flâneur

Rosalyn Bejrsuwana

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on the atlas — 19

highlights — 39

  • Chaos in the Middle East joins a long list of threats to markets, including gloomy scenarios related to artificial intelligence, trouble in private credit and a loss of faith in indebted governments. Government-bond yields have risen since the crisis began, especially in southern Europe and Britain, which depends on imported lng.
    An attack on the world economy
  • ojtaba Khamenei, now knows that energy prices are America’s weak spot. In Ukraine, which has tested drone defences, some Iranian-style machines still get through. American troops are not about to occupy Iran to stop the launches. America does not have the capacity to defend every tanker, even if it provides them with cheap insurance. Disruption to energy markets will therefore come and go with geopolitical tensions, especially if Iran concludes that it needs a nuclear weapon to be safe.
    An attack on the world economy
  • The fact that transport is a key input to so much of the world economy means that bottlenecks could cause grave harm.
    An attack on the world economy
  • he cannot restore the old energy market. Whatever happens, the world is entering a new era of energy insecurity.
    An attack on the world economy
  • Its doctrine is just the reverse of the doctrine that the end justifies the means. It proceeds, therefore, by subtle ways, and slow ones, and queer, risky ones; rather as evolution does, which is in certain senses its model.
    We can live well, even though we don’t have a higher purpose | Psyche Ideas
  • It could induce despair to give up the idea that we humans come into the world with a preset reason for living or a blueprint for how to make meaning with our lives. Taking this orientation means that there is no reason to live other than the reasons we give ourselves; we have only self-generated purposes to pursue. Instead of evoking despair, I find this idea quite beautiful. While existentially demanding, it is also ethically and politically satisfying to have no fate but what we make. We have no higher purpose. But we do have many ground-level, basic, human-scale, situated, soft, sweet, low…
    We can live well, even though we don’t have a higher purpose | Psyche Ideas
  • There is a real danger of a tit-for-tat testing dynamic unfolding. A Russian test, for instance, would likely prompt the United States, then China, then India, and then Pakistan to reciprocate. The arrows of causation could work in the other direction, too: an Indian test, for example, would likely prompt both Pakistan and China to respond in kind. The latter would then catalyze tests by the United States and then Russia.
    Major Powers Stopped Nuclear Tests in 1998. That Norm Is Now Under Threat. | Carnegie Endowment for International Peace
  • Subcritical experiments provide more technical data about the performance of nuclear weapons than full-scale tests. (By presidential direction, the United States needs to maintain the readiness to test, but that should not conflict with a commitment not to test first.) The United States’ only motivation for conducting a test would therefore be political, namely, if another state does so first. A no-first-test commitment would go a long way to forestalling that scenario.
    Major Powers Stopped Nuclear Tests in 1998. That Norm Is Now Under Threat. | Carnegie Endowment for International Peace
  • The nuclear industry of advanced industrialized countries is under significant pressure to remain competitive as the market landscape for new nuclear power opportunities changes. The relative decline of U.S. nuclear export competitiveness comes at a time when Russia is boosting its dominance in new nuclear sales, and China is doubling down on its effort to become a leader in global nuclear commerce. This report illuminates how the changing market competition among the United States, Russia, and China will affect their future relations with nuclear commerce recipient countries, and discusses wh…
    The Changing Geopolitics of Nuclear Energy: A Look at the United States, Russia, and China
  • . Instead, she wants these warnings to help people skip ahead a few steps and follow a safer path: to focus on inventions that make cars less dangerous, to build cities that allow for easy public transport, and to focus on low emissions vehicles.
    A Guide to Solving Social Problems with Machine Learning
  • One invention was about to do a great deal of harm. It would become one of the biggest causes of death—and for some age groups the biggest cause of death. It would exacerbate inequalities, because those who could afford it would be able to access more jobs and live more comfortably. It would change the face of the planet we live on, affecting the physical landscape, polluting the environment and contributing to climate change.
    A Guide to Solving Social Problems with Machine Learning
  • For people to know when to override, they need to understand their comparative advantage over the algorithm
    A Guide to Solving Social Problems with Machine Learning
  • we need people to actually use them; that is, to pay attention to them in at least some cases. It is often claimed that in order for people to be willing to use an algorithm, they need to be able to really understand how it works. Maybe. But how many of us know how our cars work, or our iPhones, or pace-makers? How many of us would trade performance for understandability in our own lives by, say, giving up our current automobile with its mystifying internal combustion engine for Fred Flintstone’s car?
    A Guide to Solving Social Problems with Machine Learning
  • Perhaps the most important example of this is how to combine human judgment and algorithmic judgment to make the best possible policy decisions. In the domain of policy, it is hard to imagine moving to a world in which the algorithms actually make the decisions; we expect that they will instead be used as decision aids.
    A Guide to Solving Social Problems with Machine Learning
  • The best way to solve this problem is to do a randomized controlled trial of the sort that is common in medicine. Then we could directly compare whether bail decisions made using machine learning lead to better outcomes than those made on comparable cases using the current system of judicial decision-making. But even before we reach that stage, we need to make sure the tool is promising enough to ethically justify testing it in the field. In our bail case, much of the effort went into finding a “natural experiment” to evaluate the tool.
    A Guide to Solving Social Problems with Machine Learning
  • For machine learning to be useful for policy, it must accurately predict “out-of-sample.” That means it should be trained on one set of data, then tested on a dataset it hasn’t seen before. So when you give data to a vendor to build a tool, withhold a subset of it. Then when the vendor comes back with a finished algorithm, you can perform an independent test using your “hold out” sample.
    A Guide to Solving Social Problems with Machine Learning
  • effect of using algorithms is the existing system – the
    A Guide to Solving Social Problems with Machine Learning
  • predictions and decisions already being made by humans. In the case of bail, we know from decades of research that those human predictions can be biased. Algorithms have a form of neutrality that the human mind struggles to obtain, at least within their narrow area of focus. It is entirely possible—as we saw—for algorithms to serve as a force for equity. We ought to pair our caution with hope.
    A Guide to Solving Social Problems with Machine Learning
  • population. In other words, we can reduce crime, jail populations and racial bias – all at the same time – with the help of algorithms.
    A Guide to Solving Social Problems with Machine Learning
  • can actually reduce race disparities in the jail
    A Guide to Solving Social Problems with Machine Learning
  • There is the possibility that any new system for making predictions and decisions might exacerbate racial disparities, especially in policy domains like criminal justice. Caution is merited: the underlying data used to train an algorithm may be biased, reflecting a history of discrimination. And data scientists may sometimes inadvertently report misleading performance measures for their algorithms. We should take seriously the concern about whether algorithms might perpetuate disadvantage, no matter what the other benefits.
    A Guide to Solving Social Problems with Machine Learning
  • Decades of behavioral economics and social psychology teach us that people will have trouble making accurate predictions about this risk – because it requires things we’re not always good at, like thinking probabilistically, making attributions, and drawing inferences. The algorithm makes the same predictions judges are already making, but better.
    A Guide to Solving Social Problems with Machine Learning
  • The impacts of the most recent executive order on the U.S. market and geopolitics will take time to play out. In the meantime, analysts, scholars, and policymakers need to consider the various levers that the United States has at its disposal and outline where the tools of new economic statecraft need to begin and end—to engage with stakeholders, both internally inside the U.S. market and externally among partners and allies, and to identify and create an institutional framework that might sustain an integrated approach to economic statecraft. This will likely involve a significant shift in th…
    Putting the Biden Administration’s “New Economic Statecraft” in Context | Lawfare
  • “de-risking” actually looks like in practice, is something policymakers must understand if the United States is to play in this new sandbox.
    Putting the Biden Administration’s “New Economic Statecraft” in Context | Lawfare
  • recognizing that governments’ attempts to exercise new, and increasingly robust, economic statecraft are here to stay.
    Putting the Biden Administration’s “New Economic Statecraft” in Context | Lawfare
  • Finally, and related to the above, allies and partners have their own response to economic statecraft—some of which might be less aligned with U.S. priorities than might be hoped. Indeed, Beijing’s knowledge that memory chips from SK hynix and Samsung (both headquartered in South Korea—a U.S. ally) could replace Micron’s contribution to the Chinese market simplified its decision to ban the U.S. firm.
    Putting the Biden Administration’s “New Economic Statecraft” in Context | Lawfare
  • And while we have spent the better part of five years pointing out that countries around the world have used various levers of economic statecraft to bolster their domestic industries viewed as strategically important (and usually associated with technology markets), the facade of trade liberalization and the surreptitious use of human capital development programs along with government procurement papered over some of the impetus to embark upon this race to the bottom.
    Putting the Biden Administration’s “New Economic Statecraft” in Context | Lawfare
  • Second, there is an inevitable race to the bottom with the application of industrial policy as countries retaliate against one another for their various forms of protection. Indeed, efforts to arrest technology and data transfer to China via export control and import bans—for example, Huawei and ZTE—have met with reciprocal measures impacting U.S. firms. Executives at Idaho-headquartered Micron represent the latest casualties in this race following China’s decision to ban Micron memory storage chips for use in systems that handle “critical information.” As a result, Micron’s competitors—South …
    Putting the Biden Administration’s “New Economic Statecraft” in Context | Lawfare
  • First, as the Sematech effort demonstrates, the costs of re-shoring are high—with downstream consequences across the value chain and, eventually, consumers. Relatedly, even where industrial policy might succeed in terms of bolstering an industry that might otherwise have struggled to survive, it is difficult for governments to pull the plug on support. Indeed, how much support is enough? And when does the government exit? From the perspective of industries and firms receiving protection, the answers to these questions are (a) as much support as possible and (b) never.
    Putting the Biden Administration’s “New Economic Statecraft” in Context | Lawfare
  • There are intrinsic costs associated with wholesale “decoupling” of supply chains between the United States and China as well as the more discrete “de-risking” of strategic industries—whether via “re-shoring” or “friend-shoring” of strategic industries and attempting to control the access of Chinese firms to U.S. and Western markets.
    Putting the Biden Administration’s “New Economic Statecraft” in Context | Lawfare
  • . Firms have long had an interest in portraying their industry as being “strategic” so as to secure protection, subsidies, and other types of state intervention to restrict competition. In the 1950s, for example, the textile industry claimed that woolen blankets were essential to protect against nuclear radiation and that lace was critical for the military to make epaulets. In the aftermath of the 2008 financial crisis, too, government responses went beyond at-the-border and traditional behind-the-border protectionist measures to what was aptly labeled “murky protectionism” in support of their…
    Putting the Biden Administration’s “New Economic Statecraft” in Context | Lawfare
  • Past efforts to promote industry in strategic sectors have often foundered on the shoals of self-serving lobbying by firms. As the Economist notes, a key question facing policymakers is which economic activities have “strategic consequences” for the state—with the attendant risk of all economic activities being designated as important for international security.
    Putting the Biden Administration’s “New Economic Statecraft” in Context | Lawfare
  • Most recently, the CHIPS Act provided $280 billion in funding with the goal of reducing U.S. reliance on overseas supply chains (though it remains to be seen how this money will be spent). The act also aims to boost the nation’s science and technology research base and address China’s anti-competitive trade practices amid broader concerns surrounding intellectual property theft. The direct link between industrial policy and investment regulation is explicit in the CHIPS Act. Specifically, it prohibits recipients from “expanding semiconductor manufacturing in China and other countries defined b…
    Putting the Biden Administration’s “New Economic Statecraft” in Context | Lawfare
  • And while economists might say that efficiency dictates markets, we suggest that the latter concern surrounding security imperatives is going to reshape the global market—whether efficient or not.
    Putting the Biden Administration’s “New Economic Statecraft” in Context | Lawfare
  • In simplest terms, think about Washington’s position as attempting to balance two imperatives. The first is market efficiency—the driving force of globalization in which goods and services were provided by firms in countries where they had a comparative advantage and, thus, prices were lower. The second is security. There are some goods and services so important that a government cannot rely on foreign firms and governments having control over their provision. And there are some goods so important to a country’s geopolitical standing that one might seek to limit its trade. Military hardware of…
    Putting the Biden Administration’s “New Economic Statecraft” in Context | Lawfare
  • Foundation models could eventually introduce several pathways for undermining state security: accidents, inadvertent escalation, unintentional conflict, the proliferation of weapons, and the interference with human diplomacy are just a few on a long list. The Confidence-Building Measures for Artificial Intelligence workshop hosted by the Geopolitics Team at OpenAI and the Berkeley Risk and Security Lab at the University of California brought together a multistakeholder group to think through the tools and strategies to mitigate the potential risks introduced by foundation models to internation…
    Confidence-Building Measures for Artificial Intelligence: Workshop proceedings | OpenAI
  • I am a research scientist at Google DeepMind (formerly at Google Brain) working at the intersection of machine learning and computer security. My most recent line of work studies properties of neural networks from an adversarial perspective. I received my Ph.D. from UC Berkeley in 2018, and my B.A. in computer science and mathematics (also from UC Berkeley) in 2013.
    Nicholas Carlini
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