This site presents the new field of applying artificial intelligence tools to assist in the process of human innovation. The majority of creative sparks, don't ignite into a full fledged innovation productivity, mostly because of human errors that can be avoided by the proper use of AI techniques. It is therefore the promise of this new field to enhance innovation across the board by enabling more creative sparks to mature into a useful innovation product. This site is dedicated to presenting the state of the art in the field, it is meant to become a conversation round table for the issues of AIAI, and a market place to connect between people who develop expertise in this new field, and researchers and developers everywhere who could benefit from these tools.
To register or express interest write to Gideon@BitMint.com.
Innovation is a walk in a mystery forest, every step taken, is marked with a measure of doubt since alternative steps present themselves, sometimes with equal attraction. The innovator needs to plow ahead through confronting uncertainty. The missteps made in the journey often doom a highly promising innovation route, or at least delay its manifestation. This challenging uncertainty may be managed via tools of AI, which the innovator may want to become well skilled in applying.
The principles of AIAI are not unique to a particular field of innovation, they are intrinstinsci to the innovation process per se. Herein lies a practical problem: subject matter experts disregard any advise given to them by laymen in that field, much as management science in it's early days was dismissed by subject matter mavens who failed to realize that there is generic know-how and wisdom in management per se, regardless of what is being managed. Same here innovation in its generic distilled form applies to innovation efforts across the board. The combination of subject matter expertise and generic innovation know how is the strategy of winners.
Early in his engineering career, Gideon Samid realized that so many of his innovative suggestions were shot down by some alien evaluator who claimed (i) "this would never work!", (ii) "it would cost too much", and (iii) "it would take too long", and finally (iv) "it is not needed really". Gideon could argue need and feasibility but was unequipped to present a counter cost and schedule statement. So he learned and became a cost engineer, specializing in putting up credible estimates of "cost to complete" and "time to finish" of innovative undertakings. Gideon eventually opened an engineering boutique (& D&G Sciences -- Innovation Productivity Corporation) specializing in his pioneering field, and returned to his alma mater to dig academically into this topic. Guided by his admired professor Epharim Kehat at the Technion -- Israel institute of technology, Gideon completed his PhD dissertation and ever since pushed the state of the art of what he originally called "innovation appraisal" (Gideon established an Innovation Appraisal center in Case Western Reserve University in Cleveland, Ohio). Over the years Gideon developed the "Innovation Solution Protocol", InnovationSP, which has now matured into "artificial intelligence assisted innovation" (AIAI).
ELon Musk and others are warning humanity of the specter of AI taking over. Surely an alarming prospect, but looking forward it is inconclusive whether it is overblown, or an imminent danger. What is conclusive is that the more creative we remain, the more productive our innovation, the better our chances. There you have it -- direct AI technology not to replace humans, but to enhance their creativity and their ultimate superiroity in the battle with this rising tide of AI
For AI to make a contribution to innovation it is necessary to codify innovation to start with. The codified process can then be made more efficient by bringing to bear the inherent advantage of AI: (i) its ability to simultaneously regard very large quantities of data, and (ii) its ability to exercise deterministic logic, heuristics, and probability reasoning uncontaminated by emotional waves that wreak havoc with human innovators.
The challenge here is to reduce the airy idea of creativity and innovation to a score and a comparable measurement. AIAI does it through economics. The reality is that innovation, research and development cost money, and take time, and one would expect that cost to be justified in light of the expected benefit of the outcome. It is therefore desirable that every innovative project will be accompanied with a credible appraisal of cost-to-complete, and time-to-finish. The problem is that inherently innovation is a process of making some unknown into known, so looking ahead the work load is 'unknown' still, and as such it resists all attempts to credibly estimate its cost-to-complete, and time-to-finish. The greater the innovation load ahead, the deeper the unknown and the less credible the estimate of cost-to-complete, and time-to-finish. Taking from the other side (and that is a basic innovative principle for AIAI), as the innovation load shrinks, the credibility of the estimate of cost-to-complete, and time-to-finish rises. By objectively measuring this credibility one acquires an objective means to gauge innovation progress. With such metric we can now build effective AI tools and improve the innovation process.
In order to rate and compare innovation pathways in terms of their innovation progress, as described above, it is necessary to draw a generic map that will apply to any innovation track, regardless of what is being innovated. AIAI developed an "Innovation Solution Protocol", InnovationSP, which says that every innovation challenge, IC0, for which the burden to resolve is P0, may be replaced by an associated challenge IC1 with a smaller burden needed for its resolution, P1, such that the burden to resolve IC0, after having resolved IC1, P'0 = [P0 | P1], is smaller than P0 - P1: P'0 + P1 < P0 . By applying this challenge-replacement strategy iteratively one will chart a universal map for the innovation process. The InnovationSP further specifies the class options for the replacement IC: (a) breakdown, (b) abstraction, (c) extension.
Once so codified the AIAI environment can capture any and all innovative processes, and apply AI techniques to guide the current innovation based on the lessons learned from the past.
Before formulating AIAI Gideon Samid had no registered IP, patents, to his name. By applying AIAI principles, a great potential was unleashed. Gideon has quickly amassed 9 granted patents, with many more in the pipeline. The secret is in avoiding loss of time doing things that AIAI can take care of. The most important principle is to persistently strive to increase the credibility of the innovation project cost-to-complete, and time-to-finish. This of course is a counter-intuitive principle, which is why we invite you to come to our seminar.