Machine learning: 50 and high on the Gartner Hype Cycle

As I've recently written Gartner's Hype Cycle is a misleading hype itself. Which actually doesn't follow its own projected trajectory at all! It seems to be at a perpetual 'peak of inflated expectations'. As they say, old dogmas hardly die, the hype cycle is a point in case. It has no descriptive or predictive power and is thus misleading instead of enlightning. I predict that blockchain will be another technology that disproves the usability of the hype cycle (as I've written extensively before). But there are also technologies that outperform the hype cycle, the best current day example is in my opinion machine learning. This article shows that it has been at the 'peak of inflated expectations' for a number of years now, which is odd. But even stranger is that the cycle completely misses the fact that machine learning has by now proven itself beyond any doubt. It has outperformed even the wildest of expectations and is nowadays being used in almost every conceivable information system. There are of course limitations with regards to applicability and biases of algorithms that should be taken very seriously, but the number of useful applications is simply staggering. AI, and its sub-field of machine learning, is about 50 years old. It would be interesting to plot one of the most powerful technological trends of our era onto a hype cycle.

Creativity, conjecturing and critical thinking

As I said in my previous post, I've been reading a lot of books lately. And they are broadly speaking topic wise all related. 
My curiosity was sparked by the question what the most fundamental drivers of human progress were. This question came to me pondering about the current state of the world. Although there are valid reasons to be critical of media coverage of the current state of affairs, it became obvious that our species is facing a number of tremendous challenges if it wants to survive and live in harmony. Climate change, erosion of truth, growing inequality, power centralization, threat of nuclear warfare, cyber security, privacy, and so forth. Even compensating for the bias of media coverage one can safely say that our species is facing a formidable challenge. Properly addressing this challenge means that one has to figure out where we are, how we ended up in this position and where it will lead us. Only insight into the fundamental driving forces behind these changes will give us the tools to make the right decisions and not go backward into the future. This boils down to answering the following three questions. As a species:
1) How did we arrived at our current state? 
2) What is our current situation? 
3) Where we are heading? 
Thinking about this there is no denying that a significant driver of change has been technological innovation. And its role will only become bigger. As this is my area of  both expertise and interest I started to wonder what mechanisms drove, and will drive, technological change. (Mind you that I'm talking about 'change' not 'progress'.) And that is not just a technical question.
Although the three questions above are very broad there is a surprising lively and focused debate going on between the world's foremost scientists and thinkers. This debate is not raging on social media but is more like a question and answer game played with books. Although there are many more books that participate in this 'debate', the following is the list of books that I've read over the past months. First and foremost I should say that everyone of these books greatly inspired me and satisfied my intellectual curiosity. The number of deep and novel insights in these books is simply staggering. One can only wonder in admiration how it's possible that so much insight can be packed into a modest stack of paper. 
The following books are broadly speaking all about the three questions I posed above. Although each from a slightly different perspective they are all by brilliant and well-respected scientists that have a track record in both asking the right questions and (at least partly) answering them as well. I recommend reading them, at least an epitome, to enlighten yourself. I won't review them here since that would take me too much time while there are many great reviews to be found elsewhere.
Human universe by Brian Cox and Andrew Cohen
In his three best sellers historian Yuval Noah Harari gives an incredible overview of the big picture from a historical perspective. Even looking into the future. He gives an excellent overview and asks all of the right questions, but he doesn't really attempt to give an answer to the question about the most fundamental driving forces. Steven Pinker in his tour the force shows his readers (with tons of facts) that the human species is doing fine thanks to the powerful mix of reason, science and humanism that has been adopted by Western societies since the Enlightenment. Like Harari, Pinker is more focused on describing than on explaining. In what is probably the most creative and deep books of this list theoretical physicist David Deutsch shows its readers that the search for 'good explanations' through creativity, conjecture and critical thinking has been the fundamental driving force. A force that started during the Enlightenment that is so powerful that he considers it to be the 'beginning of infinity' for our species.
The deep insight that I got from reading these books is, in accordance with Deutsch's view, that the proper combination of creativity, conjecture and critical thinking is the only real driver behind all the positive change our species has gone through, and will be the main driving force shaping our future. Combined these books give ample evidence supporting this insight.
With that in mind I started wondering about the current state of this, almost holy, trinity. And to be honest, going by the latest societal developments the appreciation is currently not in good shape. In other words, they could use some love. Creativity is for instance not widely regarded as a core part of fundamental research in science. An interesting book describing how technological progress is almost always inspired by play is Wonderland: How play made the modern world by respected technology writer Steven Johnson. As a probably unintended side effect his book convincingly argues that creativity is at the very heart of progress. A case made by Amy Wallace and Edwin Catmull in their somewhat more lightheartedly book Creativity, Inc.: Overcoming the Unseen Forces That Stand in the Way of True Inspiration. Creativity should thus be taken more seriously.
Proper conjecture is often mistaken for daydreaming, flights of fancy or even unscientific behaviour. It is deemed to be an unworthy way of working towards a real goal. While at closer inspection it is a paramount step in the process of coming to good explanations and realizing real progress.
Critical thinking is probably the most widely neglected skill in our current day and age. One has to have been living under a rock for the past few years when still thinking the capacity for critical thinking is still healthy in most societies. It is almost undoable avoiding blatant examples of uncritical thinking in our daily lives. In my opinion it is one of the most valuable skills one can obtain. A judgement that is abundant in another interesting book I've been reading lately. A book about what leading scientists think should be scientific concepts every human should master is This Will Make You Smarter: New Scientific Concepts to Improve Your Thinking. It is a dollection of articles from top scientists, edited by John Brockman from


