Author Archives: Espen

About Espen

For details, see www.espen.com.

Brilliance squared

Stephen Fry and Steven Pinker are two of the people I admire the most, for their erudition, extreme levels and variety of learning, and willingness to discuss their ideas. Having them both on stage at the same time, one interviewing the other (on the subject of Pinker’s last book, Enlightenment Now), is almost too much, but here they are:

(I did, for some reason, receive an invitation to this event, and would have gone there despite timing and expense if at all possible, but it was oversubscribed before I could clink the link. So thank whomever for Youtube, I say. It can be used to spread enlightenment, too.)

Interesting interview with Rodney Brooks

sawyer_and_baxterBoingboing, which is a fantastic source of interesting stuff to do during Easter vacation, has a long and fascinating interview by Rob Reid with Rodney Brooks, AI and robotics researcher and entrepreneur extraordinaire. Among the things I learned:

  • What the Baxter robot really does well – interacting with humans and not requiring 1/10 mm precision, especially when learning
  • There are not enough workers in manufacturing (even in China), most of the ones working there spend their time waiting for some expensive capital equipment to finish
  • The automation infrastructure is really old, still using PLCs that refresh and develop really slowly
  • Robots will be important in health care – preserving people’s dignity by allowing them to drive and stay at home longer by having robots that understand force and softness and can do things such as help people out of bed.
  • He has written an excellent 2018 list of dated predictions on the evolution of robotic and AI technologies, highly readable, especially his discussions on how to predict technologies and that we tend to forget the starting points. (And I will add his blog to my Newsblur list.)
  • He certainly doesn’t think much of the trolley problem, but has a great example to understand the issue of what AI can do, based on what Isaac Newton would think if he were transported to our time and given a smartphone – he would assume that it would be able to light a candle, for instance.

Worth a listen..

Beyond Default

71gkby-vpilDavid Trafford and Peter Boggis are those kinds of under-the-radar strategy consultants that ever so discreetly (and dare I say, in their inimitable British way) travel the world, advising enormous companies most civilians have never heard of about such issues as how to organise your internal departments so that they are capable of responding to technical change. (I should know, because I worked with them, first in CSC and then in the Concours Group, between 1994 and 2009.)

Now David and Peter have begot a book, Beyond Default, that provides a perspective on strategy and organisational change less built on fashionable frameworks than on solid experience. Their focus is on how organisations fail to see changes in their environment and develop strategies – real strategies – to adapt to them. The reasons are many, but most important is the fact that organisations have developed processes and measures to do what they currently do, and the focus on those particulars does not permit stepping back and seeing the bigger picture. Instead, companies carry on towards a “default” future – and, crucially, that future may be declining. Companies need to know what they don’t know and what they do not have the capabilities to do – and to acquire those capabilities when necessary. To do that, the authors advocate experiential learning – seeing for yourself what the future looks like by seeking it out, preferably as a group of managers from the same organisation experiencing and reflecting together.

The authors have a background as IT consultants, and it shows: They very much think of organisations as designed systems, with operating practices and (ideally) articulated operating principles. While eminently logical, this way of organising is hard to do – among other things, it requires thinking about organisations as tools for a purpose, and that purpose has to be articulated in a way that gives direction to its members. Thinking about your principles can make you articulate purpose, but it is very hard not to make the whole process a bit self-referential. Perhaps the key, like for Newton’s second law of thermodynamics, is to keep adding external energy, constantly identifying and understanding ramifications of technical and other change – a process that requires energy, if nothing else.

Both authors care about language and explaining and discussing what happens in a way that can be understood by the organisations they are trying to help. This means that they primarily use examples and stories, rather than frameworks (beyond simple illustrations), to convey their points. They end each chapter with a set of questions the reader can has him- or herself about the organisations they manage – and do not, in any way, try to offer simple solutions. As such, the book works best when it talks about how to explain strategic necessities and start on a strategic journey – through collective leadership, not “great man” charisma. It works less well when trying to explain strategic analysis, perhaps because the authors have too much experience to settle on a simple, all-encompassing method.

Well worth the read, not least for the senior executive trying to understand a new world and wanting an explanation held in a language that fosters understanding rather than just excitement.

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.

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?

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.

A tour de Fry of technology evolution

There are many things to say about Stephen Fry, but enough is to show this video, filmed at Nokia Bell Labs, explaining, amongst other things, the origin of microchips, the power of exponential growth, the adventure and consequences of performance and functionality evolution. I am beginning to think that “the apogee, the acme, the summit of human intelligence” might actually be Stephen himself:

(Of course, the most impressive feat is his easy banter on hard questions after the talk itself. Quotes like: “[and] who is to program any kind of moral [into computers ]… If [the computer] dives into the data lake and learns to swim, which is essentially what machine learning is, it’s just diving in and learning to swim, it may pick up some very unpleasant sewage.”)

Science fiction and the future

I am on the editorial board of ACM Ubiquity – and we are in the middle of a discussion of whether science fiction authors get things right or not, and whether science fiction is a useful predictor of the future. I must admit I am not a huge fan of science fiction – definitely not films, which tend to contain way too many scenes of people in tights staring at screens. But I do have some affinity for the more intellectual variety which tries to say something about our time by taking a single element of it and magnifying it.

So herewith, a list of technology-based science fiction short stories available on the web, a bit of fantasy in a world where worrying about the future impact of technology is becoming a public sport:

  • The machine stops by E. M. Forster is a classic about what happens when we make ourselves completely dependent on a (largely invisible) technology. Something to think about when you sit surfing and video conferencing  in your home office. First published in 1909, which is more than impressive.
  • The second variety by Philip K. Dick is about what happens when we develop self-organizing weapons systems – a future where warrior drones take over. Written as an extension of the cold war, but in a time where you can deliver a hand grenade with a drone bought for almost nothing at Amazon and remote-controlled wars initially may seem bloodless it behooves us to think ahead.
  • Jipi and the paranoid chip is a brilliant short story by Neal Stephenson – the only science-fiction author I read regularly (though much of what he writes is more historic/technothrillers than science fiction per se). The Jipi story is about what happens when technologies develop a sense of self and self preservation.
  • Captive audience by Ann Warren Griffith is perhaps not as well written as the others, but still: It is a about a society where we are not allowed not to watch commercials. And that should be scary enough for anyone bone tired of alle the intrusive ads popping up everywhere we go.

There is another one I would have liked to have on the list, but I can’t remember the title or the author. It is about a man living in a world where durable products are not allowed – everything breaks down after a certain time so that the economy is maintained because everyone has to buy new things all the time. The man is trying to smuggle home a wooden garden bench made for his wife out of materials that won’t degrade, but has trouble with a crumbling infrastructure and the wrapping paper dissolving unless he gets home soon enough…

Big Data and analytics – briefly

DFDDODData and data analytics is becoming more and more important for companies and organizations. Are you wondering what data and data science might do for your company? Welcome to a three-day ESP (Executive Short Program) called Decisions from Data: Driving an Organization with Analytics. It will take place at BI Norwegian Business School from December 5-7 this year. The short course is an offshoot from our very popular executive programs Analytics for Strategic Management, which are fully booked. (Check this list (Norwegian) for a sense of what those students are doing.)

Decisions from Data is aimed at managers who are curious about Big Data and data science and wants an introduction and an overview, without having to take a full course. We will talk about and show various forms of data analysis, discuss the most important obstacles to becoming a data driven organization and how to deal with data scientists, and, of course, give lots of examples of how to compete with analytics. The course will not be tech heavy, but we will look at and touch a few tools, just to get an idea of what we are asking those data scientists to do.

The whole thing will be in English, because, well, the (in my humble opinion) best people we have on this (Chandler Johnson og Alessandra Luzzi) are from the USA and Italy, respectively. As for myself, I tag along as best I can…

Welcome to the data revolution – it start’s here!

Recommended: appear.in

Premium_–_appear_in_–_one_click_video_conversationsTelenor has made a video conferencing service called appear.in (Twitter: @appear_in) – and it is fantastic! All you need to do is open a browser window and type

appear.in/something

where “something” is a word you choose. The other participants do the same, and you are in conference. Camera, screen sharing, everything work great, the whole thing is free (at least with up to four participants, have not tested with more). If you want your own room with your own design there is a premium version for $12 per month. No app installation, no weird settings, no drivers, no updates. Just works. Excellent!

Recommended!

(No, I am not sponsored. Just like the service.)

The coolest referral prize ever…

top-girlriding-mobile-1[From the department of irrelevant stuff…]

Like many Norwegians, I have a Tesla (it is a bit like owning a Volvo station wagon here, due to enormous tax breaks on electric cars.) I am very happy with it. Elon Musk got rich on Paypal and took some business concepts from that experience, including a referral program: If a Tesla owner refers someone who buys a Tesla (my referral link is here, hint hint), the new Tesla owner gets a $1000 rebate and free supercharging as long as they own the car. And that is a nice thing to give away.

But what is the referral prize? I did not know, but someone I have referred has bought a Tesla, and it turns out I get a Radio Flyer Tesla electric toy car. It not only looks like a Tesla S (down to the charging cable), but also has a frunk and you can connect a music player to its sound system(!).

