The flip side of generative AI: Extractive AI

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

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.

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.

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.

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

Return on … Infrastructure???

As our physical and IT infrastructure continues to grow in size, complexity, and vulnerability, people and the knowledge they possess will play an ever-increasing role.

The human capability to under-or overestimate

Yet maybe the most glaring example of underestimating humans we encounter in our work is in the world of AI. It's partly the term "intelligence" in AI that misleads so many, as AI is not intelligent in the same way that humans are intelligent. Though powerful, AI ultimately matches patterns it has learned, and even the smartest of AI systems is limited in how many patterns it can match and make sense of.

Dispatches from the edge

Edge-of-chaos decisioning means being continually informed on the critical elements needed to make better, faster decisions.

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.

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.

The twisted case of facial recognition

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

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.

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.

Coming soon to your newsfeed —Ethics and AI

People need to be sensitive to the many ways ethical judgments are being baked into the fabric of their AI projects.