Category Archives: Academically speaking

The skinny on the economic effects of IT

Wired for Innovation: How Information Technology is Reshaping the Economy Wired for Innovation: How Information Technology is Reshaping the Economy by Erik Brynjolfsson

My rating: 4 of 5 stars Erik Brynjolfsson took a look at the IT productivity paradox in the early 90s and decided to sort it out – and he did, by and large, by collecting prodigious amounts of data and tirelessly analyze them to tease out what everybody suspected but could not show empirically: That information technology contributes enormously to increases in productivity, innovation and welfare.

This short and to the point book gives an excellent overview and guide to the research on the economic effects of information technology. Each chapter has pointers to more reading, good examples, concludes with avenues for further research. I will use this as an assignment for my technology strategy students – rather than giving them a few articles, they might as well read the whole book.

(Also available through Google Booksearch. Full notes below the fold.)

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Chandler: Scale and Scope

I am teaching a doctoral class on Al Chandler’s Strategy and structure this week, so I thought I should dig out and clean up my notes on Scale and scope. And publish them here while I am at it. Caveat emptor, of course.

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Seeking M.Sc. students to study the Norwegian IT industry

The Norwegian School of Management is starting a large research project called "A Knowledge-based Norway" ("Et kunnskapsbasert Norge"), where the goal is to study Norwegian "knowledge hubs" – knowledge-intensive industries and how they create and distribute knowledge. The project is led by Torger Reve and Amir Sasoon, and will encompass 10 different industries.

I have been tasked with one of these industries – the Norwegian IT industry, and is therefore seeking M.Sc. students who wants to write their theses under this topic. This will involve studying individual companies (such as, for instance, EDB Business Partner, Accenture or Opera Software) or groups of companies (say, the Norwegian IT services sector, or software companies supporting the oil industry) to understand how they develop knowledge, interact with each other and their customers, evolve their markets and their services, and so on.

The upshot for students, of course, is that they get to learn something that is very relevant both from a research and a practical (read: career) perspective. The study starts these days and will finish in about two years, which will make it ideal for M.Sc. students starting their thesis work this or (to a lesser extent) next Fall.

Please contact me at self@espen.com if you are interested.

What is Technology Strategy?

I run a research center called Centre for Technology Strategy at the Norwegian School of Management. Inevitably, the question comes up – what is technology strategy?

In my mind, the question is simple and comes down to two things: The realization that most changes in the world are due to changes in technology, and, hence, it is vitally important for managers to understand how technology evolves and how this evolution impacts their companies.

I like to illustrate this with a diagram of such mind-boggling simplicity that it is almost embarrassing to present it here. On the other hand, it seldom fails to inform when I use it in presentations – and a number of my collaborators through the years like it enough to use it in theirs:

image

In words: Technology drivers – i.e., changes in how we do things – changes the business environment, which again imposes changes in strategies on companies. Technology strategy aims to enable companies to understand the technology drivers to be able to change their strategies before they are forced to by the business environment.

This is by no means easy. It may be hard to understand what the drivers are – if you were a producer of travel alarm clocks, would you have foreseen the use of cell phones as alarm clocks? And though the drivers may be easy to understand, you may under- or overestimate the time it takes before your business environment changes. Lastly, it may be easy to understand both the change and the timing, but just hard to deal with the change itself. Newspapers and book publishers, for instance, can easily see what is happening to the music industry, understand how the business environment is changing, yet find themselves repeating the errors of the music industry because the changes necessary goes against the norms and values of those of power, as well as their technology and their business model.

To understand technology strategy, of course, you need also to understand the current business environment – in terms of the technology currently used – and how it shapes current strategy. And you need to have an understanding of technology evolution in general and the evolution of technology in your industry in particular. Lastly, you need an understanding of how to change technology inside organizations – something which requires an understanding of not just changing technology, but also organizational structures, incentive systems, and norms and values.

(part I of a series of short and rather irreverent articles on various aspects of Technology Strategy)

PhD candidate sought – Social media for innovation

Here is the official text for this announcement. Please contact Asbjørn or me if you are interested, and/or to discuss a project proposal.

BI Norwegian School of Management and SINTEF ICT announces a PhD scholarship for the project ”NETworked POWER. Innovation by social software”

image BI Norwegian School of Management is an internationally leading business school, with a broad research production, a strong international network, and 18000 students at all levels. The school offers a four year doctoral program in business administration. The program features five areas of specialization: Finance and Economics, Strategic Management, Marketing, Leadership and Organization, and Innovation and Entrepreneurship.

image SINTEF ICT is an internationally leading research institute, delivering research-based competence, services and products.

NetPower is a four-year collaborative project between Devoteam daVinci, Innoco, Induct, Bengler, Seniornett, The Norwegian Labour Party, BI Norwegian School of Management (Department of Strategy and Logistics) and SINTEF ICT. The project is funded by the Norwegian Research Council. NetPower has funding for a PhD scholarship, and is looking for a person to fill this four-year position.

Social media for innovation

The evolution of social media the last few years has created new challenges and opportunities at many levels of society. We see the emergence of a strong culture for sharing and exchanging knowledge and experience through new, technologically facilitated network applications. This forms the background for NetPower’s objective: To create social software for innovation, with a focus on private enterprises and non-profit organizations. Within NetPower, Induct creates solutions for Devoteam daVinci and Innoco, and Bengler creates solutions for Seniornett and the Norwegian Labour Party.

The candidate’s research focus will primarily be on the innovation potential of social media for businesses. A suggested research agenda may be 1) To identify and analyze the demands and needs of users and user organizations. 2) To find solutions to challenges around broad participation, privacy and security. 3) To evaluate and analyze solutions. 4) To create knowledge about how social media can be adopted within different organizational cultures. 5) To contribute to theory about social media and strategic innovation.