With the above in mind I came to the conclusion that the most valuable thing one could currently do is to encourage everybody to improve their creative, conjecturing and critical thinking skills. By now I am convinced that is the only way to truly help our species. For now and into infinity (and beyond).

Not all messages are created equal: The fundamental problems of social media

I've been reading a lot of books lately and I'll tell you why. It's been a conscious decision to direct my intellectual curiosity at a specific area instead of letting it wander all over the place. The latter is what happens when you let social media dictate your daily information diet. I always had a love/hate relation with social media. Having been one of the earliest users of the initial crop of social media, I never really felt at home there. After spending quite a lot of time on social media and thinking about their place in the information universe I concluded that there are 2 fundamental problems. 
First of all social media doesn't 'weigh' messages. All messages are created equal and it's up to the reader to give weight to them. Users have to distinguish themselves how important a certain message is to them. The result is that social media promotes chitchat from remote connections with the same vigor as deep, emotionally relevant messages from close relatives. The result is a cacophony of messages from which it is extremely hard and cumbersome to pick out the valuable nuggets. It's like listening to music in which each not is played at the exact same loudness. It might be interesting for a minute, but it becomes very boring and exhausting after one minute. 
The other problem is when you want to share a message via social media. In real live you carefully consider with whom to share your message. Not only because of privacy reasons, but mostly because you don't want to bother all your social circles with every message you send out. Physical reality obviously helps in constraining the picking the audience for your message, but that border disappeared in the virtual realm. This is of course one of the main causes of the first problem. Interestingly enough this was one of the key insights Google+ truly understood and gave them a head start. Unfortunately Google has proven over the past decades that it refuses to take its existing user base seriously and is unable to maintain a product beyond it's beta stage (the list of killed products is as staggering as the list of Google products that are still being supported after more than a year). But I digress. 
After realizing social media were inappropriate for both consuming and sharing knowledge, and after having lost focus in satisfying my intellectual curiosity, I decided to skip social media and carefully picking books, pod casts and documentaries instead. I must say that it has been a soothing experience. Of course you miss some of the social chatter, and the occasional relevant insight or article, but on average it somehow gave me more peace of mind (to be honest I get the feeling that social media consumption doesn't make anybody happy). My regained focus and intellectual deepening really improved my life.
You might be wondering why I share this on my blog, which some might consider to be a social medium. I don't. This blog is where I write down my ideas and questions related to my professional life. Of course my professional life is never fully disconnected from my personal life, but the topic of all my messages here fall within a specific area and are therefore only interesting for a specific audience. You could say that my blog is a specific medium for my messages while social media, as far as they are used practically by most, is a general medium for any message. In my opinion Marshall McLuhan was right in concluding that the medium is the message, certainly when looking at the relation between a medium and all the messages it transmits. An interesting topic, but something for a separate post.

