algorithms

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photo of row of townhouses seen through fisheye camera lens
House Hunters International is great guilty-pleasure viewing, especially while nursing a cold or avoiding the plague. (Pro: Insider views of interesting cities. Con: Reminders of the unique pain of choosing a place to live.) It’s easy to add city center, natural light, and extra bedrooms to your wish list, but painful to accept the inevitable...
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soldiers looking at 3d map
U.S. Defense Secretary Mark Esper has announced the military’s five ethical principles for AI use. The devil will definitely be in the details because the guidelines are mostly a statement of values. But I already have concerns. Allow me to explain. Can ethical guidelines make AI GRRET again? I’ve acronymized the five principles as GRRET: “Governable....
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shopper looking thru department store merchandise
To paraphrase Raymond Chandler, too many projects deliver department store data: The most of everything but the best of nothing. Enterprise AI and analytics developers must avoid the mistake of underserving people by overengineering solutions. Designers and decision makers need straightforward tools to make them better, to save time and facilitate their best work. They...
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As data complexity grows, so does the importance of explaining. The philosophy of science can teach us about the role of explaining in high-quality, evidence-based decisions. It’s not just navel-gazing: An explanation is a statement that makes something clear, or a reason or justification given for an action or belief. It describes “a set of...
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quantamentalist, man holding playing card
Quantamental is an investment strategy combining quantitative and fundamental methods. Data and algorithms have “prompted many traditional fundamentals-centered discretionary funds to integrate data-driven tools in day-to-day decision-making.” MarketWatch says the quantamental merger of computing power and human expertise is investing’s next frontier. Example: Active trading based on a particular blend of conventional balance sheets and...
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boston-dynamics-spot-mini
1. Machines Gone Wild → Digital trust gapLast year I spoke with the CEO of a smallish healthcare firm. He had not embraced sophisticated analytics or machine-made decision making, with no comfort level for ‘what information he could believe’. He did, however, trust the CFO’s recommendations. Evidently, these sentiments are widely shared. — Tracy A...
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Translators at IFLA 2010
1. Hire analytics translators → Keep data scientists happy An emerging role – what some call the Analytics Translator – is offloading burden from data scientists, while helping business executives get better value from their technology investments. A recent HBR piece explains You Don’t Have to Be a Data Scientist to Fill This Must-Have Analytics...
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1. Recognize bias → Create better algorithmsCan we humans better recognize our biases before we turn the machines loose, fully automating them? Here’s a sample of recent caveats about decision-making fails: While improving some lives, we’re making others worse. Yikes. From HBR, Hiring algorithms are not neutral. If you set up your resume-screening algorithm to...
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Museum musings.

Pondering the places where people interact with artificial intelligence: Collaboration on evidence-based decision-making, automation of data-driven processes, machine learning, things like that.

Recent Articles

finger pointing
19 August 2021
How to blame a robot for mistakes: Do your finger pointing properly.
coronavirus pandemic curve - johns hopkins
9 April 2020
Deciding while distancing: From data viz to the hard decisions.
soldiers looking at 3d map
26 February 2020
Will military ethics principles make AI GRRET again?
woman exiting revolving door
30 January 2020
Struggling to explain AI? Try this before|after strategy.
Museum of AI entrance
8 July 2019
Can we explain AI with experiential? I say yes.