There are lots and lots of opinions on policy towards and governance of AI. A lot of those opinions are based on recycling the same sets of arguments There are lots and lots of opinions on policy towards and governance of AI. A lot of those opinions are based on recycling the same sets of arguments or facts. Some of those opinions are that others should not have opinions on these matters. Now enter Verity Harding, who has worked in government, industry, and at universities, with a book that is truly additive by bringing new ideas and insights to bear into what is already starting to feel like an old debate. It is also a really fun and stimulating read.
The bulk of Harding’s book is a history of the governance, mostly by government, of three postwar technologies: space exploration, IVF and human embryonic research, and the internet. Each of these are interesting in their own right, filled with lively characters, big stakes, and something that is much harder ex post—a sense of the many different, and worse, possibilities and paths that were not taken because of the choices that were made.
What emerges is a subtle interplay of contingency, individual government actions, the importance of ethics as a North Star and motivation, diplomacy (in some cases), and also the participation, and in some cases, centrality of businesses. The result was a treaty that space should be disarmed, a broad societal consensus in the UK on embryonic research, and the extraordinary rise of the internet as a global system that is not controlled by any one country or corporations (in part because of wise choices made in the United States).
Harding links each of these histories to their relevance but also limitations for thinking about AI. The individual histories are bracketed by a discussion of the rise of digital platforms in the Bay Area, Harding’s thrill and disappointment with them, and then a discussion of what lessons we should take from all of it.
Harding’s commitment is not to a specific policy but instead to a process that respects the importance of government but also the essential role of business, the need for ethics on the part of both players, and a passionate belief that “you” have a role to play as well....more
Chip War is an outstanding history of the microchips from their invention up until just about the current moment (hopefully the eventual paperback ediChip War is an outstanding history of the microchips from their invention up until just about the current moment (hopefully the eventual paperback edition will add some context on the significant recent U.S. policy shift on chips). It is written in a relatively easy and entertaining style that makes it easy to digest what is clearly a work of substantial depth of reporting and history. The book covers the global nature of microchips—the rise and fall of the fortunes of different companies and countries, the evolving division of labor in chip production, the ways in which government policy have been integral to developments at various stages but have also failed. Overall it serves an excellent primer on the different types of chips, their different uses and the ways in which their global supply chains operate. It also accomplishes much more with a diagnosis of the current moment and recommendations for the future.
Some of the parts were completely new to me—like the Soviet and then Russian efforts to develop their own microchip industries. Other parts added substantial depth to aspects of the microchip ecosystem we have today, like the origins of the ASML as the only supplier of high-tech and the ways in which TSMC became the only fab for the most high tech chips. One part that was not new to me, but I appreciated because so many people get it wrong, is that he emphasizes that the chip shortage in 2021 and 2022 was largely the result of dramatically increased demand for chips for consumer electronics rather than some worsening of supply—in fact he depicts the efforts China took to keep chip factories running even when everything else shut down due to the pandemic.
Chris Miller does not spend much time discussing the underlying science and engineering except insofar as it is necessary to understand the economics. The description of the cost and scientific/engineering breakthroughs needed for Extreme Ultraviolet (EUV) lithography needed to make even smaller transistors was especially vivid.
Much of the book is about the interplay of civilian and military considerations in the development of chips. Initially the military was the primary consumer of them, buying something like 90 percent (I believe, did not recheck the number). A big breakthrough was putting them in Minutemen II missiles which increased their accuracy enough to mean they could more effectively take out Russian missiles. Over time, however, consumer uses grew while military fell—not to something like 2 percent of total chip production. Moreover it is way too expensive for the military to do its own fabrication and in many cases even its own design, investments that are not just costly but would be obsolescent in a few years time. The increased reliance on consumer demand was a strength of Western and Asian allied innovation, consumer electronics in Japan played a role that the Soviet Union with its largely military industry could never match.
The main purpose of all of this history and analysis is to understand the current moment, particularly the vulnerability of the global economy to anything that happened to TSMC (a massive earthquake or a Chinese invasion/blockade), the United States impressive position as a global chokepoint for many of the most important technologies but lack of anything resembling self reliance, and China’s struggle to overcome its huge deficit in technology and relatively unimportant role in both the global supply chain and producing for its own needs. While I mostly agree with the author’s diagnosis and policy advocacy at times he was a bit overly simplistic and editorial in his judgments of the Obama administration, the battles within the Trump administration and where policy is now. Some of that read less like history and more like an oped written in the heat of the moment.
