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.

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.

Get your game on: KM skills needed for reliable use of LLMs

There is no questioning that generative AI is here to stay, but its use in mission-critical work has some way to go before it can be trusted and let loose.

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.

Look to the skies for KM opportunities

Then there's the inevitable demand for more automation, from the flight planning and clearance process to the operation of the air vehicles themselves. No human or group of humans could possibly keep track of so many constantly changing variables

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.

The Law and AI

AI is very good, and light years ahead of where it was just a decade ago, but it is far from "intelligent." Indeed, it is only as good as the data it is provided and needs close human supervision.

Getting to the future of KM

AI can and does do a good job of assisting and even augmenting knowledge work, but our "to be" state should not take the human element—however flawed—from the work.

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.

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 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.

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.

The twisted case of facial recognition

Machine translation continues to make strides forward. Facial recognition, on the other hand, has entered the twilight zone.

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.

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.