Editor | On 05, Jun 2019

Darrell Mann

One of the scariest findings from our ongoing innovation research is the amount of innovation (i.e. ‘successful step-change’) that is meaningless. Products or services that make our lives more convenient at the expense of stripping away the things that are actually meaningful to our existence. About 75% of ‘innovations’ are currently falling in to this category, thanks in no small part to the rapid rise of Artificial Intelligence technologies.

That’s not to say that AI is necessarily a bad thing. Merely that, thus far, the AI pioneers are – logically, I suppose – chomping away at low-hanging fruit applications. The fact that some of this low hanging fruit (think about nearly every app on your smartphone) is actually not as low as the Silicon Valley wizards think, is merely ‘learning curve’. A lot of it, on the other hand, is already beginning to strip away a lot of the meaningless work that humans have had to do for the last couple of hundred years. AI is better than the best radiologists at picking up problems on an x-ray. AI is better at inspecting manufactured goods. At assembling cars. Diagnosing and curing patients is a highly meaningful act, but poring over x-rays with a magnifying-glass is not a meaningful part of the process. If the AI does that part of the job, the radiologist gets more time to focus on the meaningful stuff. The worst week of my life was my time as an apprentice in the Inspection Department at Rolls-Royce. Spending a week measuring a bucket of bolts to pick out the non-compliant ones, was about as meaningless as human jobs gets. Highly meaningful that non-compliant bolts were kept away from flying machines, but the moment a computer can do the quality measurement job better than the human operator, the human should have the option to be released to go and do something more meaningful instead.

This month’s Best-Of book is AI SuperPowers by Kai-Fu Lee. Dr. Lee is the chairman and CEO of Sinovation Ventures, a China based tech focused investment firm. Previous to becoming a full-time investor, Lee held positions at Google, Microsoft and Apple. A large part of that career, Lee spent working on data and AI, both in the US and in China. In “AI Superpowers — China, Silicon Valley and the New World Order” Lee bundles his experiences and insights to describe the progress that China and the US have made and are making in the field of AI.

AI Superpowers contains a heap of valuable insights as well as predictions about the impact of technology power that both the US and China have been racking up in the journey to understanding how AI takes on the meaningless work and lets humans go back to being human.

One of the first things I take away from the book as a whole is the vital difference between American (i.e. Silicon Valley) and Chinese Government cultural views on the evolution of AI.  Lee starts the book by writing about the contrasts in business culture between the US and China: “China’s startup culture is the yin to Silicon Valley’s yang: instead of being mission-driven, Chinese companies are first and foremost market-driven.” Lee goes on to explain that the ultimate goal of Chinese companies is “to make money, and they’re willing to create any product, adopt any model, or go into any business that will accomplish that objective.” This mentality helps to explain the ‘copycat’ attitude that Chinese companies have had historically. Meituan, for example, is a group-discount website which sells vouchers from merchants for deals which started as the perfect counterpart of US-based Groupon.

But then, more as a subtle undercurrent rather than overtly stated, it also seems clear that the Chinese Government view is that, while its really useful for Chinese entrepreneurship to flourish (‘some must get rich first’), this is merely a small part in a bigger jigsaw in which a potentially unruly population is kept under control (see the emerging Chinese ‘Social Credit’ system for a – to a Westerner at least – scary look at what I’m sure the Government ultimately gets out of all of their sponsorship of AI entrepreneurship).

When the Chinese Government says something will happen, it will happen. Some must indeed get rich first. And when told that ‘mass innovation’ and AI are the ways the Government wants people to get rich, what results is a tsunami of state-endorsed entrepreneurs. And an entrepreneurial ecosystem that will quickly swamp the capitalist reliance on the emergence of lone entrepreneurs. Especially when this gets combined with the mis-named ‘copycat’ mentality that China still has. Misnamed because what is happening in China is that any entrepreneur that comes up with a ‘good idea’ knows that it will very quickly be copies by others and the only sensible response to that fact is to keep coming up with more good ideas. An ecosystem full of ‘copycats’ means successful entrepreneurs have had to evolve to become ‘gladiators’. Silicon Valley deals with copycats by trying to sue them; China, by contrast, turns them into the best innovation accelerator ever. Take one of the emerging China-based image recognition startups, Face +++, which has quickly become a market leader in face / image recognition technology, leapfrogging the likes of Google, Microsoft and Facebook along the way. Why? Because what might have started off as ‘copying’ Silicon Valley ideas quickly turned into a fierce race to stay ahead of the copycats copying the copycats. ‘Even the bad stuff is good stuff’ in the China model… pure TRIZ.