We are seeking candidates from a wide knowledge background – possible perspectives include, but are not limited to, innovation strategy, including open and/or user-centered innovation, disruptive innovation, strategy process, social network evolution, technology evolution and strategic management of knowledge and knowledge-based companies.

Formal qualifications and personal attributes

The position will be formally located at the Department of Strategy and Logistics, BI Norwegian School of Management, Oslo, Norway. A candidate should have a completed Master of Science degree with a grade B or higher, and be able to speak and understand Norwegian (for collecting and analyzing data within the target organizations). Relevant working experience is, of course, preferable. Appointment to a position as Doctoral Scholar is conditioned on admittance to BI Norwegian School of Management’s doctoral program in accordance with the admission requirements.

The Ph.D. project is central to NetPower – this is where most of the work on open innovation processes and the value of social software in business will be done. The position demands an ability to initiate and follow up relevant research work in close contact with the project partners Devoteam daVinci, Innoco and the software developer Induct. The results should primarily be academically directed, but also useful for the business partners. The candidate must be able to publish in English and deliver within tight deadlines.

We offer:

You will be part of two strong research environments. As a Ph.D. candidate your main place of work will be with the Center for Technology Strategy, a research center focusing on the relationship between technology evolution (especially within software) and the strategic potential and processes of companies. The research center, at this point, has two additional Ph. D. candidates, and is a part of the Department of Strategy and Logistics, currently with about 13 Ph.D. candidates and 45 faculty members.

As a project participant in NetPower, you will in addition collaborate closely with the HCI research group at the department of Collaborative and trusted systems at SINTEF in Oslo. This is the leading research environment on social media within Norway.

The four-year scholarship is set according to the Norwegian State Salary Scale, and currently pay NOK 344,200 per year. The scholarship involves 25% (35 hours) teaching and/or research assistant responsibilities.

For more information about the position as well as some guidance on the proposal, please contact Espen Andersen (self@espen.com, +47 4641 0452) or Asbjørn Følstad (asf@sintef.no, +47 2206 7515). Additional information about the doctoral program and admission requirements can be obtained from the Program Director Dora Sigurdardottir at BI Norwegian School of Management, (dora.s.sigurdardottir@bi.no, +47 46 41 00 57).

The application should include the following:

  • Certificate of a Master of Science degree or equivalent with a grade B or higher, please include transcripts of grades
  • Other relevant certificates and transcripts
  • A project proposal (5-10 pages including topic, proposed method, schedule)
  • CV
  • Letters of recommendations from relevant employers/tutors providing evidence of your skills as a researcher (if any)
  • Certificates from previously completed courses at the doctoral level, if requesting approval of these for the PhD programme at BI Norwegian School of Management (see last section of § 3.1.1 in our PhD Regulations on BIs website)
  • A complete list of all publications and/or other documented relevant activities. Please note that certified documentation of formal qualification must be submitted in order to be evaluated.

Applications must be submitted electronically through BI’s website (www.bi.no, see under “vacant positions”, direct link here.). Women are encouraged to apply.

The Department Council of the Institute for Strategy and Logistics will evaluate the project proposals. Candidate
s may be asked to come for a formal interview. Thereafter the project proposal will be sent to the Doctoral Programme Committee for approval.

The application deadline for this scholarship is November 1, 2009. Documentation may not be submitted after the deadline.

Stringing those dimensions together…

This video tries to do something very difficult: Explain dimensions beyond the four we are used to. And does a good job of it.

(And to my students – watch this video after having read Neal Stephenson’s In the beginning was … the command line, as an introduction to the course on technology strategy.)

(Via Cory)

Plagiarism showcased – and a call for action

image I hate plagiarism, partially because it has happened to me, partially because I publish way too little because I overly self-criticize for lack of original thinking, partly because I have had it happen with quite a few students and am getting more and more tired of having to explain even to executive students with serious job experience that clipping somebody else’s text and showing it as your own is not permissible – this year, I even had a student copy things out of Wikipedia and argue that it wasn’t plagiarism because Wikipedia is not copyrighted.

I suspect plagiarism is a bigger problem than we think. The most recent spat is noted in Boing Boing – read the comments if you want a good laugh and some serious discussion. (My observation, not particularly original: Even if this thing wasn’t plagiarized, isn’t this rather thin for a doctoral dissertation?)

The thing is, plagiarism will come back to bite you, and with the search tools out there, I can see a point in a not too distant future where all academic articles ever published will be fed into a plagiarism checker, with very interesting results. Quite a few careers will end, no doubt after much huffing and puffing. Johannes Gehrke and friends at Cornell have already done work on this for computer science articles – I just can’t wait to see what will come out of tools like these when they really get cranking. I seem to remember Johannes as saying that most people don’t plagiarize, but that a few seem to do it quite a lot.

It is high time we turn the student control protocols loose on published academic work as well. Nothing like a many eyeballs to dig out that shallowness….

From links to seeds: Edging towards the semantic web

Wolfram Alpha just may take us one step closer to the elusive Semantic Web, by evolving a communication protocol out of its query terms.

(this is very much in ruminating form – comments welcome)

Wolfram Alpha officially launched on May 18, an exciting new kind of "computational" search engine which, rather than looking up documents where your questions have been answered before, actually computes the answer. The difference, as Stephen Wolfram himself has said, is that if you ask what the distance is to the moon, Google and other search engines will find you documents that tells you the average distance, whereas Wolfram Alpha will calculate what the distance is right now, and tell you that, in addition to many other facts (such as the average). Wolfram Alpha does not store answers, but creates them every time. And it does primarily answer numerical, computable questions.

