What Today’s AI can and cannot do ? (Part 1)

1. Introduction

This last post of 2018 returns on the topic of the year : Artificial Intelligence and Machine Learning. Many new books have been published in the second part of the year, including Kai-Fu Lee’s Book “AI Superpowers – China, Silicon Valley and the new world order”. This thought-provoking book has achieved the New York Times bestseller status, rightly so in my opinion because it brings the multi-cultural insights of someone who knows very well the US AI ecosystem – having worked, for instance, both at Apple and Google – and the new vibrant China AI ecosystem. Besides, Kai-Fu Lee has made a number of provocative press conferences and interviews where he is very forceful about China and US superiority versus the rest of the world, especially  Europe, where he sees “no chances for the AI sector”.

The core argument of Kai-Fu Lee’s Book is that Artificial Intelligence has moved from Science to Engineering. This is a very strong statement, with two parts: (a) the incredible advances of the past 10 years are ready for massive deployment and will have a huge impact on the work, (b) what matters is there already and what will come next is too speculative to be a foundation for the next ten years to come – hence the implementation race has started. The first part is no surprise, the same message is at the core of our NATF report on AI and ML, or in the two previous great books that I have quoted many times : “The Mathematical Corporation - Where Machine Intelligence and Human Ingenuity Achieve the Impossible” and “Human + Machine – Reimagining work in the age of AI”. The second part is much more controversial.

Indeed, many voices are saying the opposite of the second part of the statement: AI today is still in its infancy and we have many hurdles to address before we can scale to truly significant achievement. Among those, the great paper from Melanie Mitchell, “Artificial Intelligence Hits the Barrier of Meaning” has received a lot of well-deserved attention. One could see a contradiction in the Kai-Fu Lee’s versus Melanie Mitchell’s “state of AI” viewpoints. I believe that they are both right (hence a little bit wrong as well), as is beautifully summarized by this great quote from Pedro Domingos – the author of the “The Master Algorithm” bestseller :  “People worry that computers will get too smart and take over the world, but the real problem is that they’re too stupid and they’ve already taken over the world”. My goal with this two-part blog post is to deep dive into this apparent contradiction.

Fortunately, another great book was published recently by Martin Ford, “Architects of Intelligence: The Truth about AI from the People Building it”, which is clearly the best book that I have ever read on this topic. This book is a series of 24 interviews from some of the best minds in the world – a great selection of the best-known scientists who have made AI advances in the past 30 years. This book, in my opinion, gives all the necessary clues to see all that is relevant in Kai-Fu Lee’s great book – including the call to action and the dire warnings – but also to see that this is only a small part of the story : the future of today’s AI. The other part, the future of tomorrow’s AI, is by construction much harder to speculate about but it does not entail that what we not know will not exist J The first part of this post will address the future of today’s AI following the steps of Kai-Fu Lee. The next part – in an upcoming blog post – will look at the other half of the question following the footsteps of the “Architects of AI”.

2. AI Superpowers – The upcoming revolution of today’s AI

This very synthetic book review should include a more detailed profile on the author, Kai-Fu Lee, that you may find here. It is important to understand that Kai-Fu Lee has started his career as a scientist in the field of AI  - “As a young Ph.D. student researching artificial intelligence at Carnegie Mellon University, I studied under pioneering AI researcher Raj Reddy. In 1986, I created the first software program to defeat a member of the world championship team for the game Othello”. He has worked in many prestigious Silicon Valley companies and made significant achievements in the field of natural language processing. He then returned to China as the head of “Google China” before quitting to start his own VC activity in China. The book is well written, deeply personal (I will not talk here about the last part, which you should read by yourself), and I will simply here point out some key ideas without any pretense of completeness.