Meanwhile, at the more detailed level, the book talks about “Online-to-Offline” (‘O2O”) —  the conversion of online actions into offline services. Ride-sharing services like Uber and Lyft are great examples of the new O2O model. In China, Didi copied this model and tailored it to local conditions. Didi was followed by other O2O plays such as Dianping, a food delivery service which subsequently merged with the aforementioned Meituan company, and Tujia, a Chinese version of Airbnb. Lee also mentions WeChat and Alipay, describing how both companies completely overturned China’s all-cash economy. More recently, bike-sharing startups Mobike and ofo which supplied tens of millions of internet-connected bicycles, distributing them across them about major Chinese cities and now across the globe. Dr Lee makes the compelling point that, because the China context has connected the online world to the offline world far more quickly than in the US or elsewhere, and because China has such a big population, the massive amount of real- world data this creates means O2O players probably already have an unassailable lead. The person with the data wins.

For a TRIZ person, or someone interested in S-Curves, perhaps the most important section of the book discusses ‘the four waves of AI’ and how the “AI revolution” will not happen overnight. Instead, AI will wash over us in four waves: internet AI, business AI, perception AI, and autonomous AI.

First wave: Internet AI — Internet AI is largely about using AI algorithms as recommendation engines: systems that learn our personal preferences and then serve up content hand-picked for us. Toutiao, sometimes called “the Buzzfeed of China”, is a great example of this first wave of AI; its “editors” are algorithms.

Second wave: Business AI — First wave AI leverages the fact that internet users are automatically labelling data as they browse. Business AI, the second wave of AI, takes advantage of the fact that traditional companies have also been automatically labelling huge quantities of data for decades. For instance, insurance companies have been covering accidents and catching fraud, banks have been issuing loans and documenting repayment rates, and hospitals have been keeping records of diagnoses and survival rates. Business AI mines these data points and databases for hidden correlations that often escape the naked eye and the human brain. RXThinking, an AI based diagnosis app, is a good example in this respect.

Third wave: Perception AI — Third wave AI is all about extending and expanding this power throughout our lived environment, digitising the world around us through the proliferation of sensors and smart devices. These devices are turning our physical world into digital data that can then be analysed and optimised by deep-learning algorithms. For example, Alibaba’s City Brain is digitising urban traffic flows through cameras and object-recognition. Compare this with the M4 roadworks that I’ve had to add an hour to my journey to Heathrow to get through for the last 8 months. The UK Government is ‘investing’ £848M in turning a 12 mile stretch of motorway into a ‘smart motorway’. It is doing it by digging up the roads and laying down literally tons of copper and fibre optic cable and sensors, when all the time the information needed to make the motorway ‘smart’ is already there in the Cloud. For the same money, City Brain could, I’m pretty certain, make every single road in the UK ‘smart’.

Fourth wave: Autonomous AI — Autonomous AI represents the integration and culmination of the three preceding waves, fusing machines’ ability to optimise from extremely complex datasets with their newfound sensory powers. This is the Wave – assuming Tolpuddle-like Martyrs don’t destroy the whole system – that will release humans from the meaningless and be allowed to get on with the meaningful. Or at least stay home and play on their X-boxes… although maybe not in China, where no doubt, ‘the system’ will know that you’re goofing off and need to be prodded into action to go and do something meaningful somewhere.

Political undercurrents aside, I found myself coming back to the high-level comment I made in my Systematic (Software) Innovation book: the IT geeks will control the future, but they’re the least qualified people – right now – to be trusted with the task. I’m certain this is still true. What I think Dr Lee’s book tells us is that, in the case of the Silicon Valley ‘yang’ (as proved every day by Zuckerberg) the people involved are still the least qualified. In the case of the Chinese Government, depending on your political ideology, we probably see the opposite. The exact ‘most qualified’ are pulling the strings. Whether that turns out to be a good thing or a bad thing depends on how prescient George Orwell’s 1984 turns out to be. The ‘Social Credit’ system, as it inevitably evolves to its mature state, could either be mankind’s savior or its downfall. It could deliver the ‘higher level of meaning’ that Marx was trying to describe or it could knock all the dominoes over. Interesting times. Essential reading.