Procedural CGI & the semantic programming parallel

One of my main interests within the realm of the digital revolution has been the progression in the craft of software engineering. The craft has moved to a higher abstraction level over the past, say, 70 years. Broadly speaking from machine code to assembly to C-like languages to OO-languages to the current crop of high level scripting languages. Although there have been many variations along the way, the main trend has been that the craft of programming moved to higher levels of abstraction. Instead of handwriting zeroes and ones software engineers now have languages and tools that allow them to specify for instance classes and inheritance and let the compiler turn that into machine executable instructions. As I've written before one of the questions that have been puzzling me over the past few years is why this progress seem to have stalled in the 90's. At that time the industry had quite a lot of experience with higher level languages such as COBOL (!) and there was a lively discussion surrounding 5th generation programming languages. But then the web happened and all attention was directed at writing as much software as possible to explore this vast new realm of undiscovered opportunities. Somehow that seemed to lure all attention away from fundamental progress towards the latest greatest in web development. Everybody was in a hurry to explore this new world and technologies were invented, and often reinvented, at neck-breaking speed. Today's most widely used programming language, JavaScript, was for instance developed in just 10 days at that time.

There is a problem with this stagnation, though. The demand for new software is rising so fast that there simply aren't enough humans to do the job if we stick to the labour intensive abstraction level the industry is stuck at at the moment. There is already a huge shortage of software engineers. It is actually one of the main limiters of growth in Western industries. 
But there is another problem. The low level work is prone to errors while at the same time the importance of the information systems for society at large is increasing. In other words, our lives depend on software in an accelerating rate while at the same time we ar developing the software in an archaic and error prone way. The only way out of this situation is to move the job of software engineering to the next level of abstraction. This means that domain experts should be able to express exactly what they want after which a compiler generates the information system that does just that. This has been called 'semantic programming' in the past, and it is in my opinion the only idea to move the software industry forward and prevent a lot of problems. Fortunately there is light at the end of the tunnel. In a recent post I mentioned my idea that AI seems to be the surprising candidate enabling the step towards the next level of abstraction. While in traditional information systems developers tediously wrote detailed instructions how to mangle given input to generate the required output, with AI developers just specify the required output based on a certain input and let it figure out the intermediate steps itself. This is in line with the ideas of semantic programming.
Interestingly enough there is also light at the end of another tunnel that has strikingly similar properties. After diving into the world of computer graphics (CGI), game development, AR/VR, and animation I realised that this was an industry where the exact same type of transformation was taking place. Early CGI work consisted of painstakingly typing out coordinates to, for example, draw a wireframe of a doll. Subsequently tools were developed to allowed artists to draw with a mouse and automatically tween between keyframes. Over the past decades the tools became increasingly more sophisticated, especially in the area of the notoriously complex world of 3D CGI. Each step gradually freeing the artist from tedious, repetitive tasks and allowing them to focus on expressing what they actually wanted. Interestingly enough one of the biggest trends in the CGI industry is the move towards proceduralism, where things like textures, geometry and even complete worlds are generated by procedures instead of by hand. Take a look at how the 3D modelling (if you can still call it that) software Houdini has been used to procedurally generate environments for the latest episode of the Farcry series. The artists of Farcry no longer have to draw every leaf or every road by hand. they specify the properties of their required worlds at a high level after which the algorithms of software like Houdini generate it. You could say that software like Houdini is becoming the compiler, just as AI is becoming the compiler for information systems (as previously discussed). The drive towards proceduralism in the CGI industry is the wish to focus on the high level picture and not on the low level details. But also by the need to create increasingly more complex worlds for which it would be impossible to build by hand.
I find this parallel intriguing. (Somehow my brain is wired to relentlessly look for parallels between separate phenomena.) Understanding the underlying driver enables us to see where our industry, work and tools ar heading. And it gives the creative mind a glimpse into the future.

Questions and ideas

I might have answers, but I sure have questions!
I've been thinking quite a lot about what I should trust to this blog and what not. I don't want to share too much personal stuff for obvious reasons (in case you wonder: we had social media for that awfully wrong, besides if you want to know me more personally I suggest we meet up in RL). Neither do I want the blog to be nothing more than a place where I only forward ideas of others (again, we had social media for that….). And about the topics, I like techy stuff, but as most of you know I'm a firm believer in the ecological approach towards handling a topic. Meaning that something without context has no meaning, so the context must be included. Even if the topic is highly technical. All in all I came to the conclusion that I want this blog to be the place where I share both my questions and ideas with regard to the broad field of the digital revolution. I honestly think that enforces a focus and keeps it interesting for readers while still being worthwhile for me as a place to ask my questions and dump my ideas. So from now on I'll ask myself the question if a new post is about one of my questions or ideas (or both). Ping me if I start to slack! 🙂