Oh, to be four again… On the other hand, Lena and I will be grandparents in a few months, so it will see some use.

Big Data in practice

(This is a translation of an earlier post in my Norwegian blog. This translation was done by Ragnvald Sannes using Google Translate with a few amendments. This technology malarky is getting better and better, isn’t it?).
ml_mapI have just finished teaching four days of data analytics – proper programming and data collection. We (Chandler, Alessandra and the undersigned) have managed to trick over 30 executives and middle managers in Norway to attend a programming and statistics course (more or less, this is actually what analytics basically is), while sort of wondering how we did that. The students are motivated and hard-working and have many and smart questions – in a course taught in English. It is almost enough to make me stop complaining about the state of the world and education and other things.
Anyway – what are these students going to do with this course? We are working on real projects, in the sense that we require people to come up with a problem they will find out in their own job – preferably something that is actually important and where deep data analysis can make a difference. This has worked for almost all the groups: They work on real issues in real organizations – and that is incredibly fun for the teacher. Here is a list of the projects, so judge by yourself. (I do not identify any students here, but believe me – these people face these issues every day.) Well worth spending time on:
  • What is the correct price for newly built homes? A group is working to figure out how to price homes that are not built yet, for a large residential building company.
  • What is the tax effect of the sharing economy? This group (where one student works for the Tax Administration) tries to figure out how to identify people who cheat on the tax as Uber drivers – while making suggestions on how tax rules can be adapted to make it easy to follow the law.
  • What characterizes successful consulting proposals? A major consulting firm wants to use data from their CRM system (which documents the bidding process) to understand what kind of projects they will win or lose.
  • How to recognize money laundering transactions? A bank wants to find out if any of their customers are doing money laundering through online gaming companies.
  • How to offer benefits to customers with automated analysis? A company that supplies stock trading terminals wants to use data analysis to create a competitive edge.
  • How to segment Norwegian shareholders? A company that offers online trading of shares wants to identify segments of its customers to pinpoint and improve its marketing strategy.
  • How to lower costs and reduce the risk of production stoppages in a process business? A hydropower company wants to better understand when and why your power stations need repairs or maintenance.
  • How to identify customers who are in the process of terminating? A TV company wants to understand what characterizes “churn” – how can they identify customers who are about to leave them?
  • Why are some wines more popular than others? A group will work with search data from a wine site to find out what makes some wines more sought after than others.
  • Which customers will buy a new product? A group is working on data from a large bank that wants to offer its existing customers more services.
  • How to increase the recycling rate for waste in Oslo? REN – Oslo’s municipal trash service – wants to find out if you can organize routes and routines differently to better utilize trash trucks and recycling plants.
  • How to avoid being sold out for promotional items? One of Norway’s largest grocery chains wishes to improve their ordering routines so that customers do not get to the store and find out that there is no more left of the offer they wanted.
  • How to model fraud risk in maritime insurance? An insurance company wants to build a model to understand how to find customers attempting to fraud companies or authorities.
  • Which customers are about to leave us? A large transport company wants to find out which customers are about to go to a competitor so that they can take action before it happens.
  • What characterize students who drop out? BI enters 3500 new students each year, but some of them end after the first year. How can we find evidence that a student is about to drop out?
Common to all the projects – and so it’s with all the student projects I have advised since I started in this industry – is that you start with a big question and reduce it to something that can actually be answered. Then you look for data and find that you need to reduce it even more. Then you get problems that the data is either not found, unreliable or inadequate – and one has to figure out what to do with it. Finally, after about 90% of the time and money budget is gone, one can begin to think about analysis. And then there is a risk that you find nothing…
And that is an important lesson of this course: The goal is that the student should be able to know about actual data analysis to ask the right questions and have a realistic expectation of what kind of answer you actually can get.
There is a great demand for this course – so we have set up an additional course this fall. See you there!

The reassembler

James May – Captain Slow, the butt of many Top Gear jokes about nerds and pedants – has a fantastic little show called The Reassembler, where he takes some product that has been taken apart into little pieces, and puts it together again. It works surprisingly well, especially when he goes off on tangents about corporate history, kids waiting for their birthdays to come, and whether something is a bolt or a screw.

Slow television, nerd style.

Here is one example, you can find others on Youtube:

Smarthelp: Locating and messaging passengers

 

If you are a public transportation company: How do you tell your prospective passengers that their travel plans may have to change?

