Based in Santa Fe, NM, Neil Raden is a mathematician (algebraic topology), founder of a management consulting firm, consultant to large and complex projects internationally, an industry analyst, and widely published author and speaker. His early background was in Property and Casualty actuarial R&D, and he remains active in consulting to the insurance industry.
Edge analytics is promoted as the solution to the analytics problems posed by edge devices. Let's take a harder look at the pros and cons, including security, cost, and integration with cloud analytics. Can we even call these use cases "analytics" at all?
The debate over so-called "sentient AI" is raging again. But the context is the problematic pursuit of Artificial General Intelligence - and the misguided idea that AGI should resemble the human brain.
Sustainability programs are vital, but they're not easy. Here's why - and what to do about it.
The field of AI ethics seems to generate more happy talk than potent ideas. It's rare to find an academic paper with real world teeth, but "The forgotten margins of AI ethics" stands out. Here's my critical review.
AI has the potential to aid decision-making at scale - but there are obstacles. One of the trickiest to unravel is bias, which tends to show up just where you don't want it: during the last mile of AI modeling and project delivery.
Sometimes the AI ethics conversation appears to be a morass of well-meaning platitudes, without forward direction. A notable article via the Carnegie Council for Ethics in International Affairs brings these issues to a head.
Cloud data migrations can bring significant operational benefits - not to mention opening up AI/ML use cases. That doesn't mean these migrations are easy. Getting migrations right starts with data governance.
Do data scientists really spend 80% of their time wrangling data? Last time around, we examined this notion. But when it comes to data management, how can machine learning change data platforms for the better?
Do data scientists really squander the bulk of their time cleaning data sets? Not necessarily - but for robust machine learning models, we do need better data management platforms.
Autonomous technology is racing towards us. But whether its autonomous cars or autonomous weapons, there are serious questions. This is where AI ethics comes to a head.
The global food supply is under duress, with Russia's invasion of the Ukraine applying even more pressure. With world populations mounting, can AI make a difference in farming and agricultural yields?
Supposedly, quantum computing ushers in an age of revolutionary possibilities - thanks to quantum's unprecedented computing power. But the most compelling scenarios are actually a "hybrid" mix of quantum and classical computing.
Facial recognition systems are one of the most controversial areas of real world AI. But despite the ethical qualms, facial recognition is now in use across multiple industries. Can regulation catch up?