The book starts with a recollection of AlphaGo victory over Ke Jie. After the spectacular victory over Lee Seedol that stunned the world, AlphaGo beat in May 2017 the Chinese champion considered the best world player at that time. Kai-Fu Lee emphasizes the importance of Go in Chinese culture and how it had been associated for a long time with human intelligence. Hence the “machine victory” came as a huge surprise and a very strong waking call for Chinese government and entrepreneurs: “To people here, AlphaGo’s victories were both a challenge and an inspiration. They turned into China’s “Sputnik Moment” for artificial intelligence”. Kai-Fu Lee considers that this victory over the Go champion played a critical role for the Chinese government to make a strategic investment into AI: “ And less than two months after Ke Jie resigned his last game to AlphaGo, the Chinese central government issued an ambitious plan to build artificial intelligence capabilities”. This is the starting point for the rest of the book, which describes how China has become the biggest playfield for Artificial Intelligence development in the world: “But over the past three years China has caught AI fever, experiencing a surge of excitement about the field that dwarfs even what we see in the rest of the world”.

A foundation idea of this book is that AI requires a lot of data, and the more data you have the better your AI will be: “Given much more data, an algorithm designed by a handful of mid-level AI engineers usually outperforms one designed by a world-class deep-learning researcher. Having a monopoly on the best and the brightest just isn’t what it used to be”. We see the same pattern here, the combination of a well-admitted idea “you need lots of data to develop world-class AI capabilities” – there is not one book or report that does not explain this – and a more debatable point that says that if you have more data, you will necessary win, independently of the amount of talents. Kai-Fu Lee uses the remarkable story of machine vision, and the role of the ImageNet data set, as an illustration of his vision: “Doing this requires massive amounts of relevant data, a strong algorithm, a narrow domain, and a concrete goal. If you’re short any one of these, things fall apart”.  The importance of massive data collection is the first argument about China’s future superiority. China is collecting more data because of its size, because of the advanced status of digital life in China – from WeChat being the ubiquitous digital platform to the massive deployment of connected objects and mass-market IoT, and because regulation is much more “friendly” towards leveraging value through AI : “the rich real-world interactions in China’s alternate internet universe are creating the massive data that will power its AI revolution”. There is a very strong argument here, that we shall see in Martin Ford’s book as well, that AI development thrives in a fully digitalized society. The result is the first competitive advantage of China according to Kai-Fu Lee: “China has already vaulted far ahead of the United States as the world’s largest producer of digital data, a gap that is widening by the day”.

A key conviction of Kai-Fu Lee is that, after a decade of deep learning scientific innovation, we are entering a decade of implementation, where critical masses of data, talents and energy will tell who wins the race. He sees a paradigm shift, where the question is no longer to develop new tools but to find as quickly as possible – because this is a race – where value may be found using our existing tools: “ That global shift is the product of two transitions: from the age of discovery to the age of implementation, and from the age of expertise to the age of data”. Following the two books that I quoted in the introduction, Kai-Fu Lee issues a strong call to action: “But making that distinction between discovery and implementation is core to understanding how AI will shape our lives and what—or which country—will primarily drive that progress”.  The paradigm shift towards implementation means that software and engineering skills play a critical role, which I could not agree more since this was the key message from our NATF report. There are many examples in the book where Kai-Fu Lee insists on the specificities of software architecture and solutions that were developed by Chinese entrepreneurs to adapt to their markets: “Chinese companies have never truly embraced enterprise software or standardized data storage, instead keeping their books according to their own idiosyncratic systems”.  I will quote his argument for “growing AI from local usage specificity” because it is both very true and somehow contradictory with the concept of AI worldwide power-houses: “AI has a much higher localization quotient than earlier internet services. Self-driving cars in India need to learn the way pedestrians navigate the streets of Bangalore, and micro-lending apps in Brazil need to absorb the spending habits of millennials in Rio de Janeiro.”

