828cloud

Data, Info and News of Life and Economy

Category Archives: Science

Scientists Design Skin Patch That Takes Ultrasound Images

The future of ultrasound imaging could be a sticker affixed to the skin that can transmit images continuously for 48 hours.

Researchers at Massachusetts Institute of Technology (MIT) have created a postage stamp-sized device that creates live, high-resolution images. They reported on their progress this week.

“We believe we’ve opened a new era of wearable imaging: With a few patches on your body, you could see your internal organs,” said co-senior study author Xuanhe Zhao, a professor of mechanical engineering and civil and environmental engineering at MIT.

The sticker — about 3/4-inch across and about 1/10-inch thick — could be a substitute for bulky, specialized ultrasound equipment available only in hospitals and doctor’s office, where technicians apply a gel to the skin and then use a wand or probe to direct sound waves into the body.

The waves reflect back high-resolution images of a major blood vessels and deeper organs such as the heart, lungs and stomach. While some hospitals already have probes affixed to robotic arms that can provide imaging for extended periods, the ultrasound gel dries over time.

For now, the stickers would still have to be connected to instruments, but Zhao and other researchers are working on a way to operate them wirelessly.

That opens up the possibility of patients wearing them at home or buying them at a drug store. Even in their current design, they could eliminate the need for a technician to hold a probe in place for a long time.

In the study, the patches adhered well to the skin, enabling researchers to capture images even if volunteers moved from sitting to standing, jogging and biking.

“We envision a few patches adhered to different locations on the body, and the patches would communicate with your cellphone, where AI algorithms would analyze the images on demand,” Zhao explained in an MIT news release.

A different approach tested — stretchable ultrasound probes — yielded images with poor resolution.

“[A] Wearable ultrasound imaging tool would have huge potential in the future of clinical diagnosis. However, the resolution and imaging duration of existing ultrasound patches is relatively low, and they cannot image deep organs,” said co-lead author Chonghe Wang, a graduate student who works in Zhao’s Lab.

The MIT team’s new ultrasound sticker produces higher resolution images by pairing a stretchy adhesive layer with a rigid array of transducers (they convert energy from one form to another). In the middle is a solid hydrogel that transmits sound waves. The adhesive layer is made from two thin layers of elastomer.

“The elastomer prevents dehydration of hydrogel,” co-lead author Xiaoyu Chen explained. “Only when hydrogel is highly hydrated can acoustic waves penetrate effectively and give high-resolution imaging of internal organs.”

Healthy volunteers wore the stickers on various areas, including the neck, chest, abdomen and arms. The stickers produced clear images of underlying structures, including the changing diameter of major blood vessels, for up to 48 hours. They stayed attached while volunteers sat, stood, jogged, biked and lifted weights.

They showed how the heart changes shape as it exerts during exercise and how the stomach swells, then shrinks, as volunteers drank and then eliminated juice. Researchers also could detect signs of temporary micro-damage in muscles as volunteers lifted weights.

“With imaging, we might be able to capture the moment in a workout before overuse, and stop before muscles become sore,” Chen said. “We do not know when that moment might be yet, but now we can provide imaging data that experts can interpret.”

In addition to working on wireless technology for the stickers, the team is developing software algorithms based on artificial intelligence that can better interpret the ultrasound images.

Zhao thinks patients may one day be able to buy stickers that could be used to monitor internal organs, the progression of tumors and development of fetuses in the womb.

“We imagine we could have a box of stickers, each designed to image a different location of the body,” Zhao said. “We believe this represents a breakthrough in wearable devices and medical imaging.”

The findings were published in Science.


Source: HealthDay

赴天宫相会 向星河“问天”

记者: 余建斌 . . . . . . . . .

