Assessing the commercial viability of quantum computing is no small feat. There are several quantum machines to consider - and what about the issue of hybrid quantum, and the need for orchestration? Either way, the atomic power of the qubit should not be overlooked.
How did a proprietary AI get into hundreds of hospitals - without extensive peer reviews? The concerning story of Epic's Deterioration Index
How is it possible for proprietary AI models to enter patient care, without extensive peer reviews for algorithmic transparency? That's a question we should be asking about Epic's Deterioration Index, which has been utilized for several use cases, including COVID-19 patient risk models.
In part one of our decision-making series, I made the case for distributed BI across the organization. But that's not enough. Getting better information does not necessarily yield better decisions.
With all the hype about data science and AI, we've lost track of something important: the focus on data-informed decisions. Shrill warnings about Shadow IT don't help. Here's part one of my series on better decision-making, and modern BI tools.
Zero Trust security is often hyped as the solution to enterprise data breaches. But if your corporate goal is the democratization of analytics, Zero Trust security is a flawed approach. Here's my rebuttal to Zero Trust advocates.
The problem of ineffective or biased AI training data persists. Can synthetically-generated data alleviate this, or complement real data? It's not a simple question but it's one we need to assess.
We've gone from interfaces to web services, from Hadoop to the cloud. But that's not digital transformation. Few organizations have the leadership and drive to see transformations through.
AI ethics can seem like an academic exercise - that's not the case for autonomous weapons. A defining AI ethics issue for our time is heating up.
ETL is giving way to ELT. Why does it matter? Because data preparation remains one of the toughest obstacles that a data-aware organization must overcome. Whether it's a data lake, a cloud data warehouse, or AI-enabled data prep, there are critical factors to consider.
There is no AI Ethics without data privacy. But how do we account for new privacy legislation like GPDR and CPRA - and also the resistance to it? Here's how the regulation of privacy evolved.
An effective approach to AI Ethics must reckon with bias, algorithmic discrimination, and privacy. These terms have a historical context that should be understood - if we want to deploy AI ethically. This time around, we delve into bias.
We all want "trustworthy AI" - or do we? A closer look at the semantics of trust indicate the dangers of assuming trust is ethical. Fuzzy terminology will not help our pursuit of ethical AI.