In the last year or so (and going back many decades) there has been extensive claims by major computational scientists, engineers, and others that AGI (artificial general intelligence) is 5 or 10 years away, but without a scintilla of scientific evidence, for a broad body of these claims: Computers will become conscious, have a "theory of mind," think and reason, will become more intelligent than humans, and so on. But the claims are science fiction, not science. This article reviews evidence for the following three (3) propositions using extensive body of scientific research and related sources from the cognitive and neurosciences; evolutionary evidence; linguistics; data science; comparative psychology; self-driving cars, and robotics; and the learning sciences.

Graph embedding maps graph nodes to low-dimensional vectors, and is widely adopted in machine learning tasks. The increasing availability of billion-edge graphs underscores the importance of learning efficient and effective embeddings on large graphs, such as link prediction on Twitter with over one billion edges. Most existing graph embedding methods fall short of reaching high data scalability. In this paper, we present a general-purpose, distributed, information-centric random walk-based graph embedding framework, DistGER, which can scale to embed billion-edge graphs. DistGER incrementally computes information-centric random walks. It further leverages a multi-proximity-aware, streaming, parallel graph partitioning strategy, simultaneously achieving high local partition quality and excellent workload balancing across machines. DistGER also improves the distributed Skip-Gram learning model to generate node embeddings by optimizing the access locality, CPU throughput, and synchronization efficiency. Experiments on real-world graphs demonstrate that compared to state-of-the-art distributed graph embedding frameworks, including KnightKing, DistDGL, and Pytorch-BigGraph, DistGER exhibits 2.33x-129x acceleration, 45% reduction in cross-machines communication, and > 10% effectiveness improvement in downstream tasks.

But in June 1945, just weeks after Germany had surrendered, Turing was hired by the UK's National Physical Laboratory to lead the development of an electronic version of his universal computing machine. I have spent a considerable time in this lecture on this question of memory, because I believe that the provision of proper storage is the key to the problem of the digital computer, and certainly if they are to be persuaded to show any sort of genuine intelligence. The philosopher Jack Copeland, director of the Turing Archive for the History of Computing in New Zealand, has described this paper as the first manifesto of AI, and that seems accurate as far as our present historical knowledge goes. For example, there survive minutes from an October 1949 philosophy seminar discussion between Turing, Newman, the neurosurgeon Geoffrey Jefferson and Michael Polanyi, who was then professor of social science at Manchester, on'the mind and the computing machine'. The first is a brief lecture entitled'Intelligent Machinery, A Heretical Theory', probably first broadcast in 1951, in which his stated objective is to question the commonly held belief'You cannot make a machine to think for you' by explaining, and reflecting on, the technique of reinforcement learning.

Turing never returned to the National Physical Laboratory after his research leave. Instead, in May 1948, he joined his friend Newman's Computing Machine Laboratory at the University of Manchester, where shortly afterwards the world's first electronic stored-program general-purpose digital computer, the Small-Scale Experimental Machine (commonly known as the Manchester Baby), ran its first program. Turing spent most of the remaining six years of his life continuing his research on AI. After completing the programming system of the expanded Manchester Mark I machine and the subsequent Ferranti Mark I, the world's first commercially available modern computer (manufactured by Ferranti Ltd), in early 1951 Turing began experimenting on the Ferranti. The early results of his computational modelling of biological growth were published in the paper'The Chemical Basis of Morphogenesis' (1952), which represented an important early contribution to research on artificial life.

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner What is Real #AI for Everything and Everybody Platform? The mainstream human-centric AI has some fundamental problems needing for fundamental solutions. First, it is philosophy, or rather lack of any philosophy, and blindly relying on statistics, its processes, algorithms, and inductive inferences, needing a large volume of big data as the "fuel" to train the model for the special tasks of the classifications and the predictions in very specific cases. Second, it is not a scientific AI agreed with the rules, principles, and method of science. Today's AI is failing to deal with reality and its causality and mentality strictly following a scientific method of inquiry depending upon the reciprocal interaction of generalizations (hypothesis, laws, theories, and models) and observable/experimental data. Third, there is no common definition of AI, and each one sees AI in its own way.