  7月24日,中国空间站问天实验舱成功进入太空。作为中国空间站首个科学实验舱,也是国家太空实验室的重要组成部分,问天实验舱将为航天员在轨工作生活提供更大空间,也为空间科学研究提供更大平台。

比天和核心舱更高、更大、更重

  中国空间站问天实验舱全长17.9米,直径4.2米,发射重量23吨,比空间站天和核心舱更高、更大、更重,将为航天员提供专用的生活和工作场所。问天实验舱竖起来有6层楼高,体积和重量跟北京地铁13号线列车的一节车厢差不多,是全世界现役在轨最重的单舱主动飞行器。

  问天实验舱配置了与天和核心舱一样的航天员生活设施,包括3个睡眠区、1个卫生区和厨房等设施,未来可与核心舱一起来支持两艘载人飞船轮换期间6名航天员的生活。科研人员说,问天实验舱的加入,使得空间站空间更大,航天员活动空间更充裕。比如,可以把太空自行车从天和核心舱拆到问天实验舱,增加通道通过性。未来,太空授课也会“搬”到问天实验舱进行。

  结构上,问天实验舱由用来完成科学实验的工作舱、支持太空出舱的气闸舱及储备上行物资的资源舱3部分组成。

  问天实验舱的工作舱长达9米,是目前我国航天器中体型最大、承载最重的密封舱,也是世界第二大单密封舱体,这里还是航天员的生活工作场所。工作舱的储物空间也不小,达60立方米以上。为提升航天员的居住舒适度,中国航天科技集团五院空间站结构与机构设计团队进行了大量人性化设计,如可翻转式柜门设计,让储物效率更高。航天员的3个独立“卧室”,每间自带防辐射舷窗,在休息时可安心欣赏舱外风景。舱内设置的独立卫生区,进一步提升了私密性。设计人员还在舱壁上设计了防护结构,使得密封舱能够在严酷的太空环境中坚固耐用、稳定运行。

  问天实验舱的一大特点是配置了全新的出舱气闸舱,这是未来空间站完全建成后航天员的主用出舱口。新的气闸舱出舱口朝下,更为宽敞,航天员出舱更方便。

  与以往传统密封舱不同,气闸舱首次采用“外方内圆”的构型方案,视觉效果十分独特,是空间站系统唯一一个看上去是方形的舱体,里面则为圆柱状。作为我国最大的专用气闸舱,出舱口比以往舱门更大、保护装置配备更齐全,在轨组装应急舱门则为航天员出舱活动提供了双重安全保障。航天员通过新的气闸舱进行出舱准备和舱外返回时,可以更舒展、更从容,出舱活动、开展舱外实验更为便利。由于出舱口变宽,航天员还能携带大个头的设备出舱工作,舱外工作能力大大提升。

  与天和核心舱相比,问天实验舱还具备更强的超万瓦级的供电能力、千兆级的信息传输能力。问天实验舱同时具备对空间站组合体的管理和控制功能,可以接管对空间站组合体的操作,从而在整体上提高空间站的可靠性。

柔性太阳翼单翼展开面积可达110平方米

  在外形上,问天实验舱与天和核心舱有明显不同,前者尾部有一对巨大的“翅膀”,也就是太阳能帆板或称柔性太阳电池翼。

  问天实验舱配置的是目前国内研制的最大面积可展收柔性太阳翼,单翼全展开状态下最长达27米,展开面积可达110平方米。无论是展开面积还是供电能力,这对“翅膀”都达到了天和核心舱太阳翼的两倍之多。

  在太空运行中,问天实验舱的这对太阳能帆板能以最佳角度面向太阳,避免飞行过程中被其他舱段遮挡阳光。问天实验舱的每天平均发电量,能为空间站运行提供充足的能源,足够一个普通家庭用上一个半月。

  问天实验舱是空间站系统中舱外活动部件最多的舱体,大量的舱外设施设备更好地保障了出舱活动,也为更精细的舱外操作提供了支持。在问天实验舱气闸舱外,配置了一个5米长的小型机械臂。这套7自由度的机械臂小巧、精度高,最大负荷能力达3吨,虽然拖动能力小于核心舱的大机械臂,但方便抓取中小型设备,操作更为灵巧。它既可以单独使用,也可以跟核心舱的大机械臂组合为15米长的组合臂,能在整个空间站不同舱段之间“爬行”,共同完成航天员的出舱、舱外设施照料、巡检等任务。

  在问天实验舱舱体上,还集成了结构健康监测系统,对舱体结构的健康状态进行实时监控。一旦出现空间碎片撞击或舱压异常下降事件,系统会立即自动响应、快速报警,并对撞击进行高精度定位,为航天员显示出撞击区域图形,大大减少航天员详细定位撞击漏孔的时间,进一步保障太空驻留安全。

