(In/) Visible Technology

dashboard invisible tech
One of the greatest signs of the maturing of technology is its invisibility. When something becomes the new normal, we no longer have to mention it, notice it or even call it by name. Nobody calls Uber or Starling Bank an eBusiness any more. Netflix viewers aren’t ‘online viewers’. You no longer have to specify that your mobile phone is ‘smart’. In fact, you have to clarify if you’re using a ‘feature’ phone, a quaint anachronism for a non-smart phone. When Nicholas Carr polemically stated that “IT Doesn’t Matter” – he was taking a provocative view of the fact that IT had become commonplace rather than a differentiator.
There are of course technologies that thrive on visibility. Mobile phones, televisions, wearable tech, and the latest clever technology in your electric vehicle. The reasons for the visibility of technology are obvious – sometimes it’s a selling point, or it’s a fashion accessory, or it’s display-aesthetics. Sometimes it’s value is in the visibility. An entire set of ‘front end’ technologies are designed to be visible. Your Fitbit or Applewatch, Alexa smart speaker, or your Hive thermostat or Ring doorbell. They are sometimes symbols of your progressiveness and are also used in highly explicit ways. For all these reasons they need to be visible.
Other technologies are naturally invisible. Has anybody actually seen Amazon Web Services (AWS) or Microsoft Azure? Or for that matter an RDBMS? A raft of technologies that are quite ubiquitous in making the digital world work on a daily basis just do their work quietly behind the scenes. Some of these have fundamentally changed the mechanics and the economics of the digital world. These include containerization (Docker, Kubernetes), APIs (Apigee, Mulesoft), distributed and non-relational databases (Hadoop, Mongo), and many others. Makers of these technologies have to work harder to be visible for buyers and users. None more famous than Intel who coined one of the most successful and effective technology marketing campaigns of all time with Intel inside.
There is an entirely new generation of invisible technologies around the corner. A search for IOT and sensors will immediately throw up numbers in the billions for sensors which are going to be deployed worldwide over the next few years. Chances are you will be surrounded by them but you will not notice 99% of them. They say a new car today has 200 computers onboard. Of course, you see none of them. Artificial Intelligence, Machine Learning, and Neural Networks are set to reshape the technology landscape and our jobs, businesses, and societies. Completely invisible. When AI works well, you might not even notice that it’s technology at work.
When technology becomes a part of the operating infrastructure, it is impacted by the referee effect. By which I mean that you notice it when it’s not doing its job. When it works well, you hardly think about it. Like a train station, an airport, or the government. When technology goes into everything, it needs to become fundamentally reliable, and dare I say, boring. Just doing its job. No fuss and no big deal.
Another clear sign of invisible tech is in simplicity. When complex things are well designed and work simply, you can be sure that there’s a lot of invisible tech at work. When you press the start button of your car, when you switch your TV on or when you set up your new iPad by simply bringing it into proximity of your iPhone. These simple activities which achieve miraculously complex outcomes are all the result of great technology working invisibly.
We discovered both sides of this when we recently did a study of using technology to look after elderly people in their homes. It turns out that for some people, who are living perfectly healthy lives, the technology can often be an unwelcome sign of aging. An intrusion, and a source of worry or distraction. For these people, the technology needs to be invisible. An insurance policy that exists somewhere, out of sight, until needed. For other users who are facing a loss of confidence brought about by declines in eyesight, memory or physical capability, the technology can actually be a source of comfort and a reminder that one is being looked after. Rather than the invisible insurance, technology for these users is like a carer – attentive and responsive to their needs.
As we learn to shape and design technology more and more around human needs, we therefore have the choice of making it visible or invisible. This is in some ways a great responsibility for designers and engineers, especially as more technology enters homes and many other aspects of our lives. After all the value of the technology may well depend on its ability to engage with the user. It is also quite possible that individual components of the system might need to be visible or invisible. Or that in future the technology might need to know when to become visible. Could emergency information become more visible in a car after an accident?
There is a clear trend towards decluttering in many parts of our societies. We are rediscovering minimalism. The role of technology in a world that is more ecologically sustainable, less materially extractive and with much less ‘stuff’ is ever greater. As with most things though many technologies will start being visible and soon become part of the environment and we won’t even call it technology. It will not be invisible as such, but just hiding in plain sight.

(Special thanks to my colleague Rocky Fong for the research and insight on home care)


Thinking Beyond Design Thinking

design thinking - pixabay.png

You’ve probably experienced design thinking in any digital project you’ve started or been a part of in the past few months. The tools of design thinking are highly popular in the enterprise today – from ethnographic research, contextual inquiry, and shadowing, as well as delving beneath the surface of user stories for real insight.

For enterprise long used to technology solutions that take aeons to deliver and still turn out clunky and unusable, it’s easy to understand why design thinking is so ubiquitously popular. And I’ve seen it at work in any number of client and internal projects. And I think there’s a danger that design thinking might crumple under the weight of its own expectations. This article is about why we need to use design thinking carefully.

But let’s start by reminding ourselves about why it works? The reasons are fairly obvious design thinking starts from a position of empathy. This by itself should be an obvious thing, but clearly it’s been lost in the ossification of corporate processes and taken over by the lure of experience. So people have been building solutions based on what they ‘think’ they know about what users want and how, rather than asking them. As this piece from the HBR, by Jeanne Liedtka, highlights, design thinking works because it forces us to challenge our assumptions. The more interesting questions we ask, the better the answers are.