Overall, Chip War is a fabulous guide to the global economy, appreciating one of the major security challenges facing the United States, and formulating a better understanding for handling it going forward. As an up-to-the-moment guide it leaves the reader excited to read the sequel—does Moore’s Law still hold? What happens to Intel? Does China start making higher-tech chips? Does Taiwan stay peaceful? I look forward to watching all of this play out in real time—and it might play out slightly better if policymakers and the public are better informed by reading this book....more
AI Superpowers several short books in one: a fantastic book about China’s recent internet innovation, a very good book about the current and near futuAI Superpowers several short books in one: a fantastic book about China’s recent internet innovation, a very good book about the current and near future of AI, a sometimes moving but mixed personal reflection memoir and a terrible set of predictions about the economics of AI. It also doesn’t ask some of the most fundamental questions one would want in a book like this.
Leading Chinese AI researcher and internet innovator (founder of Microsoft Research China, Google China, and now a leading venture firm Sinovention Ventures) Kai-Fu Lee sets out to compare the prospects of AI development in China and the United States. Many would give the United States the edge because of our greater degrees of creativity and cutting edge research whereas China, to date, has done more to copy, adapt and apply. Lee argues that this misses what is actually going on in AI: “the casual observer—or even expert analyst—would be forgiven for believing that we are consistently breaking fundamentally new ground in artificial intelligence research. I believe this impression is misleading. Many of these new milestones are, rather, merely the application of the past decade’s breakthroughs—primarily deep learning but also complementary technologies like reinforcement learning and transfer learning—to new problems... 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.”
Lee argues that the success of this “dirty work” will require four inputs: (1) data, (2) entrepreneurs, (3) AI scientists, and (4) a policy-friendly environment. Taking these four in turn:
DATA: Lee argues that China is collecting much more data and willing to use it, including data that intersects between the real and online world. This seems overwhelmingly true and was not exactly a novel insight. The only open question is how important will data be in the future. My guess is very and Lee’s statement “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.” will remain true. But I don’t think we’re sure, as AlphaGo Zero, for example, was better than the original AlphaGo and used no data. Lee is thinking within the current deep learning paradigm and may be understating the importance of what comes next.
ENTREPRENEURS: This was the most fascinating and novel part of the book. Lee provides a capsule history of the internet in China, the copycat businesses, the failure of American businesses in China, and the emergence of much more innovative firms. At its heart is the argument that recent Chinese entrepreneurship arguing that it is more successful than the American variant because it is hungrier, willing to work harder, less distracted by any purpose other than moneymaking, and more willing to copy instead of innovate. A lot of this seems true but I think devalues the successes of Silicon Valley and underestimates the degree to which Chinese entrepreneurs may become more like that in the future. And perhaps for obvious and forgivable reasons, but some of this discussion misses the role of the Chinese policy—for example the book portrays the exit of Google from China as an unfortunate decision by the company not the result of censorship policies in China.
AI SCIENTISTS: Lee argues there are more and more of them in China, they’re excellent, but not as innovative as Geoffrey Hinton or Yann Le Cunn. But he argues that the innovations are not needed anymore. As discussed above, this is interesting, plausible, but may undervalue the paradigm changing innovations we can’t foresee.
AI POLICY: Lee argues that China has made a concerted policy effort to encourage AI while the United States has effectively ignored it. This is true. The question is how consequential it is. Lee gives the example of China using subsidies to create an AI/innovation cluster outside of Beijing to mimic the dense networks in Silicon Valley. But by analyzing only one success case Lee fails to understand what worked about the policy. Governments around the world have done similar cluster funding with nothing like China’s success, so this hardly seems necessary (Silicon Valley did without) or sufficient. Moreover, I would think future Chinese policy is a big risk to AI if continued clamp downs discourage innovation of certain types in China.
Lee’s analysis leads him to a make predictions about four areas of AI applications and how relative US-China capabilities today will shift over the next five to ten years (e.g., from 50-50 in internet applications to 60-40 in favor of China, 10-90 in autonomous applications today in favor of the US to 50-50, with similar trends in business applications and perception AI).
Lee’s predictions about China’s development in the future and the relative balance of capabilities seems broadly reasonable, subject to the caveats above. But he never addresses the fundamental question: is this zero sum or positive sum? The “Superpowers” of the title would make one think it was zero sum, that there is a first mover advantage and whoever, for example, figures out self-driving cars will own that industry permanently. In reality, in much of AI I think “positive sum” is likely to be the better model as innovations are disseminated, copied or reverse engineered. The entire relative capabilities frame makes much less sense in this positive sum world.
