The Quest For A Universal Theory Of Intelligence

Recent findings about the capabilities of smart animals such as corvids or octopi and novel types of artificial intelligence (AI), from social robots to cognitive assistants, are provoking the demand for new answers for meaningful comparison with other kinds of intelligence. This book fills this need by proposing a universal theory of intelligence which is based on causal learning as the central theme of intelligence. The goal is not just to describe, but mainly to explain queries like why one kind of intelligence is more intelligent than another, whatsoever the intelligence. Shiny terms like "strong AI," "superintelligence," "singularity" or "artificial general intelligence" that have been coined by a Babylonian confusion of tongues are clarified on the way.

Recent findings about the capabilities of smart animals such as corvids or octopi and novel types of artificial intelligence (AI), from social robots to cognitive assistants, are provoking the demand for new answers for meaningful comparison with other kinds of intelligence. This book fills this need by proposing a universal theory of intelligence which is based on causal learning as the central theme of intelligence. The goal is not just to describe, but mainly to explain queries like why one kind of intelligence is more intelligent than another, whatsoever the intelligence. Shiny terms like "strong AI," "superintelligence," "singularity" or "artificial general intelligence" that have been coined by a Babylonian confusion of tongues are clarified on the way.

It is statistically unlikely that humans are the only intelligent species in the universe. Nothing about the others will be known until contact is made beyond a radio signal from space that merely tells us they existed when it was sent. That contact may occur tomorrow, in a hundred years, or never. If it does it will be a high-risk scenario for humanity. It may be peaceful or hostile. Relying on alien altruism and benign intentions is wishful thinking. We need to begin identifying as a planetary species, and develop a global consensus on how to respond in either scenario.

Algorithmic probability and friends: Proceedings of the Ray Solomonoff 85th memorial conference is a collection of original work and surveys. The Solomonoff 85th memorial conference was held at Monash University's Clayton campus in Melbourne, Australia as a tribute to pioneer, Ray Solomonoff (1926-2009), honouring his various pioneering works - most particularly, his revolutionary insight in the early 1960s that the universality of Universal Turing Machines (UTMs) could be used for universal Bayesian prediction and artificial intelligence (machine learning). This work continues to increasingly influence and under-pin statistics, econometrics, machine learning, data mining, inductive inference, search algorithms, data compression, theories of (general) intelligence and philosophy of science - and applications of these areas. Ray not only envisioned this as the path to genuine artificial intelligence, but also, still in the 1960s, anticipated stages of progress in machine intelligence which would ultimately lead to machines surpassing human intelligence. Ray warned of the need to anticipate and discuss the potential consequences - and dangers - sooner rather than later. Possibly foremostly, Ray Solomonoff was a fine, happy, frugal and adventurous human being of gentle resolve who managed to fund himself while electing to conduct so much of his paradigm-changing research outside of the university system. The volume contains 35 papers pertaining to the abovementioned topics in tribute to Ray Solomonoff and his legacy.

The general unified theory of intelligence addresses the cognitive functions of thinking, reasoning, and problem solving. At an abstract level, this theory construes the intellective functions of humans and computers as, respectively, restricted and directed forms of the logic of implication. In other words, human intelligence operates according to production rules. Here, Wagman presents the central tenets and research elaboration of the general unified theory of intelligence that embraces both human and artifical intelligence across the cognitive domains of scientific discovery processes, inductive and deductive reasoning, and the mechanisms basic to analogical thinking and problem solving.

By the end of the 1960s, a new discipline named computer science had come into being. A new scientific paradigm--the 'computational paradigm'--was in place, suggesting that computer science had reached a certain level of maturity. Yet as a science it was still precociously young. New forces, some technological, some socio-economic, some cognitive impinged upon it, the outcome of which was that new kinds of computational problems arose over the next two decades. Indeed, by the beginning of the 1990's the structure of the computational paradigm looked markedly different in many important respects from how it was at the end of the 1960s. Author Subrata Dasgupta named the two decades from 1970 to 1990 as the second age of computer science to distinguish it from the preceding genesis of the science and the age of the Internet/World Wide Web that followed. This book describes the evolution of computer science in this second age in the form of seven overlapping, intermingling, parallel histories that unfold concurrently in the course of the two decades. Certain themes characteristic of this second age thread through this narrative: the desire for a genuine science of computing; the realization that computing is as much a human experience as it is a technological one; the search for a unified theory of intelligence spanning machines and mind; the desire to liberate the computational mind from the shackles of sequentiality; and, most ambitiously, a quest to subvert the very core of the computational paradigm itself. We see how the computer scientists of the second age address these desires and challenges, in what manner they succeed or fail and how, along the way, the shape of computational paradigm was altered. And to complete this history, the author asks and seeks to answer the question of how computer science shows evidence of progress over the course of its second age.

Dr. Kevin Warwick warns us that robots and machine intelligence pose an enormous threat to mankind. He is deeply critical of techniques used to measure human intelligence, in particular IQ tests. He believes these are both flawed and outdated. He has developed an entirely new theory, which proposes a universal view of intelligence, within which human, animal and even artificial intelligence are united for the first time.

A bestselling author, neuroscientist, and computer engineer unveils a theory of intelligence that will revolutionize our understanding of the brain and the future of AI. For all of neuroscience's advances, we've made little progress on its biggest question: How do simple cells in the brain create intelligence? Jeff Hawkins and his team discovered that the brain uses maplike structures to build a model of the world—not just one model, but hundreds of thousands of models of everything we know. This discovery allows Hawkins to answer important questions about how we perceive the world, why we have a sense of self, and the origin of high-level thought. A Thousand Brains heralds a revolution in the understanding of intelligence. It is a big-think book, in every sense of the word. One of the Financial Times' Best Books of 2021 One of Bill Gates' Five Favorite Books of 2021