Also, design thinking gets past 2 of the biggest barriers in designing solutions. The first is assumptions. We typically have truckloads of assumptions about our users, and design thinking forces us to validate and often counter them. The second is the false comfort and lure of experience. A marketing manager with 20 years of experience thinks she understands her customers really well. Or perhaps we self reference, after all we’re all experts in retail because we shop every week.

So design thinking works, and it delivers by addressing all of these challenges and building a solution around the users needs. But while it is the mythology du jour of defining digital product experiences, it isn’t exactly new. Any product developer will tell you that this is exactly what product designers have done throughout history. My favourite example is that of the Honda engineering and design team that spent a week at Disneyland in the US just observing what families took out and put into the boot of the car.

Steve Jobs was apparently not a big fan of asking his customers what they wanted. Nor according to received wisdom was Henry Ford. But that doesn’t mean they weren’t keen observers of human behaviour and needs. The latter also suffered because he didn’t keep pace with the changes in the needs and expectations of his customers.

My current concerns, working with a number of design thinking based projects and scenarios, stems from 3 key challenges. The first is the dilution of the idea of design thinking. This is when we jump to the rituals of design thinking without actually getting to its key principles. Plenty of so called design thinking workshops are just brainstorming sessions under a different name. If the right questions aren’t asked and the right people aren’t responding, then this is just another workshop. The second is the danger of reinventing the wheel. Design thinking, when applied to a known problem space, should follow after enough secondary research has been done, else you will simply be learning the hard way what others already know. For example, while designing a shopping cart for a new ecommerce business should not be design thinking based from scratch, given that there are hundreds of examples of working shopping cards online to compare and evaluate. The third, follows from the second therefore in knowing what kind of problems to apply design thinking to. And this is typically new areas, and new products, or where there is a belief that current solutions don’t do enough. And you can’t know that unless you’ve done the research.

This article, also from the HBR, by Natasha Iskander, provides a more structured critique of design thinking – as it highlights the gatekeeping role of the designer, the inevitable subjectivity of the process and the problem of a finite and book-ended process of design that ends up preserving status quo, rather than a continuous evolution. Those of you who have been involved in agile projects will know the difficulty of fitting the commonly accepted double diamond design approach into an ongoing agile process. Usually it ends up as an early stage activity that has an end point, while product development becomes an ongoing and open ended activity. One of the most insightful points Iskander makes is the challenges faced by the prescriptive nature of the design thinking process in a world defined by continuous and evolving uncertainty. In other words, a VUCA world.

So the next time you go into a design thinking exercise, ask yourself (1) is this a problem that has been solved before? And by whom? (2) have we first made sure we’ve looked at what’s already been learnt before we go discovering for ourselves? (3) are we really following the design thinking principles, or are we going about gathering requirements in a traditional way, but just calling it something more cool and interesting? And (4) are we keeping the design and ideation process alive, or is it seeking an artificially finite solution? I have a hunch that not all your design thinking projects will pass these tests.

6 Business Lessons from the World Cup 2018

world cup russia
I spent 12 days in Russia, watching some football games and soaking up the carnival atmosphere of the World Cup. While there are many personal moments I will cherish, I’m also reflecting back on some professional and business lessons I took away from this once-in-a-lifetime experience.

(1) Clockwork Efficiency

Some 20 minutes before the World Cup final was due to start, Ronaldinho had just exited, there were performers on the pitch, the flags had to be unfurled, the anthems still had to be played, and there was mayhem all around. Yet the game started on the dot at 6 PM local time. This was true of almost every game – the last 30 minutes leading up to kick off looked chaotic, but on reflection were running like clockwork. Presumably because all the hundreds of people involved knew their cues, somebody was watching the clock, and they had practiced this to death. Kudos to FIFA and the organisers for this display of causal efficiency. Bottom line, you don’t have to go all military to retain operational efficiency. Most large events or businesses have a similar veneer of chaos, but you can always tell the ones that are operationally tightly run by the extent to which they keep to time.

Even getting close to a hundred thousand people in and out of stadiums, from and to metro stations was managed quite effectively with the help of hundreds of volunteers. Interestingly, there were a lot of police and uniformed, military or paramilitary personnel but the primary interaction with the spectators was through the volunteers. So people had a pleasant interaction with a bunch of young people, while being clear that any bad behaviour would be dealt with. The additional lesson here is to distinguish between the experience and the governance.

(2) The Impact of Willful Internal Strife

One of the stories of the world cup was the extent to which internal strife impacted the performance of the tournament favourites. The two examples here are the last two World Cup winners – Germany and Spain – and both provide interesting insights. On the eve of the World Cup, Spain hit a snag. Real Madrid announced Lopetegui, the Spain coach, as their next manager. The timing could not have been worse, and Madrid probably had their internal political issues to address. But it was done unilaterally and without consulting the Spanish FA. Consequently, the president of the FA removed Lopetegui from his post and appointed Fernando Hierro in his place. Hierro was already a part of the coaching set-up and no outsider, but it was a big ask at a critical juncture. When Spain played against Russia and passed the ball aimlessly for 120 minutes, you could question why he didn’t come up with a different tactical model or instructions. Why did he play with Koke, a defensive midfielder who was singularly unadventurous, rather than add to his goalscoring firepower? Perhaps he was adopting a safety-first approach, being a new manager? Perhaps he was out of his depth? Or the fact that Hierro was himself a defensive midfielder may have had something to do with his approach? You can question the set of decisions leading up to Hierro’s appointment, but it was made on principle, and Spain suffered.