All of the above was the meat of the book and the reason it was much more interesting than even the summaries I read. I would recommend most readers just stop there, about 60% of the way through, and I wish I did too. Instead what follows is an absurd semi-apocalyptic argument that AI will take all our jobs, cause massive inequality, and lead to a huge economic gap between the US and China and the rest of the world.
The AI jobs discussion is economically rudimentary and Lee misses some major points (e.g., he acknowledges AI will create new types of jobs but seems to miss that richer people will want more of old types of jobs, eg will eat out more and thus more restaurant servers). The GDP discussion is similarly rudimentary (I would predict US and Australia GDP closer 50 years from now than US and China GDP, he seems to not understand how these innovations can be used by countries that didn’t make them and the notion of spillovers and convergence). More importantly, there is a huge tension between Lee’s skepticism about the imminence of general AI, his belief that we will not make future paradigm innovations but instead will continue to tinker and apply, and then his claim that existing modeling attempts understate the pace of AI progress.
Finally, Lee has a sometimes moving account of his own battle with cancer, how it changed his priorities towards more balance with life, and why this tells him that future AI policy should center not around UBI but subsidies for volunteering, caring, and other human interaction. This was sometimes moving, a little thought provoking, but also had the usual shortcoming of a highly successful person who can afford to make these types of statements without giving up any of that success.
Overall, I thought I knew the main points of this book from the reviews and that it would not add much. The first 60% of the book did indeed add a lot. Unfortunately, if you paid attention to the last 40% it would also subtract a lot so maybe just skip or skim that part. ...more
An excellent book on the economics of Artificial Intelligence. Steeped in both economics and AI/ML, this book steers clear of hype (or anti-hype), appAn excellent book on the economics of Artificial Intelligence. Steeped in both economics and AI/ML, this book steers clear of hype (or anti-hype), applying standard economic concepts to a rapidly emerging phenomenon. The book is geared to business readers not economists or policymakers but it has a lot to offer to everyone.
At the heart of the book is the concept that AI/ML is a "prediction machine" that is dramatically lowering the cost of making predictions, which will lead to making it cheaper to engage in existing practices but also will open up new possibilities. The authors mostly focus on what businesses need to do in order to take advantage of those opportunities but with a some broader social/political/policy context.
Machine, Platform, Crowd is insightful, engagingly written, a nice combination of economics, business, technology and real world examples, that buildsMachine, Platform, Crowd is insightful, engagingly written, a nice combination of economics, business, technology and real world examples, that builds on The Second Machine Age. Eric Brynjolfsson and Andrew McAfee organize their book around three developments: artificial intelligence, platform technologies and crowd sourcing. The book is "speaking" to managers of businesses, talking about how they can take advantage of these developments in their own businesses. Brynjolfsson and McAfee have no shortage of enthusiasm for these developments but in all cases they present nuanced arguments about the perils of the extremes, like the importance of humans in organizations, the limits of platforms, and the best ways to combine the "crowd" and the "core." They are techno-optimists but also realists, the world they describe is much like our own, with companies, markets, and the like, just all continuing to improve through technology, big data, the cloud, and the like.
Brynjolfsson and McAfee put it very well when they say that it makes no sense to ask if technology will be good or bad for us, because technology is a tool and we can make choices about how to use the tool. The book, however, focuses on how businesses make those choices and the role of policymakers was outside of its scope.
My big disappointment was that no where in the book did the authors grapple with the significant slowdown in productivity growth, why they think it is happening, what they think the outlook is. And not just the aggregate macroeconomic data, but also the increased effort it takes to make progress in individual areas. They have a striking description of how AI is advancing productivity in farming--but they do not point out that we have been making progress in farming for over a century but despite the massive technology we are bringing to bear the progress is not getting any faster....more
Zero to One is lecture notes from a course Peter Thiel taught on startups, it is filled with practical tips and advice but also a broader theory of inZero to One is lecture notes from a course Peter Thiel taught on startups, it is filled with practical tips and advice but also a broader theory of innovation. I have not read any other books in this genre, the business tips seemed unhelpful or superfluous depending on whether you had innovative ideas already or not (and some were almost contradictory, like dominate a small market and work your way out, but if you are in too small a market you will not be able to work your way out).
But the book does an interesting and provocative job of fleshing out the thesis that progress really depends on genuinely new inventions (going from zero to one) rather than incremental improvements (the 1 to infinity) that he associates with globalization. It is not obvious that this is always right, while Thiel is scathing about the solar industries failure to generate large stock market valuations he does not appreciate the steady progress in the efficiency of solar power. And one implication he takes from his thesis is a paen to monopoly and borderline contempt for competition, which omits much of the motivation and source of innovation itself....more