Public transportation companies know a lot about their passengers’ travel patterns, but not as much as you would think – and, surprisingly, they know less now when ticket sales have been automated than they used to know before.

screen696x696

RuterBillett – a ticketing app

Let’s take a concrete company as an example: Ruter AS, the public transportation authority of Greater Oslo. Ruter is a publicly owned company that coordinates various suppliers of transportation services (bus, tram, train, some ferries) in the Oslo area. The company has been quite innovative in their use of apps, selling most of their tickets on the RuterBillett app, and having many of their customers plan their journey on the RuterReise app. The apps are very popular because they make it very easy both to figure out which bus or train to take, and to buy a ticket.

The company has a problem, though: While they know that someone bought a ticket on the ticketing app, they don’t know which particular bus, tram or other service the passenger took (a ticket typically gives you one hour of open travel on their services, no matter how many of them you use).

screen696x6961

RuterReise – a journey planning app

They could get some information from what people have been searching for, but the two apps are not linked, and they don’t know whether a passenger who searched for a particular route actually bought a ticket and did the journey – or not. There are many reasons for this lack of knowledge, but privacy issues – Norway has very strict laws on privacy – are important. Ruter does not want to track where its customers are travelling, at least not if it in any way involves identifying who a passenger actually is.

Not knowing where passengers are is a problem in many situations: It creates difficulties for dimensioning capacity, and it makes it difficult to communicate with passengers when something happens – such as a bus delay or cancellation.

Identifying travel patterns and communicating with passengers

The problem for Ruter is that they want to know where people are travelling (so they can figure out how many buses or trams they need to schedule), they ned to know who regularly takes certain journeys (so they know whom to send a message to if that route is not working) and they need to know who is in a certain area at a certain time (so they don’t send you a message about your bus being delayed if you are out of town, for instance). All of this is easy, except for one thing: Norway has very strict privacy laws – already quite similar to EU’s General Data Protection Regulation, which goes into effect in 2018 – and Ruter cares deeply about not being seen as a company that monitors where people travel.

In short, they need to know where you travel, but do not want to know who you are.

This is a seemingly impossible challenge, but Smarthelp Secure Infrastructure, in combination with Smart Decision Support, makes it possible. The communications platform creates an end-to-end encrypted communication channel between a central system and the smartphone. Using technology developed because we had to solve the problem of medical-level encrypted communication between emergency centers and individual users, Smarthelp has technology that allows someone to track specific information you allow access to – say, the fact that you are in a certain area, or that you regularly travel certain paths – without sharing other information, such as your name.

This would allow Ruter, when something happens, to send a message to people who a) regularly takes, say, bus route 85, and who b) is in an area where it is conceivable that they could take the bus, given their prior patterns, the time of day, and so on. For the individual passenger, this would mean that you only get pertinent messages – you don’t get messages about bus routes you don’t normally take (unless you actually get on the bus), and you don’t get messages when you are far enough from the bus that it is clear you are not going to take it anyway. In a world of information overload, this is extremely important – flood the user with many messages, and they do not read them.

The future of public transportation

A selective message and geolocation service, such as Smarthelp provides, is an evolutionary step, an optimization of the current way transportation is coordinated. In the long term (especially if we start to talk about seld-driving vehicles), the whole way we coordinate public transportation will change. As one Ruter employee told me: A public transportation company is “someone who takes you from a place you are not to a place you don’t want to go.”

The next step in public transportation is that the users tells the company not just that they want to get on the bus, but also where they want to go. I have been told that in an experiment, Telenor found that, one sunny summer afternoon, fully half of their employees (located at Fornebu outside Oslo) planned to go to Huk, a public beach on Bygdøy. The distance from Telenor’s headquarters at Fornebu is 10 minutes by car, but takes more than 30 minutes by public transportation, involving two bus routes. If Ruter had known about these travel plans, though, it could have just rolled up some buses and driven the employees directly, vastly improving the service – and avoiding clogging up the regular buses to Bygdøy.

And that is the future of public transportation: Instead of planning where you will go in terms of geography, you will tell the public transportation company where you want to go, and they will get you there. With self-driving cars, they will be able to tell you when you will be at your destination – but, perhaps, not willing to tell you the actual route. As a passenger, you probably will not care – after all, what matters to you is when you arrive, not by which route.

That would, in effect, mean that we have transitioned public transportation from line switching to packet switching, effectively turning the bus into the Internet. But that is for the future.

In the meantime, there is Smarthelp.


(I am on the board of Råd AS, a company that has developed the platform SmartHelp for Norwegian emergency services, allowing shared situational awareness, communication and privacy. The company is now seeking customers and collaborators outside this market.)