Germany had the same manager – Loew – who has been at the helm for one of the most successful periods in their footballing history. But their split was more internal. Following an appearance with Erdogan, the Turkish president, there was a backlash against 2 players of Turkish descent, namely Ozil and Gundogan, and reports emerged of a split between the Bavarians and the others in the team. Again, Germany’s disjointed performance was obvious for people to see and we are still seeing the aftermath of this with the German football fraternity split over Ozil’s decision to leave the national football team.

The obvious lesson is that no organisation can perform if there are significant internal schisms or disruptions to the operating model at critical junctures. If either of these incidents had happened 6 months ago, you could argue that both these teams would have gotten over them by the time the cup came around. But the more interesting question here is about principles. The Spanish FA took a stand that on the surface was a principled one (though some called it an ego issue), and paid the price. The German players of immigrant origins were dealing with their own identity issues. The bigger lesson to be learnt here is how to deal with these kinds of issues without letting it impact performance. Any answers?

(3) Leadership Lessons

Ms. Kolinda Grabar-Kitarovic was a one-person leadership lesson through the late stages of the tournament. She stood with the fans in her team jersey, as one of the people. She visited the Croatian team after the games in their dressing room after their game with Denmark, with no pomp and ceremony, to congratulate them. And on the final, as the rain came down she stood in the rain hugging her own players, and the winning French team members in a heartwarming act of humanism. Vladimir Putin deserves credit for Russia delivering a wonderful World Cup, but on that stage, he was overshadowed significantly as he stood under the umbrella, while Macron and Grabar-Kitarovic shared the rain with their compatriots.

In a sporting event so dominated by men that no other woman has been anywhere close to centre-stage, it was also quite dramatic to see Grabar-Kitarovic be effectively the only woman who was celebrated over the course of the 5 weeks of the World Cup. It was a gilt-edged opportunity to stand out and Ms. Grabar-Kitarovic rose to the occasion in all her red-and-white-chequered glory.

(4) Smart Branding

As a fan, it has always bothered me that FIFA so tightly control the branding and use of the World Cup, in connection with their Corporate Sponsors. This time, being there, I felt the benefit of the strong branding. Right from after the 2014 event the branding for the 2018 event has been in force. The Dusha font was specifically developed to work across English and Cyrillic scripts, with Asian overtones, and has been consistently used everywhere. You may have seen this in all the FIFA World Cup images and events. And the control of the branding means that the colours, fonts, and logos are instantly recognisable as official. Whether they are signs painted on the floor of the metro stations, or they are on Television, the strong branding has played a role in the instant recognition of the World Cup event, locations, merchandise, and signage. I have to say that this significantly helped to identify signage and directions around the World Cup cities as well.

(5) Harnessing the Collective

The World Cup on the pitch was clearly a victory for the collective. The GOAT (Greatest of All Time) aspirants all went home early. Messi, Ronaldo, Neymar et all were back home before the semifinals. Croatia was the very embodiment of the collective over the individual. France too were a team where individuals were all able to put the team above themselves. Whether it was Mbappe, or Pogba, or Griezman, you could see they were all playing for the team, not for personal glory. While it was reassuring to note that teams still win over individuals, it begs the question of what great teams and managers do to instill the right culture. The France manager, Deschamps was himself a World Cup-winning player, but played in an unglamorous role, which was derided by Eric Cantona as a ‘water carrier’. The Croatia manager was largely unknown before the World Cup. If anything, this eschewing of the star culture was a real reassurance for those who have always believed in the team over the individual.

This isn’t a call for removing individualism. Great performers will always shine – as Modric, Mbappe, and Pogba all did, but great teams are built around overarching team ethics that both elevate and subsume the stars. That is the mark of great management.

(6) Technology Wins

One of the biggest talking points in the World Cup was the use of VAR. FIFA made a bold move to implement VAR (Video Analysis Replays) in the World Cup which allowed referees to use video replays where there was a reasonable doubt about the decision. A bank of assistant referees was continuously watching the replays and signaling to the on-field referee where there was a potential need for a replay.

In the main, the technology was a success. A number of decisions were given or reversed based on the video evidence. By and large, the right result was achieved. You would have to say this was a big step forward for football. The one notable case where people had doubts was the call in the final to award France a penalty. Still, you can say that the referee looked at this closely and from a number of angles, and made his best call. The rules in football still leave room for subjectivity and judgment, so this allows the referee to base that judgment on more data than ever before.

If anything, FIFA have erred in setting the expectations of this technology by announcing that only clear and obvious cases will be reviewed. Why? The only driver for the technology should be that the right decisions are reached every time, as best as possible. This idea of ‘clear and obvious’ has led to a lot of quibbling between pundits on panels. Whereas really, all we should be concerned with is that the technology enabled the right decisions to be achieved, and in 9 out of 10 cases was an unmitigated success.

One area where more work needs to be done, and a point I’ve argued before, is the ability of VAR to engage the audience, especially in the ground. The tv audience gets to see the replays, as do the referees. There’s no reason why the audience at the ground shouldn’t see the same replays in the way tennis and cricket audiences do. If anything this reflects on the organisers’ view of the maturity of the spectators to behave with decorum no matter what the final decision of the referee is.