As previously said, this paradigm shift comes with the – debatable – conviction that AI today’s toolbox is now complete or good enough: “Much of the difficult but abstract work of AI research has been done, and it’s now time for entrepreneurs to roll up their sleeves and get down to the dirty work of turning algorithms into sustainable businesses”. This viewpoint is derived from the conviction that the time from the initial idea to market-readiness of software technology is long, may decades: “Deep learning marked the largest leap forward in the past fifty years, and advances on this scale rarely come more than once every few decades. Even if such a breakthrough does occur, it’s more likely to emerge out of the open environment of academia”. It is clear that this is where many would disagree, especially Rodney Brooks, whom we shall meet in the second part of this blog, and his famous paper “The Seven Deadly Sins of AI Prediction”.

The core consequence of the previous analysis is that China will inevitably rise as one of world AI “superpowers” (hence the title) because it combines precisely the four following strengths. Kaifu-Lee gives his list of the four components that are necessary for a successful AI ecosystem: “ Harnessing the power of AI today—the “electricity” of the twenty-first century—requires four analogous inputs: abundant data, hungry entrepreneurs, AI scientists, and an AI-friendly policy environment”.  We have already talked about the “data collection advantage”, another fascinating part of the book is the description of the internet entrepreneur ecosystem in China. As a venture capitalist, Kai-Fu Lee is in a very good position to describe and explain the strength and the differences – versus Silicon Valley and Europe startup ecosystems. One should keep in mind though that, as an investor, this not an unbiased viewpoint – Kai-Fu Lee has definitely “much skin in the game”. To him, China’s ecosystem is simply “the most cutthroat competitive environment on the planet. They live in a world where speed is essential, copying is an accepted practice, and competitors will stop at nothing to win a new market”.  I will let you discover the – sometimes caricatural – description of the extraordinary energy and willingness to work long hours in the Chinese startups. As a VC, Kai-Fu Lee points out the rapid adoption of “Lean Startup concepts” (MVP, pivoting, etc.) in the Chinese ecosystem: “Core to its philosophy – The Lean Startup Book - is the idea that founders don’t know what product the market needs—the market knows what product the market needs”. The race that motivates Chinese entrepreneurs is the race of disruption through AI, they do not see themselves as technology partners to larger incumbent companies, but as challengers: “Instead of helping those companies access AI, these startups want to disrupt them using AI. They aim to build AI-first companies from the ground up, creating a new roster of industry champions for the AI age”. As mentioned earlier, Kai-Fu Lee sees that China is not simply a country with more data – because of its size and a government that is indeed more AI-friendly in its data privacy regulation – but also a country with better data : “But China’s data advantage extends from quantity into quality. The country’s massive number of internet users—greater than the United States and all of Europe combined—gives it the quantity of data, but it’s then what those users do online that gives it the quality. The nature of China’s alternate universe of apps means that the data collected will also be far more useful in building AI-driven companies … that data is tailor-made for building profitable AI companies”.

What makes the “AI race” exciting is that the goal is nothing short than a profound revolution and the opportunity to acquire new leader positions in the future economy. Because of the digital nature of most those AI-related opportunities, there is indeed a race: “The AI world order will combine winner-take-all economics with an unprecedented concentration of wealth in the hands of a few companies in China and the United States … The positive-feedback loop generated by increasing amounts of data means that AI-driven industries naturally tend toward monopoly, simultaneously driving down prices and eliminating competition among firms”. This is nothing new, but AI will amplify the opportunities: “But as remarkable as these accomplishments have been, these changes will pale in comparison to what these entrepreneurs will do with the power of artificial intelligence”.  Kai-Fu Lee gives numerous examples of Chinese startups that he sees as future world leaders of their fields. For instance, Face++ is described as “a world leader in face- and image-recognition technology”. He quotes another example from his own field of expertise – but one should keep in mind that, as a VC, he is necessarily biaised : “Nearly twenty years and dozens of AI competition awards later, iFlyTek has far surpassed Nuance in capabilities and market cap, becoming the most valuable AI speech company in the world”.  There is   an interesting discussion about the availability of skills and talents in China, and Kai-Fu Lee gives convincing arguments that China is ready to develop the successive waves of AI : “The complete AI revolution will take a little time and will ultimately wash over us in a series of four waves: internet AI, business AI, perception AI, and autonomous AI”. One could object that this enthusiasm, about Chinese investments and government strong push, reminds us  of the Japanese “Fifth Generation” program in the 80s. Kai-Fu Lee is aware of this : “While all of this may sound exciting and innovative to the Chinese landscape, the hard truth is that no amount of government support can guarantee that China will lead in autonomous AI”. His topic of interest is not the next generation of AI or how government funding may steer that race, but the current generation of AI and how a well-structured ecosystem will deliver faster growth.

