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.
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.
Perspective on knowledge: Approximately causal
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.
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.
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?
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.
Perspective on Knowledge: 250 Columns later
Knowledge management has indeed become a multi-threaded discipline, embracing just about everything related to knowledge.
Perspective on Knowledge: The challenge of emergence
Traditionally, we humans have succeeded at building complex structures by breaking plans down into a multitude of simple, predictable, knowable causes and effects.
Boosting knowledge worker engagement through mentoring
If your employees aren't engaged, knowledge simply can't flow to the extent that's needed in order to compete in the global economy.
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.
Perspective on Knowledge: Journalism’s new landscape
What's happening to news is a microcosm of what's happening to knowledge overall.
Perspective on knowledge: Bring back blogging
The case for internal blogging within a corporate environment is strong and the risk is far lower because the participants are vetted and known.
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.
Data and our future: too much of a good thing? Not enough? How will we know?
In today's AI-exploded world, analysts and business people loudly call for more data, complaining that cognitive computing and other AI applications need more raw material to build better models and more accurate predictions.
The convergence of convergence
The more systems and subsystems we attempt to stitch together, the greater the unpredictability.
Perspective on knowledge: 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.
Perspective on knowledge: Rewriting the world
Traditional computing upholds the traditional method of applying general rules to particulars. With machine learning, you skip the generalized abstraction and feed in the particulars.