Alan Turing is often praised as the foremost figure in the historical process that led to the rise of the modern electronic computer. Particular attention has been devoted to the purported connection between a "Universal Turing Machine" (UTM), as introduced in Turing's article of 1936,27 and the design and implementation in the mid-1940s of the first stored-program computers, with particular emphasis on the respective proposals of John von Neumann for the EDVAC30 and of Turing himself for the ACE.26 In some recent accounts, von Neumann's and Turing's proposals (and the machines built on them) are unambiguously described as direct implementations of a UTM, as defined in 1936. "What Turing described in 1936 was not an abstract mathematical notion but a solid three-dimensional machine (containing, as he said, wheels, levers, and paper tape); and the cardinal problem in electronic computing's pioneering years, taken on by both'Proposed Electronic Calculator' and the'First Draft' was just this: How best to build a practical electronic form of the UTM?"9 "[The] essential point of the stored-program computer is that it is built to implement a logical idea, Turing's idea: the universal Turing machine of 1936."18 This statement is of particular interest because, in his authoritative biography21 of Turing (first published 1983), Hodges typically follows a much more nuanced and careful approach to this entire issue. For instance, when referring to a mocking 1936 comment by David Champernowne, a friend of Turing, to the effect that the universal machine would require the Albert Hall to house its construction, Hodges commented that this "was fair comment on Alan's design in'Computable Numbers' for if he had any thoughts of making it a practical proposition they did not show in the paper."21 "Did [Turing] think in terms of constructing a universal machine at this stage? There is not a shred of direct evidence, nor was the design as described in his paper in any way influenced by practical considerations ... My own belief is that the'interest' [in building an actual machine] may have been at the back of his mind all the time after 1936, and quite possibly motivated some of his eagerness to learn about engineering techniques. But as he never said or wrote anything to this effect, the question must be left to tantalize the imagination."21 Discussions of this issue tend to be based on retrospective accounts, sometimes even on hearsay. The most-often quoted one comes from Max Newman, who had been Turing's teacher and mentor back in the early Cambridge days and, later, became a leading figure in the rise of the modern electronic computer, sometimes collaborating with Turing. "The description that [Turing] gave of a'universal' computing machine was entirely theoretical in purpose, but Turing's strong interest in all kinds of practical experiment made him even then interested in the possibility of actually constructing a machine on these lines."6

Intelligence amplification (IA) (also referred to as cognitive augmentation and machine augmented intelligence) refers to the effective use of information technology in augmenting human intelligence. The idea was first proposed in the 1950s and 1960s by cybernetics and early computer pioneers. IA is sometimes contrasted with AI (artificial intelligence), that is, the project of building a human-like intelligence in the form of an autonomous technological system such as a computer or robot. AI has encountered many fundamental obstacles, practical as well as theoretical, which for IA seem moot, as it needs technology merely as an extra support for an autonomous intelligence that has already proven to function. Moreover, IA has a long history of success, since all forms of information technology, from the abacus to writing to the Internet, have been developed basically to extend the information processing capabilities of the human mind (see extended mind and distributed cognition).

But the condition is so rare that it's still far from fully understood. Though it's undeniably horrific for those experiencing it, Cotard's Syndrome presents a fascinating conundrum for those studying the disorder. The condition's central contradiction -- how can someone articulate the thought that they don't exist? A 2013 case study of a Cotard's sufferer showed low activity in the brain network associated with awareness of the body. It's only one example (as with much of Cotard's Syndrome research, because the condition is so rare), but unpacking how the brains of those with the syndrome work offers hints as to how normally-functioning brains develop a sense of existence.

A basic problem in information theory is that of transmitting information reliably over a noisy channel. An analogous problem in computing machines is that of reliable computing using unreliable elements. This problem has been studies by von Neumann for Sheffer stroke elements and by Shannon and Moore for relays; but there are still many open questions. The problem for several elements, the development of concepts similar to channel capacity, the sharper analysis of upper and lower bounds on the required redundancy, etc. are among the important issues. Another question deals with the theory of information networks where information flows in many closed loops (as contrasted with the simple one-way channel usually considered in communication theory).

As anyone who can operate a personal computer knows, the way to make the machine perform some desired task is to open the appropriate program stored in the computer's memory. Life was not always so simple. The earliest large-scale electronic digital computers, the British Colossus (1944) and the American ENIAC (1945), did not store programs in memory. To set up these computers for a fresh task, it was necessary to modify some of the machine's wiring, re-routing cables by hand and setting switches. The basic principle of the modern computer--the idea of controlling the machine's operations by means of a program of coded instructions stored in the computer's memory--was conceived by Alan Turing.