Imagination Engines, Inc., Home of the Creativity Machine


The Big Bang of Machine Intelligence!

Imagination Engines, Inc., Home of the Creativity Machine
The simple
  • Three Generations of Creativity Machines

    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.


Wall Street Journal: Can an AI System Be Given a Patent?

Fast Company: Can a robot be an inventor?

BBC: AI system 'should be recognised as inventor'

Financial Times: Patent agencies challenged to accept AI inventor

Futurism: Scientists are trying to list AI as the inventor on a new patent

The Disruption Lab: The disruption that is DABUS: Beyond AI

ACT-IAC: The dawn of conscious computing

WIRED: This artificial intelligence is designed to be mentally unstable



IEI's Patented Creativity Machine® Paradigm

Summary - An artificial neural network that has been trained upon some body of knowledge, and then perturbed in a specially prescribed way, tends to activate into concepts and/or strategies (e.g., new ideas) generalized from that conceptual space. These continuously perturbed networks are called 'imagination engines' or 'imagitrons'. If another computational agent, such as a traditional rule-based algorithm or, even better, another trained neural network is allowed to filter for the very best of these emerging ideas, in terms of novelty, utility, or value, we arrive at an extremely valuable neural architecture, the patented Creativity Machine. Optional feedback connections between this latter computational agent and the imagination engine assure swift convergence toward useful ideas or strategies. This new AI paradigm is vastly more powerful than genetic algorithms (GA), efficiently generating new concepts on mere desktop computers rather than on the computational clusters required of GAs. This generative neural network paradigm can and has been extended to whole assemblies of perturbed neural nets generating complex ideas as a multitude of neural modules watch, selectively reinforcing those notions offering novelty, utility, or value of any kind.

Simple Creativity Machines - Ordinary neural networks excel at learning from raw data, cumulatively learning to associate one pattern with another, as when raw sensory inputs from our five senses activate mental images or feelings. Note, however, that such a direct link between the external world, and our own internal mental life is only a small part of brain activity. To produce a more faithful emulation of human cognition, some mechanism must be established that provides an internal genesis of thoughts and ideas that draws upon cumulative experience, rather than what our senses are telling us at the moment.

Creativity Machines represent a new kind of neural network paradigm that is capable of generating rather than just associating patterns. They are based upon what we believe to be a significant scientific discovery: that a neural network exposed to any conceptual space and then internally irritated, in a specially prescribed way, tends to generate coherent and plausible ideas derived from its absorbed wisdom. Another neural network called an "Alert Associtive Center" (AAC), trained to filter out the most salient of these notions, patrols the former net's outputs, accumulating useful concepts (i.e., new drug or automobile designs) or using these output patterns in real time to devise strategies (i.e., robotics and control systems). Furthermore, feedback from the AAC may increase the intensity of perturbations being injected into the imagination to produce more novel concepts, or decrease such noise to permit reinforcement learning of more meritorious notions.

Perhaps the most appropriate benchmark for Creativity Machines is the highly popularized genetic algorithm (GA) wherein mathematically simulated 'genes' randomly combine and mutate to produce new potential offspring that in turn represent new concepts, designs, or strategies. The problem with such systems is that they work on geologic time scales relative to the inherent processing speed of digital computers. Furthermore, GAs fail to deliver solutions as the dimensionality of the problem increases and the algorithm's generative process leads to combinatorial explosion and nonsensical offspring. 

Note that problem dimensionality is not an issue for Creativity Machines. In a nutshell, this advantage stems from the fact that this neural architecture emulates the thalamocortical loop of the brain (i.e., the seat of intelligence and consciousness) rather than blind, and excruciatingly slow processes of mutation and natural selection. Furthermore, the actual time to build a Creativity Machine is negligible to genetic programs, since the CM is a self-organizing system. GA's, in contrast are not, and require that human experts hardwire myriad constraint relationships into their human-originated computer codes. In other words, genetic algorithms must be written by human programmers, whereas Creativity Machines build themselves!

Compound Creativity MachinesAdvanced Creativity Machines - Of course, since the mid 80s, Creativity Machines have become much more sophisticated, and by the mid 90s the canonical architecture discussed above had significantly grown to include a plurality of perturbed neural nets monitored and controlled by yet another plurality of critic nets (Thaler, 1996, 1998, 1999, 2013). To deal with the growing complexity of these generative neural architectures, a special nomenclature was devised, examples of which from Thaler, 1996,  are shown to right, in which multiple nets may simultaneously activate into complex ideas whose salience is evaluated by critic nets, O (Thaler, 1996). In this nomenclature, Ui and Vi respectively represent intact memories and confabulations activating within the ith neural net. Feedback control was achieved through a mixture of noise injection as well as reinforcement learning, both processes governed by a variety of criteria sensed by critic nets (e.g., novelty, utility, value, etc.). In these new schemes, the memories of notions and/or strategies offering utility or value to AACs could be selectively strengthened within the generative component at the expense of those deemed less valuable. Continuing this process into the early 2000s, more promising notions could be reinforced based upon their predicted consequences (i.e., the resulting chain of events). Currently, these advanced, generative neural architectures are serving as laboratories for the study of human cognition and consciousness (Thaler, 2012, 2014, 2016), in many cases revealing the neurodynamic connection between various psychopathologies and creativity (Thaler, 2016, 2017).

Future Creativity Machines - Over the last few years, IEI has begun the development of Creativity Machine having as many as one trillion neurons and millions of individual neural nets, all in an effort to create brain-like neural systems that can create, invent, and discover on unprecedented levels. For a taste of what can be done artistically with such extensive Creativity Machines, see Leech, 2016.


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