Making the jump to hyperdrive
The new, all-digital workforce will be made from a combination of AI, machine learning, computer vision, naturallanguage understanding, robotics, and more.
The critical part of critical infrastructure
Whether we're talking about infrastructure to support the flow of goods or the flow of knowledge, all require energy, and lots of it.
The coming blue wave
It should come as no surprise that topping the list of requirements to create and sustain a vibrant blue economy are innovation, learning, and collaboration.
We need to look at the major challenges we're facing as we enter the millennium's third decade from the perspective of the global economy as a wholly integrated system.
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.
At long last, the conference of the future
In past epochs, usually when a civilization is at or near its peak, the architecture of prominent structures masterfully blends the physical and the cognitive.… we need to be thinking along the same lines as we build platforms for interacting in an increasingly virtual world, including virtual conferences.
Disruptive innovation: No better time
With the push to move more functions online, disruptive technologies such as robotic process automation are rendering old skill sets obsolete, while at the same time creating the need for new ones.
Building the enterprise of the future: If not now, when ?
It should be plainly clear that we need knowledge management now more than ever. You can be sure that the COVID-19 crisis won't be the last crisis to come our way. And the next one might be even more severe because our supporting systems have taken some serious hits.
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.
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.
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.
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.
The convergence of convergence
The more systems and subsystems we attempt to stitch together, the greater the unpredictability.
The future of food: a fresh look
There's a growing demand for the ability to facilitate the integration of knowledge generated by widely diverse communities from multiple disciplines.
The future of education
Today, we find ourselves in a highly networked knowledge-based economy. This new world demands radically different learning approaches in alignment with complex behaviors of natural systems.
Trends for the ’20s
Providing the right information to the right people at the right time can only be accomplished through greater openness.
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.
From just-in-time to just-ahead-of-time
Through our many decades of research into the knowledge sciences, we've determined that if AI is ever going to live up to its promises, it needs an architecture that integrates three levels of functionality: memory, awareness and anticipation.
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.