Imagination Engines, Inc., Home of the Creativity Machine
Imagination Engines, Inc., Home of the Creativity Machine
Imagination Engines, Inc., Home of the Creativity Machine
  • IEI Patent Overview

    The simple, elegant, and inevitable path to human level machine intelligence and beyond, the Creativity Machine Paradigm, US Patent 5,659,666 and all subsequent foreign and divisional filings.

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The Ultimate Idea - The Creativity Machine Paradigm
What is the ultimate idea?

We, along with many others, believe that grandest of all notions is how ideas themselves are formed within the biological neural networks of the brain. If the principle behind this highly prized cognitive mechanism can be captured, understood, and then implemented within lightning fast machine intelligence, then we have attained the ultimate idea, the one that can generate all subsequent ideas.

In 1994, this company achieved that goal, announcing the accomplishment through a patent called the "Creativity Machine," a computational paradigm, already 20 years in the making, that came the closest yet to emulating the fundamental neurobiological mechanisms responsible for idea formation. Within months of this patent's issue, international corporations, the US government, and the press (e.g., MSN, Scientific American, New Scientist, and Economist, among others) were asking for more information, leading to diverse contracts in materials discovery and consumer products. In 1997 this same sentient neural paradigm was chosen by a major US defense contractor as the 'brains' behind a satellite communications system that contemplated how best to distribute petabytes of critical information to the country's armed forces around the world. Similarly, the intelligence community frantically approached the company in August of 2001, requesting a cost-free license to its Creativity Machine based semantic parsing software to mine the Internet for terrorist activities. Of course, out of our sense of altruism and national loyalty, we naturally agreed and donated our time and resources to this very worthwhile effort.

It was at this point that this neural paradigm inspired, and some cases, produced derivative neural network technology, leading to even more, fundamental artificial intelligence patents. Chief among these new concepts were artificial neural network objects that trained without recourse to the standard, human-conceived learning algorithms. Instantiated with billions of connection weights, they could learn from live video streams involving 30 million bytes per second, using the personal computer technology of 1998! Soon systems of these so-called STANNOs could self-organize themselves into vast brain-like structures called SuperNets. rather than be manually arranged in successive layers by human software engineers. It was these networks of networks that paved the way to such innovative automotive machine vision applications that we all know and love, such as lane departure warning, drowsiness detection, automatic high-beam control, and pedestrian/vehicle detection. Then, these compound nets could be stimulated, just as in the fundamental Creativity Machine architecture, to produce even more complex ideas, and in the case of battlefield robots and space exploration platforms, improvise clever strategies to autonomously deal with the unexpected.

Recently, the company has teamed with the government in the area of health informatics and space exploration, but our real love is in the design of what is arguably both conscious and sentient machine intelligence. In this vein, IEI will soon unveil a new technology, codenamed "Sunshine Transit," that vastly exceeds so-called "deep learning" techniques in its pattern recognition capabilities, while enabling idea formation on a scale that can't be touched by modern day supercomputer technology. Arguably the first sentient machine intelligence, it is able to select those notions and strategies it feels are the most appropriate for a given situation or challenge.

Though partially occluded by a torrent of publicity regarding so-called "cognitive computing" and "machine sentience," both of which are being used as marketing rather than scientific terms, IEI is about to make yet another mark on the world of artificial intelligence, making good the promise of a well known NASA visionary that the Creativity Machine is AI's best bet at creating human to trans-human machine intelligence. That this fundamental AI principle could be applied across so many different problem areas such as theoretical chemistry, art, music, law, machine vision, medicine, and robotics, serves as testament to the power and flexibility of this all neural, general problem solving methodology that this company invented and memorialized through dozens of U.S. and international patents. 

Recommended Reading

An early explanation of the Creativity Machine - Neural Nets That Create and Discover, PC AI , May/June, 16-21, 1996.

Using the Creativity Machine to defend Alan Turing's thesis that machines may become both intelligent and conscious - The Creativity Machine Paradigm: Withstanding the Argument from Consciousness, APA Newsletters, Volume 11, Number 2, Spring 2012.

Basic theory and history of the Creativity Machine - The Creativity Machine Paradigm, Encyclopedia of Creativity, Invention, Innovation, and Entrepreneurship, (ed.) E.G. Carayannis, Springer Science+Business Media, LLC, 2013.

A comprehensive theory of consciousness based upon the Creativity Machine - A Synaptic Perturbation and Consciousness, International Journal of Machine Consciousness, Vol. 06, No. 02, pp. 75-107, 2014.

How Creativity Machines quantitatively predict the tempo with which we think and their distinct advantage in building synthetic brains - Pattern Turnover within Synaptically Perturbed Neural Systems, Procedia Computer Science, 88, Elsevier, 2016.

The inevitable tradeoff between creative brilliance and cognitive malfunctions in both Creativity Machine-based synthetic and human intelligence - Cycles of Insanity and Creativity within Contemplative Neural Systems, Medical Hypotheses, 94:138-147, Elsevier, 2016.

 

   
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