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Edge analytics hype versus reality - a discerning look at the pros and cons This article is sponsored by:

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?

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The flawed quest for Artificial General Intelligence - do you need to know how the mind works for AGI? This article is sponsored by:

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.

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The forgotten margins of AI ethics - a paper on AI ethics with teeth This article is sponsored by:

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.

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Think AI can aid decision-making? Only if you address the risk of bias in the last mile This article is sponsored by:

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.

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Why are we failing at the ethics of AI? A critical review This article is sponsored by:

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.

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The cloud data migration challenge continues - why data governance is job one This article is sponsored by:

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.

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Solving the data wrangling conundrum - can machine learning transform data management? This article is sponsored by:

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?

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Data science myths and realities - do data scientists really spend 80% of their time wrangling data? This article is sponsored by:

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.

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From autonomous cars to autonomous weapons, the AI ethics issues can't be ignored This article is sponsored by:

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.

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Quantum computing scenarios - the case for hybrid computing models This article is sponsored by:

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.

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Here's looking at you, kid - the problematic adoption of facial recognition systems This article is sponsored by:

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?

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