Category Archives: Machine learning

AI caution – explained

I hear so much weird stuff about AI these days that I tend to just block it out – including people talking about “an AI” as in “we need an AI for that”. So if it quite fun when John Oliver more or less nails it in this widely viewed video:

(And, well, with more than 3 million views and counting, it is not like he needs the mention. But my puny little intelligence need a place to store my references, and this blog is as good as any other place…)

Analytics VI: Projects

Another year, another list of exciting projects (previous ones here, here, and here) from the course Analytics for Strategic Management, which I teach with my excellent colleague Chandler Johnson). In this course, students work on real data analysis projects for real companies – and here is a (rather disguised) list:

  • Avinor, the Norwegian Airport Authority, wants to predict TOBT (Target Off-Block Time) for Gardermoen Airport. TOBT is a measure of when the plane will leave the gate, and very important for planning access to runways and other congested areas of the airport.
  • Norsk Tipping AS wants to improve its marketing of certain products through predicting customers’ likeliness to adopt them (see this (Norwegian) article for a former, very successful project with this company)
  • An international company in the shipping supply business wants to predict prices for some of its products. A key issue here (as is often the case) is finding data from orders that were not accepted (and, hence, not registered anywhere). You cannot know what price a customer will accept unless you have access to cases where the price was too high.
  • A news agency wants to predict the uptake of its articles to prioritise its editorial resources. The get news articles from news agencies and other sources around the world, and need to know which of those to spend money on translating and editing for the Norwegian market.
  • A grocery wholesaler wants to predict demand for its products. in the grocery industry, most stock levels are determined either by having minimum levels or by going by what you bought last year with some adjustments. This group wants to see if they can improve on that.
  • An insurance company wants to predict churn for some of its products. This problem is common to any company running a subscription business – which is increasingly true for more and more companies.
  • An large business school wants to predict grades for large exams. Manually reading through thousands of exams is boring work – not to mention expensive – can machine learning in some form be used to automate some of the work?
  • A large engineering company wants to predict employee churn. Engineers and other specialists are difficult to find, and it is much cheaper to retain a good employee than to find a new one.
  • Brønnøysundsregistrene, a Norwegian register for, amongst other things, company annual accounts, wants to predict late submissions. If they can predict this, they can make efforts to follow up more carefully on those companies, rather than send out nagging reminders to everyone.

One problem we often have in these projects is difficulty in getting data. This is not the case this year. Whether this is a result of more companies saving more data, the students getting better at defining problems based on data they have, or just plain coincidence, remains to be seen. But it is a welcome development!

EU’s new AI regulation: GDPR for machine learning?

EU has recently release a proposal for regulating the use of AI in companies and regulations. As far as I can see, it is modelled on the GDPR regulations: Assigning responsibility to board and top management, sanctions expressed in terms of percentages of revenues, and (hopefully) som sort of “safe harbor” rules so you can be somewhat confident in that you are not doing anything wrong.

An interesting aspect here is that the EU is early with regards to the use of AI (yes, I know “AI” is a really diffuse concept, but leave that be for a moment) and is again taking the lead in regulation where Silicon Valley (and China) leads in implementation.

Elin Hauge

This means that managers, board members and researchers will need to learn more. I plan to do this by attending a webinar at Applied Artificial Intelligence Conference 2021. This webinar (May 27, at CDT 1430-1600) is open for everyone who registers. It will be facilitated by Elin Hauge, who is a member of one of the EGN networks I lead.

Recommended – see you there!

Analytics IV and V: Projects

asm_topLast year (with Chandler Johnson and Alessandra Luzzi) and this year (with Chandler, Jadwiga Supryn and Prakash Raj Paudel), I teach a course called Analytics for Strategic Management. In this course executive students work on real projects for real companies, applying various forms of machine learning (big data, analytics, whatever you want to call it) to business problems. Here is a list (mostly anonymised, except for public organizations) list from this year:

  • One group wants to use machine learning to predict fraud in public security contracts in a developing country
  • A credit agency wants to predict which of their customers will pay their bills by the end of the month
  • An engineering company wants to predict the number of hours needed to meet demand for each month in each department
  • One group wants to predict housing prices within Oslo, to help house sellers get a realistic estimate of what their property is worth
  • A higher education provider wants to predict which students are likely to fail or not qualify for an exam, to be able to intervene early
  • A couple of municipalities want to predict who will accept a kindergarten allocation or not
  • A telecommunications company wants to predict which customers will churn
  • An Internet product company wants to predict necessary capacity for picking and shipping work every day
  • One group wants to predict the likelihood of a road closing due to bad weather, in order to warn truck drivers so they can detour
  • One group wants to predict the future financial health of companies based on employee engagement numbers
  • One group wants to predict efficiency of production in a wind power park

And last year we had these projects:

  • An investment company wanted to predict bankruptcies from media events
  • Ruter, Oslo’s public transportation authority wanted to predict the number of passengers (for each station, to great precision) for one line on the metro
  • A telecommunications company wanted to predict customer feedback scores from analyzing customer interactions (so the customer does not have to answer a survey afterwards)
  • The Norwegian Health directorate wanted to predict general physician “fastlege” churn
  • A commercial TV station wanted to predict subscriber churn
  • An insurance company wants to identify customers likely to buy a group insurance package
  • An online gaming company wanted to predict customer churn
  • A large political party wanted to predict membership churn
  • One group wanted to start a company based on using machine learning to diagnose hearing problems
  • A large retail chain wanted to predict churn based on customer purchase patterns