The enterprise of the future: Yesterday, today, and tomorrow
Today, much of the knowledge we need is readily available. The problem is having the courage and fortitude to properly act on it.
The rise of machine teaching
In contrast to some jobs that can indeed be automated and removed from the human payroll, KM practitioners have the potential to see their skills in much higher demand and volume in the future.
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.
Thinking about KM differently
Moving to a push rather than a pull mentality simply means that we now have the technology to tag, manage, and interpret information automatically and near instantly—automatically pushing the right information to the right person (or application) at the right time.
The twisted case of facial recognition
Machine translation continues to make strides forward. Facial recognition, on the other hand, has entered the twilight zone.
The eureka moment
AI is beginning to develop some support for the thought process. As the technology improves, it's possible that AI will eventually be able to offer relationships and connections that still seem far-fetched.
Thinking beyond the status quo
The technologies exist today to achieve almost any corporate or departmental goal. What is lacking is the nerve to think big and think beyond the status quo—to break barriers, to collaborate, and to share.
What happens when AI meets a pandemic?
This is what we can see clearly after some months of reading, watching, and listening to the pronouncements on the novel coronavirus crisis from around the globe: Content challenges continue to dog AI.
The right time for knowledge management
A new generation is coming in—one that sees order in the chaos, spots previously invisible patterns, and not only embraces technology but grew up with it.
Enterprise of the future update: More disruption ahead
The concept of a phyle has experienced a resurgence, driven in part by the frustration people are feeling about being forced into making binary choices regarding the groups with which they want to be identified: public versus private, capitalist versus socialist, and liberal versus conservative.
Science will not give up on hypotheses. But it already is becoming more willing to accept results based on the sorts of statistical analyses performed by machine learning. And it may be thatwhen science does rely on theories and laws, we will recognize that no matter how ironclad they are as generalizations, their application to a world of confetti will always and necessarily render them approximate and probabilistic.
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?
Talk a little, type a lot - Will conversational interfaces survive Siri and Alexa?
For the next generation of conversational computing, it is hard to avoid the conclusion that the only companies that have enough researchers, enough processing resources, enough motivation, and, above all, enough data to deliver the much- needed improvements are the consumer giants.
How robotic is your process ?
To break out of the structured process world, RPA will need to address the full range of cognitive computing capabilities.
Bringing adult supervision to machine learning and AI
Human and machine knowledge governance has many moving parts. No governance means leaving things to chance. Too much governance means clogging up the system and slowing things down to a crawl. The trick is achieving the right balance based on your organization's size, goals, strategy, and risk profile.
Flipping data science
No matter how much "intelligence" is programmed into a computer, it will very likely never understand the results it produces. Doing so takes human cognition, intuition, judgment, and other ways we humans make sense out of data.
Ethical issues in AI and cognitive computing
Many innovations from the past needed the insight of entrepreneurs as well as technologists to change the world. That's also the case with machine learning and AI.
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.