Introducing technology is always fraught with risk, as most businesses know. The lesson here is to be clear about the value of technology and data on better decisions, and also focusing on the user experience of customers.

On a side note, the other big technology winner at the World Cup was Google Translate. A point already highlighted in the Guardian newspaper. Google translate was the saviour for the hundreds of thousands of people coming to Russia from across the world. A quarter of a million people just from South America alone, for example. Most of these people were communicating with shopkeepers, taxi drivers, and restaurant staff via Google Translate and doing so quite effectively. Our taxi driver from the airport to our apartment in Moscow had google translate set up so he would talk into it in Russian while driving and it would repeat the words in English. He even managed to point out some sights and tell us a couple of jokes, over the course of our half hour journey.

There are probably many more if you look hard enough, but these are the ones that stand out for me. What are yours?

20 Takeaways From The CogX Event, London

IMG_5043First, a clarification. I visit events such as the CogX events to be stimulated, to have new thoughts and to have the neurons in my brain fired in new ways. I go to learn, not to network. So I agonise over sessions to attend, and importantly the sessions I miss. Of which there is an overwhelming majority, as this event was running 5–7 conference tracks at any time, as well as a lot of other small stage events. By and large though it’s a weirdly monastic experience, surrounded by people, but very much alone in my head, to the point where I’m actually a little bit annoyed when somebody wants to talk to me! This then is the list of things that made me think.

