Distributed artificial intelligence system
10. Automated warfare may begin in 2019. Modern automated warfare began with the Aegis Ballistic Missile Defense System, which has roots in the 1980s Reagan era. The potential now exists for high-scale automated offensive attacks, whether used by terrorist groups or state actors at massive scale. The risk of a billion drones attacking complete with primitive… Read More
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… Read More
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. Read More
Mark Montgomery reflects on Memorial Day 2018 with regard to the incumbency challenges facing the U.S. and Department of Defense in distributed AI systems. Read More
Kyield founder welcome to KyieldOS.com followed by 15 min discussion on exponential productivity with AI systems Read More
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. Read More
It is truly an honor to share our recent announcement and welcome Vice Admiral Phil Wisecup USN (Ret.) to our board of directors. Phil joins Dr. Robert Neilson who is now special advisor to the board. As their bios only partially reflect, Phil and Rob are exceptional additions to Kyield’s leadership. Read More
This is a personal story about our real-world experience, which contains little resemblance to most of what is written about entrepreneurism and technology commercialization. While our journey has been longer than most, scientific commercialization (aka deep tech) typically requires two decades or more from theory to market. Read More
Even though some companies may seem well positioned, the fundamental economic and business environment is rapidly changing. To the best of my awareness, survival from this point forward will essentially require a strong AI OS for the super majority of organizations. Read More
I wanted to share a general pattern that is negatively impacting organizations in part due to the compounding effect it has on the broader economy. Essentially this can be reduced to misapplying the company’s playbook in dealing with advanced technology (AI systems). Read More
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] Read More
The focus should be maximize benefits from our inventions, engineered systems and technologies to recreate a sustainable competitive advantage. One benefit of lagging behind other countries in infrastructure is that much progress has been made in recent years. Future projects can be embedded with hardware that enable intelligent networks, which can then be managed with distributed operating systems enhanced with artificial intelligence (AI) to meet the diverse needs of our society.
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Learn about the background of Kyield and the multi-disciplinary science involved with AI systems, with a particular focus on AI augmentation for knowledge work and how to achieve a continuously adaptive learning organization (CALO). Read More
Recent investment in AI is primarily due to the formation of viable components in applied R&D that came together through a combination of purposeful engineering and serendipity, resulting in a wide variety of revolutionary functionality. However, since investment spikes also typically reflect reactionary herding, asset allocation mandates, monetary policy, and opaque strategic interests among other factors, caution is warranted. Read More
This post is in response to an excellent article Tom Davenport wrote for the WSJ (now on LinkedIn) ‘Lessons from the Cognitive Front Lines: Early Adopters of IBM’s Watson’. Read More
This is a clip from an E-book nearing completion titled: The Kyield OS: A Unified AI System; Rapid Ascension to a Higher Level of Performance. Read More
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. Read More
Consider that learning algorithms are very likely (or soon will be) improving the intelligence quotient and operational efficiency of your chief competitors at an extremely rapid rate. Read More
While artificial intelligence (AI) has roots as far back as Greek mythology, and Aristotle is credited with inventing the first deductive reasoning system, it wasn’t until the post WWII era of computing that we humans began to execute machine intelligence with early supercomputers. The science progressed nicely until the onset of AI Winter in the 1960s, representing the beginning of severe cycles in technology. A great deal of R&D needed to evolve and mature over the next five decades prior to wide adoption of applied AI — here is a brief history and analysis of the rapidly evolving field of artificial intelligence and machine learning. Read More
As one who has invested a significant portion of my life studying crises and designing methods of prevention, including for many years the use of artificial intelligence (AI), I feel compelled to offer the following definitions of AI with a few of what I consider to be plausible scenarios on how AI could prevent or mitigate catastrophes, as well as brief enlightenment on how we reduce the downside risk to levels similar to other technology systems. Read More
I just completed an in-depth paper on how our work and system can help life science and healthcare companies overcome the great challenges they face, so I wanted to share some thoughts while still fresh. The paper is part of our long-term commitment to healthcare and life sciences, requiring a deep dive over the past… Read More
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. Read More
Physics won this debate before anyone had a vision that a computer network might someday exist, but biology played an essential role on the team. The reason of course is that all living things, including humans and our organizations, are unique in the universe—for our purposes anyway—until that identical parallel universe is discovered. Even perfectly… Read More
Those tracking business and financial news may have observed that a little bit of knowledge in the corner office about enterprise architecture, software, and data can cause great harm, including for the occupant, often resulting in a moving van parked under the corner suite of corporate headquarters shortly after headlines on their latest preventable crisis. Exploitation of ignorance in the board room surrounding enterprise computing has become mastered by some, and is therefore among the greatest challenges for emerging technology that have the capacity for significant improvement. I spend more time and energy on dispelling myths than I would prefer necessary, but so be it. The issues surrounding neural networks requires total emersion for extended duration. Many organizations lack the luxury of time, so let’s get on with it.
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An article in the New York Times reminds us once again that without a carefully crafted and highly disciplined governance architecture in place, perceived misalignment of personal interests between individuals and organizations across cultural ecosystems can lead to catastrophic decisions………While not unexpected by those who study crises, rather yet another case where brave individuals raised red flags only to be shouted down by the crowd, the article does provide instructive granularity that should guide senior executives, directors, and policy makers in planning organizational models and enterprise systems. (click to continue to article) Read More
Prevention of human-caused catastrophes has long been a top priority of our R&D. We have a desire and an obligation to provide insight into what can be done to prevent school shootings and other similar human-caused catastrophes. I sincerely hope this modest attempt to help will assist in taking specific pragmatic actions to save lives. Read More