The difference between Google (and other search engines) and Wolfram Alpha is not so clear-cut, of course. If you ask Google "17 mpg in liters per 100km" it will calculate the result for you. And you can send Wolfram Alpha non-computational queries such as "Norway" and it will give an informational answer. The difference lies more in what kind of data the two services work against, and how they determine what to show you: Google crawls the web, tracking links and monitoring user responses, in a sense asking every page and every user of their services what they think about all web pages (mostly, of course, we don’t think anything about most of them, but in principle we do.) Wolfram Alpha works against a database of facts with a set of defined computational algorithms – it stores less and derives more. (That being said, they will both answer the question "what is the answer to life, the universe and everything" the same way….)

While the technical differences are important and interesting, the real difference between WA and Google lies in what kind of questions they can answer – to use Clayton Christensen’s concept, the different jobs you would hire them to do. You would hire Google to figuring out information, introduction, background and concepts – or to find that email you didn’t bother filing away in the correct folder. You would hire Alpha to answer precise questions and get the facts, rather than what the web collectively has decided is the facts.

The meaning of it all

Now – what will the long-term impact of Alpha be? Google has made us replace categorization with search – we no longer bother filing things away and remembering them, for we can find them with a few half-remembered keywords, relying on sophisticated query front-end processing and the fact that most of our not that great minds think depressingly alike. Wolfram Alpha, on the other hand, is quite a different animal. Back in the 80s, I once saw someone exhort their not very digital readers to think of the personal computer as a "friendly assistant who is quite stupid in everything but mathematics."  Wolfram Alpha is quite a bit smarter than that, of course, but the fact is that we now have access to this service which, quite simply, will do the math and look up the facts for us. Our own personal Hermione Granger, as it is.

I think the long-term impact of Wolfram Alpha will be to further something that may not have started with Google, but certainly became apparent with them: The use of search terms (or, if you will, seeds) as references. It is already common to, rather than writing out a URL, to help people find something by saying "Google this and you will find it". I have a couple of blogs and a web page, but googling my name will get you there faster (and you can misspell my last name and still not miss.) The risk in doing that, of course, is that something can intervene. As I read (in this paper) General Motors, a few years ago, had an ad for a new Pontiac model, at the end of which they exhorted the audience to "Google Pontiac" to find out more. Mazda quickly set up a web page with Pontiac in it, bought some keywords on Google, and quite literally Shanghaied GM’s ad.

Wolfram Alpha, on the other hand, will, given the same input, return the same answer every time. If the answer should change, it is because the underlying data has changed (or, extremely rarely, because somebody figured out a new way of calculating it.) It would not be because someone external to the company has figured out a way to game the system. This means that we can use references to Wolfram Alpha as shorthand – enter "budget surplus" in Wolfram Alpha, and the results will stare you in the face. In the sense that math is a language for expressing certain concepts in a very terse and precise language, Wolfram Alpha seeds will, I think, emerge as a notation for referring to factual information.

A short detour into graffiti

Back in the early-to-mid-90s, Apple launched one of the first pen-based PDAs, the Apple Newton. The Newton was, for its time, an amazing technology, but for once Apple screwed it up, largely because they tried to make the device do too much. One important issue was the handwriting recognition software – it would let you write in your own handwriting, and then try to interpret it. I am a physician’s son, and I certainly took after my father in the handwriting department. Newton could not make sense of my scribbles, even if I tried to behave, and, given that handwriting recognition is hard, it took a long time doing it. I bought one, and then sent it back. Then the Palm Pilot came, and became the device to get.

The Palm Pilot did not recognize handwriting – it demanded that you, the user, wrote to it in a sign language called Graffiti, which recognized individual characters. Most of the characters resembled the regular characters enough that you could guess what they were, for the others you either had to consult a small plastic card or experiment. The feedback was rapid, to experimenting usually worked well, and pretty soon you had learned – or, rather, your hand had learned – to enter the Graffiti characters rapidly and accurately.

Wolfram Alpha works in the same way as Graffiti did: As Steven Wolfram says in his talk at the Berkman Center, people start out writing natural language but pretty quickly trim it down to just the key concepts (a process known in search technology circles as "anti-phrasing".) In other words, by dint of patience and experimentation, we (or, at least, some of us) will learn to write queries in a notation that Wolfram Alpha understands, much like our hands learned Graffiti.

From links to seeds to semantics

Semantics is really about symbols and shorthand – a word is created as shorthand for a more complicated concept by a process of internalization. When learning a language, rapid feedback helps (which is why I th
ink it is easier to learn a language with a strict and terse grammar rather than a permissive one), simplicity helps, and a structure and culture that allows for creating new words by relying on shared context and intuitive combinations (see this great video with Stephen Fry and Jonathan Ross on language creation for some great examples.)

And this is what we need to do – gather around Wolfram Alpha and figure out the best way of interacting with the system -and then conduct "what if" analysis of what happens if we change the input just a little. To a certain extent, it is happening already, starting with people finding Easter Eggs – little jokes developers leave in programs for users to find. Pretty soon we will start figuring out the notation, and you will see web pages use Wolfram Alpha queries first as references, then as modules, then as dynamic elements.

It is sort of quirky when humans start to exchange query seeds (or search terms, if you will).  It gets downright interesting when computers start doing it. It would also be part of an ongoing evolution of gradually increasing meaningfulness of computer messaging.