  1. If there was one session that made attending the event worthwhile for me, it was Zavain Dar’s session on the New Radical Empiricism (NRE). His argument is that the traditional scientific method is based on certain rational assumptions — which are now challenged. In the classic method, you would hypothesise that the earth was round, find the right experiments to run, collect data and prove/ disprove your hypothesis. This runs into trouble when the computational models are too complex and / or changing too often — such as gene sequencing or macroeconomic data. Also this is not efficient when the range of options is vast and we don’t know what data might be relevant — e.g. curing cancer. The traditional methods may yield results, but it might take a lifetime of research and work to get there. What Dar calls the NRE is the opposite — a data driven view which allows machine learning to build hypotheses based on patterns it finds in the data. So in the NRE world, rather than starting with whether the earth is round, you would share a lot of relevant astronomical data and ask the machine to discover the shape of the world. This approach works best in areas where we have a data explosion such as genomics and computational biology. Or where there is plenty of data but is shackled by traditional hypotheses based methods, such as macroeconomics. An additional problem that NRE solves is where the problem space is simply to complex for human minds to compute — both the examples above are instances of this complexity. You may know that Radical Empiricism is by itself a construct from the late 19th century by William James — which eschews intuition and insists on physical experiences and concrete evidence to support cause and effect. Its worth noting that there are plenty of examples of environments where quantifiable data is not yet abundant, where experts still follow the traditional method driven by hypotheses. VC investing, ironically, is such an area!
  2. This also led to a discussion on Deeptech led by Azeem Azhar of Exponential View and panelists from Lux, Kindred Capital and Episode1 Ventures. Deeptech is defined from an investment perspective as companies and start ups who are building products which involve technical risk. Not using existing tech to solve new problems. Usually involving products and ideas which a few years ago would have to subsist on research grants and be housed by academic institutions.
  3. Jurgen Schmidhuber’s session on LSTM was another highlight. Schmidhuber’s PhD thesis on LSTM (Long Short Term Memory), in 1997 was a foundation of the AI advancement which was used by a number of technology products and subsequent development. Schmidhuber presented an excellent timeline of the evolution of AI in the past 20 years and ended with a long view where he explored the role of AI and ML in helping us reach resources that were not on earth but scattered across the solar system, the galaxy and beyond. And how we might perceive today’s technology and advancement in a few thousand years.
  4. One of Schmidhuber’s other points was around curiosity driven learning. Mimicking the way an infant learns, by exploring his or her universe. This is the idea that a machine can learn through observation and curiosity, about it’s environments.
  5. Joshua Gans, the author of Prediction Machines, and professor of Economics and Tech Innovation, talked about AI doing to prediction what computers did to arithmetic. Essentially they dramatically reduced the cost of complex arithmetical operations. AI does the same for prediction or inference. Which is essentially making deductions about the unknown based on the known. And bringing down the cost of prediction has a massive impact on decision making because that’s what we’re doing 80% of the time, at work, as managers.
  6. Moya Green, the CEO of Royal Mail talked about the transformation that Royal Mail went through — including an increase in technology team size from 60 to over 550 people. She also made the comment that most managers still under-appreciate the value of tech, and overestimate their organisations capability to change, and absorb new tech.
  7. Deep Nishar of Softbank used an excellent illustrative example of how AI is being used to provide personalised cover art for albums by digital streaming and media providers, based on users choices and preferences.
  8. Jim Mellon, long time investor and current proselytiser of life-extending tech suggested that Genomics would be a bigger breakthrough than semiconductors. He was joined by the chief data officer for Zymergen, which works on bio-manufactured products, based on platforms which work with microbial and genetic information.
  9. A very good data ethics panel pondered the appropriate metaphors for data. We’ve all heard the phrase data is the new oil. Yet that may be an inadequate descriptor. Experts on the panel posited metaphors such as ‘hazardous material’, ‘environment’, ’social good’ etc. because each of these definitions are useful in understanding how we should treat data. Traditional property based definitions are limited and it was mentioned that US history has plenty of examples of trying to correct social injustice via the property route (reservations for native Americans), which have not worked out. Hence we need these alternative metaphors. For example, the after-effects of data use is often misunderstood, and sometimes it needs to be quarantined or even destroyed, like hazardous material, according to Ravi Naik of ITN Solicitors.
  10. Michael Veale of the UCL suggested that ancient Greeks used to make engineers sleep under the bridges they built. This principle of responsibility for data products needs to be adopted for some of the complex products being built today by data engineers. Data use is very hard to control today, so rather than try and control it’s capture and exploitation, the focus perhaps should be on accountability and responsibility.
  11. Stephanie Hare made the excellent point that biometric data can’t be reset. You can reset your password or change your email, phone number, or even get a completely new ID. But you can’t get new biometrics (yet). This apparent permanence of of biometrics should give us pause to think even harder about how we collect and use it for identification, for example in the Aadhaar cards in India.
  12. Because of the inherently global flows of data and the internet, the environmental model is a good metaphor as well. Data is a shared resource. The lines of ownership are not always clear. Who owns the data generated by you driving a hired car on a work trip? You? Your employer? The car company? The transport system? Clearly a more collective approach is needed and much like social goods, such as the environment, these models need to validate the shared ownership of data and it’s joint stewardship by all players in the ecosystem.
  13. Stephanie Hare, who is French Historian by education provided the chilling example of how the original use vs ultimate use of data can have disastrous consequences. France had a very sophisticated census system and for reasons to do with it’s muslim immigrants from North Africa captured the religion of census correspondents. Yet, this information was used to round up all the jewish population and hand them over to the Nazis because that’s what the regime at the time felt justified in doing.
  14. On a much more current and hopeful note, I saw some great presentations by companies like Mapillary and SenSat, and Teralytics which focus on mapping cities with new cognitive tools. Especially for cities which are of less interest to tech giants, and using crowdsourced information and data, which may include mobile phone and wifi usage, or street level photographs all used with permission, for example.
  15. At a broader level, the smart cities discussions, strongly represented by London (Theo Blackwell) and TFL (Lauren Sager Weinstein) shows the transition from connected to smart is an important one. Very good examples by TFL on using permission based wifi tracking at platforms to give Line Managers for each of the tube lines much more sophisticated data on the movement of people, to make decisions about trains, schedules and crowd management, over and above the traditional ways which include CCTV footage or human observation on platforms.
  16. At a policy level, a point made by Rajiv Misra, CEO of Softbank Investment Advisors (aka the Vision Fund) is that while Europe leads in a lot of the academic and scientific work being done in AI, it lags in the commercial value derived by AI, notably to China and the US. A point echoed by the House of Lords report on AI which talks about the investments and commitment needed to sustain the lead the UK enjoys in AI, currently. Schmidhuber’s very specific solution was to mimic the Chinese model — i.e. identify a city and create an investment fund of $2bn to put into AI.
  17. I also sat through a few sessions on Chatbots and my takeaway is that chatbots are largely very much in the world of hype machines. There is very little ‘intelligence’ that it currently delivers. Most platforms rely on capturing all possible utterances and coding them into the responses. Even NLP is still at a very basic stage. This makes chatbots basically a design innovation — where instead of finding information yourself, you have a ‘single window’ through which to request all sorts of information. Perhaps its a good thing that the design challenges are getting fixed early, so that when intelligence does arrive, we won’t stumble around trying to design it.
  18. Within the current bot landscape, one useful model that I heard is ‘Treat a bot like a new intern that doesn’t know much’ and let it have a similar personality so that it provides responses that are appropriate and also sets expectations accordingly. It might just start with a ‘hello, I’m new so bear with me if I don’t have all the answers’, for example.
  19. Dr Julia Short, who has built Spot — a chatbot to handle workplace harassment provided a very interesting insight about the style of questions such a bot might ask. A police person’s questions on the one hand are all about capturing in detail exactly what happened and making sure that the respondent is clear and lucid about events, incidents, and the detail. A therapists questions and line of discussion on the other hand is all about helping a victim get over some the details and get on with their lives. This suggests that you need to be clear whether your bot is an extension of the law enforcement or a counselling body. It also suggests that you might want to do the former before the latter.
  20. A really important question that will not leave us is: what do we do if the data is biased? If we are conscious of certain biases which are to do with gender, race or age, then we can guard against them either at the data level or at the algorithmic level, but we also need to be able to detect biases. For example, the example which I’ve now read in a few places of how the leniency of sentences handed out by judges in juvenile courts in the US vary inversely with the time since the last meal of the judge.

Clearly all of this really represents under 20% of the great discussions over the 2 days. Please do add your own comments, takeaways and thoughts.

Hail Mary (Meeker)!

Hail Mary (Meeker)



On the 29th of May, Mary Meeker released her annual compendium of the digital state of the world – the KPCB Internet Trends. For those who may not remember, Mary Meeker was a veteran who survived the dot.com crash and also the financial crisis of 2008, as the head of tech research for Morgan Stanley. She was named as among the 10 smartest people in Tech. She now serves as a partner at KPCB (Kleiner Perkins Caufield and Byers) and has been publishing her annual opus for a few years now.

The problem is that when you’re Mary Meeker, you can get away by putting out a deck with 294 slides. For us mere mortals, reading and absorbing this encyclopaedia of information is a challenge by itself. Every year I get this and carefully save the deck to read in detail and of course, it never happens. So this year, with the benefit of a relatively free weekend, I thought I would do a first pass and pull out some of the most interesting things that I found in the report. So here are my top 10 interesting things to take away from the Mary Meeker report – some of them confirm what we know, while others are what we didn’t know, or are truly counter-intuitive.

