For decision makers grappling with data, Bayesian Networks are an overlooked asset. Affordable? Yes. Performance and applicability to edge devices? Yes again. Here's a practical guide to how Bayes Nets can solve enterprise problems.
Can Bayesian Networks provide answers when Machine Learning comes up short? It's a question of probabilities
Machine Learning gets all the marketing hype, but are we overlooking Bayesian Networks? Here's a deeper look at why "Bayes Nets" are underrated - especially when it comes to addressing probability and causality.
Does applying AI to insurance ratings and FICO scores improve fairness? The insurance industry says yes, but the data points to AI fairwashing. It's time for a change.
As a job title, Chief Revenue Officers are trending. But is CRO a needed role? And if so, how is it defined? Are there other ways to oversee revenue growth and accountability?
Too often, the AI startup field comes off as a VC-funded money grab. But commercial NLP healthcare player John Snow Labs is doing things differently. Their Spark NLP open source library includes support for 375 languages. Here's why their approach stands out.
The problem with 'Ethics by Design' - why this WEF report gets AI Ethics wrong, and 25 techniques for producing trustworthy AI
A recent WEF report called "Ethics by Design" reinforces problematic misconceptions about AI ethics. Here's where I think the report went wrong - and my own personal 25 point list for avoiding AI project speed bumps.
Struggling with metadata is nothing new, but there's a misconception that advanced data tech and cloud storage solved this problem. That's not the case - so what is the way forward?
Cloud data warehouses aren't trendy enough - now we evidently need data lakehouses as well. But how should enterprises sort these terms? And has the data lake outlived its usefulness?
Most of us have heard of cognitive bias - and the problem it can pose. Less well known is the practice of moral licensing. But it's an issue AI teams need to consider.
Human-robot interaction is upon us - we're in dire need of a framework that makes sense. Asimov's three laws of robotics are one model, but is it applicable to today's robots? An alternative based on robot "empowerment" is worth a close look.
Supercomputers used to be the domain of scientists and the military. Now the enterprise use cases are picking up steam. But in a way, all computers are supercomputers. Here's a look at how the field has evolved - and what's next.
Big Tech has been on the defensive lately, and for good reason. What was once perceived as a way to foster democracy has given way to algorithmic dystopia. But Facebook's algorithmic dangers are tied to an ad-server-based model we must dismantle. Rant time.
Opacity in AI used to be an academic problem - now it's everyone's problem. In this piece, I define the issues at stake, and how they tie into the ongoing discussion on AI ethics.