  据航天员系统专家介绍,此次问天实验舱还搭载了航天员生活、工作所需的部分产品,包括全套厨房设备。这相当于空间站组合体有了两套太空厨房,提高了航天员生活的便利性。为了方便航天员在轨使用手机、平板电脑和其他便携式电子产品,问天实验舱也配套了与天和核心舱相同的充电设备,和地面使用的电源适配器功能类似,有效扩展了空间站的便携式电子产品充电能力。

  此外,航天员系统还在问天实验舱内配套了全套舱外航天服的出舱支持设备,出舱活动任务期间可支持航天员的舱载供氧、制冷等过闸功能。平时气闸舱可支持舱外航天服贮存、在轨检测、航天员训练。

  当问天实验舱和天和核心舱对接到位,航天员将会使用专用扳手打开实验舱闸门,启动舱内生命维持系统,完成科学实验柜的组装,并开展科学实验。

将进行空间生命科学研究

  以天和核心舱、问天实验舱和梦天实验舱为基本构型的天宫空间站完成建造后,意味着国家太空实验室也将建成,并将开展长期、多领域、大规模空间科学与应用研究。

  载人航天工程空间应用系统副总师、中科院空间应用中心研究员吕从民介绍,问天实验舱以生命科学和生物技术研究为主,在空间生命科学与生物技术、微重力流体物理、空间材料科学、空间应用新技术试验等领域规划部署了研究主题。通过这些科学项目的实施,关注生命生长发育和人的健康,探索人类长期太空生存所面临的一系列科学问题。

  作为空间站内进行空间生命科学研究的主要场所,问天实验舱舱内配置了生命生态实验柜、生物技术实验柜、科学手套箱与低温存储柜、变重力科学实验柜等科学实验设施,就像把一个大型科学实验室搬到了太空。其中,两个生命科学实验柜和变重力科学实验柜是开展科学实验的场所,科学手套箱为航天员对科学样品精细操作提供安全、高效支持,低温装置用于实验样品在轨存储。

  吕从民说,生命生态实验柜以多种类型的生物个体为实验样品,将开展拟南芥、线虫、果蝇、斑马鱼等生物的空间生长实验,揭示微重力对生物个体生长、发育、代谢的影响,促进人类对生命现象本质的理解。这意味着空间站里也会“种草”“养鱼”。

  在问天实验舱舱外,还部署了能量粒子探测器、等离子体原位成像探测器等,用于获取空间环境要素数据,为航天员健康、空间站安全运营提供保障支持,并用于空间环境基础研究。


Source : 新华网

Infographic: Earth’s Tectonic Plates

See large image . . . . . .

Source : Visual Capitalist

Infographic: Elements Making Up the Human Body

See large image . . . . . .

Source : Visual Capitalist

Soy Sauce’s Salt-enhancing Peptides

Soy sauce deepens the flavor of soup stocks, gives stir-fried rice its sweet-salty glaze and makes a plate of dumplings absolutely enjoyable. But what exactly makes this complex, salty, umami sauce so tasty? Now, researchers reporting in ACS’ Journal of Agricultural and Food Chemistry have discovered the proteins and other compounds that give soy sauce its distinctive flavors and they say that proteins and peptides help make it salty.

Understanding how foods taste the way they do can help producers tailor their growing or manufacturing methods or modify the final product to boost certain flavors. Decoding the flavors of fermented foods like soy sauce is particularly challenging because they arise from complex processes, including the microbial breakdown of proteins and other compounds, that happen over a long period of time.

Though several compounds in soy sauce are known, no complete profile of its flavor agents has been developed. So, Thomas Hofmann and colleagues wanted to carry out a full assessment of the chemicals behind soy sauce’s flavor profile and test the completeness of this profile by using the compounds to recreate the seasoning’s distinctive taste.

The team started by trying to recreate soy sauce’s taste with a mixture of compounds known to be involved in its flavor. A panel of taste experts found that this recreated soy sauce wasn’t quite right — it wasn’t as salty or as bitter as the authentic product.

The team then searched for other, unknown flavor compounds, hypothesizing that small proteins could potentially be the missing ingredient. Using various chemical and taste analysis methods, they identified a collection of proline-modified dipeptides and other larger, newly identified proteins that enhanced umami and other flavors.

Several of the proteins were discovered to contribute to a salty sensation, which, in soy sauce, had only previously been attributed to table salt and other minerals. After mixing a sample containing over 50 individual compounds, the team was finally able to recreate the complex taste of soy sauce.

This profile could help producers optimize fermentation conditions to boost desirable compounds and tailor the taste of the final product, the researchers say.


Source: American Chemical Society

Cambodian Catches World’s Largest Recorded Freshwater Fish

Jerry Harmer wrote . . . . . . . . .

The world’s largest recorded freshwater fish, a giant stingray, has been caught in the Mekong River in Cambodia, according to scientists from the Southeast Asian nation and the United States.

The stingray, captured on June 13, measured almost four meters (13 feet) from snout to tail and weighed slightly under 300 kilograms (660 pounds), according to a statement Monday by Wonders of the Mekong, a joint Cambodian-U.S. research project.

The previous record for a freshwater fish was a 293-kilogram (646-pound) Mekong giant catfish, discovered in Thailand in 2005, the group said.

The stingray was snagged by a local fisherman south of Stung Treng in northeastern Cambodia. The fisherman alerted a nearby team of scientists from the Wonders of the Mekong project, which has publicized its conservation work in communities along the river.

The scientists arrived within hours of getting a post-midnight call with the news, and were amazed at what they saw.

“Yeah, when you see a fish this size, especially in freshwater, it is hard to comprehend, so I think all of our team was stunned,” Wonders of the Mekong leader Zeb Hogan said in an online interview from the University of Nevada in Reno. The university is partnering with the Cambodian Fisheries Administration and USAID, the U.S. government’s international development agency.

Freshwater fish are defined as those that spend their entire lives in freshwater, as opposed to giant marine species such as bluefin tuna and marlin, or fish that migrate between fresh and saltwater like the huge beluga sturgeon.

The stingray’s catch was not just about setting a new record, he said.

“The fact that the fish can still get this big is a hopeful sign for the Mekong River, ” Hogan said, noting that the waterway faces many environmental challenges.

The Mekong River runs through China, Myanmar, Laos, Thailand, Cambodia and Vietnam. It is home to several species of giant freshwater fish but environmental pressures are rising. In particular, scientists fear a major program of dam building in recent years may be seriously disrupting spawning grounds.

“Big fish globally are endangered. They’re high-value species. They take a long time to mature. So if they’re fished before they mature, they don’t have a chance to reproduce,” Hogan said. “A lot of these big fish are migratory, so they need large areas to survive. They’re impacted by things like habitat fragmentation from dams, obviously impacted by overfishing. So about 70% of giant freshwater fish globally are threatened with extinction, and all of the Mekong species.”

The team that rushed to the site inserted a tagging device near the tail of the mighty fish before releasing it. The device will send tracking information for the next year, providing unprecedented data on giant stingray behavior in Cambodia.

“The giant stingray is a very poorly understood fish. Its name, even its scientific name, has changed several times in the last 20 years,” Hogan said. “It’s found throughout Southeast Asia, but we have almost no information about it. We don’t know about its life history. We don’t know about its ecology, about its migration patters.”

Researchers say it’s the fourth giant stingray reported in the same area in the past two months, all of them females. They think this may be a spawning hotspot for the species.

Local residents nicknamed the stingray “Boramy,” or “full moon,” because of its round shape and because the moon was on the horizon when it was freed on June 14. In addition to the honor of having caught the record-breaker, the lucky fisherman was compensated at market rate, meaning he received a payment of around $600.


Source : AP

A Glucose Meter Could Soon Say Whether You Have SARS-CoV-2 Antibodies

Over-the-counter COVID tests can quickly show whether you are infected with SARS-CoV-2. But if you have a positive result, there’s no equivalent at-home test to assess how long you’re protected against reinfection. In the Journal of the American Chemical Society, researchers now report a simple, accurate glucose-meter-based test incorporating a novel fusion protein. The researchers say that consumers could someday use this assay to monitor their own SARS-CoV-2 antibody levels.

Vaccines against SARS-CoV-2 and infection with the virus itself can guard against future infections for a while, but it’s unclear exactly how long that protection lasts. A good indication of immune protection is a person’s level of SARS-CoV-2 antibodies, but the gold standard measurement – the enzyme-linked immunosorbent assay (ELISA) – requires expensive equipment and specialized technicians.

Enter glucose meters, which are readily available, easy to use and can be integrated with remote clinical services. Researchers have been adapting these devices to sense other target molecules, coupling detection with glucose production. For example, if a detection antibody in the test binds to an antibody in a patient’s blood, then a reaction occurs that produces glucose — something the device detects very well. Invertase is an attractive enzyme for this type of analysis because it converts sucrose into glucose, but it’s difficult to attach the enzyme to detection antibodies with chemical approaches. So, Netzahualcóyotl Arroyo-Currás, Jamie B. Spangler and colleagues wanted to see whether producing a fusion protein consisting of both invertase and a detection antibody would work in an assay that would allow SARS-CoV-2 antibody levels to be read with a glucose meter.

The researchers designed and produced a novel fusion protein containing both invertase and a mouse antibody that binds to human immunoglobulin (IgG) antibodies. They showed that the fusion protein bound to human IgGs and successfully produced glucose from sucrose. Next, the team made test strips with the SARS-CoV-2 spike protein on them. When dipped in COVID-19 patient samples, the patients’ SARS-CoV-2 antibodies bound to the spike protein. Adding the invertase/IgG fusion protein, then sucrose, led to the production of glucose, which could be detected by a glucose meter. They validated the test by performing the analysis with glucose meters on a variety of patient samples, and found that the new assay worked as well as four different ELISAs. The researchers say that the method can also be adapted to test for SARS-CoV-2 variants and other infectious diseases.


Source: American Chemical Society

中国十年來科技事业发生历史性、整体性、格局性重大变化

记者: 胡喆、张泉、温竞华、王琳琳、徐鹏航 . . . . . . . . .

  重大创新成果接连涌现,从载人航天到深海探测再到中国高铁、中国大坝、中国桥梁,我国建成一大批世界级工程;高质量科普服务惠及我国更广泛人群,深化科学基金改革、不断提升资助效益、促进基础研究高质量发展……

  十年来,在党中央坚强领导下,在全国科技界和广大科技工作者的共同努力下,我国科技事业发生历史性、整体性、格局性重大变化,成功进入创新型国家行列,走出一条从人才强、科技强,到产业强、经济强、国家强的发展道路。

  中共中央宣传部6日举行“中国这十年”系列主题新闻发布会的第六场,聚焦“实施创新驱动发展战略 建设科技强国”。

科技事业蓝图已经画就 在不断向前发展

  党的十八大以来,以习近平同志为核心的党中央,把创新作为引领发展的第一动力,摆在党和国家发展全局的核心位置,立足中国特色,着眼全球发展大势,把握阶段性特征,对新时代科技创新谋篇布局。

  “在目标上,我们建设创新型国家和科技强国;在摆位上,把科技自立自强作为国家发展的战略支撑;在战略上,我们持续深入实施创新驱动发展战略;在路径上,我们坚定不移走中国特色自主创新道路。我国科技事业的蓝图已经画就,我们的科技创新事业在不断向前发展。”科技部部长王志刚说。

  我国全社会研发投入从2012年的1.03万亿元增长到2021年的2.79万亿元,研发投入强度从1.91%增长到2.44%;世界知识产权组织发布的全球创新指数排名,中国从2012年的第34位上升到2021年的第12位……

  王志刚指出,中国在全球创新版图中的地位和作用发生了新的变化。中国既是国际前沿创新的重要参与者,也是共同解决全球性问题的重要贡献者。

  “下一步,我们还是要坚持改革,以改革促创新,以创新促发展,不断地推动中国科技创新发展,支撑引领经济社会、国家安全、人民健康等方面的提升和发展。”王志刚说。

重大创新成果接连涌现

  “十年来,中科院科研人员攻坚克难、勇攀高峰,产出了一批具有标志性、引领性的重大创新成果。”中国科学院院长侯建国说。

  悟空、墨子、慧眼等一批科学卫星提升我国空间科学国际竞争力;凝聚态物理、纳米材料等一批重要前沿方向研究进入世界第一方阵;“中国天眼”“人造太阳”等国际领先的重大科技基础设施成为科研利器……

  侯建国说,中科院紧扣国家战略需求,在保障国家重大工程、突破“卡脖子”技术等方面发挥了关键作用。与此同时,瞄准科技前沿加强基础研究,持续提升原始创新能力,在衡量基础研究水平的“自然指数”排名中,中科院已连续9年位列全球科教机构之首。

  近十年,中科院累计向社会转化了约11万项科技成果,为高质量发展提供强大助力。例如,“曙光”超级计算机、人工智能芯片等促进了相关新兴产业的发展;煤制乙醇、煤制低碳烯烃等多项技术成功实现了商业化,为煤炭清洁高效利用和能源安全提供了科技解决方案。

  下一步,中科院将进一步发挥国家战略科技力量主力军作用,努力取得更多重大创新成果,为加快建设世界科技强国、实现高水平科技自立自强做出新的贡献。

工程科技进步最大、实力提高最快的十年

  “可以说,这十年是我国工程科技进步最大、实力提高最快的十年。”中国工程院院长李晓红说,这十年,我国建成了一大批世界级工程,从载人航天到深海探测再到中国高铁、中国大坝、中国桥梁……这是充分发挥了新型举国体制优势的结果。

  与此同时,工程科技的发展实实在在造福了人民,在确保粮食安全、助力抗击疫情、支撑生态环境改善中都发挥了重要作用。

  李晓红说,中国工程院将建设国内一流、世界知名的工程科技高端智库,为国家发展建言献策,为地方提供战略咨询。

  “我们的院士遍布全国各地,中国工程院可以把他们凝聚起来,组织跨学科、跨领域的院士专家进行联合攻关、服务国家战略,打好关键核心技术攻坚战。”李晓红说,中国工程院将持续强化国家战略科技力量,不断深化院士制度改革,促进院士在国家重大工程、核心关键技术方面发挥更大的作用。

  “我们今后要以更加开放的思维和举措来推进国际工程科技的创新开放合作,提高我国工程科技的国际化水平和影响力。”李晓红说,在全球工程科技治理中要发出中国声音。

高质量科普服务惠及我国更广泛人群

  科技创新、科学普及是实现创新发展的两翼。没有全民科学素质普遍提高,就难以建立起宏大的高素质创新大军,难以实现科技成果快速转化。

  “过去十年,得益于科学普及的推广,我国公民具备科学素质的比例大幅提升,2020年达到10.56%,比2015年的6.2%提高了近1倍。”中国科协分管日常工作副主席、书记处第一书记张玉卓说。

  党的十八大以来,我国不断提高科普组织力动员力,构建省域统筹政策和机制、市域构建资源集散中心、县域组织落实,以新时代文明实践中心(所、站)、党群服务中心、社区服务中心(站)等为阵地,以科技志愿服务为手段的基层科普组织动员体系,打造“品牌、平台、机制、队伍、改革、阵地”六位一体的高质量科普服务体系。

  中国特色现代科技馆体系服务线下公众超8.5亿人次,“科普中国”平台传播量达416亿人次,213万名科技工作者实名注册科技志愿者,连续4年举办世界公众科学素质促进大会深化国际合作……十年来,高质量科普服务惠及我国更广泛人群。

  张玉卓说,接下来将着力营造“人人科普、科普人人”的良好氛围,并引导科普资源和服务向欠发达地区尤其是西部地区倾斜,持续推进科普助力乡村振兴。

促进基础研究高质量发展

  国家自然科学基金是我国资助基础研究的“基本盘”,为基础研究人员提供了最稳定的项目来源。十年里,国家自然科学基金共受理项目申请约201万项,资助约43万项,覆盖自然科学各个领域,形成了完整的资助体系。

  国家自然科学基金委主任李静海认为,当前应对全球挑战与科研范式变革交织,在这关键历史时期,深化科学基金改革、不断提升资助效益、促进基础研究高质量发展,是必须肩负的历史使命和时代责任。

  为此,国家自然科学基金以明确资助导向、完善评审机制、优化学科布局三项任务为核心进行了系统性改革。

  同时,国家自然科学基金大幅简化申请代码,代码数量由3500多个压缩到1300多个。资助管理机制改革方面,国家自然科学基金改革联合基金,针对不同合作对象明确出资比例,引导多元主体加大投入。

  “下一步,科学基金将以转变科研范式与提升凝练科学问题能力为抓手,更加主动开拓未来,为把握新发展阶段,贯彻新发展理念,构建新发展格局,推动高质量发展提供坚实的支撑。”李静海说。


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

Infographic: Companies with the Most Patents Granted in 2021

See large image . . . . . .

Source : Visual Capitalist