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.

Neil Raden

Man leaping across gap in rocks against sunburst on blue sky © GIS - Fotolia.com

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Still rife with ethical issues, data sharing has a bright side too

Data brokers and personal data collection continues to cross ethical lines. But there are bright spots - including supply chain data sharing startup Aperity. I talked with their CEO about how their approach is different, and why AI and machine learning play a crucial data processing role.

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tightrope

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When model drift becomes a deluge - the Coronavirus pandemic wreaks havoc with data science and ML models

All predictive models are wrong - but some are very wrong. How did we wind up in the predicament of flawed Machine Learning models, just when we need them the most?

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The technical realities of functional quantum computers - is Google’s ten-year plan for Quantum Computing viable?

Google's quantum computing exploits have garnered plenty of attention in recent months. But how might they apply this commercially? A contrasting technical view comes by way of Itamar Sivan, CEO of Quantum Machines.

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Hitachi Vantara - Lumada

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Hitachi Vantara DataOps NEXT Virtual Conference review - Lumada's AI ambitions stand out

This year, Hitachi Vantara's DataOps NEXT event went virtual. Here are the themes that made an impression - including Lumada's AI pursuits.

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Telemedicine adoption amidst a pandemic - can we overcome the barriers?

Far from being a cure-all, telemedicine brings a whole new set of pros and cons. The barriers are not insignificant. However, a review of telemedicine adoption in the U.S. and beyond provides a good incentive to press forward.

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Can supercomputers play a role in the fight against COVID-19?

Yes, supercomputers are fast. But can high performance computers can provide something unique in the fight against Coronavirus? Here's how supercomputers have evolved, and a view of the COVID-19 work underway.

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Will telemedicine finally disrupt the health care industry?

In the U.S., regulatory pressures and per-state requirements have kept telemedicine at a standstill. Emergency relaxation of telemedicine guidelines have changed the picture for now - but will it lead to lasting change?

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After COVID-19, where does the US pharma and biotech industry stand?

The push for COVID-19 treatments has stoked the fires on big questions about the US pharmaceutical industry, including drug pricing and conflicting data on drug effectiveness. Here's my unvarnished take.

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Surveillance AI with a thermal heat twist - another look at Athena Security, with COVID-19 in mind

The ethical questions raised by AI-powered surveillance are numerous. Athena Security has some thoughtful answers - but what happens when we extend those capabilities into thermal heat detection?

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Is digital transformation dependent on pervasive analytics? Not really

Is the gap between data scientists and business analysts something the Alteryx platform can bridge? Here's Alteryx CEO Dean Stoecker's view - and my critique.

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Anxiety as an algorithm - leadership lessons for anxious times with Adam Goldstein

Can we draw algorithmic lessons from our immune response? How do leaders cope with anxiety? Does treating anxiety as an algorithm help us to shift behaviors? I explored these questions with startup executive Adam Goldstein.

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COVID-19 pandemic models - are Machine Learning models useful?

Applying Machine Learning to Coronavirus data is tempting - but deeply problematic. DataRobot shared lessons on working with smaller data sets, but the predictive limitations of ML for assessing pandemics go much further.

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puzzle-pieces

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Solving data integration at scale - DataOps, knowledge graphs and permissioned blockchains emerge

Despite shiny new AI and data science tools, the problem of data integration at scale hasn't gone away. But promising new approaches from vendors like StreamSets and FlureeDB are worth a closer look.

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