Run Jupyter notebooks on hosted GPU for free: Google Colaboratory

I've been refreshing my AI skills lately since they were a little rusty. After getting my master's in AI in 1998, during which the historical victory of Deep Blue over Kasparov took place, the next AI winter set in. For about 15 years AI was about as sexy as Flash is now. But the well known advance in processing power and the staggering price drop of storage AI, and specifically machine learning, became viable. Actually at the time of writing machine learning specialist is probably the most sought after specialism. So it was time for me to get back into the AI game. But mostly to cure my curiosity what the new state of the art was actually about and what was new that could be done. I mean, over the past years we've seen self driving cars, robots, algorithms for social media, all kinds of medical applications, etcetera etcetera come into being solely because of the advances in AI and the needed hardware/software infrastructure. In other words, the AI playground was revived and full of enthusiastically playing children. First thing that surprised me is that although there were a lot of refinements but not many fundamental new technologies. Of course, you run stuff in the cloud, there are more neural net variations, and the time it takes to train a network has decreased dramatically. But the underlying principles are still largely the same. Another thing didn't change were the hoops you have to run through to get your stuff up and running. It was a bit like the current web development situation (those in the know will nod their heads in understanding). Instead of focussing on the algorithms you spend most of your time trying to get your toolset installed and build/deploy street up and running. And that's a bad thing. Fortunately I stumbled across a hidden gem called Google Colaboratory. It's a free service that let's your run Jupypter notebooks on a hosted GPU….for free! If you want you can store the notebooks themselves on Google Drive, or, if you don't want that load them from elsewhere. That is quite amazing and an amazing boon for those that want to get up and running with machine learning as soon as possible. The amount of resources you get for free is, of course, limited, but it's more than enough the experiment and design your data processing pipeline and design, train and test your models. Once your content with your trained model you can take it to more beefy hardware (in case needed). Or to train it on huge training data sets. All in all quite an amazing service that will benefit the machine learning community a great deal. The nice thing about Jupyter notebooks is that you can take them elsewhere and run them there. You are in no way tied to Google, which is a good thing.

AI is the new compiler

What I think is one of the most interesting trends of this moment goes unnoticed by the general public, but surprisingly also by the majority of software professionals. Now that the latest AI winter is over (there have been a few in the past) and every self-respecting information system has at least a bit of AI in its bowels, the way we design and implement information systems is drastically changing. Instead of programmers writing down exact instructions that a computer must execute, machine learning specialists specify and modify both the input and output (I/O) of the system and leave it to AI to find the algorithm that does so on a training and testing set of data. (As a side note, the latter is important to realise, the exact algorithm is very hard to understand and so are predictions of it on unknown input data.) AI thus compiles the requirements into an executable algorithm.

I have been interested in ways we could improve the way we design and implement information systems for a long time. It is my belief that the current state of the art in software engineering is temporary and that we must move on to improve our profession for economic, safety, and societal reasons. Our current way of working is very cumbersome and leads to a lot of problems while at the same time software is controlling an ever increasing part of our daily lives. There is a long and very interesting history of software development from the 1940's up to the present day that I have been following for many years. If there is one continuous thread I would say that it's the fact that the software engineering profession keeps moving towards higher levels of abstraction. From machine code to assembly to C to C++ to Java to 'scripting' languages. But it seems the industry got stuck somewhere around the beginning of the 1990's. When the industry exploded with the advent of the web, so did the number of tools, but none of them was at a fundamental higher abstraction level than languages such as C. So what we got was basically more of the same. Unfortunately 'the same' turned out to be not good enough for our fast changing world. We simply don't have enough software developers to keep up with global demand while at the same time the stakes are rising for every piece of new software that gets released. Admitted, there has been research into more 'semantic' programming languages, but none of them left the academic realm to conquer the software industry.
With the advent of AI something interesting happened though. As said traditional software engineers writing down machine instructions are slowly being replaced by machine learning specialists selecting the right estimator and specifying the I/O. The latter work on higher semantic level, they are concerned with the properties they want the algorithm (system) to have, not about the instructions the machine should execute. This is a fundamental difference that fits neatly with the long historical trend of the software profession moving towards a higher semantic level. Of course the machine learning approach does not fit every use case, there will still be many systems that must be specified procedurally (or functionally if you prefer so), but the variety of use cases for machine learning surprised me (even as an AI veteran).
With machine learning specialist being the most sought after profession at the moment it is my guess that this trend is just picking up steam and we're only starting to scratch the surface of what I believe to be a fundamental change for the software industry at large.