When computers – or, if you will, programs – needed to exchange information in the early days, they did it in a machine-efficient manner – information was passed using shared memory addresses, hexadecimal codes, assembler instructions and other terse and efficient, but humanly unreadable encoding schemes. Sometime in the early 80s, computers were getting powerful enough that the exchanges gradually could be done in human-readable format – the SMTP protocol, for instance, a standard for exchanging email, could be read and even hand-built by humans (as I remember doing in 1985, to send email outside the company network I was on.) The world wide web, conceived in the early 90s and live to a wider audience in 1994, had at its core an addressing system – the URL – which could be used as a general way of conversing between computers, no matter what their operating system or languages. (To the technology purists out there – yes, WWW relies on a whole slew of other standards as well, but I am trying to make a point here) It was rather inefficient from a machine communication perspective, but very flexible and easy to understand for developers and users alike. Over time, it has been refined from pure exchange of information to the sophisticated exchanges needed to make sure it really is you when you log into your online bank – essentially by increasing the sophistication of the HTML markup language towards standards such as XML, where you can send over not just instructions and data but also definitions and metadata.

The much-discussed semantic web is the natural continuation of this evolution – programming further and further away from the metal, if you will. Human requests for information from each other are imprecise but rely on shared understanding of what is going on, ability to interpret results in context, and a willingness to use many clues and requests for clarification to arrive at a desired result. Observe two humans interacting over the telephone – they can have deep and rich discussions, but as soon as the conversation involves computers, they default to slow and simple communication protocols: Spelling words out (sometimes using the international phonetic alphabet), going back and forth about where to apply mouse clicks and keystrokes, double-checking to avoid mistakes. We just aren’t that good at communicating as computers – but can the computers eventually get good enough to communicate with us?

I think the solution lies in mutual adaptation, and the exchange of references to data and information in other terms than direct document addresses may just be the key to achieving that. Increases in performance and functionality of computers have always progressed in a punctuated equilibrium fashion, alternating between integrated and modular architectures. The first mainframes were integrated with simple terminal interfaces, which gave way to client-server architectures (exchanging SQL requests), which gave way to highly modular TCP/IP-based architectures (exchanging URLs), which may give way to mainframe-like semi-integrated data centers. I think those data centers will exchange information at a higher semantic level than any of the others – and Wolfram Alpha, with its terse but precise query structure may just be the way to get there.

Stephen Wolfram talk on Wolphram Alpha

Enough said, watch it. As a colleague twittered: This will change computing.

(That being said, this is a very poor filming – there are no pictures of the screen, aside from a glimmer now and then.)

Notes from Stephen Wolfram webcast

These are my raw notes from the session with Stephen Wolfram on the pre-launch of the Wolfram Alpha service at the Berkman center. Unfortunately, I was on a really bad Internet connection and only got the sound, and missed the first 20 minutes or so running around trying to find something better.

Notes from Stephen Wolfram on Alpha debut

…discussion of queries:
– nutrition in a slice of cheddar
– height of Mount Everest divided by length of Golden Gate bridge
– what’s the next item in this sequence
– type in a random number, see what it knows about it
– "next total solar eclipse"

What is the technology?
– computes things, it is harder to find answers on the web the more specifically you ask
– instead, we try to compute using all kinds of formulas and models created from science and package it so that we can walk up to a web site and have it provide the answer

– four pieces of technology:
— data curation, trillions of pieces of curated data, free/licensed, feeds, verify and clean this (curate), built industrial data curation line, much of it requires human domain expertise, but you need curated data
— algorithms: methods and models, expressed in Mathematica, there is a finite number of methods and models, but it is a large number…. now 5-6 million lines of math code
— linguistic analysis to understand input, no manual or documentation, have to interpret natural language. This is a little bit different from trad NL processing. working with more limited set of symbols and words. Many new methods, has turned out that ambiguity is not such a bit problem once we have mapped it onto a symbolic representation
— ability to automate presentation of things. What do you show people so they can cognitively grasp what you are, requires computational esthetics, domain knowledge.

Will run on 10k CPUs, using Grid Mathematica.
90% of the shelves in a typical reference library we have a decent start on
provide something authoritative and then give references to something upstream that is
know about ranges of values for things, can deal with that
try to give footnotes as best we can

Q: how do you deal with keeping data current
– many people have data and want to make it available
– mechanism to contribute data and mechanism for us to audit it

first instance is for humans to interact with it
there will be a variance of APIs,
intention to have a personalizable version of Alpha
metadata standards: when we open up our data repository mechanism, wn we use that can make data available

Questions from audience:

Differences of opinion in science?
– we try to give a footnote
– Most people are not exposed to science and engineering, you can do this without being a scientist

How much will you charge for this?
– website will be free
– corporate sponsors will be there as well, in sidebars
– we will know what kind of questions people ask, how can we ingest vendor information and make it available, need a wall of auditing
– professional version, subscription service

Can you combine databases, for instance to compute total mass of people in England?
– probably not automatically…
– can derive it
– "mass of people in England"
– we are working on the splat page, what happens when it doesn’t know, tries to break the query down into manageable parts
300th largest country in Europe? – answers "no known countries"

Data sources? Population of Internet users. how do you choose?
– identifying good sources is a key problem
– we try do it well, use experts, compare
– US government typically does a really good job
– we provide source information
– have personally been on the phone with many experts, is the data knowable?
– "based on available mortality data" or something

Technology focus in the future, aside from data curation?
– all of them need to be pushed forward
– more, better, faster of what we have, deeper into the data
– being able to deal with longer and more complicated linguistics
– being able to take pseudocode
– being able to take raw data or image input
– it takes me 5-10 years to understand what the next step is in a project…

How do you see this in contrast with semantic web?
– if the semantic web had been there, this would be much easier
– most of our data is not from the web, but from databases
– within Wolfram Alpha we have a symbolic ontology, didn’t create this as top down, mostly bottom-up from domains, merged them together when we realized similarities
– would like to do some semantic web things, expose our ontological mechanisms

At what point can we look at the formal specs for these ontologies?
– good news: All in symbolic mathematical code
– injecting new knowledge is complicated – nl is surprisingly messy, such as new terms coming in, for instance putting in people and there is this guy called "50 cent"
– exposure of ontology will happen
– the more words you need to describe the question, the harder it is
– there are holes in the data, hope that people will be motivated to fill them in

Social network? Communities?
– interesting, don’t know yet

How about more popular knowledge?
– who is the tallest of Britney Spears and 50 cent
– popular knowledge is more shallowly computable than scientific information
– linguistic horrors, book names and such, much of it clashes
– will need some popularity index, use Wikipedia a lot, can determine whether a person is important or not

The meaning of life? 42….

