+1 720 515 7230 frontdesk@museumof.ai
Contact
analytics & machine learning

Category

kitten on keyboard awww!

Algorithm reluctance, home-visit showdown, and the problem with wearables.

28 July 2017By
Yikes, evidence-based decisions are taking on water. Decision makers still resist handing the car keys to others, even when machines make better predictions. And government agencies continue to, ahem, struggle with making evidence-based policy.  — Tracy Altman, editor 1. Evidence-based home visit program loses funding.The evidence base has developed over 30+ years. Advocates for home visit programs –...
Read More
photo of people grouped together

False dichotomy: Data-driven vs. gut-feel

21 July 2017By
Smart decision-making is more complicated than becoming ‘data-driven’, whatever that means exactly. We know people can make better decisions if they consider relevant evidence, and that process is getting easier. But too often tech enthusiasts dismiss people’s decisions as based on gut feel, as if data will save us from ourselves. Let’s put an end to...
Read More

Equity crowdfunding algorithms, decision-making competitions, and statistical wild geese.

16 March 2017By
1. CircleUp uses algorithm to evaluate consumer startups. Recently we wrote about #fintech startups who are challenging traditional consumer lending models. CircleUp is doing something similar to connect investors with non-tech consumer startups (food, cosmetics, recreation). It’s not yet a robo adviser for automated investing, but they do use machine learning to remove drudgery from...
Read More

ROI from evidence-based government, milking data for cows, and flu shot benefits diminishing.

17 February 2017By
1. Evidence standards → Knowing what works → Pay for success Susan Urahn says we’ve reached a Tipping Point on Evidence-Based Policymaking. She explains in @Governing that 24 US governments have directed $152M to programs with an estimated $521M ROI: “an innovative and rigorous approach to policymaking: Create an inventory of currently funded programs; review...
Read More
patient value

Valuing patient perspective, moneyball for tenure, visualizing education impacts.

28 December 2016By
1. Formalized decision process → Conflict about criteria It’s usually a good idea to establish a methodology for making repeatable, complex decisions. But inevitably you’ll have to allow wiggle room for the unquantifiable or the unexpected; leaving this gray area exposes you to criticism that it’s not a rigorous methodology after all. Other sources of...
Read More

Everyone’s decision process, C-Suite judgment, and the Golden Gut.

28 December 2016By
1. SPOTLIGHT: MCDA, a decision process for everyone. ‘Multiple criteria decision analysis’ is a crummy name for a great concept (aren’t all big decisions analyzed using multiple criteria?). MCDA means assessing alternatives while simultaneously considering several objectives. It’s a useful way to look at difficult choices in healthcare, oil production, or real estate. But oftentimes,...
Read More
book cover Data-Driven Organization

Data-driven organizations, machine learning for C-Suite, and healthcare success story.

14 November 2016By
1. Great stuff on data-driven decision making in a new O’Reilly book by Carl Anderson (@LeapingLlamas), Creating the Data-Driven Organization. Very impressive overview of the many things that need to happen, and best practices for making them happen. Runs the gamut from getting & analyzing the data, to creating the right culture, to the psychology...
Read More

Game theory for Jeopardy!, evidence for gun control, and causality.

12 November 2016By
1. Deep knowledge → Wagering strategy → Jeopardy! win Some Jeopardy! contestants struggle with the strategic elements of the show. Rescuing us is Keith Williams (@TheFinalWager), with the definitive primer on Jeopardy! strategy, applying game theory to every episode and introducing “the fascinating world of determining the optimal approach to almost anything”. 2. Gun controls...
Read More

Data blindness, measuring policy impact, and healthcare analytics.

29 September 2016By
1. Crowdsourcing → Machine learning → Micro, macro policy evidence Premise uses a clever combination of machine learning and street-level human intelligence; their economic data helps organizations measure the impact of policy decisions at a micro and macro level. @premisedata recently closed a $50M US funding round. 2. Data blindness → Unfocused analytics → Poor...
Read More
analytics disconnected from strategy

Analytics disillusionment, evidence-based presentation style, and network analysis.

10 September 2016By
1. Visualizing networks. @Polinode builds innovative tools for network analysis. One nifty feature allows creation of column charts using a set of nodes. A recent post explains how to use calculated network metrics such as centrality or betweenness. 2. Analytics are disconnected from strategic decisions. An extensive study suggests analytics sponsors are in the trough...
Read More
1 2 3 4 5

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

muscle car by bing/create
20 June 2023
Stolen cars and AI ‘moral self-correction’
person in silhouette with orange background, pondering AI input for an evidence based decision
9 May 2023
Can you trust AI with your next decision? Part 3 in a series on fact-checking/citation
image generated by bing image creator bottle on apothecary shelf
25 April 2023
How is generative AI referencing sources? Part 2 in our series
15 March 2023
Can AI replace your CEO?
reference to Google's Bard AI and Microsoft's Bing AI compared to conflict at Sopranos Bada Bing
28 February 2023
What’s state-of-the-art when an AI cites sources of evidence? Part 1 in our series