Hype cycle considered harmful

As mentioned in my previous post the downfall in popularity of blockchains is considered to be perfectly in line with Gartner's well-known Hype Cycle. But there are so many fundamental problems with it that it's dangerous to assume a bright future of your technology of choice solely on applying the hype cycle 'theory'. The most obvious one, as pointed out here as well, is that by far the vast majority of technologies fail, despite the fact that they went through a period of hype (being aroused interest). After the hype they just die. It is incredible naive to think every technology will pick up steam again after it has lost interest from the general public. Besides that many successful technologies never go through a 'through of disillusionment'. Just as many technologies never become a hype while still becoming extremely successful in the long run. Sure, a few technologies followed Gartner's Hype Curve perfectly, but most didn't. So predicting every technology will follow the curve is misleading and could be harmful.

Blockchains: It’s not even funny anymore

Halfway 2017 I wrote quite an extensive article about our adventure in the world of blockchains. By that time my colleagues and me had spent over three years deep diving into both the technical and societal aspects of blockchains. How we went from awestruck by the sheer genius of Sakamoto’s idea to the disillusionment in the applicability, the engineering standards, and the cult of ignorance fostered by the ‘blockchain visionaries’. We built companies, products, communities and technologies to get this blockchain revolution going, but we ended up concluding that the only way you could earn a living with blockchains is by either talking about it or building proof of concepts. So we spent years trying to tell the truth about the true potential of blockchains but we were regarded as party poopers. The audience simply didn’t have the technical frame of reference to understand why most use cases would never work. Their response basically boiled down to, “All fine and dandy all this technical mumbo jumbo of yours, but look at all these billions and billions and billions and billions and billions and billions and billions of dollars spent on it. and all these incredible nerds, and all these successful entrepreneurs. They can’t all be wrong, can they?”. Read all the details in the article but in the end we abandoned ship and never looked back. Over the past years I haven’t been following blockchain news, I haven’t visited conferences, I haven’t talked to companies that wanted to ‘do something with blockchains’ (I spent my time reading books and learning skills in an area that actually does have a future, AI). In the meantime blockchains have apparently entered the mainstream. It’s on the news, it’s in the newspapers, it’s on the website of every big corporation and it’s in at least one slide of a presentation of some middle manager.


