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Daily Archives: May 26, 2022

Charts: U.S. Pending Home Sales Plunged in April 2022

Source : Bloomberg

Music Video: Someday We’ll Be Together

Diana Ross & The Supremes

Watch video at You Tube (3:31 minutes) . . . .

中國6方面33项措施!国务院进一步部署稳经济一揽子措施

记者: 申铖 . . . . . . . . .

日前召开的国务院常务会议进一步部署稳经济一揽子措施,包括6方面33项措施,努力推动经济回归正常轨道、确保运行在合理区间。

当前经济下行压力持续加大,许多市场主体十分困难。对此,会议指出,发展是解决我国一切问题的基础和关键。“围绕稳增长、促发展,此前召开的国务院常务会议已部署了多项举措。此次会议进一步部署了一揽子措施,彰显政策力度和强度,着力稳住经济大盘。”粤开证券首席经济学家罗志恒说。

会议决定,实施6方面33项措施,主要包括:

一是财政及相关政策。着力稳市场主体稳就业。

在更多行业实施存量和增量全额留抵退税,增加退税1400多亿元,全年退减税总量2.64万亿元;将中小微企业、个体工商户和5个特困行业缓缴养老等三项社保费政策延至年底,并扩围至其他特困行业,预计今年缓缴3200亿元;将失业保险留工培训补助扩大至所有困难参保企业……会议部署了多项措施。

“财政方面的相关政策力度超市场预期,体现以政府过‘紧日子’换市场主体过‘好日子’的决心。”罗志恒说,此次会议明确留抵退税政策进一步扩大行业范围、增加退税规模,有利于进一步增加企业现金流,提高企业抗风险能力。

二是金融政策。将今年普惠小微贷款支持工具额度和支持比例增加一倍。对中小微企业和个体工商户贷款、货车车贷、暂时遇困个人房贷消费贷,支持银行年内延期还本付息;汽车央企发放的900亿元商用货车贷款,要银企联动延期半年还本付息。将商业汇票承兑期限由1年缩短至6个月。推进平台企业合法合规境内外上市。

三是稳产业链供应链。优化复工达产政策,完善对“白名单”企业服务。保障货运通畅,取消来自疫情低风险地区通行限制,一律取消不合理限高等规定和收费。客货运司机等在异地核酸检测,同等享受免费政策。增加1500亿元民航应急贷款,支持航空业发行2000亿元债券。有序增加国内国际客运航班,制定便利外企人员往来措施。

四是促消费和有效投资。放宽汽车限购,阶段性减征部分乘用车购置税600亿元。因城施策支持刚性和改善性住房需求。优化审批,新开工一批水利特别是大型引水灌溉、交通、老旧小区改造、地下综合管廊等项目,引导银行提供规模性长期贷款。启动新一轮农村公路建设改造。支持发行3000亿元铁路建设债券。加大以工代赈力度。

五是保能源安全。落实地方煤炭产量责任,调整煤矿核增产能政策,加快办理保供煤矿手续。再开工一批水电煤电等能源项目。

六是保障基本民生。做好失业保障、低保和困难群众救助等工作。视情及时启动社会救助和保障标准与物价上涨挂钩联动机制。

“总的看,此次会议部署的一揽子措施,着眼于稳定供给、扩大需求、稳定预期。这些政策不仅在宏观层面推动经济稳定,更在微观层面着力保市场主体、保就业、保特定困难群体,政策有力度且针对性强,将推动国民经济加快恢复。”罗志恒说。


Source : 中国政府网


Read also at SCMP

China has 33 ways to get economy back on track, but critics say ‘adjusting zero-Covid strategy is key’ . . . . .

Type 2 Diabetes Accelerates Brain Aging and Cognitive Decline

Scientists have demonstrated that normal brain aging is accelerated by approximately 26% in people with progressive type 2 diabetes compared with individuals without the disease, reports a study published today in eLife.

