On Chat AI and BS

So, I'm sticking with hallucinations for all of chat AI's statements, true or false. But that leaves us with a question: Why isn't there a word that perfectly expresses this situation? The answer is easy: LLMs are doing something genuinely new in our history. Our lack of a perfectly apt verb proves it.

Inefficient at the speed of light

While process mining started years ago as a mainly data-driven exercise, its stated goal is to be knowledge-driven. Given KM's multidisciplinary scope, we can play a major role in achieving that goal. Any process, no matter how simple, has the potential to reach across an entire business ecosystem, including all stakeholders. This seems like a perfect match for collaborative workflow, AI/ML, knowledge graphs, human sensemaking, and many of the other arrows in our KM quiver.

Pushing the boundaries of knowledge curation

Knowledge democratization occurs in two directions, seemingly engaged in an endless tug of war: acquisition and dissemination.

Truth, lies, and large language models

The good news is that the problem of chat AI's proclivity for hallucinating is well-recognized by the organizations creating these marvels, and they realize that it is a danger to the world and to their success, not necessarily in that order of priority. Until that problem is solved, chat AI engines need to lose their self-confidence and make it crystal clear that they are the most unabashed and charming liars the world has ever seen.

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.

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.