But a change has been brooding over the past year in the technically more advanced circles. As is often the case the ones with a thorough understanding of the subject matter are the only ones that can grasp the true potential of a technology. (This insight was for me personally the reason to complement my alpha master’s with a beta master’s and PhD.) The word ‘blockchain’ nowadays results in a quirky smile on their faces. The result of having heard so many stories from uninformed ‘visionaries’ that the revolution is neigh and everything will change that the only reaction to so much naïvety they have left is to laugh. I personally got so fed up that about a year ago I presented my tongue-in-cheeck game “Berco Beute’s Blockchain Bullshit Bingo”. On the bingo card were all the cliché statements every apostel of the church of blockchain uttered. For example:
–  It’s going to disrupt EVERYTHING. It’s following the Gartner hype curve, but it will be successful in the long run.
– We don’t need trust anymore.
– Nor trusted third parties.
– Bitcoin will replace fiat currencies.
– Etcetera…
So the word ‘blockchain’ and its cult following are slowly becoming the laughing stock of the industry. Mind you, I’m not talking about the technologists working on it simply because they love to work on interesting technical puzzles. I’m talking about the ‘visionaries’ not bothered by knowledge that fail to apply some modesty to their behaviour. At the time of this writing even mainstream media start to question whether blockchains could ever fulfil all the promises they have been attributed by the visionaries. Yesterday the Dutch newspaper De Volkskrant published an extensive writeup about the failure of all the blockchain projects to come up with truly viable solutions. And even the projects that lived past the ‘pilot’ phase are so simple that most experienced software professionals would agree they could be implemented much faster and cheaper with other technologies.
Although this recent trend makes for some good laughs and a few told-you-so’s, we shouldn’t dismiss it so easily. Not because the promise it still holds, but because the damage it has done. “Damage it has done!?”, you might react, but yes, let me explain. The blockchain is a hype that deserves its own category solely by the sheer amount of resources it has consumed of the past decade.
1. Energy. The proof-of-stake algorithm by now consumes about as much energy as Ireland. Or roughly twice as much as mining copper and gold. Combined. What we get in return is bitcoins, a mysterious entity that’s neither money nor gold, but has proven itself to be an effective way to buy nerds around the globe a couple of Tesla’s (each).
2. Money. Companies, investors and governments have invested billions and billions of dollars in blockchain technologies of the past years. A truly staggering amount of money has flowed to startups, ICO’s, consultancy companies, schools, etc. The awkward aspect of this is that blockchains were invented to create money (bitcoin), not vaporise it.
3. Brainpower. Likely even bigger than the energy consumption has been the brainpower that got sunk into the intellectual blockchain blackhole. Sure, there have been a few technological innovations but that pales in comparison to the amount of intellectual effort that led to nothing. The buzz, billions, technological marvel attracted most of the greatest brains of our era.
Think about it, these resources combined could have been invested in healthcare, fundamental research, battling climate change, fighting cancer, and many other truly important causes. But instead it was spent on a pipe dream. A troubling conclusion for which I think those responsible should be held accountable. All these so-called visionaries that got rich by misleading the general public with smoke, mirrors and visions of the promises land should repay the societal damage they have caused. Although I realise it is highly unlikely this will ever happen, I do think it’s important that this message gets out. As I said, it’s not even funny anymore.

Voice interface hype

Despite all the buzz surrounding smart home assistants such as Google Home, Amazon Lexa and Apple Siri, I still think the expectations with regards to voice interfaces should be scaled back. This Venturebeat article has some nice background reading. For instance, we’ve had voice-to-text for decades now and still almost nobody uses it. Who do you know that dictates his message? And if you know someone, is he/she the odd one out or one of many? While a few decades ago you could still blame the inferior technology for the low adoption rate, nowadays speech recognition has become so good that that’s no longer valid. And still almost nobody regularly uses a voice interface. And to be honest, I don’t think it will ever be really successful. And there is a very simple reason for that. When interacting with information systems users are mostly in a situation where talking is not very handy. They are in company of others that they don’t want to disturb, or they don’t want eavesdroppers, or they’re in a noisy surrounding (public spaces), or they simply want to take some time to order their thoughts before blurting them out. If you think about it there are actually not many situations where users would feel comfortable talking out loud to a computer.

Just as author Neal Stephenson had to point out in his insightful 1999 book “In the Beginning was the Command Line”, don’t just throw away old interface paradigms when a new one comes along. You might misunderstand what made the original paradigm so successful. The same goes for the envisioned switch from text to voice interfaces. To paraphrase an old saying, “A wise man once said….nothing…..but typed it in”.