The authors evaluated the relationship between typical brain aging and that seen in type 2 diabetes, and observed that type 2 diabetes follows a similar pattern of neurodegeneration as aging, but which progresses faster. One important implication of this finding is that even typical brain aging may reflect changes in the brain’s regulation of glucose by insulin.

The results further suggest that by the time type 2 diabetes is formally diagnosed, there may already be significant structural damage to the brain. Sensitive ways to detect diabetes-associated changes to the brain are therefore urgently needed.

There is already strong evidence linking type 2 diabetes with cognitive decline, yet few patients currently undergo a comprehensive cognitive assessment as part of their clinical care. It can be difficult to distinguish between normal brain aging that begins in middle age, and brain aging caused or accelerated by diabetes. To date, no studies have directly compared neurological changes in healthy people over the course of their lifespan with changes to those experienced by people of the same age with diabetes.

“Routine clinical assessments for diagnosing diabetes typically focus on blood glucose, insulin levels and body mass percentage,” says first author Botond Antal, a PhD student at the Department of Biomedical Engineering, Stony Brook University, New York, US. “However, the neurological effects of type 2 diabetes may reveal themselves many years before they can be detected by standard measures, so by the time type 2 diabetes is diagnosed by conventional tests, patients may have already sustained irreversible brain damage.”

To define the impact of diabetes on the brain over and above normal aging, the team made use of the largest available brain structure and function dataset across human lifespan: UK Biobank data from 20,000 people aged 50 to 80 years old. This dataset includes brain scans and brain function measurements and holds data for both healthy individuals and those with a type 2 diabetes diagnosis. They used this to determine which brain and cognitive changes are specific to diabetes, rather than just aging, and then confirmed these results by comparing them with a meta-analysis of nearly 100 other studies.

Their analysis showed that both aging and type 2 diabetes cause changes in executive functions such as working memory, learning and flexible thinking, and changes in brain processing speed. However, people with diabetes had a further 13.1% decrease in executive function beyond age-related effects, and their processing speed decreased by a further 6.7% compared to people of the same age without diabetes. Their meta-analysis of other studies also confirmed this finding: people with type 2 diabetes had consistently and markedly lower cognitive performance compared to healthy individuals who were the same age and similarly educated.

The team also compared brain structure and activity between people with and without diabetes using MRI scans. Here, they found a decrease in grey brain matter with age, mostly in a region called the ventral striatum – which is critical to the brain’s executive functions. Yet people with diabetes had even more pronounced decreases in grey matter beyond the typical age-related effects – a further 6.2% decrease in grey matter in the ventral striatum, but also loss of grey matter in other regions, compared with normal aging.

Together, the results suggest that the patterns of type 2 diabetes-related neurodegeneration strongly overlap with those of normal aging, but that neurodegeneration is accelerated. Moreover, these effects on brain function were more severe with increased duration of diabetes. In fact, progression of diabetes was linked with a 26% acceleration of brain aging.

“Our findings suggest that type 2 diabetes and its progression may be associated with accelerated brain aging, potentially due to compromised energy availability causing significant changes to brain structure and function,” concludes senior author Lilianne Mujica-Parodi, Director of the Laboratory for Computational Neurodiagnostics, Stony Brook University. “By the time diabetes is formally diagnosed, this damage may already have occurred. But brain imaging could provide a clinically valuable metric for identifying and monitoring these neurocognitive effects associated with diabetes. Our results underscore the need for research into brain-based biomarkers for type 2 diabetes and treatment strategies that specifically target its neurocognitive effects.”


Source: elife

中國南玉高铁跨黎湛铁路特大桥成功转体

转体前的转体梁

5月24日凌晨,由中铁二局集团承建的南玉高铁跨黎湛铁路特大桥成功转体57度。

此次转体的桥体转体梁长99米,重5810吨。

南玉高铁是南深高铁的重要组成部分,设计时速350公里。

转体过程中

完成转体

Source : 新华网

The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence

Erik Brynjolfsson wrote . . . . . . . . .

Abstract

In 1950, Alan Turing proposed a test of whether a machine was intelligent: could a machine imitate a human so well that its answers to questions were indistinguishable from a human’s? Ever since, creating intelligence that matches human intelligence has implicitly or explicitly been the goal of thousands of researchers, engineers, and entrepreneurs. The benefits of human-like artificial intelligence (HLAI) include soaring productivity, increased leisure, and perhaps most profoundly a better understanding of our own minds. But not all types of AI are human-like–in fact, many of the most powerful systems are very different from humans–and an excessive focus on developing and deploying HLAI can lead us into a trap. As machines become better substitutes for human labor, workers lose economic and political bargaining power and become increasingly dependent on those who control the technology. In contrast, when AI is focused on augmenting humans rather than mimicking them, humans retain the power to insist on a share of the value created. What is more, augmentation creates new capabilities and new products and services, ultimately generating far more value than merely human-like AI. While both types of AI can be enormously beneficial, there are currently excess incentives for automation rather than augmentation among technologists, business executives, and policy-makers.


Alan Turing was far from the first to imagine human-like machines. According to legend, 3,500 years ago, Dædalus constructed humanoid statues that were so lifelike that they moved and spoke by themselves. Nearly every culture has its own stories of human-like machines, from Yanshi’s leather man described in the ancient Chinese Liezi text to the bronze Talus of the Argonautica and the towering clay Mokkerkalfe of Norse mythology. The word robot first appeared in Karel Čapek’s influential play Rossum’s Universal Robots and derives from the Czech word robota, meaning servitude or work. In fact, in the first drafts of his play, Čapek named them labori until his brother Josef suggested substituting the word robot.

Of course, it is one thing to tell tales about humanoid machines. It is something else to create robots that do real work. For all our ancestors’ inspiring stories, we are the first generation to build and deploy real robots in large numbers. Dozens of companies are working on robots as human-like, if not more so, as those described in the ancient texts. One might say that technology has advanced sufficiently to become indistinguishable from mythology.

The breakthroughs in robotics depend not merely on more dexterous mechanical hands and legs, and more perceptive synthetic eyes and ears, but also on increasingly human-like artificial intelligence (HLAI). Powerful AI systems are crossing key thresholds: matching humans in a growing number of fundamental tasks such as image recognition and speech recognition, with applications from autonomous vehicles and medical diagnosis to inventory management and product recommendations.

These breakthroughs are both fascinating and exhilarating. They also have profound economic implications. Just as earlier general-purpose technologies like the steam engine and electricity catalyzed a restructuring of the economy, our own economy is increasingly transformed by AI. A good case can be made that AI is the most general of all general-purpose technologies: after all, if we can solve the puzzle of intelligence, it would help solve many of the other problems in the world. And we are making remarkable progress. In the coming decade, machine intelligence will become increasingly powerful and pervasive. We can expect record wealth creation as a result.

Replicating human capabilities is valuable not only because of its practical potential for reducing the need for human labor, but also because it can help us build more robust and flexible forms of intelligence. Whereas domain-specific technologies can often make rapid progress on narrow tasks, they founder when unexpected problems or unusual circumstances arise. That is where human-like intelligence excels. In addition, HLAI could help us understand more about ourselves. We appreciate and comprehend the human mind better when we work to create an artificial one.

These are all important opportunities, but in this essay, I will focus on the ways that HLAI could lead to a realignment of economic and political power.

The distributive effects of AI depend on whether it is primarily used to augment human labor or automate it. When AI augments human capabilities, enabling people to do things they never could before, then humans and machines are complements. Complementarity implies that people remain indispensable for value creation and retain bargaining power in labor markets and in political decision-making. In contrast, when AI replicates and automates existing human capabilities, machines become better substitutes for human labor and workers lose economic and political bargaining power. Entrepreneurs and executives who have access to machines with capabilities that replicate those of humans for a given task can and often will replace humans in those tasks.

