The flip side of generative AI: Extractive AI

Extractive AI takes a more comprehensive and transparent approach to machine intelligence.

Should we go back to paper-based KM?

The sheer volume of largely useless data we have accumulated across the years severely limits the ability of AI to work well, and it comes at a heavy environmental and financial cost.

The trust problem with GenAI

2023 has been the year of ultra-hyping GenAI, and who is paying for this deluge of marketing? Technology vendors that want us to buy it. Again, it's impressive stuff, but when we shift from selling to buying and ultimately using it, many tough questions need to be asked.

8 billion and counting

The message is clear: No single person or committee or group can weave the best paths through the infinite maze of possible event chains. Only humans and machines working together, side by side, can produce a better result than would ever be possible from either one alone.

When is good enough enough?

Our goal should be to improve the quality of knowledge assets and their accuracy and relevance in use. Much of this will come from human expertise and effort, increasingly combined with the power of AI.

Are you data-driven or knowledge-driven?

We no longer need to blindly accept the output of even the most sophisticated AI/ML platforms. In fact, we should not consider any artifact, whether produced by humans or machines, as valid knowledge unless it contains not only supporting data and analyses, including provenance, but also an explanation of the underlying plausibility.

AI technologies upending traditional KM

If we are not careful and proactive about it, the concept and importance of knowledge itself may soon become blurred or lost.

The undiscovered country

Capturing and sharing what you already know is good; and with today's data and text analytics tools, it has become much easier than when we'd first begun this journey.

Finding the weakest link

Though traditional and often reluctant to change, the supply chain sector is now reassessing its lack of embrace of technology and, significantly, rethinking long-established processes.

From robots to digital workers

As more firms use the term "digital workers" in place of bots, a spotlight is being shone on the role, importance, and increasing controversy surrounding enterprise automation.

The way of the scenario

The Delphi technique has become less effective in recent years, especially in crisis situations in which conditions, assumptions, and other variables are changing faster than the group is able to respond.

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 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.

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.

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.

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 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.

Usability testing for effective interactivity

Connecting the seeker to the information she seeks is not a new problem. Interaction design has been a stumbling block since the age of the card catalog.

AI: The issue is execution

By demonstrating on Jeopardy! that a machine could understand and analyze many fields of human knowledge and answer questions faster and more accurately than the reigning human experts, Watson's victory created an instant global brand.