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.

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.

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.

Dispatches from the edge

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

AI’s ways of being immoral

The most powerful ML can require the resources of wealthy organizations. Such organizations usually have at best mixed motivations, to be charitable about it.

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.

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.

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 twisted case of facial recognition

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

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.

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.

The convergence of convergence

The more systems and subsystems we attempt to stitch together, the greater the unpredictability.

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.

Cognitive Computing: Balancing the risks with the rewards from AI

The fact is that the effects of AI and cognitive computing will be even broader than current traditional computing systems. As we incorporate more and more data sources for better results, we also increase the likelihood of affecting more lives and more organizations.

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.

Insights from AIIM