning of words like human.Turing Test is similar to a teacher testing a student;Before Test,tester must teach the content of the test questions to UI machine first;after UI machine learning,tester asks testee questions;to check testee has remembered what he taught;to check testee can think among character,pronunciation,and meaning of words.This paper demonstrates that testee can remember what testee taught;and answer all 6 questions correctly by thinking.UI machine passes Turing Test easily and successfully with score 100.Following on,the works related to this study is briefly introduced.At last,this paper concludes that UI machine is based on word encoding,can form word,form concept,can possess brain like intelligence,also can possess human like Intelligence;therefore UI machine passes Turing Test easily and successfully.On the contrary,AI machine is based on character encoding;can’t form word;can’t form concept,AI machine can’t possess brain like intelligence,nor possess human like Intelligence.Therefore,AI machine can’t pass Turing Test.