The World's Most Advanced Distributed Artificial Intelligence Operating System
The KYield operating system is based on the theorem ‘yield management of knowledge’ developed by KYield’s founder in 1997. The KYield OS provides optimal network management at the confluence of human and machine intelligence. The patented AI system core is fully adaptive and tailored to the unique profiles of each entity with a simple natural language interface. Among many benefits of the KYield OS include data optimization at vast speed and scale that delivers enhanced governance, security, prevention, productivity, innovation, discovery, and continuous learning for each individual, team, and organization. Data ownership and control remains with customers unless required by regulations or per agreement.
Metamorphic Transformation with Enterprisewide Artificial Intelligence
Most CEOs and boards today realize that in order to remain competitive they must apply artificial intelligence across their companies, but according to a recent McKinsey survey, only 21 percent report embedding A.I. into multiple business units or functions. Clearly, the piecemeal approach to A.I. is not serving the strategic or operational needs of business or government.
In addition to the barriers found by McKinsey, our direct engagement with hundreds of large organizations over the last decade found cultural resistance due to fear of displacement from A.I., which exacerbates all other barriers to A.I. adoption.[ii] While fear of A.I. has been declining over the past few years, the combination of barriers to enterprise-wide A.I. systems represents existential risk to companies and nations (Continue reading article on our blog...).
The DOD JEDI program provides a clear example of how not to do it
The list of priority career objectives for pioneers in artificial intelligence (AI) rarely include trillion dollar weapons programs, institutional turf battles, stifling bureaucracy, or systemic corruption — even for those who desperately want America and democracy to prevail. Highly intelligent scientists, engineers, entrepreneurs, and architects are typically motivated by solving the myriad of other challenges facing the planet, not least in saving the planet itself and each species within it, including our own.....→
The greatest risk from AI facing companies and nations is slow adoption of advanced AI systems and/or poor execution. The fast follow approach that became such a common strategy over the past two decades with incumbents is failing fast in AI systems. An incremental approach is more than just high risk behavior during revolutionary change–it’s certainty of failure. Indeed, the “let’s wait and see” strategy in the case of AI systems should be viewed as suicidal, whether driven by a corporate Luddite culture, incompetence, or complacency.....→
When we first approached senior executives about the benefits of our work in AI systems more than a decade ago, we were limited by physical constraints to the small group of organizations who happen to own supercomputers that could perform extreme computing tasks. Obstacles in physics and economics at the time still prevented us from achieving our original goal of distributed AI systems across networks and organizations.....→
Among the most important lessons in human history is that those who adopt innovation in the most advantageous manner often triumph over competitors. This has never been truer than in the rapidly evolving artificial intelligence revolution underway, where we face great risk from a tripartite of totalitarian nations, corporate oligopolies and complacent democracies....→
AI systems create value by converting human knowledge to digital form that can then be converted to other forms of energy, including kinetic. At the atomic level knowledge created by human intelligence and augmented by machine learning can be viewed and expressed as an extension of relativity discovered by Einstein more than a century ago....→
The Amazon acquisition of Whole Foods represents yet another confirmation of our rapidly changing business environment driven by opportunities at the confluence of technology and network dynamics. Although only the latest in a powerful trend initially impacting in this case the grocery industry, the business and technology issues driving the strategy are relevant to most and serves as a reminder that digital convergence is not confined to traditional thinking or industry lines.. Please keep that in mind while reading......→
Every year, natural catastrophes (nat cat) are highly visible events that cause major damage across the world. In 2016 the cost of nat cats were estimated to be $175 billion, $50 billion of which were covered by insurance, reflecting severe financial losses for impacted areas.[i] The total cost of natural catastrophes since 2000 was approximately $2.3 trillion.[ii] Much less understood is that human-caused catastrophes (hum cat) have resulted in much greater economic damage during the same period and have become increasingly preventable.......→
Those of us who have been through a few tech cycles have learned to be cautious, so for the second article in this series I thought it might be helpful to examine the state of AI algorithms to answer the question: what’s different this time? I reached out to leading AI labs for their perspective, including Jürgen Schmidhuber at the Swiss AI Lab IDSIA. Jürgen’s former students include team members at Deep Mind who co-authored a paper recently published by Nature on deep reinforcement learning........→
While it may be an interesting question whether the seasons are changing in artificial intelligence (AI), or to what extent the entertainment industry is herding pop culture, it may not have much to do with future reality. Given recent attention AI has received and the unique potential for misunderstanding, I thought a brief story from the trenches in the Land of Enchantment might shed some light........→
Inspired by Nature. Managed by Humans. Assisted by AI.
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