Integration with CYC?
– CYC is most advanced common sense reasoning system
– CYC takes what they reason about things and make it computing strengths
– human reasoning not that good when it comes to physics, more like Newton and using math

Will you provide the code?
– in Mathematica, code tends to be succinct enough that you can read it
– state of the art of synthesizing human-readable theorems is not that good yet
– humans are less efficient than automated and quantitative qa methods
– in many cases you can just ask it for the formula
– our pride lies in the integration, not in the models, for they come from the world
– "show formula"

Will this be integrated into Mathematica?
– future version will have a special mode, linguistic analysis, pop it to the server, can use the computation

How much more work on the natural language side?
– we don’t know
– pretty good at removing linguistic fluff, have to be careful
– when you look at people interacting with the system, but pretty soon they get lazy, only type in the things they need to know
– word order irrelevant, queries get pared down, we see deep structure of language
– but we don’t know how much further we need to go

How does this change the landscape of public access to knowledge?
– proprietary databases: challenge is make the right kind of deal
– we have been pretty successful
– we can convince them to make it casually available, but we would have to be careful that the whole thing can’t be lifted out
– we have yet to learn all the issues here

– have been pleasantly surprised by the extent to which people have given access
– there is a lot of genuinely good public data out there

This is a proprietary system – how do you feel about a wiki solution outcompeting you?
– that would be great, but
– making this thing is not easy, many parts, not just shovel in a lot of data
– Wikipedia is fantastic, but it has gone in particular directions. If you are looking for systematic data, properties of chemicals, for instance, over the course of the next two years, they get modified and there is not consistency left
– the most useful thing about Wikipedia is the folk knowledge you get there, what are things called, what is popular
– have thought about how to franchise out, it is not that easy
– by the way, it is free anyway…
– will we be inundated by new data? Encouraged by good automated curation pipelines. I like to believe that an ecosystem will develop, we can scale up.
– if you want this to work well, you can’t have 10K people feeding things in, you need central leadership

Interesting queries?
– "map of the cat" (this is what I call artificial stupidity)
– does not know anatomy yet
– how realtime is stock data? One minute delayed, some limitations
– there will be many novelty queries, but after that dies down, we are left with people who will want to use this every day

How will you feel if Google presents your results as part of their results?
– there are synergies
– we are generating things on the fly, this is not exposable to search engines
– one way to do it could be to prescan the search stream and see if wolfram alpha can have a chance to answer this

Role for academia?
– academia no longer accumulates data, useful for the world, but not for the university
– it is a shame that this has been seen as less academically respectable
– when chemistry was young, people went out and looked at every possible molecule
– this is much to computer complicated for the typical libraries
– historical antecedents may be Leibniz’ mechanical and computational calculators, he had the idea, but 300 years too early

When do we go live?
… a few weeks
– maybe a webcast if we dare…

Steroids for the flighty-minded

An excellent and truly scary article by Margaret Talbot in the New Yorker about the use of neuroenhancers by people who are not ill. Which is comparable to recreational plastic surgery, which I don’t like either.

Is it just me, or is cheating seen as more and more normal and not to be punished or even held in contempt? When I catch students plagiarizing (which happens with a depressing frequency, partly because the tools for doing so have gotten so much better) their defense is more and more that this is normal, that you cannot expect them to come up with something original when everything is available out there on Google and Wikipedia. My retort is that I need to judge them on their own work, not others’, and that they therefore need to make it clear to me what they have done themselves and what they have found somewhere else. And their answer is that they put "Source: Wikipedia" at the bottom and therefore they are scot free, so there.

I would get angry if this wasn’t so depressing and so pointless. I am tempted to just fail them. Not for plagiarism – which entails disciplinary committees and all sorts of make-work. Rather an F for outright stupidity.

It is some consolation that creativity is one area where neuroenhancers don’t seem to work. But they might, as the article finds,  help these modern-day multitaskers concentrate on one specific task (hoping that it is a productive one and not, say, obsessively alphabetizing your library.) But neuroenhancers won’t make your ideas better – they won’t assist in spotting the prey, only in bringing it home. In the most dreary way possible:

Every era, it seems, has its own defining drug. Neuroenhancers are perfectly suited for the anxiety of white-collar competition in a floundering economy. And they have a synergistic relationship with our multiplying digital technologies: the more gadgets we own, the more distracted we become, and the more we need help in order to focus. The experience that neuroenhancement offers is not, for the most part, about opening the doors of perception, or about breaking the bonds of the self, or about experiencing a surge of genius. It’s about squeezing out an extra few hours to finish those sales figures when you’d really rather collapse into bed; getting a B instead of a B-minus on the final exam in a lecture class where you spent half your time texting; cramming for the G.R.E.s at night, because the information-industry job you got after college turned out to be deadening. Neuroenhancers don’t offer freedom. Rather, they facilitate a pinched, unromantic, grindingly efficient form of productivity.

If you find that tempting, be my guest. I am sure you can find directions via Google.