The important question of data ownership

Over the past months I’ve read 4 books that largely about the same theme: Where do we (as humans) come from, what is our current situation, and what could be our future. The books are Yuval Noah Harari’s ‘Sapiens’, ‘Homo Deus’ and ’21 Lessons for the 21st Century’, and Steven Pinker’s ‘Enlightenment Now: The Case for Reason, Science, Humanism, and Progress’. These books currently lead my list of best books I’ve ever read (with probably a slight win for Steven Pinker) and I encourage everybody to read them. These writers do an amazing job in sketching out the big picture for us humans, clarifying where we’re coming from, where we’re at and we might be heading, while in the same time ask all the right question all of us should be asking. I’ll come back to those books in later posts, since there are so many ideas in there that relate to my mission and the questions I have, but I want to pick out one insight that is particularly relevant for this blog. Both authors mention it, but Harari says it most clearly in his ’21 Lessons for the 21st Century’: The most important question we have to answer is ‘who is going to own the data?’. More details on this later, but the negative and unfixable consequences answers like ‘I don’t know’ or ‘the big tech company CEO’s’ will have are impossible to overestimate. Harari ventures to say that this is the single most important question to be answered in the history of human kind. And that’s a question whose time has come and which WE have to answer. Let that sink in for a moment. As Harari points out data is the resource that will set the global balance of power and over which wars will be fought. He warns not to make the same mistake native Americans for instance made when deceived by imperialists with beads and gold. In other words, realise what you’re giving away and understand the consequences that might have.
One of the biggest problems I see is that most humans have no idea what data is theirs, why this is important and what they should do. The human tragedy of the tendency to value short term positive consequences higher than long term negative consequences. So I’m not convinced we should depend on humans to make the right decisions. The only option we have is to design media that respect mentioned data ownership requirements and make sure those media are available and easy to use.
In one of my previous companies (Contracts11) we worked on solution that I think deserves more attention. Our idea was a new way to build information systems that respected the following requirements:
1. There is only one source of every piece of data
2. Data ownership is clear for everybody
3. Data exchange is always governed by a contract
Ad 1. This idea is one of the deep insights from Georg Gilder’s ” Telecosm: The World After Bandwidth Abundance” (2002) that copies are no longer needed given enough bandwidth. A single source suffices. The practical consequences of this insight are quite amazing if you think about it, but one of the most profound is that it becomes easier to assign ownership and stay in control. This might have seem as a pipe dream for the last decades, but we are slowly but surely moving in this direction. The slow migration of almost every conceivable software service to the cloud is unstoppable. Music (Spotify), games (Steam), films (Netflix), productivity applications (Office 360, Google Suite, Photoshop), etc. It already doesn’t make much sense anymore to buy a multi-terrabyte laptop (although they are available on the market), since you won’t store any films, photo’s, applications, etcetera on them any more. A clear example that we are heading towards the world Georg Gilder described. You could say that files are replicated in ‘the cloud’, but conceptually your dealing with one piece of data (there is one access point to it, and one owner).
Ad 2. Possibly one of the biggest tragedies of the last decade is that it has been unclear who owns which data and the big tech companies (Google, Amazon, Alibaba, Facebook, etc) stepped forward and claimed their turf. A bit like the British colonised many parts of the globe by ‘the cunning use of flags’ (as hilariously pointed out by comedian Eddie Izzard). The natives were so impressed by the flag, the free beads, gold, email/chat/doc services that they gave away something of which they only later realised its worth. By then it was simply too late to turn back the clock and set the record straight.
Ad 3. To prevent misuse of data rightful owners should be able to enforce everybody to play by the rules. Fortunately there is an institute that was designed just for that, the nation state. With its trias politica nation states have a mechanism to create, set and enforce the rules through politics, the military and the legal system. The only thing owners of data have to do is set the conditions under which their data can be used and what will happen if others don’t abide to those rules. This can, obviously, be set in a contract.
So what we at Contracts11 have built were contract-based information systems. They consisted of data sources whose ownership was clear, which contained original data (instead of copies), and where every data exchange was governed by a contract. For every use of a certain piece of data consumers had to sign the contract and in case they misused it they could expect legal consequences. What the contract enforced was of course up to the owner of the data, but generally they would state requirements such as:
– The owner of the data
– Purposes (processes) for which the data might be used
– Who could use the data
– Whether the data could be temporarily stored by the consumer
– Which court of law would be used in case of a dispute
There were a number of insights taken away from a number of pilot projects we did. First of all it turned out that such a contract-based system was no less user friendly than regular applications. On the contrary, instead of having to fill out forms for e.g. an address (with the chance of making spelling errors in copies of that data), users could simply read the contract and check a box.
Another insight was that having no copies had a large number of unforeseen positive consequences. Because it became increasingly clear that ‘big data’ was more often a burden than a blessing. You had to store, maintain, protect, clean, copy, backup, etcetera it, while at that same time it was unclear why you needed all that data in the first place.
This resulted in another insight, that such contract-based information systems enforce data-consumers to only require data that they really need. In other words, they would rethink their processes and model them in such a way that they would lead to the desired state with as minimal data as possible. We called this ‘data minimalism’ and often explained this with the Albert Heijn Sperziebonen example (sorry if you’ve never been to The Netherlands). AH have been tracking most of their costumers for many years via their bonus card. This card is an excellent example of consumers letting short term gains prevailing over long term losses because they don’t fully understand what they are giving away. The strange thing is that in the name of ‘big data all the things’ AH has been harvesting an incredible amount of information while the only answers they needed were often pretty simple and could be asked directly, without the need for this whole ‘big data’ circus. They might for instance be interested wether you (as a customer walking in the store on a Wednesday) would be interested in sperziebonen. Your answer would be a simple yes, no or maybe, and that would have been enough for AH to fire off some process of, for instance, actually offering you sperziebonen for a special price. No need to collect all kinds of relevant data and process, maintain and protect it.
This approach is a practical solution that could help answer the all important question of ‘who owns the data’, as stated in the beginning of this post. It shows that we have to think about, and work on, the medium through which the information flows. It should enforce proper behaviour, adapt to its users, be transparant for any kind of message, not alter the message, etc. This is a big undertaking that requires the combination of many disciplines, from deeply technical to highly philosophical, but it can be done. And should be done. And that is one of my interests and topics of this blog.

