Author Archives: Espen

About Espen

For details, see www.espen.com.

Neural networks – explained

As mentioned here a few times, I teach an executive course called Analytics for strategic management, as well as a short program (three days) called Decisions from Data: Driving an Organization on Analytics. We have just finished the first version of both of these courses, and it has been a very enjoyable experience. The students (in both courses) have been interested and keen to learn, bringing relevant and interesting problems to the table, and we have managed do what it said on the tin (I think) – make them better consumers of analytics, capable of having a conversation with the analytics team, employing the right vocabulary and being able to ask more intelligent questions.

Of course, programs of this type does not allow you do dive deep into how things work, though we have been able to demonstrate MySQL, Python and DataRobot, and also give the students an understanding of how rapidly these things are evolving. We have talked about deep learning, for instance, but not how it works.

But that is easy to fix – almost everything about machine learning is available on Youtube and in other web channels, once you are into a little bit of the language. For instance, to understand how deep learning works, you can check out a series of videos from Grant Sanderson, who produces very good educational videos on the web site 3 blue one brown.

(There are follow-up videos: Chapter 2, Chapter 3, and Chapter 3 (formal calculus appendix). This Youtube channel has a lot of other math-related videos, too, including a great explanation of how Bitcoin works, which I’ll have to get into at some points, since I keep being asked why I don’t invest in Bitcoin all the time.)

Of course, you have to be rather interested to dive into this, and it certainly is not required read for an executive who only wants to be able to talk intelligently to the analytics team. But it is important (and a bit reassuring) to note the mechanisms employed: Breaking a very complex problem up into smaller problems, breaking those up into even smaller problems. solving the small problems by programming, then stepping back up. For those of you with high school math: It really isn’t that complicated. Just complicated in layers.

And it is good to know that all this advanced AI stuff really is rather basic math. Just applied in an increasingly complex way, really fast.

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Analytics projects

asm_topTogether with Chandler Johnson and Alessandra Luzzi, I currently teach a course called Analytics for Strategic Management. In this course (now in its second iteration), 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. We have just finished the second of five modules, and the projects are now defined.

Here is a (mostly anonymised) list:

  • The Agency for Public Management and eGovernment (Difi) wants to understand and predict which citizens are likely to reserve themselves against electronic communications from the government. The presumption is that these people may be mostly old, not on electronic media, or in other ways digitally unsophisticated – but that may not be true, so they want to find out.
  • An electric power distribution company wants to investigate power imbalances in the electric grid: In the electric grid, production has to match consumption at all times, or you will get (sometimes rather large) price fluctuations. Can they predict when imbalances (more consumption that production, for instance) will occur, so that they can adjust accordingly?
  • A company in the food and beverage industry want to offer recommendations to their (business) customers: When you order products from them, how can they suggest other products that may either sell well or differentiate the customer from the competition?
  • A petroleum producing company wants to predict unintended shutdowns and slowdowns in their production infrastructure. Such problems are costly and risky, but predictions are difficult because they are rather rare – and that creates difficulties with unbalanced data sets.
  • A major bank wants to look into the security profiles of their online customers and investigate whether some customers are less likely to be exposed to security risks (and therefore may be able to use less cumbersome security procedures than others).
  • An insurance company wants to investigate which of their new customers are likely to leave them (churn analysis) – and why. They want to find them early, while there is still time to do something to make them stay.
  • A ship management company wants to investigate the use of certain types of oil and optimise the delivery and use of it. (Though the oil is rather specialised, the ships are large and the expense significant.)
  • Norsk Tipping runs a service helping people who are in danger of becoming addicted to gaming, an important part of their societal responsibility which they take very seriously. They want to identify which of their customers are most likely to benefit from intervention. This is a rather tricky and interesting problem – you need to identify not only those who are likely to become addicted, but also make a judgement as to whether the intervention (of which there is limited capacity) is likely to help.
  • A major health club chain wants to identify customers who are not happy with their services, and they want to find them early, so they can make offers to activate them and make them stay.
  • A regional bank wants to identify customers who are about to leave them, particularly those who want to move their mortgage somewhere else. (This is also a problem of unbalanced data sets, since most customers stay.)
  • A major electronic goods retailer wants to do market basket analysis to be able to recommend and stock products that customers are likely to buy together with others.

