My team investigated closed vs. open systems of innovation through the lens of a particular technology: Artificial Intelligence. I took a pretty large risk in taking such a deep mathematical tone in the beginning, but think I did well to keep it accessible and relevant.
<ul><li> 1. WHAT IS ARTIFICIAL INTELLIGENCE?</li></ul>
<p> 2. 3. CONTRADICTIONS Infinity is infinity but infinity doesnt adequatelycontain itself. Russells Paradox of class of all classes which arenot members of themselves Illustrated: Barber of Seville Each man in town either shaves himself, or goes to thebarber The barber shaves only those who do not shavethemselves Who shaves the barber? 4. GDELS INCOMPLETENESS THEOREMS Proved that the systems of mathematical logic areflawed. No matter how large you make your set of axioms,in arithmetic there will always be statements thatare true, but cannot be proven so. Another way of saying this, for us technologymanagers: No matter how much data you have,even infinitely many data bits, you cannot prove alltrue statements. 5. FROM ESOTERIC TO CONCRETE - DISRUPTIVEINNOVATION IN MATHEMATICS Alan Turing The Halting Problem For mathematicians, how do you know if the problemyou are working on is inherently unsolvable (HilbertsSecond Problem), or extraordinarily difficult (FermatsLast Theorem)?In conceiving an answer, Turing turned to something morebasic: uncomputability. What are the limits ofcomputation? The machine he constructed, The TuringMachine, was the conceptual creation of what we todaycall the computer. 6. WHY DOES THIS MATTER? Every single one of us will have our lives inexorablyand profoundly changed over the coming decadesby AI. It is important because it tells us what AI is NOT. Internet TV, only better AI human intelligence, only better (perfect memory) Establishes a respect for the AI technology, but adeep and abiding admiration for the naturaltechnology of the human mind. 7. FROM IDEA TO INNOVATION Innovation = Commercialization of Ideas. AIdeveloped only as an idea until the hardware couldcatch up. Now, with the situations somewhatreversed, funding is pouring into AI research. DARPAs CALO project in 2003 Trapit and SIRI 8. VOICE RECOGNITION-CHALLENGES speaker dependence, continuity of speech, difficulty of identifying word boundaries - as in "youth inAsia" and "euthanasia. vocabulary size Large vocabularies cause difficulties in maintainingaccuracy, but small vocabularies restrict the speaker. 9. APPLE >50,000employees and with annual revenueapproaching $100 billion grow 60% a year Multi-focused structure in which product,function, and geography are emphasized all atonce Better alignment between functional anddivisional goals Simplicity is key. It is deceptively straightforward with none of thedotted-line or matrixed responsibilities popularelsewhere in the corporate world A corporate dictator who makes every critical decision(Steve Jobs) 10. APPLE A cutting-edge startup rather than the consumer-electronics behemoth The attention to detail, the secrecy, the constant feedback-- into processes Passionfor innovation and an uncompromisingcommitment to bringing great products to market. Smart technology also needs to be beautiful technology 11. HOW APPLE WORKS Accountability from top on down a series of weekly meetings never any confusion as to who is responsible for what. The "DRI" or directly responsible individual. Ability to move nimbly Ability to focus on just a few things at a time Still a startup at heart Most notably by putting small teams on crucial projects Do-more-with-less mentality 12. HOW APPLE MANAGE Value-driven business-model innovation Smart technology (ipad, phone) Voice recognition is a disruptivetechnology, but they apply it as a sustaininginnovation Acquired SIRI (2010) ability to correlate data ability to interpret meaning If improved upon,... 13. LESSONS FROM APPLE Network Innovation In pursuit of Simplicity Fail Wisely Not All Innovation is Equal Innovation Doesnt Generate Growth. Management Does 14. GOOGLES VOICE RECOGNITION Application Simultaneous subtitle in video Perspective Translation A supercar in Knight Rider and Green Hornet Searching by oral inputs 15. GOOGLES CHALLENGES Challenges Vocabulary Accent Automatic skip Translation 16. MICROSOFT VOICE RECOGNITION WindowsSpeech Recognitionempowers users to interact withtheir computers by voice. Itwas designed for people whowant to significantly limit theiruse of the mouse and keyboardwhile increasing their productivity. 17. MICROSOFT VOICE RECOGNITION Schools-Teacherscan use speech recognitions toimprove students second language. Offices- People send email and do their projectsefficiency by speech recognition. Research Center- Scientists improveproductivity by speech recognition. Military-Commanders can control anyequipments easily and safety by speechrecognition. 18. MICROSOFT-FEATURESCommanding "Say what you see" control applications and tasks, such as formatting and saving documents; opening and switching between applications; and opening, copying, and deleting files; and browse the Internet by saying the names of links.Correction Efficiently fix incorrectly recognized words by selecting form alternatives for the dictated phrase or word or by spelling the word.InteractiveThe interactive tutorial teaches people how to usetutorial windows speech recognition and teaches system what your voice sounds like.Personalization The system keep adapting both your speaking style and accent continually improves speech recognition accuracy. 19. PROBLEMS AND CHALLENGES FOR MICROSOFT Problems-1. Voice distinguish2. Commands error on system Challenges-1. How to develop a new smart system2. Strengthen distinguish system3. Operating speed. 20. COMPARISON Managing Innovation Potentially disruptive technologies used in a sustaininginnovation framework (ecosystem) Apple Product ecosystem- iPad Google Search ecosystem- Android Microsoft Windows ecosystem- Office Products 21. COMPARISON 22. KLINE: SHARING THE CORPORATE JEWELS "Strategic licensing is emerging against the backdrop of intensified efforts bycorporate America to maximize the return on its intellectual property assets,which now account for 50% to 70% of the market value of all publiccompanies. To judge from the results of such initiatives to date, the most powerfulbenefits are economic. No company demonstrates this better than IBM, whichearned an astounding $1.7 billion from technology licensing in 2000 alone.These revenues came with a 98% profit margin and accounted for roughly20% of the companys net income in that year. Imagine the possibilities Artificial Intelligence applications could have in thisregard. 23. THE FUTURE OF AI Another DARPA creation, the internet, was in a similarposition, not too long ago. Tim Berners-Lees World Wide Web Netscape Browser AI also needs the concurrent development of enablingtechnologies, like: a semantically linked web, populatedwith a web of things, and robotics. Until then, this space is best doing a lot of the same as itis doing now until such time as the disruptive technologyfinds a model that can make it into a truly disruptiveinnovation.</p>