Machine Knowledge

Knowledge and Intelligence

By |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 [...]

Assessing AI

By |2019-03-08T10:45:09-04:00September 16th, 2015|MK|

Measuring Language Comprehension How intelligent will our sapiens become?  For the first time in the history of computing, the language comprehension of a software technology can be measured with tools designed to assess human comprehension.  We are already finding that such tools can be usefully applied to assess our technology’s increasing sophisticated language comprehension.  The performance level of a sapiens is determined solely by the scope and fidelity of its world model.  There is no limit to how well the world can be modeled as the history of human knowledge attests.  However, the computational bandwidth and memory capacity of an individual human brain is forever bounded in ways computer technology is not. We expect the baseline language comprehension to climb quickly through the grade levels, continuing to college, graduate levels, and beyond.  Such a notion has been inconceivable for any other approach because, without world models, they have no language comprehension to measure and no thoughts to articulate.  Since its beginnings in the 1980s, the AI community has been rife with hyperbole and vague claims of programs that “think like humans,” but always without measurable results. We believe that era is now in the past.  With quantifiable comprehension, we foresee that New Sapience’s Machine Knowledge will demonstrate a breakthrough potential to move into a field of machine-human interface applications that is basically unlimited as compared to the technologies currently available. Blooms Taxonomy of Learning Bloom’s Taxonomy provides an important framework teachers use to focus on higher order thinking. [...]

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