Jon Udell on observable work

Jon Udell has a great presentation over at Slideshare on how to work in observable spaces – something that should be done, to a much larger extent, by academics. I quite agree (and really need to get better at this myself):

Mind, heart, and hands: Lifelong learning and teaching in the digital agehttp://static.slidesharecdn.com/swf/ssplayer2.swf?doc=ocwc-090423142114-phpapp01&stripped_title=mind-heart-and-hands-lifelong-learning-and-teaching-in-the-digital-age

View more presentations from judell.

What if you could remember everything?

I was delighted when I found this video, where James May (the cerebral third of Top Gear) talks to professor Alan Smeaton of Dublin City University about lifelogging – the recording of everything that happens to a person over a period of time, coupled with the construction of tools for making sense of the data.

In this example, James May wears a Sensecam for three days. The camera records everything he does (well, not everything, I assume – if you want privacy, you can always stick it inside your sweater) by taking a picture every 30 seconds, or when something (temperature, IR rays in front (indicating a person) or GPS location) changes. As it is said in the video, some people have been wearing these cameras for years – in fact, one of my pals from the iAD project, Cathal Gurrin, has worn one for at least three years. (He wore it the first time we met, where it snapped a picture of me with my hand outstretched.)

The software demonstrated in the video groups the pictures into events, by comparing the pictures to each other. Of course, many of the pictures can be discarded in the interest of brevity – for instance, for anyone working in an office and driving to work, many of the pictures will be of two hands on a keyboard or a steering wheel, and can be discarded. But the rest remains, and with powerful computers you can spin through your day and see what you did on a certain date.

And here is the thing: This means that you will increasingly have the option of never forgetting anything again. You know how it is – you may have forgotten everything about some event, and then something – a smell, a movement, a particular color – makes you remember by triggering whatever part (or, more precisely, which strands of your intracranial network) of your brain this particular memory is stored. Memory is associative, meaning that if we have a few clues, we can access whatever is in there, even though it had been forgotten.

Now, a set of pictures taken at 30-second intervals, coupled together in an easy-to-use and powerful interface, that is a rather powerful aide-de-memoire.

Forgetting, however, is done for a purpose – to allow you to concentrate on what you are doing rather than using spare brain cycles in constant upkeep of enormous, but unimportant memories. For this system to be effective, I assume it would need to be helpful in forgetting as well as remembering – and since it would be stored, you would actually not have to expend so much remember things – given a decent interface, you could always look it up again, much as we look things up in a notebook.

Think about that – remembering everything – or, at least being able to recall it at will. Useful – or an unnecessary distraction?

Shirky on newspapers

Clay Shirky, the foremost essayist on the Internet and its boisterous intrusion into everything, has done it again: Written an essay on something already thoroughly discussed with a new and fresh perspective. This time, it is on the demise of newspapers – the short message is that this is a revolution, and saving newspapers just isn’t going to happen, because this is, well, a revolution:

[..]I remember Thompson [in 1993] saying something to the effect of “When a 14 year old kid can blow up your business in his spare time, not because he hates you but because he loves you, then you got a problem.” I think about that conversation a lot these days.

[..]

Revolutions create a curious inversion of perception. In ordinary times, people who do no more than describe the world around them are seen as pragmatists, while those who imagine fabulous alternative futures are viewed as radicals. The last couple of decades haven’t been ordinary, however. Inside the papers, the pragmatists were the ones simply looking out the window and noticing that the real world was increasingly resembling the unthinkable scenario. These people were treated as if they were barking mad. Meanwhile the people spinning visions of popular walled gardens and enthusiastic micropayment adoption, visions unsupported by reality, were regarded not as charlatans but saviors.

[..]

That is what real revolutions are like. The old stuff gets broken faster than the new stuff is put in its place. The importance of any given experiment isn’t apparent at the moment it appears; big changes stall, small changes spread. Even the revolutionaries can’t predict what will happen. Agreements on all sides that core institutions must be protected are rendered meaningless by the very people doing the agreeing. (Luther and the Church both insisted, for years, that whatever else happened, no one was talking about a schism.) Ancient social bargains, once disrupted, can neither be mended nor quickly replaced, since any such bargain takes decades to solidify.

And so it is today. When someone demands to know how we are going to replace newspapers, they are really demanding to be told that we are not living through a revolution. They are demanding to be told that old systems won’t break before new systems are in place. They are demanding to be told that ancient social bargains aren’t in peril, that core institutions will be spared, that new methods of spreading information will improve previous practice rather than upending it. They are demanding to be lied to.

That simple. He draws the line back to the Gutenberg printing press and the enormous transition that caused – much more chaotic that you would think with 500 year hindsight.

Highly recommended. And another piece of reading for my suffering students….

The perils of openness

Mary Beard has a really interesting perspective on the consequences of openness: Transparency is the new opacity. In the absence of confidential channels (which, given today’s storage and search capabilities, you have no guarantee will remain confidential) very little actual information gets transmitted in student appraisals.

And the only difference between job appraisals and student appraisals, I assume, lies in vocabulary. As a technologist, I could envision all kinds of technical fixes to this, assuming that those in charge of the specifications acknowledge that they are necessary: Fields for comments hidden from the subject, fields that terminate after a certain time after reading, filters to search engines that handle confidentiality – including the fact that there is a confidential comment in the first place (which turns out to be surprisingly hard to do.)

But the more natural fix is the quick conversation in the pub, the hallway, or on the private cell phone – impervious to search, storage and documentation – where the real information can be exchanged. The electronic equivalent? Encrypted Twitter, perhaps, if such a thing exists.

What we need is online coffee shops, offering the same discreet, transient and history-less marketplace for information. Now I spend time on the phone with my colleagues for that, but that doesn’t work well across time zones. So – what would it look like and how to build it?

