Decentralized knowledge management
Decentralization, though a boon to technology vendors, poses a unique set of challenges and risks for information and knowledge managers to grapple with.
The twisted case of facial recognition
Machine translation continues to make strides forward. Facial recognition, on the other hand, has entered the twilight zone.
Data is never just data
As with all tools, data has uses because of complex contexts that include other objects, physics, social norms, social institutions, and human intentions.
Deep project management
Given the increased negative media exposure that comes from project failure, organizations need more tightly integrated, intelligent project management systems, in addition to people who have the requisite skills. This need will grow as systems continue to become more complex and timelines more tightly compressed.
Cognitive computing and AI begin to grow together
How do we manage the hype and promise for new inventions while making sure that they represent a realistic opportunity? Can we invent self-driving cars or a Boeing 737 MAX without exposure to the risks these innovations can pose to our lives?
The convergence of convergence
The more systems and subsystems we attempt to stitch together, the greater the unpredictability.
Behind the scenes of Everyday Chaos
Machine learning builds up a model that connects data points in complex, multi-dimensional ways, usually without yielding the sort of general principles we're accustomed to reasoning from.
Rebooting the information refinery
In the field of knowledge management, of course, the idea of turning data into information into knowledge has been a foundation concept for knowledge managers. But frankly, the ability to achieve this alchemy of data to knowledge has not been broadly demonstrated in practice. A next generation information refinery is required to make something meaningful and valuable out of the raw data flying around the firm and throughout the internet economy.
Crossing the epistemic divide
As the world races ahead, purely data-driven approaches will become less attractive. Instead, we need to start gaining a deeper understanding of how to bridge the great divide which separates the artificial and the natural.
A deep future approach to KM
We're familiar with the near-term portion of the time spectrum—from femtosecond lasers used in eye surgery to high-frequency trading in milliseconds on the major securities exchanges. Unfortunately, the extreme opposite end of the time spectrum, the "deep future" receives little if any attention. Decisions in fields such as genetic engineering, nuclear energy, geopolitics and the like can have serious implications for human civilization. But the impact of those decisions might not become apparent for many thousands of years and hundreds of generations.
Cognitive Computing: Balancing the risks with the rewards from AI
The fact is that the effects of AI and cognitive computing will be even broader than current traditional computing systems. As we incorporate more and more data sources for better results, we also increase the likelihood of affecting more lives and more organizations.
Picked up from the podium
Two themes are top of mind at this stage of the new AI era: "Training data is the new ‘oil' for the AI economy," and "deep learning has left the labs and become mainstream.
What do we mean by a cognitive computing application?
But what is a cognitive computing application exactly? Would you know one if you saw one? And would you have a reasonably intelligent way to differentiate a cognitive application from the applications we are familiar with in this early cloud/big data period?
Amazon vs. The Librarians! The Fight of the Century!
But many of us believe—I do—that we have a cultural and societal interest in expanding our horizons. A librarian is likely to help us to that end.