AGI – Page 2 – New Sapience

AI at Google
AGI, Competition

AI at Google

By bcruse|2018-07-14T13:23:18-04:00September 20th, 2016|AGI, Competition|

Representation of a neural network Artificial Neural Networks & Natural Language When we explain our Compact Knowledge Model technology and describe it's far reaching implications for Artificial General Intelligence a common reaction is "but surely Google and the other big tech companies are doing something similar." As we know, Google (and all of the big tech companies) have been making massive investments in the (we think misnamed) "cognitive computing" technology that is now considered almost synonymous with AI by common usage. "Cognitive computing" is jargon for artificial neural networks (ANNs). Neural networks are "trained" over vast numbers of iterations on supercomputers to recognize patterns in equally vast databases. A very expensive process, but one that works reasonably well for things like pattern recognition in photographs, though even here, there are limitations, because ANNs lack any knowledge of the real world objects they are being trained to recognize. Applications of neural networks to natural language processing proceed in the same way as with images. The networks are trained under the control of algorithms designed to find certain patterns in huge databases, in this case, of documents, which from the standpoint of the program, are just an array of numbers (exactly as a photograph is nothing but an array of numbers to such programs.) The applications process these text databases but they have no reading comprehension as humans recognize it - no notion whatsoever about the content or meaning of the text. Humans curate the databases to limit the [...]

The Third Singularity

By bcruse|2019-03-06T10:13:11-05:00September 20th, 2015|AGI, Foundations, MK|

The Third Singularity Are Super Artificial Intelligences going to make humanity obsolete? If you’re not worried about this maybe you should be since some of the leading technical minds of our time are clearly very concerned. Eminent theoretical physicist, Stephen Hawking said about AI: “it would take off on its own, and re-design itself at an ever increasing rate. Humans who are limited by slow biological evolution, couldn’t compete, and will be superseded.” Visionary entrepreneur and technologist Elon Musk said: “I think we should be very careful about artificial intelligence. If I had to guess at what our biggest existential threat is, it’s probably that. So we need to be very careful,” No less than Bill Gates seconded his concern: "I agree with Elon Musk and some others on this and don't understand why some people are not concerned." The scenario Hawking refers to, of A.I.s redesigning themselves to become ever more intelligent is called The Singularity. It goes like this: once humans create A.I.s as intelligent as they are, then there is no reason to believe they could not create A.I.s even more intelligent, but then those super A.I.s could create A.I.s more intelligent than themselves and so on ad-infinitum and in no time at all A.I.s would exist as superior to humans in intelligence as humans are to fruit flies. The term Singularity is taken from mathematics where it refers to a function that becomes undefined at a certain point beyond which its behavior becomes impossible [...]

Knowledge and Intelligence

By bcruse|2018-07-14T13:39:17-04:00September 20th, 2015|AGI, MK|

Understanding Intelligence Alan Turing, in his 1950 paper “Computing Machinery and Intelligence,” proposed the following question: “Can machines do what we (as thinking entities) can do?” To answer it, he described his now famous test in which a human judge engages in a natural language conversation via a text interface with one human and one machine, each of which try to appear human; if the judge cannot reliably tell which is which, then the machine is said to pass the test. The Turing Test bounds the domain of intelligence without defining what it is. We recognize intelligence by its results. John McCarthy, who coined the term Artificial Intelligence in 1955, defined it as "the science and engineering of making intelligent machines." A very straight-forward definition, yet few terms have been more obfuscated by hype and extravagant claims, imbued with both hope and dread, or denounced as fantasy. Over the succeeding decades, the term has been loosely applied and is now often used to refer to software that does not by anyone’s definition enable machines to “do what we (as thinking entities) can do.” The process by which this has come about is no mystery. A researcher formulates a theory about what intelligence or one of its key components is and attempts to implement it in software. “Humans are intelligent because we can employ logic” and so rule-based inference engines are developed. “We are intelligent because our brains are composed of neural networks” and so software neural networks are [...]