The End of Management?

endIt’s not very often that you realise that a startling change is underway in the world. But I had one of those epiphanic moments at the recently concluded TCS European Summit at Budapest.

At the Summit, Ruud Gullit, who has the personality to take over the room when he speaks, spoke about his key frustration with today’s footballers. His key complaint was that today’s players have lost the element of personality and opinion and have become followers of instructions. They no longer have their own views about how they should play, or where they should position themselves. They leave that entirely to the manager. This seems to mirror how American football is played, where the coach does all the thinking and the players are effectively automatons on the pitch, following instructions. This is an armchair debate we’ve all had about our favourite sport — what’s the role of the captain on the pitch? How much does the coach or manager influence or control the decision making? For coaches like Pep Guardiola, it seems that there is a very high level of control and specificity about his instructions and players are, in fact, urged to simply follow the plan. For others, the level of separation between management and execution may vary. But I was intrigued by Gullit’s idea that football is being taken over by management.

Management as we know it is, of course, a product of the industrial revolution. Mass production and factories created production lines and turned people into automatons, where they were required to do, not think. And the control, evaluation, governance and thinking was all abstracted to a supervisory layer of people whose job was to think and not do. And thus modern management was born. While this has always existed in the historical context, in armies and in the construction of castles, pyramids and fortresses from the earliest days, the emergence of a global, scaled middle class came only with the dramatic rise in mass production of goods and services. One of the most dramatic changes in production philosophies — lean manufacturing — actually involves getting factory production workers to think. But conversely, even in white-collar work in a lot of businesses, there is an abundance of people doing apparently cerebral jobs like talking to customers, who have been turned into automatons who have to follow a script. They’ve been reduced to doing, rather than thinking.

Today the world over, Management has been enshrined as the backbone of how every organisation works. It is seen as the difference between success and failure. Managers are rewarded many times more handsomely than the most productive worker. Decision making is seen as the preserve of the manager. To serve this need, business schools have long produced people trained to be managers. It is both an art and a science. It involves people skills, domain knowledge, technical skills, risk-taking ability, leadership, problem-solving and many others. So much so that management is synonymous with leadership today.

So when Michael Clijdsdale from ING suggested that we should basically get rid of management, a ripple of laughter went around the room. It was one of those jokes speakers make. But it became obvious in his next few sentences that he was in fact, serious. And over the following days, having spoken with other leaders, and correlating with what I’ve been reading and reflecting on, I’ve come to realise that this wasn’t just a stray opinion and that there is indeed a big change underway. This is based on 3 pillars:

The first is the now universally accepted idea that software is, in fact, eating the world. More and more of the value of business and commercial activities are tied to the software rather than the hardware or human activity involved. In the book Breaking Smart, the author Venkatesh Rao articulates this very well. He talks about how software helps industries ‘break smart’ by moving into a world of software-driven value which allows them to change the business models, and reorganise themselves around a new set of economic and externalities. This reorganisation often requires rethinking organisational structures and processes, often increasing technology infrastructure and software and cognitive processes, rather than the industrial and management processes.

The second is the evolution of how software is actually created. The Agile Manifesto was created in 2000 by a group of seasoned developers who believed that better software could be developed, more efficiently, if they reduced the separation between management (thinking, planning) and execution (coding). Agile has come a long way since then and is now at the heart of a lot of significant enterprise platforms. Agile puts the focus and the responsibility back into the self-managed team and reduces management overload. Agile teams today set their own pace, course-correct as necessary, think and act in equal measure and end up delivering more than teams that are externally managed.

The third is the VUCA world we find ourselves in. The term was created by the US military but increasingly has become more mainstream. It stands for volatile, uncertain, complex and ambiguous — an apt description for the world most businesses find themselves in. From uncertainty about the outcome of political negotiations such as Brexit, or trade wars, or the volatility of extreme weather conditions, the complexity of interlinked economic events, or the uncertainty of disruptive technologies. VUCA conditions reduce the impact of planning and control and instead reward agility, adaptability and empowered choices.

When you add these 3 up, you start to understand why this might be the end of management as we know it — a layer of people whose only job is to manage other people and own the thinking and planning role without actually being able to execute the tasks themselves. This is not to say we won’t need management, thinking, and planning — we will. It’s just that these tasks will also be done by those actually doing the work. And conversely, we may see the end of the pure execution role. Those tasks that are performed by people acting as automatons, will, in fact, be done by machines in the future. What we’ll get is a new category of empowered workers. Collaborative teams that can plan, think, build and run. To give you a military analogy, a general who plans a battle but sits in headquarters is of very limited use when the battle conditions change frequently and strategy and tactics need to be adapted in near real time. Conversely, soldiers who can’t (or aren’t allowed to) adapt to the changing needs are also likely to be doomed to failure.