What I knew or suspected.

1. The devices story mobile device shipments growth has shrunk to zero. This confirms what we’ve known for a while – device evolution has stalled since Steve Jobs. And since Samsung, the largest manufacturer has a ‘follow Apple’ strategy. Will we see a new device redefine growth or will the we see a decline in shipment numbers next year? HMD – are you watching? (Slide 6)

2. The decline in desktop use despite overall growth. While mobile internet growth is expected, it’s the ‘other devices’ that is interesting. This will presumably include netbooks, etc. but also smart things. I expect in future this category will be broken out to reflect the detail on Internet of Things. (Slide 11)

3. The privacy paradox will be one to watch – after all data is how every single provider improves their services, while keeping prices low, which leads to user spending more time and sharing more data. Versus the regulators needs to protect consumers and protect data use. This will be a key axis of debate going forward and will determine the balance between innovation and protection. Unfortunately Meeker’s slides don’t carry too much insight on this by way of data. (Slides 31-36)

What I didn’t know (I’m intentionally using the singular, as you may well be aware of this)

1. While we’re aware that big tech now dominates the market cap list, what should worry the rest of the pack is how they dominate the R&D spending list, which points to a continuation of their dominance at the top. The top 15 R&D investors list is dominated by 6 technology firms, with 2 each from automotive, petroleum, telcos, Pharma), with GE as the only conglomerate. The top 5 in the list are Amazon, Alphabet, Intel, Apple, and Microsoft. Also, tech firms report the highest growth in R&D, with 9% CAGR and 18% YoY growth. (Slides 40-41)

2. We know that image recognition is an area where AI has now passed the human levels of accuracy leading to all kind of applications across scan analysis in healthcare, and more controversial applications such as face recognition. Now, voice-based natural language recognition is another areas as also demonstrated recently by Google. This should drive a revolution in customer contact centres and in human-computer interfaces in general. (Slide 25)

3. The extent to whichAmazon & Google are getting to dominate the enterprise AI race. To be honest, we know instinctively that the AI race will be one by players with the largest data stockpile. But the range of services being offered for enterprise customers is still an eye-opener. We’ve just started playing around with Google’s Dialogflow, but they also have Tensor (cloud-based H/w), the recently announced AutoML (machine learning), and Vision API (Image recognition), while Amazon has AWS based tools such as Rekognition (image recognition), Comprehend (NLP), Sagemaker (ML framework), and of course their AWS GPU clusters. (Slide 198 – 200)

4. The growth of Fortnight and Twitch on the gaming front – pushes forward what we saw with Pokemon Go. The sweet spot between the hardcore platform based gamers and the casual gamers and kids where millions of people get just a little bit more involved about game, that does not need a special platform – is the story behind Fortnight (Slide 24)

What I didn’t expect

1. The highest increase in spending in enterprise IT is in networking equipment. This is a surprise. I haven’t found the data on this yet, and while the 2nd and 3rd place results don’t surprise me, (AI and hyper-converged infrastructure), my curiosity is definitely piqued by why companies are spending more on networking equipment – connecting to cloud environments from the enterprise perhaps? More connected devices and environments?

2. I’m seeing a lot more confirmation of the models of lifelong learning. This is repeated by Meeker, but her really interesting insight is around how much more learning freelancers invest in compared to their presumably complacent employee counterparts. Perhaps unsurprisingly the top courses sought include AI & related subjects, cryptocurrency, maths and English. (Slides 236 and 233)

3. Meeker makes a great point but Slack and dropbox and I wouldn’t have picked these 2 companies as the flagbearers of consumer-grade technology in the enterprise. But clearly, they are among the most penetrated consumer style tools in the corporate environment. (Slides 264-268)

Meeker has a big section on the Job market, on-demand jobs and future jobs. She also makes the same point others have made about how all technologies so far has created net new jobs. While I agree with this backlog, history is not always the best predictor of the future. And the fact that there will be net new jobs tends to gloss over the significant short-term and geographical disruption in livelihoods that is likely to occur. Think Detroit or Sheffield. There may be more automotive and steel manufacturing jobs today than in 1980 but they are in China, not in Detroit or Sheffield. And so of not much solace to the unemployed factory worker and his / her family in these towns. This may well be the story of AI – but potentially at a larger scale and possibly in a shorter time frame. (See slides 147-163).

There are also useful slides on the gig economy and on-demand jobs now being a scaled phenomenon. (Slides 164-175)

There are also entire sections on China, Immigration and Advertising – which I’ve not delved into as they are currently of less interest to me personally. The E-commerce section also didn’t have anything that jumped out at me as noteworthy. Happy to be corrected!

Seven in 7: Amazon’s Infinite Monkey Theorem Defence, GDPR Impact on Innovation, Ocado’s Successful Transformation, and More…

Seven for 7: Alexa sends the wrong message; does GDPR take us backwards? Uber crash – design flaw; future gazing with Michio Kaku; AI Winners; Ocado transformation and Energy Industry Updates.

