Neil Raden

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Neil Raden, based in Santa Fe, NM, is an active industry analyst, consultant and widely published author and speaker and also the founder of Hired Brains Research. Hired Brains provides thought leadership, context and advisory consulting and implementation services in Information Management, Analytics/ Data Science, Machine Learning/AI and IoT for clients worldwide. Hired Brains also provides consulting, market research, product marketing and advisory services to the software industry. Neil is the co-author of Smart (Enough) Systems: How to Deliver Competitive Advantage by Automating Hidden Decisions, Prentice-Hall. He welcomes your comments at nraden@hiredbrains.com.

Archive: Neil Raden

facial recognition threat

Beyond the evil facial recognition myth - can AI play an ethical role in predictive threat detection?

The stories on facial recognition advances lean strongly towards the concern side, with a host of consequences poorly addressed during rollouts. But is there an AI-for-good role in threat prediction?

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Hand of business man using smartphone and laptop on AI concept background with blue tone © TippaPatt - shutterstock

Dismissing ethical issues means AI has a long way yet to go in the enterprise

The time has come think critically about the value of AI as it stands, and whether to be concerned that a concerted effort to press it forward to true intelligence bypasses ethical questions.

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cancer

Supercomputing and cancer research - is computational biology useful yet?

What can you do with 1,000,000,000,000,000,000 Floating-point calculations per second (EXAflops)? Simulate nuclear weapons? Yes. Cure cancer? Maybe...

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Hitachi Vantara

Hitachi Vantara Next2019 - conference takeaways

A round-up of the happenings at the Hitachi Vantara jamboree.

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team solving puzzle

Can we finally solve the demand and out-of-stock problem? NextOrbit says yes

Retailers' attempts to solve out-of-stock and predict demand have a long history. But have we finally turned a corner? A look at AI startup NextOrbit points to new possibilities.

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Image of a human brain abstracted with AI

DataOps challenge - the complicated art of making things simple

Variations on Dolly Parton - making things simple is pretty complicated.

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health-care-tech

To understand AI advancements in health care, there are two storylines we must follow

Yes, health care needs AI - but maybe not in the ways we think. A new book on AI's medical potential needs a critical eye. With AI, there is always a human consequence beyond the tech storyline.

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code of ethics

Data for good and AI ethics - a movement or just a conversation?

The issue of AI ethics has sharpened - ideas for governing AI and ethical oversight are gaining a foothold. But will they have any teeth? And what about the possibility that AI can oversee itself?

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woman-and-algorithm

Can we create better algorithms for screening candidates - and reduce hiring bias?

A new research paper from Georgia Tech takes a surprising position on algorithmic bias in hiring. Their view: we can reduce screening bias if algorithms take the impacted demographic groups into account. Here's my critique.

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people-with-questions

New York state regulators pushed back on data brokers for insurance - will other states and industries follow?

A warning letter from New York State's Department of Financial Services (DFS) raised far-reaching data privacy questions for the insurance industry. With the increasing role of algorithmic claims processing, this is an ethics debate we can't ignore.

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dominos fall

Big data didn't fall with MapR - Hadoop is fading, but data lakes are not

The fall of MapR caused a rush to judgement about the future of Hadoop. To understand what this means for data initiatives, the viability of Hadoop and data lakes must be separately examined.

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AI robot finger on laptop keyboard with bokeh background © Zapp2Photo - shutterstock

Are hiring decisions ready for AI? How repeatable algorithms can harm people

AI marketing literature extols the benefits of algorithmic hiring. But the problem of algorithmic bias and hiring fairness raises serious questions.

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judging

Data brokers and the implications of data sharing - the good, bad and ugly

The term "data sharing" is expanding, but in a problematic way that raises flags for companies and consumers alike. Neil Raden provides a deeper context for data sharing trends, dividing them into the good, bad and ugly.

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