The first place we will see this is in software and technology development and operations. Software development is the sharp end of this change, and some of the newest thinking about work and its organisation in the recent past has come from the software and technology world. Open source, collaborative working, mobility, network effects and platformisation, and the gig economy, just to name a few. It is likely that as software continues to eat the world, and our organisations, we will see it swallow the traditional (industrial era) style of management, in favour of empowered doers. Ironically, therefore, even as technology replaces some jobs, in the longer term, it may be the best thing that happened to workers.

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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)

Internet

 

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.

https://www.youtube.com/watch?v=K1EZWYqm-5E&feature=youtu.be

(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
https://towardsdatascience.com/who-is-going-to-make-money-in-ai-part-i-77a2f30b8cef

(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.

https://www.ajbell.co.uk/news/index-reshuffle-looks-set-deliver-ocado-ftse-100
https://www.theguardian.com/business/2018/may/17/us-deal-boosts-ocados-stock-market-value-above-5bn

(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.
https://medium.com/@BlakeGossard/the-underrepresentation-of-you-in-medical-research-85289b591ba

 

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.

Reading List: 7 for 7 – April 23: Palantir > Facebook, Generative Design, Alexa With Eyes, and More…

The 7 most interesting things I’ve read over the past 7 days.
 future doors
(image credit: Pixabay)

(1) The One Thing You Should Read about AI this week: 
In March, we ran a TCS DEX event where we posed the question to our partners and clients, around whether every company should have an AI strategy. While there was general agreement about the need for an AI strategy, there was no clear starting point. This may be the challenge for most companies. And perhaps the first steps towards a strategy are gathering information and running experiments.
If you read one thing this week, read this AI paper by the McKinsey Global institute – they publish results from a comprehensive survey and analysis of AI across industries, functions, and use cases and by the relevance of the different techniques, such as Transfer Learning, Reinforcement Learning, and Deep Learning Neural Networks. If some of that sounds obscure to you, I suggest reading up a little bit as these will become common business parlance in the not-too-distant future, and clients will be asking about them. In any case, succinct explanations are provided in the paper. It will probably take you a couple of hours to read (not skim) the 40 odd pages. Here are some of the very high-level takeaways:
  1. Industries where the number of use cases are the highest, include (1) Insurance, (2) Banking, (3) Retail, and (4) Automotive & Assembly.
  2. Functions with the highest number of use cases include (1) Supply Chain management and manufacturing, and (2) Marketing and Sales
  3. Specific domains where the impact might be the highest include (1) customer service & management (2) risk modelling (3) predictive service / intervention (4) workforce productivity and efficiency (5) analytics-driven hiring and retention, and (6) yield optimisation.
Some other takeaways:
  • The highest absolute impact of AI is to be found in Retail, but Travel and transport & logistics can extract the highest incremental value over other analytics techniques.
  • Image data is the highest value, after structured and time series data, and ahead of text.
  • Challenges and limitations: (1) labelling training data (2) obtaining large enough data sets (3) explaining the outcomes and decisions in clear enough terms – e.g. for product classification or regulatory (4) transferring of findings to adjacent use cases, and (5) risk of bias in data/ algorithms

(2) Data: Facebook is a misguided amateur compared to Palantir 
Palantir is much more dangerous than FB. Why? (1) Because Peter Thiel, the founder is a man of metamorphosis – he has quixotic views of the world – such as ‘freedom is not compatible with democracy’; (2) because Palantir is a much more shadowy and secretive organisation but built specifically for next-generation analytics for powerful clients. (3) Because this kind of analytics power can be destructive if individuals go rogue – the article talks about Peter Caviccia who ended up running his own spying operation within JPMorgan in what is described in the article as Wall Street meets Apocalypse Now, and (4) because tools like this are being used by police forces such as LAPD to predict crime – but also to do that to build deep and intricate views of a lot of individuals and their lives. The article also provides a very good visual model of Peter Thiel’s incredible original Paypal team and network which includes Elon Musk, Reid Hoffman (LinkedIn), Steve Chen (Youtube) and many others.

(3) Design: Welcome to Generative 3D Design 
What do you do when you need to design and build a spinal implant that needs to be appropriately strong, light and pliant? You use an algorithm-driven design process called generative design with 3D printing. Algorithmic design takes in your specifications or requirements and generates a number of options, which are developed faster than humans and enables a lot more personalisation of complex materials. In future, these will probably be custom built to specs in a way that humans simply can’t. It also uses the least amount of material possible (it’s one of the constraints/ objectives). This story in the Wired magazine talks about how Nuvasive does this using AI and 3D.