(1) Amazon Echo: message in a bottle

The technology story of the week is undoubtedly the one about Amazon Echo and the message it inadvertently sent. ICMYI, a couple in Oregon had a call from an acquaintance to say that Alexa had sent them a recording of a private conversation of the couple, without their permission, or even knowledge. Amazon’s explanation is that this is the rare combination scenarios where a normal conversation between the couple somehow triggered all the keywords and responses that made Alexa record, validate and send the conversation to the acquaintance. This feels like the equivalent of the money, typewriter and Shakespeare problem, only, it’s not an infinite amount of time.

Here’s Amazon’s explanation: https://www.recode.net/2018/5/24/17391480/amazon-alexa-woman-secret-recording-echo-explanation

(2) GDPR  – impact on marketing and innovation.

I’m sure you’ve all received hundreds of emails in the past week exhorting you to stay in touch and re-sign up for all the emails you’ve been getting from people you didn’t know were sending you emails. But now that the moment has come, how will marketing work in a GDPR world? In one way this will take marketing backwards – as there is now a ban on algorithmic decision making based on behavioural data. It’s a moot point whether advertising falls into this category but companies may want to play it safe and in any case, the confusion will create a speed breaker in the short term. We may now be back in the world where if you’re watching or reading about champions league football you will see a beer ad irrespective of who you are. Not just marketing – a lot of innovation will also come under fire – both because of safety first practices, but also because some organisations will use GDPR as a shield for enabling innovation stifling practices, as highlighted by John Battelle of NewCo Shift. He argues that the regulation favours ‘at scale first parties’ – large tech platforms that provide you with a direct service such as Netflix, Facebook, or Uber – where users are likely to still give consent for data use more readily than to smaller, upcoming or relatively new and unproven services.

Dipayan Ghosh in the HBR – GDPR & advertising: https://hbr.org/2018/05/how-gdpr-will-transform-digital-marketing

John Battelle on GDPR & Innovation: https://shift.newco.co/how-gdpr-kills-the-innovation-economy-844570b70a7a

(3) Driverless / Uber/ Analysis

The analysis of Uber’s recent driverless crash has now thrown some light on what went wrong. And the answers aren’t great for Uber. In a nutshell, the problem is design and not malfunction. Which means that all the components did exactly what they were designed to do. Nothing performed differently and no components were at fault for failing to do their job. But as a collective, the design itself was flawed. The car had 6 seconds and 378 feet of distance to do something about the pedestrian crossing the street with her cycle. But it was confused about what the object was. The human in the car only engaged the steering 1 second before the crash and started breaking 1 second after the collision. The car was not designed to warn the human driver about any possible threats. A lot of the inbuilt safety systems in the Volvo vehicle including tracking driver alertness, emergency braking and collision avoidance, were disabled in the autonomous mode. In a nutshell, the responsibility lies with Uber’s design of autonomous cars. Uber has stopped testing in Arizona but has now started exploring flying taxis. Not a project that might fill you with confidence!

Uber crash analysis: https://sf.curbed.com/2018/5/25/17395196/uber-report-preliminary-arizona-crash-fatal

(4) A glimpse of the future: Michio Kaku & Jack Ma

The robotics industry will take over the automobile industry. Your car will become a robot – you will argue with it. Then your brain will also be robotised and brain net will allow emotions and feelings to be uploaded. You will be able to mentally communicate with things around you. Biotech allows us to create skin, bone, cartilage and organs. Alcoholics may be able to replace their livers with artificial ones. You may be able to scan store goods with a contact lens and see the profit margin on goods. The first 7 mins of this video tells you all of this through the eyes and experience of futurist Michio Kaku. Jack Ma (14 mins in) also talks about trusting the next generation. And how we are transitioning from the industrial era where we made people behave like machines, to a world where we are making machines behave like people. Believe the future before you see it, to be a leader, according to Ma.


(5) Who’s Winning The AI Game?

With the whole world hurtling towards an AI future, this piece looks at who exactly wins the AI game – across 7 different layers. It won’t surprise you to know that China is making amazing gains as a nation – their face recognition can pick out a wanted man in a crowd of 50,000. But it might surprise you to note that Nvidia’s stock is up 1500% in the past 2 years on the back of the success of their GPU chips. Meanwhile, Google is giving away Tensorflow free. All this points to a $3.9 tn market for enterprise AI in 2022. Are you ready for the challenge?

Who wins AI, across 7 layers

(6) Ocado – digitally transformed.

When Ocado launched in 2000, it was on the heels of Webvan, a category of providers who set up to focus on eCommerce fulfilment, as an arm of Waitrose. Cut to 2018, and Ocado is a story of successful digital transformation. Ocado is today a provider of robotic technology for warehouse automation. Having become profitable in 2014, it now has a valuation of $5.3 bn and is set to become a part of the FTSE 100.


(7) Understanding statistics: What medical research reports miss

When a drug is tested and the outcome suggests a 5% chance of a possible side effect, this does not mean that you have a 5% chance of being impacted if the drug is administered to you. It means there is a 5% chance that you will have the condition which leads to you having a 100% likelihood of being impacted. This is a subtle but very important distinction in how we interpret the data. But continuing down this stream of thought, it points to the lack of personalisation of medicine, not just the misinterpretation of data.