The relative strong positioning of China in AI is not simply a matter of size, it is the consequence of a deliberate plan to develop and strengthen an ecosystem. I strongly urge any regulator to read carefully the story of Guo Hong, a startup founder who became a key player for the Chinese government and played a critical role in setting up the Chinese AI ecosystem. Guo Hong worked hard to understand “what really made Silicon Valley tick”, his first mission “to build the Avenue of the Entrepreneurs was just the first trickle of what in 2014 turned into a tidal wave of official policies pushing technology entrepreneurship”. A lot of thinking about emergent ecosystems and the role of data went into play “In the age of AI implementation, the impact of these divergent data ecosystems will be far more profound. It will shape what industries AI startups will disrupt in each country and what intractable problems they will solve”.   In comparison, Kai-Fu Lee is not impressed with the approach that Europe in general, and France in particular, is taking to succeed this AI race because they focus too much on research and not enough on usage ecosystems: “Canada, the United Kingdom, France, and a few other countries play host to top-notch talent and research labs, but they often lack the other ingredients needed to become true AI superpowers: a large base of users and a vibrant entrepreneurial and venture-capital ecosystem”. Our proclivity toward issuing reports is gently mocked, whether there are European or American reports: “But the report—issued by the most powerful political office in the United States—had about the same impact as a wonkish policy paper from an academic think tank”. Very logically, Kai-Fu Lee is not impressed with GDPR and the European desire to control rather than to develop AI-based usage of data: “While Europe has opted for a more heavy-handed approach (fining Google, for example, for antitrust and trying to wrest control over data away from the technology companies), China and the United States have given these companies greater leeway, letting technology and markets develop before intervening on the margins”.

Although Kai-Fu Lee is bullish on the future impact of AI on society, he is also quite aware of the complexities and difficulties of the journey ahead. He is especially sensitive to the social risk that AI automation is likely to cause:  “Does that mean I see nothing but steady material progress and glorious human flourishing in our AI future? Not at all. Instead, I believe that civilization will soon face a different kind of AI-induced crisis”. His vision of the possible impact on job loss is not very different from the Frey-Osborne study : “Based on the current trends in technology advancement and adoption, I predict that within fifteen years, artificial intelligence will technically be able to replace around 40 to 50 percent of jobs in the United States...  Actual job losses may end up lagging those technical capabilities by an additional decade, but I forecast that the disruption to job markets will be very real, very large, and coming soon”.  He proposes his own analysis in his book, based on a number of job characteristics, such as requiring or not dexterity, using a structured versus unstructured context and environment, or the level of social interaction that is required. With a two-axis / four quadrant approach, he makes his own predictions about which jobs will be impacted the most by AI-automation: “While the simplest and most routine factory jobs—quality control and simple assembly-line tasks—will likely be automated in the coming years, the remainder of these manual labor tasks will be tougher for robots to take over”.  I read where something that I first head two years ago at the Singularity University : “contrary to popular assumptions, it is relatively easy for AI to mimic the high-level intellectual or computational abilities of an adult, but it’s far harder to give a robot the perception and sensorimotor skills of a toddler”. There is an interesting discussion about the need for solidarity, inclusion and some form of redistribution – Kai-Fu Lee is not a proponent for Universal Basic Income, but what he calls for may not be that different : “I want to create a system that provides for all members of society, but one that also uses the wealth generated by AI to build a society that is more compassionate, loving, and ultimately human”.  This vision requires a massive investment in education: “To be clear, I do believe that education is the best long-term solution to the AI-related employment problems we will face … But the scale and speed of the coming changes from AI will not give us the luxury of simply relying on educational improvements to help us keep pace with the changing demands of our own inventions”. Eventually, many of the future jobs will turn to be “interaction jobs”, but we need to find a way to redistribute some of the AI-created wealth, since those jobs have no “market invisible hand” that would push their values forward: “Private companies already create plenty of human-centered service jobs—they just don’t pay well.”. As is proposed by most futurists, Kai-Ful Lee quotes “health and personal care” as a great example of such a domain: “The U.S. Bureau of Labor Statistics has found that home health aides and personal care aides are the two fastest growing professions in the country, with an expected growth of 1.2 million jobs by 2026”.

3. Temporary Conclusion  

To summarize, I believe that there are some excellent points in this book that make it a must-read, especially for anyone who is planning to use AI in their own business:

• The importance of data collection is well spelled-out, not only the quantity but also the quality and the virtuous circle that links usage data, data collection, data enrichment and value creation.

• The description of the Chinese digital and AI entrepreneur ecosystem is fascinating. Kai-Fu holistic vision of what it takes to succeed should be required reading for anyone in a public position planning to “foster” AI development and competitiveness.

• The book is full of great examples of applying today’s AI to real-life situations. This book echoes the NATF recommendation to companies: what matters to succeed with AI is the willingness to try and the speed at which you execute.

On the other hand, there are a few debatable points. They do not change my positive opinion about the book, but they mean that this book does “not cover the whole story”. More precisely:

• The book puts a very high emphasis on data and describes “a new world where expertise may be harvested from data”.  Actually, in most cases, data-driven knowledge benefits from prior expertise and does not replace existing knowledge but augment it. There is more to AI than Deep Learning, and there is more to creating value with deep learning than brute-force application independently of the expertise put in labelling.

• There is a strong claim that “this era will reward the quantity of solid AI engineers over the quality of elite researchers”. Here also, this statement applies to the “present of AI” but not to what will happen in the future. As is pointed out by Melanie Mitchell, there is so much that we do not know how to do that we clearly need “elite researchers” to push out new boundaries. Let us illustrate this with a thought experiment: If we are in 2028 and look at AI usage, what do you think the ratio will be, of the value created by today’s methods versus the new methods that will be invented in the decade to come: 10, 1, or 10% ? I have no crystal ball and thus no answer. But I would disagree with anyone to claims to know.

• As I stated earlier, some of the strong claims made by Kai-Fu Lee about AI newcomers, such as “the widespread adoption of deep learning in 2013 turbocharged these capabilities and gave birth to new competitors, such as Element AI in Canada and 4th Paradigm in China. These startups sell their services to traditional companies or organizations, offering to let their algorithms loose on existing databases in search of optimizations”, will have to be checked a few years from now.

I have been following the comments about the book on Twitter and found that I agreed with both sides. I am a big fan of this book, especially because of the wonderful insights about the China ecosystem. I have had the privilege to work with Chinese teams and partners in my past three jobs and to visit the Shanghai startup ecosystem. Reading the book has helped me to better understand what I saw (in the same way that reading “Start-up nation” is a must to go and work with Tel-Aviv entrepreneurs). I have also agreed with the negative comments that said approximately : “wait … there so much more about AI than what is said here” and those opportunities could be explored everywhere in the world, including France and Europe.

As stated in the introduction, this paradox can be easily explained with the reading of Martin Ford’s “Architects of Artificial Intelligence”. This will be the topic of “Part 2”.