McCarthy


The fourth industrial revolution: a primer on Artificial Intelligence (AI) – MMC writes

#artificialintelligence

From Amazon and Facebook to Google and Microsoft, leaders of the world's most influential technology firms are highlighting their enthusiasm for Artificial Intelligence (AI). While there is growing interest in AI, the field is understood mainly by specialists. Our goal for this primer is to make this important field accessible to a broader audience. We'll begin by explaining the meaning of'AI' and key terms including'machine learning'. We'll illustrate how one of the most productive areas of AI, called'deep learning', works.


The fourth industrial revolution: a primer on Artificial Intelligence (AI) – MMC writes

#artificialintelligence

From Amazon and Facebook to Google and Microsoft, leaders of the world's most influential technology firms are highlighting their enthusiasm for Artificial Intelligence (AI). While there is growing interest in AI, the field is understood mainly by specialists. Our goal for this primer is to make this important field accessible to a broader audience. We'll begin by explaining the meaning of'AI' and key terms including'machine learning'. We'll illustrate how one of the most productive areas of AI, called'deep learning', works.


Arguing A.I.: The Battle for Twenty-first-Century Science: Sam Williams: 9780812991802: Amazon.com: Books

@machinelearnbot

Is research and thinking on artificial intelligence stuck in a local minimum? Those in the field have attested to major advances in the last decade, but are these advances merely a renaming of approaches that were taken decades ago? This book does not address these questions as its major goal, but instead attempts to give a broad overview of how A.I. got started and where it is now, and where it might be going. The reader is lead to ask the questions above though after reading the book, for the author seems to ask them implicitly. Its validity as a science are questioned, and the future of A.I. is addressed in detail.


The future cyber economy ANZ BlueNotes

#artificialintelligence

In 1959, Marvin Minsky and his colleague John McCarthy founded the MIT Artificial Intelligence Project. The men were convinced computers could be made as smart as humans – and then smarter. McCarthy thought functioning AI systems were only a decade away. Around the same time, another researcher, Herbert Simon of CMU, predicted a computer would be the world chess champion by 1967. McCarthy and Simon were wildly wrong – so much so that in 1972 Hubert Dreyfus, a philosophy professor at the University of California, Berkeley, wrote What Computers Can't Do, citing "game playing, language translating, problem solving, and pattern recognition" as key things humans can do and that computers can't.


Why we need a legal definition of artificial intelligence

#artificialintelligence

When we talk about artificial intelligence (AI) – which we have done lot recently, including my outline on The Conversation of liability and regulation issues – what do we actually mean? AI experts and philosophers are beavering away on the issue. But having a usable definition of AI – and soon – is vital for regulation and governance because laws and policies simply will not operate without one. This definition problem crops up in all regulatory contexts, from ensuring truthful use of the term "AI" in product advertising right through to establishing how next-generation automated weapons systems (AWSs) are treated under the laws of war. True, we may eventually need more than one definition (just as "goodwill" means different things in different contexts).


John McCarthy

#artificialintelligence

McCarthy was a true "progressive" in that he appreciated the rapid and dramatic improvements in human living standards brought about by innovation. It was from McCarthy's website that I first learned of Thomas Babington Macaulay's remarks, in the Edinburgh Review, that I often quote -- "We cannot absolutely prove that those are in error who tell us that society has reached a turning point, that we have seen our best days. But so said all before us, and with just as much apparent reason ... On what principle is it that, when we see nothing but improvement behind us, we are to expect nothing but deterioration before us".


EPISTEMOLOGICAL PROBLEMS OF Al / 459

Classics (Collection 2)

EPISTEMOLOGICAL PROBLEMS OF ARTIFICIAL INTELLIGENCE John McCarthy Computer Science Department Stanford University Stanford, California 94305 Introduction In (McCarthy and Hayes 1969), we proposed dividing the artificial intelligence problem into two parts - an epistemological part and a heuristic part. This lecture further explains this division, explains some of the epistemological problems, and presents some new results and approaches. The epistemological part of Al studies what kinds of facts about the world are available to an observer with given Opportunities to observe, how these facts can be represented in the memory of a computer, and what rules permit legitimate conclusions to be drawn from these facts. It leaves aside the heuristic problems of how to search spaces of possibilities and how to match patterns. Considering epistemological problems separately has the following advantages: I. The same problems of what information is available to an observer and what conclusions ...


Circumscription--A Form of Non-Monotonic Reasoning

Classics (Collection 2)

Circumscription formalizes such conjectural reasoning. McCarthy [6] proposed a program with common sense' that would represent what it knows (mainly) by sentences in a suitable logical language. It would decide what to do by deducing a conclusion that it should perform a certain act. Performing the act would create a new situation, and it would again decide what to do. This requires representing both knowledge about the particular situation and general common sense knowledge as sentences of logic.


PATRICK J. HAYES

Classics (Collection 2)

The frame problem arises in attempts to formalise problem--solving processes involving interactions with a complex world. It concerns the difficulty of keeping track of the consequences of the performance of an action in, or more generally of the making of some alteration to, a representation of the world. The paper contains a survey of the problem, showing how it arises in several contexts and relating it to some traditional problems in philosophical logic. In the second part of the paper several suggested partial solutions to the problem are outlined and compared. This comparison necessitates an analysis of what is meant by a representation of a robot's environment.


SESSION 1 PAPER 3 PROGRAMS WITH COMMON SE

Classics (Collection 2)

John McCarthy, born at Boston, Mass. in 1927, received his B.S. degree in mathematics at the California Institute of Technology in 1948, and his Ph.D. also in mathematics at Princeton University in 1951. He is at present Assistant Professor of Communication Sciences at the Massachusetts Institute of Technology. His present interests are in the artificial intelligence problem, automatic programming and mathematical logic. He is co-editor with Dr. C. E. Shannon of "Automatic Studies". However, certain elementary verbal reasoning processes so simple that they can be carried out by any non--feeble--minded human have yet to be simulated by machine programs.