https://bit.ly/3ZaakJ7 - the book to read this summer if interested in #AI. This link provides my detailed review with some additional points. Mustafa Suleyman #TheComingWave #books #bookreview

The Coming Wave: Technology, Power, and the Twenty-first Century's Greatest Dilemma by Mustafa Suleyman
My rating: 5 of 5 stars

Before the review, it is important to mention that this reviewer has rarely agreed with any books more than this one on almost any topic. I have written on at least a dozen topics covered in the book in various past reviews and my LinkedIn posts with almost identical conclusions on AI's impact. Now on to the long review:

Mustafa Suleyman's The Coming Wave offers a compelling narrative of the promises and perils of artificial intelligence, emphasizing the urgency of collective action to mitigate its risks before they spiral out of control. The book excels in illustrating the far-reaching impacts of AI, although these sections are short and sporadic. It falls short in its central theme of potential solutions.

As the founder of DeepMind, Suleyman provides an insider's, authoritative perspective on the recent advances. While AI optimism pervades the book, with the topic being its risks, these sections' message on transformative power is often too hurried.

That said, the book convincingly argues that we have moved beyond the rudimentary applications of AI, such as chatbots and photo editing tools, to a new era where machines can think on their own. Techniques like deep learning and transformers have enabled AI to tackle complex real-world tasks like protein folding, autonomous driving, and understanding of human languages that were far beyond their capabilities until now. The author effectively argues these are not incremental advances but a paradigm shift – AI can now learn and reason independently in ways that were unimaginable even a few years or even quarters ago. Many who feel AI has been around for years completely miss this point: various giant thresholds have been crossed, and we all need to think about AI anew to appreciate its impact from hereon truly.

The main positive effect of AI is how it has begun to turbocharge innovation across sectors globally. It can deal with the complexity of orders higher than ours and exponentially rising. In this reviewer's language, we have been tackling all the life's NP-hard problems at a particular level defined by the limitations of our 100W-powered biological neural networks, aka brains. Our solutions have been the best so far, as nothing in our toolkit could process the most elementary of our level complexity issues, let alone anything higher. This last fact is no longer true.

In other words, machines of higher capabilities will have repeated relooks at the complicated problems of all life's domains, with the promises of solutions that will revolutionize fields as far away as synthetic biology and robotics to drug discovery and new chemicals and everything in between including quantum computing, battery alternatives, superconducting candidates, etc. Whether linked to machine vision or solving micro-scale climate tech issues, supply chains, policy regulations, and finance - none of our domains will likely remain untouched. More importantly, the changes will be hyper-paced, with the risks of better technologies around the corner forever even before any new solution settles, all accentuated by these technologies building on each other, with quantum computing plus AI an obvious hyperscaling candidate.


This ability to acquire and deploy knowledge at a more complex level makes AI a universally applicable technology. Suleyman illustrates this through examples like AlphaFold cracking protein folding, then showing how the same system can learn physics or math.

On the one side, The Coming Wave refreshingly looks beyond common Silicon Valley chat/edit examples of AI's emerging impact. On the other, the book's focus is squarely on risks. The author balances every positive prognostication with a historian's lens, cautioning against blind techno-optimism. He convincingly argues against optimists who ignore logical risks through the simplistic interpretation of history based on a small number of data points, most famously those who love to site a Malthus or a Ludd.

The author's starting point is simple: AI should not be contained, given the positives, but it cannot be contained given the distance we have already covered in our world of competitive nations, corporates, and ego-driven people. Paradoxically, his eventual suggested solutions are too idealistic and rhetorical to have any chance of succeeding in a world where no two central authorities have the same moral, ethical, political, or legal framework, not just across nations but even within the same nation between rival parties.

To this reviewer, the author should have focussed on the best defensive strategies given his technological skillsets and understanding. Good, conscientious folks should continuously work on mitigating risks by building rival technologies - like anti-virus or anti-missile technologies - that perpetually monitor early signs of malfeasance. Yes, policymakers, too, must come together to do their best on global guardrails, but it is unlikely high-level global agreements can prevent much of anything the author warns about through the book.

Other societal risks, like technological unemployment, are more important, but they do not get the same treatment as risks of malintent or machine errors. Most discussions on these topics, including issues like bias, transparency, and privacy invasions, have little freshness.

In fairness, few books could offer definitive solutions to challenges this enormous and complex. If anything, this ambiguity leaves readers recognizing their own role and agency in shaping the AI future. The book succeeds most in giving readers a conceptual foundation to wrestle with the coming wave.

The book dwells heavily on historical examples to frame AI. While partly instructive for context, extended discussions about innovations like electricity feel dated and distracting compared to the remarkable technological forces described elsewhere. More perspective from the bleeding edge of research could have reinforced the book's vital message around AI's transformational potential.

The discussions on the complexity above are my own, although they mirror those of the author's. Before leaving the review, let me add some more personal views on the same topic, although this has no connections with anything in the book. The book does not recognize that we have likely built a completely different form of intelligence using the latest neural network methods, one that is likely far superior to human intelligence and constantly improving. This new intelligence may or may not beat The Turing Test. We may constantly encounter examples where humans do things better or differently, but this will not contradict our machines' new ability to deal with higher forms of complexity.

It is the machines' ability to deal with a higher level of complexity that is revolutionary and transformative. Ever since Turing, the arguments that machines would need to mimic human behavior to be considered intelligent have been fundamentally flawed and, at times, regressive. We don't need to understand a dolphin's language to know that we are more intelligent, and similarly, machines don't need to mimic us to surpass us in intelligence.

Lastly, through unsupervised learning, machines create their own languages, classifications, tags, etc., to analyze structures like genes, proteins, vision, and everything else. This allows them to approach various scientific queries in ways our languages, including coding languages, were incapable of. For example, deciphering a gene would never be possible using our language dictionaries and any human-driven categorization. The same happens in LLMs through machines' indecipherable ways of dealing with tokens and converting them into usable symbols. This is another new methodical change with far-reaching implications everywhere.

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