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.”)
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 stool made for his wife, but has trouble with a crumbling infrastructure and the wrapping paper dissolving unless he gets home soon enough…
Data 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!
Telenor 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
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!
(No, I am not sponsored. Just like the service.)
[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 no 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.
(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?).
I have just finished teaching four days of data analytics – proper programming and data collection. We (Chandler
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.
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: