Kuttner: Trump And Trade: A Plus For Workers? [feedly]

Trump And Trade: A Plus For Workers?
http://www.huffingtonpost.com/entry/trump-and-trade-workers_us_5857dd8de4b08debb789ca5e

On a good day, Donald Trump can fool some people into thinking that he will be a change for the better on trade policy, and by extension on American jobs. He's for keeping more jobs in the US, renegotiating NAFTA, and taking a tougher line with China.

He did a cute publicity stunt, strong-arming Greg Hayes, the CEO of Carrier's parent corporation into keeping several hundred jobs in Indiana (lubricated by tax breaks). 

Progressives were on the verge of killing the misconceived Trans-Pacific Partnership, when Donald Trump administered the coup de grace—and took the credit.

Trade deals like TPP, and NAFTA before it, signaled American workers that trade policy was mainly for corporate and financial elites, not for regular people. Despite the repeated claims that these deals would produce expanded benefits for all, the benefits went to the top.

The fact that Bill Clinton, Barack Obama, and Hillary Clinton all promoted NAFTA and TPP (until Hillary awkwardly tried to walk back her support), split the progressive coalition and helped Trump.

Some pro-business economic nationalists, such as Alan Tonelson, have contended that progressives, therefore, ought to be applauding Trump's trade initiatives. 

Should they?

Trump's top adviser on trade, Dan DiMicco, is former CEO of Nucor Steel, a very successful (and viciously anti-union) mini-mill producer, which has on occasion filed trade complaints against China. It's not clear whether DiMicco will get a job in the administration, but DiMicco supports U.S. manufacturing and is very familiar with the games that China plays. 

Trump's Commerce Secretary-designate Wilbur Ross is also a longtime critic of the U.S. government's failure to get tougher with China.

Trump's people are already reaching out to some progressive activists on trade. It makes sense to listen, even to make suggestions, but then to be very, very skeptical of the results.

If we go back to first principles, what's wrong with U.S. trade policy?

For one thing, it has promoted a set of global rules that define ordinary forms of financial, labor, health, safety and environmental regulation as violations of free trade.

Secondly, trade policy has promoted deals like NAFTA that not only make it easier to export and outsource jobs, but create extra-legal private tribunals to which corporations and banks can file complaints and not have the decisions subject to court review.

Third, trade policy has failed to challenge the mercantilist practices of other nations that close foreign markets to U.S. exports and leave American producers vulnerable to subsidized imports. That has caused the Midwest to hemorrhage jobs—and again, opened the door to Trump.

In the 1970s and 1980s, U.S. trade policy displayed these odd indulgences because state-led economies like Japan and Korea were good Cold War allies. More recently, American presidents have failed to get tough with China—no ally―in part because China cut a deal with American financiers to give them a piece of the action (thank you, Robert Rubin) and in part because U.S.-based multinationals are quite happy to produce in China's low-wage, subsidized factories.

In other words, trade policies under both Democratic and Republican presidents have helped American industry and finance sell out American workers. This was the year that somebody called them on it, and workers noticed.

But what will Trump do now, and where, if anywhere, is there common ground with progressives?

Photo-ops with executives pressured into keeping a few more jobs at home may be smart politics for Trump, but they don't add up to a trade policy.

It helps to remember that America's misguided trade policies are part of a suite of policies that have been bad for workers. The others include financial deregulation, inadequate labor regulation, tax policies that promote outsourcing, insufficient public investment and a war on unions.

Trump's policies in all of these other areas are likely to make things worse, not better. Just look at who he is appointing to key labor, environmental and regulatory posts.

It also helps to remember that Trump's administration is turning out to be corporatist. If Trump tries to tell his business allies where to produce at more than token levels, the corporate pushback will be yuge.

Photo-ops with executives pressured into keeping a few more jobs at home may be smart politics for Trump, but they don't add up to a trade policy.

That said, Trump has decided to ally with Russia and get tougher with China. You could imagine Trump taking a harder line against China's subsidized exports. The U.S. government has the authority to initiate anti-dumping trade cases, but with America's kid-gloves policy towards China, that not has been done since the 1980s.

In industry after industry, complaints and the cost of pursuing them have had to come from private parties―unions and companies. If Trump were to change that policy, it would be hard for progressives not to applaud, even while holding their noses.

For instance, New York City just signed a contract to use public money give a Chinese state-owned company, the China Railway Rolling Stock Corporation (CRRC) the contract to build at least 1,025 new subway cars. CRRC has already built about 1,000 subway cars for Boston and Chicago. As part of the New York deal, the Chinese state company gets to acquire a U.S. producer of rail cars. That aspect of the deal required the approval of President Obama, and certification that the deal did not have national security implications, over the objections of a rare bipartisan group of 42 senators. 

Deals like this happen all the time. If would not be hard for Trump, as a good New Yorker, to insist that this contract go to an American producer. That would be a nice symbolic demonstration of concern for U.S. industry and jobs, as well as a way of showing up the Democrats.

Trump is a master of the symbolic stunt, and on trade he actually has some advisers who know what they are doing.

Trump may try to keep more jobs at home―but by destroying social standards he assures that they will be low-wage jobs. For decades, progressives have been calling for a new global trade regime that helps raise rather than lower social standards, in labor, the environment, health, and human rights. Whatever else Trump delivers, he will not deliver that.

Trump may try to keep more jobs at home―but by destroying social standards he assures that they will be low-wage jobs.

What he might deliver, though, is a form of economic nationalism that helps his corporate allies, while doing little if anything for American workers, with the exception of workers in extractive industries, a relative handful of production workers, and some construction jobs if he gets serious about infrastructure (though he also supports killing the Davis Bacon Act which supports construction wages).

As the outlines of his policies become clearer, there may be occasional points of convergence, such as the mercy-killing of TPP, and the retention of some jobs at Carrier (though it only took Trump a little while to trash the president of the union local), and some get-tough stuff with China.

Here is the real risk. A moderately tougher trade policy could take the spotlight off the net effect on workers. Regulatory relief and lower taxes for industry plus the trashing of unions and labor standards may create more jobs, but with wages and career horizons that are even lower.

As Trump goes through the motions of a pursuing a trade policy that serves the people who voted for him, our job is to be very careful not to be gulled or co-opted, to keep pointing out what a real pro-worker trade policy looks like, and to challenge Trump to support one.

Robert Kuttner is co-editor of The American Prospect and professor at Brandeis University's Heller School. His latest book is Debtors' Prison: The Politics of Austerity Versus Possibility.

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Keepin’ it real on the growth slowdown: the first of many factoids from the 2017 ERP [feedly]

Keepin' it real on the growth slowdown: the first of many factoids from the 2017 ERP
http://jaredbernsteinblog.com/keepin-it-real-on-the-growth-slowdown-the-first-of-many-factoids-from-the-2017-erp/

President Obama's Council of Economic Advisers just released their final Economic Report of the President and it is the perfect holiday gift for your favorite econo-nerd. (It's also, I fear, the last cogent ERP we might see for a bit.)

So, for the next few days, I'll highlight some of the arguments in there that really resonated with me and some of my colleagues.*

Episode 1:

Whenever people bemoan the slowdown in GDP growth in recent years, part of me moans with them but part of me doesn't, because some of the growth deceleration is a function of slower population growth. Remember, growth is basically productivity plus labor input, and an aging population tends to slow the latter.

Thus, whenever you're making long-term historical comparisons over periods where this population growth factor is in play, you must account for it, by looking not simply at real GDP growth, but at some measure of per-capita growth.

Think of it this way. Suppose GDP's growing at 3 percent, and the population is growing at 1 percent. Thus, GDP/capita is growing at 2 percent. Now, suppose both slow half-a-percent. You could complain about slower aggregate growth, but on a per-capita basis, growth hasn't changed at all. And that means there's the same amount of income per person to go around (obviously, we're not talking about inequality yet–that's to come in later posts).

The figure below shows the sharp deceleration in the growth rate of the working-age population, meaning we'd expect overall growth to slow, just based on the decelerating trend of this input.

Source: 2017 ERP

The next figure is one I made, but totally ripped off from the ERP (Fig 2-viii; I remade it so I could calculate the "slowdown" bar, which isn't in their figure; the current cycle looks a little different in my figure, maybe because I used more recent data). Overall GDP growth slows by a very significant 1.9 percent in the first set of bars. But the deceleration is half that much if we use the working-age population, and about a third if we look at "per labor-force participant." (I'd argue that the slowdown under this latter measure is a bit biased by weak labor force participation having to do more with insufficient demand than demographics; i.e., it's "endogenous".)

Source: My version of Fig 2-viii from 2017 ERP, (BEA, BLS)

Trust me when I tell you that none of us is downplaying the importance of that slower growth rate under these working-age population-adjusted measures. If anything, those negative bars represent what I (and I believe CEA) consider our most pressing macroeconomic challenge: the slowdown in productivity growth (about which the ERP has many excellent figures which I'll parade out soon).

But it is somewhere in between incomplete and misleading to complain about the slowdown in GDP growth without accounting for the sharp slowdown in the growth of the working-age population.

*Do not confuse this with my Best CBPP Charts of 2016, as that's still to come. IKR!: an embarrassment of riches.

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Bernstein: Inequality, technology, globalization, and the false assumptions that sustain current inequities [feedly]

Case: What is it that makes "fair" trade -- trade that justly spreads the gains from train -- "impossible"?? -- only politics, IMO!

Inequality, technology, globalization, and the false assumptions that sustain current inequities
http://jaredbernsteinblog.com/inequality-technology-globalization-and-the-false-assumptions-that-sustain-current-inequities/

Here's a great interview with inequality scholar Branko Milanovic wherein he brings a much-needed historical and international perspective to the debate (h/t: C. Marr). Many of Branko's points are familiar to my readers: yes, increased trade has upsides, for both advanced and emerging economies. But it's not hard to find significant swaths hurt by globalization, particularly workers in rich economies who've been placed into competition with those in poorer countries. The fact that little has been done to help them is one reason for president-elect Trump.

As Milanovic puts it:

The problems with globalization arise from the fact that gains from it are not (and can never be) evenly distributed. There would be always those who gain less than some others, or those who lose even in absolute terms. But to whom can they "appeal" for redress? Only to their national governments because this is how the world is politically organized. Thus national governments have to engage in "mop up" operations to fix the negative effects of globalization. And this they have not done well, led as they were by the belief that the trickle-down economics will take care of it. We know it did not.

But I'd like to focus on a related point from Branko's interview, one that gets less attention: the question of whether it was really exposure to global trade or to labor-saving technology that is most responsible for displacing workers. What's the real problem here: is it the trade deficit or the robots?

Branko cogently argues that "both technological change and economic polices responded to globalization. The nature of recent technological progress would have been different if you could not employ labor 10,000 miles away from your home base." Their interaction makes their relative contributions hard to pull apart.

I'd argue that the rise of trade with China, from the 1990s to the 2007 crash, played a significant role in moving US manufacturing employment from its steady average of around 17 million factory jobs from around 1970 to 2000, to an average today that's about 5 million less (see figure below; of course, manufacturing employment was falling as a share of total jobs over this entire period).

Source: BLS

The relative trade balances, shown in the next figure, underscore this point, as China's surplus grew sharply in the 2000s while the deterioration of our trade deficit accelerated (source: Autor et al; I'll discuss the reversal of these trends below).

Source: Autor et al

Finally, the "robots did it!" story requires an acceleration in productivity growth. The next figure shows manufacturing productivity growth (yr/yr) since 1987 (also, read Sue Houseman's very important work for the full story here). It's awfully jumpy, so I've plotted a 5-yr rolling average. There's a bit of acceleration in the 1990s or 2000s, and I'm not denying automation has played a role in the pattern in the employment figure above. But it's unlikely to be the whole story and that story is particularly strained today, as manufacturing productivity growth has crashed in recent years.

But my main point here is that it's a mistake to either believe that trade and technology are clearly separable forces, or to think that it matters to workers which force is displacing them. Too often, policy makers seem to assert that, because "it's not trade, it's technology!"—typically offered without much evidence—displaced workers should somehow be assuaged. Hey, all they need to do is go from running a drill press to designing, building and programming drill-press-running robots!

True, trade and China are much more tangible targets, and I get that they sometimes carry more symbolic weight than they should. That's particularly true regarding China right now, which is trade enemy #1 in the Trump playbook, despite the fact that their trade surplus has been falling and, if anything, they appear to be trying to prop up the value of their currency (the trade play is to depreciate).

But remember, automation and labor-saving technology are ongoing forces that have been with us forever. Historically, increased demand for the extra output created by faster productivity growth absorbed displaced workers, sometimes in new sectors.

We must understand why that hasn't happened in recent years. The productivity evidence over the past decade contradicts an accelerating automation explanation. I think the evidence instead supports an explanation that exposes false assumptions:

–the winners from expanded trade would compensate the losers
–regressive tax cuts would trickle down
–trade deals centered on corporate interests would somehow help laborers
–full employment would automatically persist (even in the face of large, growth-draining trade deficits)
–austere fiscal policies amidst weak private demand would magically have anything other than their predicted, negative effect on growth
–the safety net, minimum wages, and other labor standards must be diminished to create the right micro-incentives

All of the above distribute income upward, and not just income, but just as importantly, power and the political influence that makes reversing these false assumptions an extremely heavy lift. The system effectively insulates itself from progressive change.

I don't have the answer to the question of what breaks this extremely damaging chain, but I'm sure it involves some serious organizing of the many hurt by this dominant agenda against the minority helped by it. At any rate, a good place to start is a clear-eyed identification of the problem.

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UN Special Rapporteur offers sharp criticism of American temporary foreign worker programs [feedly]

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UN Special Rapporteur offers sharp criticism of American temporary foreign worker programs
// Economic Policy Institute Blog

On December 19, one day after International Migrants' Day, Maria Grazia Giammarinaro, the United Nations Special Rapporteur on Trafficking in Persons, Especially Women and Children, issued a statement regarding her official visit to the United States to assess the country's state of affairs on human trafficking. During her trip, Giammarinaro met with government officials, diplomats, trafficking survivors, and representatives from civil society. While she praised the United States for developing "an impressive number of laws and initiatives which focus on the protection of victims," especially the Victims of Trafficking and Violence Protection Act and its subsequent reauthorizations, she offered up sharp and insightful criticisms of the nonimmigrant visa programs that temporarily authorize migrants to work in the United States:

The legal framework governing temporary visas for migrant workers, especially H-2A visa for temporary or seasonal agricultural work and H-2B visa for temporary or seasonal non-agricultural work visas, is of particular concern as it exposes applicants to the risk of exploitation, including human trafficking. Workers holding these temporary visas are tied to a specific employer who can exercise extensive control over them. Employers often confiscate passports, withhold wages, terminate contracts arbitrarily and threaten employees with job loss and deportation. Some live in deplorable housing conditions, commute long distance and enjoy low benefits. This is a serious problem in itself, but it is exacerbated by the fact that concerned workers may fear that if they report abuses, they will be deported or denied future visa applications. This situation creates vulnerabilities to labour exploitation, such as unsafe working conditions and isolation, especially in rural areas where there are fewer service providers. In order to prevent further harm, it will be essential to amend the regulation governing these temporary visas, as well as to those of Exchange visitor (J-1) and domestic workers (G-5) visas, and make visa "portable" to allow workers to change abusive employers.

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What to Learn from US Govt Strategy on AI [feedly]

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What to Learn from US Govt Strategy on AI
// Digitopoly

[This post was co-authored with Ajay Agrawal and Avi Goldfarb. A shorter version was published in HBR Online on 21st December 2016. Also, the post does not review a new White House paper on AI and its impact released on 20th December 2016, that cites some posts on this blog.]

On October 12, 2016, President Obama's Executive Office published two reports that received less media attention than they might have otherwise because the United States was gripped by the final weeks of a presidential campaign race. In these two reports, the administration laid out its plans for the future of artificial intelligence (AI). Depending on one's view of AI's potential impact, the actions resulting from these reports may be more influential on the long arc of history than the outcome of that election.  The combined reports include eighty-eight pages and twenty-five recommendations. What does all this mean for other countries? We summarize the seven most important insights for governments of other countries, such as Canada, that collaborate, trade, and compete with the US.

#1 – The US government's approach to AI reflects a sense of urgency.

The two reports were prepared with remarkable speed, especially relative to the traditional pace of government.  On May 3, 2016, the White House announced the formation of a new National Science and Technology Council (NSTC) subcommittee, "Machine Learning and Artificial Intelligence," along with a series of public workshops.[1] Six weeks later, on June 15, 2016, that subcommittee directed another subcommittee to draft a separate AI plan focused on R&D.[2] Four months later, the two subcommittees each published a substantive report, both of which articulate a series of recommendations for action.[3]  Thus, in only six months, the US government set a priority, enlisted key individuals, and produced a comprehensive national plan for AI.

#2 – They're not treating AI as a science project; it's commercially important.

The plans call for a significant increase in both the human and financial capital allocated to AI. The wide-ranging importance of AI is reflected in the people assigned to the NSCT Subcommittee on Machine Learning and Artificial Intelligence, which includes representatives from the Departments of Commerce, Treasury, Transportation, Energy, Education, Justice, Health and Human Services, Labor, State, as well as the Department of Defense, Department of Homeland Security, Central Intelligence Agency, National Security Agency, and the National Security Council.  Furthermore, in a separate report, the Chair of President Obama's Council of Economic Advisers, Jason Furman, wrote: "The biggest worry I have about AI is that we will not have enough of it, and that we need to do more…"[4] Echoing this point, President Obama subsequently remarked: "The analogy that we still use when it comes to a great technology achievement, even 50 years later, is a moonshot. And somebody reminded me that the space program was half a percent of GDP. That doesn't sound like a lot, but in today's dollars that would be $80 billion that we would be spending annually … on AI."[5]

#3 – China is a leader, not a follower or a copycat.

Perhaps the most perplexing part of the report is the stark contrast between a dramatic illustration and the accompanying blasé text.  Figures 1 and 2 (copied below) plot the number of scientific and technical publications mentioning "deep learning" or "deep neural network" by nation over the nine-year period 2007-2015. Although slightly different approaches are used to count publications, both figures illustrate the same striking finding. Over the past four years, the US and China grew their research output in AI significantly faster than other countries, with the US initially emerging as the worldwide leader. However, over the last two years, China surpassed the US by these measures of research output.  Given the importance of AI reflected in every other aspect of this report, it's surprising that the rise of China and implications for competition in research and human capital receives only one sentence in the text: "The trends also reveal the increasingly global nature of research, with the United States no longer leading the world in publication numbers, or even publications receiving at least one citation."  That's all the report says.  China is never explicitly mentioned even once in the text of either report.

Given the likely increasing returns in this area such that small leads are likely to grow into larger leads due to network effects (better AIs attract more users, which in turn generate more data and further improve the AIs), localized learning spillovers, and virtuous circles attracting talent (stars attract other stars), it's surprising that both reports are silent on the topic of competition with China.  Perhaps the authors of the report believe the data reported in Figures 1 and 2 are not good measures of meaningful research. Alternatively, perhaps the real policy response (an aggressive international recruiting campaign for top AI research talent) is not meant for public consumption. The founder of Chinese company Baidu, Robin Li, expresses his view on the importance of this technology for China: "when the age of AI arrives, the [internet of things] will become a big market and completely change manufacturing. I think that in the future all manufacturing will be a part of the AI industry… China is a manufacturing giant, and I think we need to really pay attention to AI tech development…"[6] Every country with AI research expertise that wishes to participate as producers, not only consumers, on the frontier of this technology and the associated economic and social opportunities should prepare for intensifying labor market competition, especially from the US and China.



Figures 1 & 2 from The National AI R&D Strategic Plan (p. 13)

#4 – There is no clear vision regarding where to focus research funding.

The R&D report notes that the private sector is investing heavily in AI and at an increasing pace, such that it is important for the government to focus their resources on the types of AI research that the private sector will be less likely to support: "…this plan assumes that some important areas of research are unlikely to receive sufficient investment by industry as they are subject to the typical underinvestment problem surrounding public goods." (p. 6) However, although the report provides a long list of research topics, it does not delineate which are commercial versus requiring government support.  Perhaps that is because it is so difficult to predict which areas of research are unlikely to have reasonably immediate commercial value.

At present, AI is a field where the line between basic and applied research is so blurred and the future so poorly understood that even the most advanced governments have little insight other than the generic theme to focus on "fundamental and long-term" R&D.  This is consistent with the observation that several of the most advanced companies in the field uncharacteristically appointed academics to lead initiatives critical to their business rather than recruit full-time employees to do the job (e.g., Yann LeCun – Director of AI Research at Facebook and Professor at NYU; Andrew Ng – Chief Scientist at Baidu and Professor at Stanford; Ruslan Salakhutdinov – Director of AI Research at Apple and Professor at CMU).  This lack of clarity presents an opportunity for other countries. It is precisely how a small country like Canada was able to compete with the US – a country ten times its size – to become a leader in the development of two important subfields of machine learning: deep learning and reinforcement learning. Nations that back domestic star scholars or junior researchers with a promising trajectory who are focused on unique approaches to fundamental problems may develop expertise in areas that are critical to the advancement of the field and thus earn a competitive position in the affected markets and industries.

#5 – The US government has a good roadmap for AI infrastructure.

While neither report offers clear insight into how to allocate resources to research, the reports do offer a series of compelling approaches to other forms of support for AI that are likely to be underserved by private-sector investment.  The four most salient are:

1) Regulation: The bottleneck to deployment of AI-enabled, society-enhancing products and services in a number of domains is shifting from technology to regulation, such as those associated with the use of autonomous vehicles and unmanned aircraft systems.  The report notes: "where regulatory responses to the addition of AI threaten to increase the cost of compliance, or slow the development or adoption of beneficial innovations, policymakers should consider how those responses could be adjusted…" Well-designed regulations will influence the rate and direction of innovation by creating incentives for the private sector to invest along trajectories that will most benefit society.  For example, regulations regarding transparency (provision of an explanation for an AI-generated determination, e.g., "Why is the AI recommending a treatment that is different from what the human doctor recommends? What is its reason?"), efficacy, and fairness, based on evidence-based verification and validation, will create incentives for the private sector to invest in research to address these issues (e.g., the provision of transparency), which will make AI more governable.

Regulation offers another area for regional differentiation and competition. For example, jurisdictions that are first movers in regulations that are friendly towards self-driving cars may attract significant investment, not only from car companies, but also from complementary industries (e.g., intelligent road infrastructure, delivery services, autonomous fueling services, disabled person services, etc.), even though that region is not a leader in AI research. The many regulations that will be challenged by AI-enabled technologies in industries as diverse as medicine, finance, transportation, safety, and justice will create significant opportunities for non-market competition. Countries do not need to be leaders in AI research to compete on this dimension.

2) Education: The reports recommend investing in developing AI talent at all levels and also investing in a data-literate citizenry that will enhance the value of all humans in an economy that employs an increasing level of machine intelligence. The returns to investment in education will vary across countries, depending on the extent to which their labor force participates in industries that either produce or consume AI-enabled products and services.

3) Public Service: The government can use AI itself to serve the public faster, more effectively, and at lower cost.  This includes mundane tasks such as faster bureaucratic processes (e.g., issuing driver's licenses) as well as complex applications such as cybersecurity (lower cost, more agile) and weapon systems (safer, more humane).  This will be an important area for competition across jurisdictions. National and regional leaders who understand the opportunities made available through the development of AI will incorporate that into their strategies and invest accordingly.  Those who invest early may benefit not only from increased productivity but also from learning about how AI-enabled products and services work that may provide additional advantages in subsequent strategy decision-making. Of course, early adopters also risk exposure to the costs of learning and less-tested technologies.

4) Information: The reports call for the US government to "monitor the state of AI in other countries" and also calls on industry to "keep government updated on the general progress of AI in industry, including the likelihood of milestones being reached soon" (p. 41). These recommendations underscore the unpredictable and nonlinear nature of AI research. The committee is concerned that sudden significant increases in AI capability are likely, have non-market implications (security, massive shifts in the distribution of wealth, etc.), and may emerge in other countries or in the private sector. It is in every country's interest to follow this sage recommendation to "monitor the state of AI" as developments could be far-reaching and invoke significant economic and social effects.

#6 – The US government wants to expand their AI workforce.

After 36 pages of discussion, the R&D report concludes with two recommendations: 1) coordinate across federal agencies, and 2) create and sustain a "healthy" AI R&D workforce.  Expanding the AI R&D workforce is a key theme across both reports as well as Chair Furman's paper. There are two ways to grow the workforce: 1) by training students at home, and 2) by importing talent from abroad.  The reports are silent on how much emphasis the government should place on each approach.  This depends partly on how long it takes to develop talent domestically. With an expanding set of resources for lowering the barrier to entry in the field (e.g., open-source software libraries for machine learning such as TensorFlow, Caffe, Theano, and MOOCS for learning machine learning online) some skills can be developed in short order.  However, developing the PhD level expertise required to conduct research and make fundamental breakthroughs takes time.  This level of talent likely needs to be aggressively recruited to the US in order to achieve the stated objectives with the urgency implied in the reports.  Other countries that have significant pools of AI talent, most likely in their university computer science departments, and that have aspirations to participate at the frontier of AI-related fields, should brace for increased competition to keep their human capital at home.

#7 – The US government is not designing policy for general intelligence.

The reports define artificial general intelligence (AGI) as "a notional future AI system that exhibits apparently intelligent behavior at least as advanced as a person across the full range of cognitive tasks." The NSTC subcommittee takes the position that current policy should not be influenced by aspirations to achieve AGI: "The NSTC Committee on Technology's assessment is that long-term concerns about super-intelligent General AI should have little impact on current policy." The committee takes this position for three reasons: 1) many experts believe that AGI is not feasible in the short or medium run; 2) the authors assume the best way to prepare for AGI is to "attack risks" from narrow AI, such as security, privacy, and safety; and 3) the policy recommendations for AGI are unknown and may conflict with those for narrow AI, which is more certain and with immediate economic implications (implied). This approach to AGI, that it should have "little impact on current policy," is interesting because it stands in stark contrast to the views advanced by organizations that focus specifically on AGI such as the Future of Life Institute at MIT, the Future of Humanity Institute at the University of Oxford, and the Machine Intelligence Research Institute at the University of California at Berkeley.  This creates an opportunity for other countries to differentiate by designing policies predicated on the assumption of achieving AGI sooner, creating an environment that is more attractive for AGI-related investment (e.g., regulatory frameworks that grant property rights and agency to machines).

Conclusion

Overall, these reports represent an important first step in the US government's AI strategy.  The new administration will decide which of these recommendations, if any, it will follow.  One of the recommendations calls for "The Executive Office of the President to publish a follow-on report by the end of this year, to further investigate the effects of AI and automation on the US job market, and outline recommended policy responses."  If that recommendation is followed, then the resultant new report will set the tone, not only for the US, but for all OECD countries with respect to how they prepare to compete amidst this backdrop of technological advance and its effect on the workforce.

[1] https://www.whitehouse.gov/blog/2016/05/03/preparing-future-artificial-intelligence

[2] This was assigned to the subcommittee on Networking and Information Technology Research and Development.

[3] The two reports are: 1) "Preparing for the Future of Artificial Intelligence," developed by the Executive Office of the President, NSTC Committee on Technology – Subcommittee on Artificial Intelligence and Machine Learning: https://www.whitehouse.gov/sites/default/files/whitehouse_files/microsites/ostp/NSTC/preparing_for_the_future_of_ai.pdf; and 2) "The National Artificial Intelligence Research and Development Strategic Plan," developed by the AI Task Force at the request of the Networking Information Technology Research and Development subcommittee of the NSTC: https://www.whitehouse.gov/sites/default/files/whitehouse_files/microsites/ostp/NSTC/national_ai_rd_strategic_plan.pdf.

[4] "Is This Time Different? The Opportunities and Challenges of Artificial Intelligence" by Jason Furman, Chair, Council of Economic Advisors, July 7, 2016: https://www.whitehouse.gov/sites/default/files/page/files/20160707_cea_ai_furman.pdf

[5] Interview on August 24, 2016 published in WIRED: https://www.wired.com/2016/10/president-obama-mit-joi-ito-interview/

[6] https://www.techinasia.com/baidu-founder-ai-future


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Krugman: phony populism

http://mobile.nytimes.com/2016/12/23/opinion/populism-real-and-phony.html?_r=0&referer=

Eastern Panhandle Independent Community (EPIC) Radio: Epic radio on holiday till Dec 30

http://www.enlightenradio.org/2016/12/epic-radio-on-holiday-till-dec-30.html