(4) eCommerce and Retail – change of guard, and disruption for the economy
This week we had a direct comparison between M&S vs ASOS: M&S is a struggling brand – losing share in apparel, and under pressure on foods. Other brick and mortar retailers like New Look are also in trouble. ASOS sales, on the other hand, hit £1.9 bn 2017 which amounted to a 33% increase. It’s also instructive to note that eCommerce contributes some 25% of British clothes retail numbers. In fact, the UK has the highest amount of online commerce (as a % of overall retail numbers – almost 18%), but the retail industry also accounts for 10% people and 10% of the economy – so significant disruptions lie ahead.

(5) Asset-light business models 
We’ve seen them in telecoms (MVNOs), in retail, and also in utilities. Lightweight, direct to consumer competitors who don’t carry the baggage of their larger competitors. They have no legacy IT and are built ground up on digital platforms, for a start, and also have a much more nimble operating model. Companies like Asos and Ovo energy are successful because they attract a particular consumer niche, operate in an agile way and are not weighed down by the legacy business and IT challenges of their larger peers (zero inventory, for example). This trend goes all the way down to micro brands in the consumer goods space. Many of these businesses will die or stay micro, but once in a generation, they will lead to the next FB or Amazon.

(6) Alexa Fashion – a glimpse of the future 
What’s Alexa’s next trick? How about a camera that can give you fashion feedback? Amazon’s Echo Look (not yet launched to the public, but on invitation only basis) has a camera and lets you take selfies and gives you feedback on what you’re wearing. For those worried about whether Amazon was listening to all your conversations, this will definitely be a step too far! This piece is a good take on the social and psychological implication of a tool like this. Of course, if you want algorithmic advice but don’t want something that invasive, you can always turn to Miquela

(7) Battery Wars 
We all know that a move to electric cars is a ‘when’ and not an ‘if’ question by now. What that means, however, is a near insatiable demand for batteries and a huge spotlight on battery technology. Currently, the minerals that go into batteries such as Lithium and Magnesium are seeing a huge spurt in demand. It turns out that DR Congo is the worlds dominant source of Magnesium. In all of this, the UK is seeking to play a leadership role in battery technology. But is it either feasible or desirable? On the other hand, Williams has been working on safer batteries which are tough-tested in the Formula E competition – where electric-only cars race, collide and crash.

Why Are We Suddenly So Bad At Predicting the Future?

Imagine that a monkey got into the control room of the universe and spent the year clicking random buttons. Imagine him hopping about on the ‘one musician less’ button, stomping on the ‘auto-destruct’ lever and gurgling while he thumped repeatedly on the ‘introduce chaos’ switch. Close your eyes and picture him dropping a giant poo on the bright red panel marked ‘do not touch under any circumstances’. That my friends is the only way to think about 2016 – after all, it was the year of the monkey in the Chinese zodiac. It was the year when rational thinking took a beating, when meritocracy became a bad word, when liberalism escaped from the battlefield to a cave in the mountains to lick its wounds. And not surprisingly, a year when projections, predictions and polls made as much sense in the real world as an episode of Game of Thrones on steroids.
monkey-prediction
Given much of our lives are spent in productively engaging with the future and making decisions based on big and small decisions about the possible future, this last point is more important than just the schadenfreude of laughing at pollsters and would be intellectuals. The present passes too quickly, so really every decision you’ve ever made in your life is counting on future events to turn out in ways that are favourable. Getting this wrong is therefore injurious to health, to put it mildly. And yet our ability to predict the future has never been under such a cloud in living memory. Why is this so?

Fundamentally, we’re wired to think linearly in time, space and even line of sight. We are taught compound interest but we get it intellectually rather than viscerally. When you first encounter the classic rice grains and chessboard problem, as a smart person, you know that it’ll be a big number, but hand on heart, can you say you got the order of magnitude right? i.e. the total amount of rice on the chessboard would be 10x the world’s rice production of 2010? Approximately 461,168,602,000 metric tons? This problem of compounding of effects is incredibly hard to truly appreciate, even before you start to factor in all the myriad issues that will bump the rate of change up or down, or when the curve hits a point of inflexion. The Bill Gates quote  – ‘we over-estimate the impact of technology in 2 years, and under-estimate the impact over 10’ – is a direct reframing of this inability to think in a compound manner.