Automation increases productivity. Moreover, there are many tasks that are dangerous, dull, or dirty, and those are often the first to be automated. As more tasks are automated, a fully automated economy could, in principle, be structured to redistribute the benefits from production widely, even to those people who are no longer strictly necessary for value creation. However, the beneficiaries would be in a weak bargaining position to prevent a change in the distribution that left them with little or nothing. Their incomes would depend on the decisions of those in control of the technology. This opens the door to increased concentration of wealth and power.

This highlights the promise and the peril of achieving HLAI: building machines designed to pass the Turing Test and other, more sophisticated metrics of human-like intelligence. On the one hand, it is a path to unprecedented wealth, increased leisure, robust intelligence, and even a better understanding of ourselves. On the other hand, if HLAI leads machines to automate rather than augment human labor, it creates the risk of concentrating wealth and power. And with that concentration comes the peril of being trapped in an equilibrium in which those without power have no way to improve their outcomes, a situation I call the ­Turing Trap.

The grand challenge of the coming era will be to reap the unprecedented benefits of AI, including its human-like manifestations, while avoiding the Turing Trap. Succeeding in this task requires an understanding of how technological progress affects productivity and inequality, why the Turing Trap is so tempting to different groups, and a vision of how we can do better.

Artificial intelligence pioneer Nils Nilsson noted that “achieving real human-level AI would necessarily imply that most of the tasks that humans perform for pay could be automated.” In the same article, he called for a focused effort to create such machines, writing that “achieving human-level AI or ‘strong AI’ remains the ultimate goal for some researchers” and he contrasted this with “weak AI,” which seeks to “build machines that help humans.” Not surprisingly, given these monikers, work toward “strong AI” attracted many of the best and brightest minds to the quest of–implicitly or explicitly–fully automating human labor, rather than assisting or augmenting it.

For the purposes of this essay, rather than strong versus weak AI, let us use the terms automation versus augmentation. In addition, I will use HLAI to mean human-like artificial intelligence, not human-level AI, because the latter mistakenly implies that intelligence falls on a single dimension, and perhaps even that humans are at the apex of that metric. In reality, intelligence is multidimensional: a 1970s pocket calculator surpasses the most intelligent human in some ways (such as for multiplication), as does a chimpanzee (short-term memory). At the same time, machines and animals are inferior to human intelligence on myriad other dimensions. The term “artificial general intelligence” (AGI) is often used as a synonym for HLAI. However, taken literally, it is the union of all types of intelligences, able to solve types of problems that are solvable by any existing human, animal, or machine. That suggests that AGI is not human-like.

The good news is that both automation and augmentation can boost labor productivity: that is, the ratio of value-added output to labor-hours worked. As productivity increases, so do average incomes and living standards, as do our capabilities for addressing challenges from climate change and poverty to health care and longevity. Mathematically, if the human labor used for a given output declines toward zero, then labor productivity would grow to infinity.

The bad news is that no economic law ensures everyone will share this growing pie. Although pioneering models of economic growth assumed that technological change was neutral, in practice, technological change can disproportionately help or hurt some groups, even if it is beneficial on average.

In particular, the way the benefits of technology are distributed depends to a great extent on how the technology is deployed and the economic rules and norms that govern the equilibrium allocation of goods, services, and incomes. When technologies automate human labor, they tend to reduce the marginal value of workers’ contributions, and more of the gains go to the owners, entrepreneurs, inventors, and architects of the new systems. In contrast, when technologies augment human capabilities, more of the gains go to human workers.

A common fallacy is to assume that all or most productivity-enhancing innovations belong in the first category: automation. However, the second category, augmentation, has been far more important throughout most of the past two centuries. One metric of this is the economic value of an hour of human labor. Its market price as measured by median wages has grown more than tenfold since 1820. An entrepreneur is willing to pay much more for a worker whose capabilities are amplified by a bulldozer than one who can only work with a shovel, let alone with bare hands.

In many cases, not only wages but also employment grow with the introduction of new technologies. With the invention of the airplane, a new job category was born: pilots. With the invention of jet engines, pilot productivity (in passenger-miles per pilot-hour) grew immensely. Rather than reducing the number of employed pilots, the technology spurred demand for air travel so much that the number of pilots grew. Although this pattern is comforting, past performance does not guarantee future results. Modern technologies–and, more important, the ones under development–are different from those that were important in the past.

In recent years, we have seen growing evidence that not only is the labor share of the economy declining, but even among workers, some groups are beginning to fall even further behind. Over the past forty years, the numbers of millionaires and billionaires grew while the average real wages for Americans with only a high school education fell. Though many phenomena contributed to this, including new patterns of global trade, changes in technology deployment are the single biggest explanation.

If capital in the form of AI can perform more tasks, those with unique assets, talents, or skills that are not easily replaced with technology stand to benefit disproportionately.18 The result has been greater wealth concentration.

Ultimately, a focus on more human-like AI can make technology a better substitute for the many nonsuperstar workers, driving down their market wages, even as it amplifies the market power of a few. This has created a growing fear that AI and related advances will lead to a burgeoning class of unemployable or “zero marginal product” people.

As noted above, both automation and augmentation can increase productivity and wealth. However, an unfettered market is likely to create socially excessive incentives for innovations that automate human labor and provide too weak incentives for technology that augments humans. The first fundamental welfare theorem of economics states that under a particular set of conditions, market prices lead to a pareto optimal outcome: that is, one where no one can be made better off without making someone else worse off. But we should not take too much comfort in that. The theorem does not hold when there are innovations that change the production possibilities set or externalities that affect people who are not part of the market.

[ . . . . . . . . ]

In sum, the risks of the Turing Trap are increased not by just one group in our society, but by the misaligned incentives of technologists, businesspeople, and policy-makers.

The future is not preordained. We control the extent to which AI either expands human opportunity through augmentation or replaces humans through automation. We can work on challenges that are easy for machines and hard for humans, rather than hard for machines and easy for humans. The first option offers the opportunity of growing and sharing the economic pie by augmenting the workforce with tools and platforms. The second option risks dividing the economic pie among an ever-smaller number of people by creating automation that displaces ever-more types of workers.

While both approaches can and do contribute to productivity and progress, technologists, businesspeople, and policy-makers have each been putting a finger on the scales in favor of replacement. Moreover, the tendency of a greater concentration of technological and economic power to beget a greater concentration of political power risks trapping a powerless majority into an unhappy equilibrium: the Turing Trap.

The backlash against free trade offers a cautionary tale. Economists have long argued that free trade and globalization tend to grow the economic pie through the power of comparative advantage and specialization. They have also acknowledged that market forces alone do not ensure that every person in every country will come out ahead. So they proposed a grand bargain: maximize free trade to maximize wealth creation and then distribute the benefits broadly to compensate any injured occupations, industries, and regions. It has not worked as they had hoped. As the economic winners gained power, they reneged on the second part of the bargain, leaving many workers worse off than before.48 The result helped fuel a populist backlash that led to import tariffs and other barriers to free trade. Economists wept.

Some of the same dynamics are already underway with AI. More and more Americans, and indeed workers around the world, believe that while the technology may be creating a new billionaire class, it is not working for them. The more technology is used to replace rather than augment labor, the worse the disparity may become, and the greater the resentments that feed destructive political instincts and actions. More fundamentally, the moral imperative of treating people as ends, and not merely as means, calls for everyone to share in the gains of automation.

The solution is not to slow down technology, but rather to eliminate or reverse the excess incentives for automation over augmentation. A good start would be to replace the Turing Test, and the mindset it embodies, with a new set of practical benchmarks that steer progress toward AI-powered systems that exceed anything that could be done by humans alone. In concert, we must build political and economic institutions that are robust in the face of the growing power of AI. We can reverse the growing tech backlash by creating the kind of prosperous society that inspires discovery, boosts living standards, and offers political inclusion for everyone. By redirecting our efforts, we can avoid the Turing Trap and create prosperity for the many, not just the few.


Source : Dædalus