Regulating Digital Businesses – Like Chasing Trains

chasing train

I don’t know if you’ve ever had the experience of running for a train that’s just started to move? I’ve had to do it a few times. Yes, I was younger and more foolish then. But it was usually within seconds of the train moving that I was on it. It’s only in old movies that you see the protagonists dashing down the platform as the train picks up speed. Usually, you just have the platform length and the problem is that the train is accelerating. There is a finite window of opportunity after which you’re just going to be left on the platform. This is my very long-winded analogy for regulators and technology. As technology accelerates – it’s getting harder for regulators to keep pace and in fact, in many areas they are just like the proverbial train chasers, running desperately after an accelerating train – often in a futile bid to control a business or industry that is on the verge of leaving the station of regulatory comfort. You can pick from a range of visual metaphors – a man trying to control seven unruly horses, or grabbing a tiger by the tail, but you get the idea. Regulators are in a fix.

The sight (and sounds) of the congressional hearing of Mark Zuckerberg did not bode well for regulators. They should have had Zuckerberg dead to rights over it’s (willing or unwilling) culpability in the Cambridge Analytica imbroglio. Yet he came out with barely a scar to show for 2 days of grilling. Many of the people asking him questions came across as the stereotypical grandparent trying to figure out the internet from their grandchild, even if these are very exaggerated caricatures. There is arguably a 40 year age gap between the average lawmaker and the average entrepreneur. But the age challenge is just a minor problem. Here are some bigger ones.

Technology businesses are shape-shifting enterprises invariably redefining industries. Platforms cannot be regulated like their industrial counterparts. Uber is not a taxi company. Facebook is not a media business. Airbnb is not a hotel. No matter how convenient it might be to classify and govern, or how often someone points out that the world’s biggest taxi company doesn’t have taxis. No, these are data and services platforms, and they need an entirely new definition. You could argue that the trouble with Facebook has come about because they were being treated like a media organisation, rather than a data platform. And let’s not forget that the only reason Facebook was in the dock is because of the success of Cambridge Analytica in actually influencing an election. Not for the misuse of customer data on a daily basis which may have gone on for months and years by Cambridge Analytica as well as other similar firms. While governments’ focus on Uber stems largely from incumbent and licensed taxi services, nobody seems to be worried that Uber knows the names, credit card details and the home and office residences of a majority of its users.

Tech businesses, even startups, are globally amorphous from a very early age. Even a 20 person startup barely out of its garage can be founded in California, have it’s key customers in Britain, its servers in Russia, its developers in Estonia and pay taxes in Ireland. Laws and governments are intrinsically country bound and struggle to keep up with this spread of jurisdiction. Just think of the number of torrent services that have survived by being beyond the reach of regulation.

These are known problems and have existed for a while. Here’s the next challenge which is a more fundamental and even an existential one for lawmakers. With the emergence of machine learning and AI, the speed of technology change is increasing. Metaphorically speaking, the train is about to leave the station. If regulators struggle with the speed and agility of technology companies today, imagine their challenge in dealing with the fast-evolving and non-determinate outcomes engendered by AI! And as technology accelerates, so do business models, and this impacts people, taxes, assets, and infrastructure. Imagine that a gig-economy firm that delivers food home builds an AI engine that routes its drivers and finds a routing mechanism that is faster but established as being riskier for the driver. Is there a framework under which this company would make this decision? How transparent would it need to be about the guidance it provides to its algorithms?

I read somewhere this wonderful and pithy expression for the challenge of regulation. A law is made only when it’s being broken. You make a law to officially outlaw a specific act or behaviour. Therefore the law can only follow the behaviour. Moreover, for most countries with a democratic process, a new law involves initial discussion with the public and with experts, crafting of terms, due debate across a number of forums and ultimately a voting process. This means we’re talking in months, not days and weeks. And if technology is to be effectively regulated and governed, a key challenge to address is the speed of law-making. Is it possible to create an ‘agile’ regulatory process? How much of the delay in regulation is because the key people are also involved with hundreds of other discussions. Would lawmaking work if a small group of people was tasked to focus on just one area and be empowered to move the process faster in an ‘agile’ manner? We are not talking about bypassing democratic processes, just moving through the steps as quickly as possible. A number of options are outlined in this piece from Nesta website – including anticipatory regulation (in direct contravention of the starting point of this paragraph), or iterative rather than definitive regulation. All of these have unintended consequences so we need to tread cautiously. But as with most businesses, continuing as present is not an option.

Then there’s the data challenge. The big technology platforms have endless access to data which allows them to analyse them and make smarter decisions. Why isn’t the same true of regulators and governments? What would true data-driven regulation look like? We currently have a commitment to evidence-driven policymaking in the UK (which has sometimes been unkindly called policy driven evidence making!) but it involves a manual hunt for supporting or contradicting data, which is again time-consuming. What if a government could analyse data at the speed of Facebook, and then present that to the experts, the public, and legislators in a transparent manner? The airline industry shares all the data about every incident, accident and near miss, across its ecosystem, competitors, and regulators, and this is a significant contributor to overall airline safety. (Outlined in the book Black Box Thinking, by Matthew Syed.) Why isn’t the same true for cybersecurity? Why isn’t there a common repository for all the significant cyber attacks, which can be accessed by regulators armed with data science tools and skills, so that they can spot trends, model the impact of initiatives and move faster to counter cyber attacks? If attacks seem to originate from a specific territory or impact a specific vulnerability of a product, pressure can be brought to bear on the relevant authorities to address those.

running after train
These are non-trivial challenges and we need to be aware of risks and unintended consequences. But there is no doubt that the time has come for us to think of regulation that can keep pace with the accelerating pace of change, or governments and regulators will start to feel like the protagonists of movies where people run after trains.