Then there’s the matter of space and line of sight. The way the future unfolds is dramatically shaped by network effects. The progress of an idea depends on it’s cross fertilisation across fields, geographies and disciplines, across any number of people, networks and collaborations. These collaborations can be engineered to a point or are the result of fortuitous clustering of minds. In his book ‘Linked’ – Ablert-Lazlo Barabasi talks about the mathematician Erdos who spent his life nomadically, travelling from one associates’ home to another discussing mathematics and ironically, network theory. Not surprisingly, a lifestyle also practiced for many years by a young Bob Dylan, if you substitute mathematics for music. Or consider the story of the serial entrepreneur in Rhineland in the 1400s, as told by Steven Johnson, in ‘Where Good Ideas Come From’. Having failed with a business in mirrors, he was working in the wine industry, where the mechanical pressing of grapes had transformed the economics of winemaking. He took the wine press, and married it with a Chinese invention – movable type, to create the worlds first printing press. His name of course, was Johannes Gutenberg. This kind of leap is not easy to predict, not just for the kind of discontinuity they represent (more on that later), but also because of these networked effects. Our education system blinkers us into compartmentalised thinking which stays with us through our lives. Long ago, a student of my mothers once answered a question about the boiling point of water by saying “in Chemistry, it’s a 100 degrees Centigrade, but in Physics, I’m not sure”. We are trained to be specialists, becoming more and more narrow as we progress through our academic career, ending up more or less as stereotypes of our profession. Yet human progress is driven by thousands of these networked, collaborative, and often serendipitous examples. And we live in a world today with ever expanding connections, so it’s not surprising that we have fallen behind significantly in our ability to understand how the network effects play out.

If you want to study the way we typically make predictions, you should look no further than sport. In the UK, football is a year round sport, so there are games every weekend for 9 months and also mid week for half the year. And with gambling being legal, there is an entire industry around football gambling. Yet, the average punter, fan or journalist makes predictions which are at best wilfully lazy. There is an apocryphal story about our two favourite fictitious sardars – Santa Singh and Banta Singh, who decide to fly a plane. Santa, the pilot, asks Banta, the co-pilot to check if the indicators are working. Banta looks out over the wing and says “yes they are, no they aren’t, yes they are, no they aren’t…” – this is how a lot of predictions are made in the world of premier league football today. Any team that loses 3 games is immediately in a ‘crisis’ while a team that wins a couple of games are deemed to be on their way to glory. Alan Hansen, an otherwise insightful pundit and former great player, will always be remembered for his one comment “You can’t win anything with Kids” – which he made after watching a young Manchester United side lose to Aston Villa in the 1995-96 season. Manchester United of course went on to win the season and dominate the league for the next decade and a half. Nobody predicted a Leicester City win in 2016 of course, but win they did. The continuous and vertiginous increase in TV income for football clubs has led to a relatively more equal playing field when it comes to global scouting networks, so a great player can pop up in any team and surprise the league. Yet we find it hard to ignore all the underlying trends and often find ourselves guilty of treating incidents as trends.

The opposite, is amazingly, also true. We are so caught up with trends that we don’t factor in the kinks in the curve. Or to use Steve Jobs’ phrase – the ding in the universe. You can say that an iPhone like device was sure to come along sooner or later. But given the state of the market – with Nokia’s dominance and 40% global market share, you would have bet your house on Nokia producing the next breakthrough device eventually. Nobody saw the iPhone coming, but when it did it created a discontinuous change that rippled across almost every industry over the next decade. The thing is, we like trends. Trends are rational and they form a kind of reassuring continuity so that events can fit our narratives, which in turn reaffirm our world view. And unless we’re close to the event, or perennial change seekers and nomads ourselves, it’s hard to think of countercyclical events. It’s now easy to see how in 2016 we were so caught up in the narrative of progressive liberalisation and unstoppable path to globalisation, we failed to spot those counter-cyclical events and cues that were right there in our path.

In fact there are any number of cognitive biases we are guilty of – on an everyday basis. This article just lists a dozen of them. My favourites in this list are the confirmation bias and the negativity bias. Both of these are exacerbated by social media and digital media. While social media has led us to the echo-chambers – the hallmarks of 2016, our projection bias is also accentuated by our ability to choose any media we want to consume, in the digital world, where access is the easy part. Similarly, bad news spreads faster on social networks and digital media today than at any time before in history. Is it possible that despite knowing and guarding against these biases in the past, we’ve been caught out by the spikes in the impact and incidence of a couple of these, in the digital environment we live in today?
To be fair, not everybody got everything wrong. Plenty of people I know called the Donald Trump victory early in the game. And amongst others, John Batelle got more than his share of predictions right. There is no reason to believe that 2017 will be any less volatile or unpredictable than 2016, but will our ability to deal with that volatility improve? One of the more cynical tricks of the prediction game is to make lots of predictions at many different occasions. People won’t remember all your bad calls, but you can pick out the ones you got right, at leisure! This is your chance, then, to make your predictions for 2017. Be bold, be counter-cyclical. And shout it out! Don’t be demure. The monkey is history, after all. This is the year of the rooster!