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. He founded Hired Brains Research in 1985 to provide thought leadership, context and advisory consulting and implementation services in Data Architecture, Analytics, AI, Data Science and organizational change for clients worldwide across many industries. His current portfolio includes data warehouse modernization, AI Last Mile and Complex Supply Chain analytics. Neil is a recognized authority on AI Ethics, author of more than fifty articles on the subject on Diginomica, and the author of the foundational report for the Society of Actuaries, “Ethical Use of Artificial Intelligence for Actuaries.” He, with James Taylor, is the co-author of the first book on Decision Management, “Smart (Enough) Systems.” Clients welcome his practical and valuable advice and counsel. He welcomes your comments at [email protected].

 

Articles by Neil Raden

Reverse supply chains and sustainability - making the business case for retail returns This article is sponsored by:

The amount of waste involved in retail returns is staggering, yet it doesn't have to be that way. Sustainable retail returns can be a viable part of the circular economy - early adopters are proving why.

Circular economy - hand holding grass sustainability loop with choice of globes © BsWei - shutterstock
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We're stuck in the AI ethics fishbowl - so how do we get out? This article is sponsored by:

Live AI systems, already operating at scale, can have dangerous consequences - and concerning downsides. In theory, the AI ethics movement should provide a corrective force. Yet so far, it has failed to do so. What is AI ethics getting wrong?

Goldfish moving to larger bowl change management improvement concept © Romolo Tavani - Shutterstock
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What just happened to Southwest Airlines? A cautionary tale about underfunding key IT technology This article is sponsored by:

By now, the world knows about Southwest's massive holiday logistical meltdown, which stranded millions of passengers. Southwest blamed the system-wide breakdown on winter weather, but that doesn't hold up. A deeper look at Southwest's predicament surfaces harsh lessons on the problem of technical debt.

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The concerning state of rural medicine - a big missing piece in healthcare transformation This article is sponsored by:

The US healthcare narrative is about "patient outcomes" and personalized medicine, supported by permissioned data. But the data on the state of rural hospitals tells a very different story. Unless we reckon with this, healthcare transformation is off the table.

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Can Graphcore's new processor change AI and high performance computing? A closer look at Graphcore's Good Computer plans This article is sponsored by:

UK-based Graphcore has announced that it will deliver, by 2024, the world's first "ultra-intelligence AI computer" - called the Good Computer. Here's a deeper look at what they're planning, and the implications.

Code with brain artificial intelligence AIOps concept © Antonov Serg - shutterstock
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The space economy matters - an enterprise view on space as the International Space Station heads towards de-orbit This article is sponsored by:

The international space station has been orbiting for 21 years - but change is afoot. Here's why enterprises should care about the emerging space economy.

Cloud euro sign under magnifying glass on blue sky © spacezerocom
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Data overload is a real thing - can causal emergence reframe the problem? This article is sponsored by:

Data overload is a real problem, but we keep piling it on. After all, AI and machine learning require massive data sets. But are we framing the problem correctly? Causal emergence makes a different argument - and could impact the "big data" obsession.

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AI needs foundational models - so what can we learn from GPT-3, BERT, and DALL-E 2? This article is sponsored by:

Foundational models address a fundamental flaw in bespoke AI. But foundational and large language models have limitations. GPT-3, BERT, and DALL·E 2 garnered gushing headlines, but models like these deserve scrutiny.

Robot hand in trust AI machine learning © zapp2photo
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