PS: Come to think of it, Skype is encrypted, at least the phone calls.

Shannon, explained…

Peter Cochrane has a simple and very useful explanation of Claude Shannon’s mathematical law of communication, complete with diagrams. And a warning that, when it comes to technology, magic won’t work there, either.

We might thus imagine the energy of a signal dispersed inside such a solid form in the same way that water is retained by the skin of a balloon. We can change the shape of the balloon but the amount of water stays the same. Similarly, different coding and modulation schemes can alter the ratios of the sides presented by Shannon’s equation.

We can certainly trade off signal power against noise and/or bandwidth and time, but we can never exceed the bounds set by nature.

Seeking PhD candidates for iAD project

(Note: This is not the official announcement, which you can find here, where you will also find a link to the application program. I post this here because this blog is easier to update, allows me to link to pertinent information more easily, allows pictures, and allows comments and questions.)

 

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Announcement: Available Ph. D. Scholarships in Technology Strategy

 

BI Norwegian School of Management is inviting applications for scholarships in technology strategy. The scholarships are made available through the iAD Center for Research-based Innovation, an eight-year research project funded by the Norwegian Research Council and hosted by FAST Search and Transfer, a Microsoft Company. The candidates will pursue their Ph. D. through the doctoral programs of BI Norwegian School of Management and do their thesis research on topics of interest to the iAD project.

Continue reading →

Clayton Christensen on health care disruption

Here is Clayton Christensen giving a talk on disruptions in health care (but really a good introduction on disruption in general) at MIT:

http://mitworld.mit.edu/flash/player/Main.swf?host=cp58255.edgefcs.net&flv=mitw-01023-esd-innovator-prescription-christensen-13may2008&preview=http://mitworld.mit.edu//uploads/mitwstill-01023-esd-innovator-prescription-christensen-13may2008.jpg

 

Note that Clay uses Øystein Fjeldstad’s Value Configurations framework a little before 1:00:00 – a result of many conversations aboard the "Disruptive Cruise" which I arranged last year…. don’t say we aren’t doing our part over here….

Liveblogging from Sophia Antipolis

This are my running notes from visiting Accenture’s Technology Labs in Sophia Antipolis, as part of a Master of Management program called "Strategic Business Development and Innovation" for the Norwegian School of Management.

Accenture’s Technology Labs is a relatively small organization: 200 researchers, 180000 employees in Accenture. There are four tech labs: Silicon Valley, Chicago (the largest), Sophia Antipolis, Bangalore, they should be able to do everything, but in practice there is specialization. The four main activities of the tech labs are technology visioning, research, development of specific platforms, and innovation workshops (with clients, press, consultants etc.) The themes pursued are mobility and sensors; analytics and insight; human interaction & performance; Systems Integration (architecture, development methods); and infrastructure (virtualization, cloud computing).

Kelly Dempski: Power Shift: Accenture Technology vision

The visioning used to be far-thinking, visionary etc., now have a much more immediate focus, want to look at things that you can implement today, make it much more "grounded in reality"

Eight critical trends:

  • 1: Cloud computing and SaaS: Hardware cloud (amazon.com, IBM, Google (now the third largest producer of servers in the world)), desktop cloud (Google, Zimbra, MS Office Live Workspace), SaaS cloud (Netsuite, CrownPeak, salesforce.com), and services cloud (Google Checkout, Amazon web services, eBay, Yahoo)
    • examples: Flextronics has changed over their HR applications to an SaaS model. AMD emulates chips on software for testing purposes, now contract with Sun to do that in the cloud. New York Times had 4Tb of articles that they wanted to translate to PDF: Translated it all twice (because there was a bug the first time), someone went on Amazon with their credit card, uploaded 4Tb, processed it (24h), there was a bug, had to do it again, 48h, total cost $250 on someone’s credit card.
    • issues:
      • data location (where is the data)
      • privacy and security
      • performance
  • 2: Systems – regular and lite
    • SOA as the integration paradigm (regular), mashups (lite)
    • traditional back-end apps vs. end-user apps
    • small number of apps maintained by CIOs vs. large number of User and user-group created applications (long tail)
    • examples:
      • REST is a light architectural approach for interoperability & data extraction
      • Mashups (JackMe (trading platform tools), Serena, Duet (SAP and Microsoft), IBM) becoming more important in the enterprise arena
      • Widgets and gadgets are light-weight desktop UIs that continually update some data
  • 3: Enterprise intelligence at scale
    • combination of internet-scale computing, petabytes of data, and new algorithms
    • almost all the large systems vendors have partnered with or acquired some analytics oriented software company (such as Microsoft acquiring FAST)
    • rampant use of data: evolution through access, reporting, external & internal, unstructured etc.
  • Trends 1-2-3 together: The new CIO
    • hardware and software procured from the cloud
    • business units, end-users create their own lightweight apps
    • The new CIO:
      • "Data Fort Commander" – ensure security, privacy, integrity of corporate data and manage back-end apps
      • "Chief Intelligence Officer" – provide data analysis services & insights to business units
  • 4: Continuous access
    • mobile device "first class" IT object
    • No concept of enterprise desktop/laptop
    • location-based services
  • 5: Social computing
    • amplify and support the value of the community
    • three major directions: Platformization, inter-operability, identity management
  • 6: User-generated content
    • community-based CRM (users making videos about how to run certain kinds of software or build something from IKEA)
    • new forms of entertainment
    • revenue erosion of traditional media companies
    • this has marketing implications: You can measure the sentiment out there in the user community. You switch from advertising to engaging.
  • 7: Industrialization of software development
    • converging trends will increase integration: Predictive metrics, model-driven development, domain-specific languages, service-oriented architecture, agile-development & Forever Beta.
  • 8: Green computing
    • global warming, energy prices, consumer pressure, compliance and valuation
    • switch out energy-intensive processes for information-intensive processes: Electronic collaboration; Warehousing, supply chain & logistics optimization; Smart factories, plants, buildings & homes; and new businesses such as carbon auditing and trading