English? Nederlands?

I've been thinking about which language to use for this blog for a long time. Although English is my working language and the potential audience for English text dwarfs the potential of Dutch, the latter is my mother tongue and no matter how much English I read, write and speak, I will never be able to put in the finesse that I can with Dutch. So I figured, "why not both?', just write articles that are only relevant for a Dutch audience in Dutch and the rest in English. Since this blog is mainly related to my professional domain, it will mostly be English, but don't be surprised to bump into a Dutch article once in a while. And hey, it's a great opportunity for all the non-Dutch to learn some Dutch!

Why ‘kunnis’?

‘Kunnis’ is the agglutination if the Dutch words ‘kunde’ and ‘kennis’. I made up the term when the term ‘knowledge economy’ (or ‘kenniseconomie’) was all the rage in The Netherlands. That was somewhere halfway during the nineties. What I found to be missing in that rage was a proper appreciation of the ones that could actually create things. Hence the Dutch word ‘kunde’, which means ‘being able to do something’. The word ‘kennis’ means ‘knowledge’. Knowing something and being able to actively do something with that knowledge is in my eyes what everybody should aspire. Over the past 25 years I’ve been an active promotor of a higher appreciation of the engineers, the tinkerers, the programmers, the makers, etc. I’ve written about it, held hackathons before it became a buzzword, talked to schools about programming for kids (before…), made a case for the engineers in the companies I ran, etc. Nowadays every self respecting marketing department is organising hackathons and every board member of every company says all their employees should learn how to ‘code’. Although the latter sounds like a good idea, on deeper thought that might in the end not be the real solution, but I’ll come back to that later (and more often) in this blog. So although from a distance it seems that the makers are back on their pedestal, but that’s just superficial. Beware that I’m taling about The Netherlands because it is definitely different in other parts of the world. There is still a ton of work to do to set the record straight between the ‘talkers’ and the ‘creators’, so I’m sticking to the word ‘kunnis’. And I like the ring it has and the fact that you can easily google it.


For a plethora of reasons I've been silent on all the usual digital platforms for almost a year. It's not just that I've been silent, I haven't been reading or following there much either. And I must say it has been a soothing experience. There were simply more pressing things to do in my life than constantly reading posts on Twitter, LinkedIn, etc. It felt like a constant background buzz slowly dying out and things that really matter slowly coming in perspective in its place. I spent more time with my family, read many great books, honed my 3D modelling skills, refreshed my AI knowledge and skills, met interesting new folks, made new music and played a lot of guitar. But most of all I pondered, with my feet on the table, about the (future) state of the world, the role of technology in that journey and the role that I could or should play in it. There is no simple answer to that question, but I do have an almost unlimited number of ideas about it that could explain or set the course of that journey. I feel a deep urge to share those ideas because it forces me to structure my thoughts and truly hope that someone someday finds value in them. That's why I'm starting this blog. I've tried every possible medium for sharing my ideas (hello Blogger, Google Buzz, Google+, Facebook, Twitter, LinkedIn, Medium,….), and got disappointed again and again by the apparently inescapable route to walled garden-like situations on those corporate-led platforms. So I'm now going back to writing on my own blog, on the open web, where I'm in control and nobody else. This choice directly touches upon one of my deepest interests: The role that new media have, can and will play in our lives. The importance of a value-free medium for our societies at large is hard to over-estimate. And this is not just a philosophical or societal discussion, but increasingly also a technical one. And this broad realm is where my ideas are rooted.

For whatever it is worth, I hope my ideas clarify and inspire, but I'm not aiming to push them through the throats of those that are not genuinely interested. It simply isn't worth my energy, but the former is.