All in all, a fairly typical set of examples of the use of machine learning and analytics in business – and I certainly like to work with practical examples with very clearly defined benefits. Now – a small matter of implementation!

Concorde moment

british_airways_concorde_g-boac_03I recently searched for the term “Concorde moment” and did not find it. The term has appeared on Top Gear some years ago (though I can’t find the clip), probably mentioned by James May (who knows something about technology evolution) or Jeremy Clarkson (who certainly lamented the passing of the Concorde many times.) What “Concorde moment” means, essentially, is (as Clarkson says in the video below) “a giant step backward for mankind”.

The Concorde is still the fastest passenger jet ever made (3.5 hours from London to New York) and still the most beautiful one. In the end, it turned out to be too noisy, too polluting, and too expensive, never really making money. But it sure looked impressive. I never got to go on one, despite working in an international consulting company and jetting back and forth across the pond quite a bit. But my boss once bamboozled someone into bleeding for the ticket, and lived off the experience for a long time.

palm_graffiti_gesturesA Concorde moment, in other words, is a situation where a groundbreaking technology ceases to be, despite clearly being (and remaining) best in class, for reasons that seem hard to understand. Other examples may include

  • the Palm Pilot with its Graffiti shorthand system, once used by businesspeople all over the world (and by my wife to take impressive notes in all her studies)
  • the Apollo space program – we last went to the moon in 1972, with Apollo 17, and have not been back since. 45 years without going back has resulted in some impressive conspiracy theories, but again, the lack of any scientific or economic reason for going there is probably why it hasn’t happened.
  • the Bugatti Veyron, at least according to Top Gear. Personally, I find the announced new Tesla Roadster much more exciting, but, well, everyone is entitled to an opinion.
  • and, well, suggestions?

Cool ad. Literally speaking.

This is our (NI Norwegian Business School) new ad for attracting international students. Which, if they do master’s courses in strategy, might mean that they will meet me. Be forewarned, I am just like this guy, with somewhat shorter hair and beard…

Looking forward to seeing you!

Spam on LinkedIn

Got this LinkedIn invite this morning. I don’t know, but somehow I think this is not legit. If not, I am sure the real Erling Persson Family Foundation will be in touch…

Inbox__48 093_messages_

So far, spammy invites on LinkedIn has largely been young women with very short CVs. This is new, and something LinkedIn should pay attention to, as the network is one where the signal-to-noise ratio is unusually high.

The nastiness of immigrant fear

This piece (http://crookedtimber.org/2017/10/23/working-to-rule/) by Maria (Farrell?) is a long and very insightful read about the emotional impact on immigrants from the Brexit debacle – and more generally, about the nastiness of reducing immigrants (or, for that matter, any foreigner) to a number and a category.

Anyone who thinks being an immigrant, even a deluxe EU three million-type immigrant, is easy, should try it. We compete on equal terms with all comers, but with no social or economic safety net and, for many, hustling like mad in second and third languages. No dole, no network of couches to sleep on, no contacts and no introductions; qualifications from institutions you’ve never heard of, references from employers you aren’t sure are real but can’t be bothered to check, acting as daily fodder for stereotypical jokes we laugh off to show we’re one of you. You don’t hear us complaining about it because it’s just part of the deal. But when the terms of the deal change, and you tell us we’re social welfare parasites who are also, somehow, taking all the jobs and are the reason the country is failing, then the deal is probably dead.

How anyone can think shutting yourself off from the world and fantasise about going back to a nonexistent 1960s idyll is in any way beneficial is beyond me. And this nastiness is not limited to Britain or Trump’s USA, far from it, Norway has its share of little people with big fears as well.

To get new ideas, increase the variety of sources, expose yourself to new experiences, and embrace that which you cannot understand.

Assuming you want new ideas, of course.