Cyrille Bataller: Biometric Identity Management

Biometric identification is coming, driven by increasing demand and technological progress. Biometric identification is defined as "automated recognition of individuals based on their physiological and/or behavioral characteristics. Physiological can be face, iris, fingerprint; behavioral can be signature, voice, or walk. Involves a tradeoff, as with all security systems, between the level of security and the convenience of the system. Fingerprint is most used (38%), face is the most natural, iris the most accurate. Many others: Finger/hand vein, gait, ear shape, electricity, heat signature, hand geometry and so on…

Balance between FMR (false (positive identification) m rate) and FNMR, called equal error rate. Iris has an EER of .002%, 10 fingerprints .01%, fingerprint .4%, signature 3%, face recognition 6%, voice 8%. Many parameters in addition to this.

Securimetrix has something called HIIDE, a mobile unit that does a number of biometrics, used in Iran. Voice is very interesting because it can be done over the phone, interesting for call centers, banks etc. Multimodal important, because it is hard to spoof.

Airports is a good example of what you can do with proper identification: You can move 99.9% of the check-in away from the airport. Bag drop can also be almost fully automated. Portugal is the leader in the EU, have automated passport control with facial recognition (scan, use electronic passport etc.). Most people are not concerned very much with privacy given some assurance and convenience. Likely to see lost of automated border clearance for the masses, but also registered travelers that go through even quicker and are interoperable across many airports. One common misunderstanding is that automated identity checking is moving away from 100% accuracy, but human passport/security control is an error-ridden process and mostly automated processes are more accurate.

Antoine Caner: Next Generation Branch

This is a showcase exhibit of best practice banking technology and processes. This showroom has about 40 companies (banks, mostly) visits per year.

Most banks have a multi-channel strategy, have returned from a strategy of getting rid of branches but want to redefine it. Rather than doing low-value transactions, the branches are seen as a mesh network for business development.

Key principles behind the branch of the future:

  • generating and taking advantage of the traffic
  • flexibility throughout the day
  • adaptation to client’s value
  • sell & service oriented
  • modular space according
  • entertaining and attractive
  • focused on customer experience

Examples:

  • turning the branch windows into an interactive display (realty, for instance)
  • Bluetooth-enabled push information
  • swipe card at entrance to let branch know you are there, let your account manager know, apply Amazon-like features
  • digital displays for marketing
  • avatar-based teller services
  • biometric-based ATMs to allow for more advanced transactions, as well as more opportunistic sales applications
  • do both identification and authentication
  • digital pen user interface for capturing data from forms
  • RFID-based or NFC (Near Field Communication) in brochures, swipe and get info on screen
  • "interactive wall" for interaction with clients in information seeking mode
  • visual tracking of movement in the branch
  • modular office that can change shape during the day, reconfigurable furniture

What impressed me was not the individual applications per se – though they were impressive – but way everything had been put together, with a back-office application that can be used by the branch manager to track how this whole customer interface  (i.e., the whole bank branch) works.

Alexandre Naressi: Emerging Web Technologies

Alexandre leads the rich Internet applications community of interest within Accenture. He started off giving some background on Web 2.0 and used Flickr as an example of a Web 2.0 application, where a company use user-generated content and tagging to get network effects on their side. Important here is not only the user interface but also having APIs that allow anyone to create applications and to have your content or services embedded into other platforms. Dimpls is another example. More than one billion people have Internet access, 50% of the world has broadband access, which allows for richer applications. Customers’ behavior is changing – it is now a "read-write" web. It has also gotten so much cheaper to launch something: Excite cost $3m, JotSpot $200k, Digg cost $200.

Rich Internet Application and Social Software represent low-hanging fruit in this scenario. RIA allows the functionality of a fat client in a browser interface, with very rich and capable components for programmmers to play around with.

Two families of technologies: Jacascript/Ajax (doesn’t require a plugin, advocated by Google), and three different plugin-based platforms: Silverlight (Microsoft), Flash/Flex from Adobe, and JavaFX from Sun. All of them have offline clients that can be downloaded as well. A good example is Searchme.com, which gives a better user interface – Accenture has developed something similar for their internal enterprisesearch.

Social Software: Accenture has its own internal version of Facebook. Youtube is also a possible corporate platform where people can contribute screencasts of all kinds of interesting demos and prototypes.

Kirsti Kierulf: Nordic Innovation Model for Accenture and Microsoft

Accenture and Microsoft collaborating (own a company, Avanade, together), and have set up an Innovation lab in Oslo called the Accenture Innovation Lab on Microsoft Enterprise Search. Three agendas: Network services, enterprise search (iAD), and service innovation. Running a number of innovation processes internally. This happens on a Nordic level, so collaboration is with academic institutions and companies all over.

Have made a number of tools to support innovation methodologies: InnovateIT, InnovoteIT, and InnomindIT (mind maps), as well as a method for making quick prototypes of systems and concepts for testing and experimentation: 6 weeks from idea to test.

Current innovation models are not working for long-term, risky projects. Closed models do not work – hence, looser, more informal and open innovation models with shorter innovation cycles. Pull people in, share costs throughout the network, Try to avoid the funnel which closes down projects with no clear business case and NIH. Try to park ideas rather than kill them.

Important: Ask for advice, stay in the question, maintain relationships, don’t spend time on legalities and financials.