Smarthelp is a platform technology consisting of, at present, three elements: Smarthelp Rescue, an app for iPhone and Android that allows users to transmit their position to an emergency service; Smarthelp Decision Support, a decision support system which allows an operator to locate and communicate with users (both with the app and without), and Smarthelp Secure Infrastructure, a granularly encrypted communications platform for secure, private communication. If you want more information, please contact me or Fredrik Øvergård, CEO of SmartHelp.

After Moore: Landauer

Very interesting blog by the very readable Ted: Is computing in reverse the next big thing?

As Moore’s law continues, it will reach certain physical limitations, such as electrons behaving less dependently the thinner the conduits become (think individual electrons instead of a more predictable stream. Another (they are linked, I suspect) is Landauer’s principle, which dictates that there is a certain lower limit on how much power that is necessary to flip a bit, and that forms a hard stop in terms of how much you can lower power consumption (and with it, heat dissipation.) (See Denning, P. J. and T. G. Lewis (2016). “Exponential laws of computing growth.” Communications of the ACM 60(1): 54-65, for an excellent discussion of Moore’s law and its remaining life.)

Turns out computing capability as a function of electric power consumption might be the next big obstacle (or at least measurement.) The BitCoin miners certainly know that.

Reverse, computing, which Ted writes about, is essentially computing where the power can be reversed, recreating the initial state. While difficult technically, it certainly would reduce power consumption to almost nothing.

To learn how, read the article. Recommended!

SmartHelp: Locating employees in a crisis

If there is a crisis – do you know where your people are?

Imagine the situation: An event (terrorist attack, industrial accident, public transportation accident) of some proportion happens. Many people are hurt, lots of rumors abound, emergency services are responding. Almost immediately, the question arises: Are any of my employees affected by this – and do they need help?

At present, most organizations locate their employees by calling them or sending emails. This is slow and ineffective – when Norway was hit by a terrorist bomb in the Oslo city centre in 2011 during the summer holiday, it took one of the large newspapers more than two days of frantic telephoning to find all their employees. Most of the employees were, of course, just fine, but the company still had to locate them all. In such a situation, knowing who is not in danger quickly is very important, because it lets you concentrate resources on those who need help.

Smarthelp Decision Support, the emergency service communication platform, allows an organization to quickly – within minutes – determine where its employees are and whether they need help. Smarthelp does this while maintaining privacy of the individual employee.

Most large organizations have a system where employees register where they travel on business. For this service to work, the employee has to remember to update it, though for some companies, this happens automatically if they purchase their tickets through a specific travel agency. While this may help, people travel for pleasure, deviate from their itineraries, forget to register their travels, and purchase their tickets from the cheapest, rather than the official source. Consequently, nobody knows where they really are.

SmartHelp Decision Support (see picture) allows the company to set up a geographical area surrounding the event, and contact all their employees (based on lists of telephone numbers) to determine whether they are inside this area or not.

terroreksempel

Here is another example: You are responsible for security in a large company facility – say, an office building. The company receives a bomb threat which necessitates evacuating the building with thousands of employees. If the employees have SmartHelp on their phones, you can communicate with them all, and determine whether they (or at least their smartphones have left the building (limited by GPS accuracy). You can define a rallying point or area and get an automatic message as soon as someone enters the area, allowing you to quickly determine who is not accounted for. (At this point, GPS location – which we use – does not allow precise location inside a building, but that could change as WiFi locationing services get better.)

rumorsparisAnother advantage is information: In the November 2015 terrorist attack in Paris happened, there where (as is usual) lots of rumors circulating in the hundreds of thousands of Twitter messages and other social channels. With SmartHelp, the authorities would have been able to send targeted messages to specific areas, conveying a precise and autorative message across a cacophony of noise and misinformation.

SmartHelp works anywhere in the world where there is mobile reception (I have used it to signal my position to my host in Shanghai, for instance.) Privacy is handled through an ingenious cryptographic architecture that is secure and fast – the platform is certified for the medical information under the Norwegian data privacy laws, among the strictest in the world.

If you want more information, please contact me or Fredrik Øvergård, CEO of SmartHelp.


(I am on the board of Råd AS, a company that has developed the platform SmartHelp for Norwegian emergency services, allowing shared situational awareness, communication and privacy. The company is now seeking customers and collaborators outside this market.)

Smarthelp is a platform technology consisting of, at present, three elements: Smarthelp Rescue, an app for iPhone and Android that allows users to transmit their position to an emergency service; Smarthelp Decision Support, a decision support system which allows an operator to locate and communicate with users (both with the app and without), and Smarthelp Secure Infrastructure, a granularly encrypted communications platform for secure, private communication. If you want to see how the system works in a 911 central situation, see this video: