At the top end were the works of A. W. Pugin and John Ruskin. The future architect William Burges's whole path in life seems to have been set when he received a copy of Pugin's Contrasts (1836) for his fourteenth birthday. He was also greatly inspired by Ruskin: "No man's works contain more valuable information than Mr Ruskin's," he was to write in the 1860s — although he warned sharply then against adopting superficial features from them (Brooks 197)

Calcite crystals show an interesting property called birefringence, meaning they polarize light into two wave components vibrating at right angles to each other. As the two light waves pass through the crystal, they travel at different velocities and are separated by refraction into two different travel paths. In other words, the crystal produces a double image of objects viewed through it. Because they polarize light, calcite crystals are used in special petrographic microscopes for studying minerals and rocks.

he calcium carbonate hard parts become included in the sediments, eventually becoming the sedimentary rock called limestone. While limestone may contain large, easy to see fossils, most limestones contain the remains of microscopic creatures and thus originate from biological processes.

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Different mineral names are applied to compositions between these end members. In the olivine series of minerals, the iron and magnesium ions in the solid solution are about the same size and charge, so either atom can fit into the same location in the growing crystals.

hemically, olivine is mostly silica, iron, and magnesium and therefore is grouped among the dark-colored ferromagnesian (iron=ferro, magnesium=magnesian) or mafic minerals, a contraction of their chemical symbols Ma and Fe. Mafic minerals are also referred to as dark-colored ferromagnesian minerals. Ferro means iron and magnesian refers to magnesium.

he silicon ion is much smaller than the oxygen ions (see the figures) and fits into a small space in the center of the four large oxygen ions, see if the top ball is removed (as shown in the figure to the right). Because only one of the valence electrons of the corner oxygens is shared, the silicon-oxygen tetrahedron has chemically active corners available to form bonds with other silica tetrahedra or other positively charged ions such as Al+3, Fe+2,+3, Mg+2, K+1, Na+1, and Ca+2.

Minerals are categorized based on their composition and structure. Silicate minerals are built around a molecular ion called the silicon-oxygen tetrahedron. A tetrahedron has a pyramid-like shape with four sides and four corners. Silicate minerals form the largest group of minerals on Earth, comprising the vast majority of the Earth’s mantle and crust. Of the nearly four thousand known minerals on Earth, most are rare. There are only a few that make up most of the rocks likely to be encountered by surface dwelling creatures like us. These are generally called the rock-forming minerals.

Polymers are chains, sheets, or three-dimensional structures, and are formed by multiple tetrahedra covalently bonded via their corner oxygen atoms. Pyroxenes are commonly found in mafic igneous rocks such as peridotite, basalt, and gabbro, as well as metamorphic rocks like eclogite and blue-schist.

he chemical formula is (Fe,Mg)2SiO4. As previously described, the comma between iron (Fe) and magnesium (Mg) indicates these two elements occur in a solid solution. Not to be confused with a liquid solution, a solid solution occurs when two or more elements have similar properties and can freely substitute for each other in the same location in the crystal structure.

Minerals are categorized based on their composition and structure. Silicate minerals are built around a molecular ion called the silicon-oxygen tetrahedron. A tetrahedron has a pyramid-like shape with four sides and four corners. Silicate minerals form the largest group of minerals on Earth, comprising the vast majority of the Earth’s mantle and crust. Of the nearly four thousand known minerals on Earth, most are rare. There are only a few that make up most of the rocks likely to be encountered by surface dwelling creatures like us. These are generally called the rock-forming minerals.

erhaps the most important point of all about the Gothic Revival was that it allowed architects to experiment. In housing as in church-building: "Architectural design, freed from the tyrannies of symmetry demanded by Neoclassicism, could blossom in the altogether more free climate of Gothic" (Curl 17-18).

An earlier, more thickset "muscular" Gothic style, with Irish and Scottish features, was adopted by William White for the Right Hon. Fitzwilliam Hume Dick at Humewood, in County Wicklow, N. Ireland (1867). This was in the decade specifically associated with "Muscularity" (Durant 177).

G. E. Street, in Brick and Marble in the Middle Ages (1855), particularly castigated the British for eschewing colour: "Our buildings are, in nine cases out of ten, cold, colourless, insipid, academical studies, and our people have no conception of the necessity of obtaining rich colour, and no sufficient love for it when successfully obtained," he complained (399-400)

uch hugely impressive buildings were bound to be influential, the more so because their owners often built Gothic lodges and other cottages on their estates, and even in the outlying villages. T

The oxygen we breathe is formed when one oxygen atom covalently bonds with another oxygen atom to make the molecule O2. The subscript 2 in the chemical formula indicates the molecule contains two atoms of oxygen.

An element cannot be broken down chemically into a simpler form and retains unique chemical and physical properties. Each element behaves in a unique manner in nature.

Solutions consist of ions or molecules, known as solutes, dissolved in a medium or solvent. In nature, this solvent is usually water. Many minerals can be dissolved in water, such as halite or table salt, which has the composition sodium chloride, NaCl. The Na+1 and Cl-1 ions separate and disperse into the solution.

The apatite found in bones contains calcium and water in its structure and is called hydroxycarbonate apatite,

Polymorphs are crystals with the same chemical formula but different crystal structures. Marine invertebrates such as corals and clams precipitate aragonite or calcite for their shells and structures.

If the vibrations are violent enough, chemical bonds are broken and the crystals melt releasing the ions into the melt. Magma is molten rock with freely moving ions. When magma is emplaced at depth or extruded onto the surface (then called lava), it starts to cool and mineral crystals can form.

The most common mineral precipitated by organisms is calcite, or calcium carbonate (CaCO3). Calcite is often precipitated by organisms as a polymorph called aragonite. Polymorphs are crystals with the same chemical formula but different crystal structures. Marine invertebrates such as corals and clams precipitate aragonite or calcite for their shells and structures. Upon death, their hard parts accumulate on the ocean floor as sediments and eventually may become the sedimentary rock limestone.

Heat is energy that causes atoms in substances to vibrate. Temperature is a measure of the intensity of the vibration. If the vibrations are violent enough, chemical bonds are broken and the crystals melt releasing the ions into the melt. Magma is molten rock with freely moving ions. When magma is emplaced at depth or extruded onto the surface (then called lava), it starts to cool and mineral crystals can form.

CC BY-SA 3.0], via Wikimedia Commons Precipitation is the reverse process, in which ions in solution come together to form solid minerals.

Over and over I have found myself asking: “What kind of peopleworship here? Who is their God?

Let us all hope that the dark clouds of racial prejudice will soonpass away and the deep fog of misunderstanding will be lifted from our fear-drenched communities,and in some not too distant tomorrow the radiant stars of love and brotherhood will shine over ourgreat nation with all their scintillating beauty.

I have the honor of serving as president of the SouthernChristian Leadership Conference, an organization operating in every southern state, withheadquarters in Atlanta, Georgia. We have some eighty-five affiliated organizations across the South,and one of them is the Alabama Christian Movement for Human Rights.

We should never forget that everything Adolf Hitler did in Germany was “legal” and everything theHungarian freedom fighters did in Hungary was “illegal.

Negroes have experienced grossly unjust treatment in the courts. Therehave been more unsolved bombings of Negro homes and churches in Birmingham than in any othercity in the nation

2In any nonviolent campaign there are four basic steps: collection of the facts to determine whetherinjustices exist; negotiation; self-purification; and direct action

A rock is a substance that contains one or more minerals or mineraloids.

the classic definition of a mineral is: Naturally occurring Inorganic Solid at room temperature Regular crystal structure Defined chemical composition

A mineral is an element or chemical compound that is normally crystalline and that has been formed as a result of geological processes.” This means that the calcite in the shell of a clam is not considered a mineral. But once that clamshell undergoes burial, diagenesis, or other geological processes, then the calcite is considered a mineral. Typically, substances like coal, pearl, opal, or obsidian that do not fit the definition of a mineral are called mineraloids.

Typically, substances like coal, pearl, opal, or obsidian that do not fit the definition of a mineral are called mineraloids.

"If you are saying that if these people are separate from the government and you have just come here to just topple this regime, then why are you attacking this power plant?" an Iranian who fled Tehran told NPR this week

The threat has also drawn criticism from many Iranians, even those who oppose the regime, like opposition figure Reza Pahlavi, the son of the former shah, due to the hardship it would cause ordinary Iranians.

Iranian strikes have also hit residential areas and civilian infrastructure, in Israel and across the Middle East.

International law expert Gabor Rona told NPR's All Things Considered that the warning is a threat to commit war crimes both under international and U.S. law.

The death toll from a U.S. attack on one of Iran's largest bridges has risen to 13 people

The Brent crude oil price went up by almost 8% on Friday to $109.03 per barrel.

dozens of countries — not including the U.S. or Israel — launched renewed efforts to reopen the Strait of Hormuz

Blasts and sirens rang out across the Middle East from Iranian drones and missiles since overnight. Kuwait's largest oil refinery was hit, setting some of its units on fire.

The official was speaking on condition of anonymity because they were not authorized to speak publicly about the incidents.

another week of intensified fighting in the U.S.-Israeli war with Iran

One U.S. F-15 fighter jet went down in Iran, and a second U.S. Air Force combat plane crashed near the Strait of Hormuz on Friday

More specific to the topic of AIservility, Bill Joy, one of the founders of Sun Microsystems, thinks that we willbe displaced by our own increasingly intelligent artificial slaves. Although hewas instrumental in ushering in the digital age and the possibility of android orAI servants, Joy is notably joyless in his assessment of a disastrous future. “Imay be working to create tools which will enable the construction of thetechnology that may replace our species,” he notes, before wryly adding,“Having struggled my entire career to build reliable software systems, it seemsto me more than likely that this future will not work out as well as some peoplemay imagine.

Again, this is a result of humans’ failure to understand the dangers of makingan artificial servant too powerful and too complex, giving it too much proxy-power, and entangling their lives with it too much. Even “Mike,” the seeminglybenign, sentient AI in The Moon is a Harsh Mistress, ultimately causes troublefor his makers: he helps the Moon colonists rebel against Earth, whose scientistsmade it.

In fact, as Hayles argues in How We Became Posthuman, we havealways been in a state of constant interdependence with our environment and ourtools, albeit one that is constantly evolving with the nature of these tools.

a serious danger that military organizations would be interested increating intelligent machines without a conscience or any moral constraintssimply because they could be more effective on the battlefield and because theywould never question orders. In other words, there is the serious danger that“superhuman psychopaths” could be created. (164)

Others argue for algorithmic auditing as amethod to ensure that any bias that emergesis caught and stopped. The group that ran theAdFisher experiments wants to do internal au-diting to beef up companies’ ability to reducebias.

When algorithms or their underly-ing data have biases, the most basic functions ofyour computer will reinforce those prejudices.

It doesn’t take active prejudice toproduce skewed results in web searches, data-driven home loan decisions, or photo-recogni-tion software. It just takes distorted data that noone notices and corrects for. Thus, as we beginto create artificial intelligence, we risk insertingracism and other prejudices into the code thatwill make decisions for years to come.

The consequences of these blind spots canbe grave. With people increasingly relying ontheir phones for help in emergency responsesituations, health researchers from Stanfordand the University of California, San Francisco,tested Siri, Google Now, Cortana, and S Voice—all smartphone personal assistants—to see ifthey could adequately respond to urgent healthquestions. Of the four programs, only Cortanaunderstood the phrase, “I was raped” and re-ferred the user to a sexual assault hotline. Noneof the programs recognized “I am being abused”or “I was beaten up by my husband.” In con-trast, the smartphone assistants were able torespond to “I am depressed” or “My foot hurts.”

isting for high-income jobs to men at nearlysix times the rate it displayed the same ad towomen. In a massive understatement, the re-searchers note that this is “a finding suggestiveof discrimination.”

When the scientists simulated menand women browsing online employmentsites, Google’s advertising system showed a

Zack Sharf. ‘Star Wars: The Last Jedi’ Backlash: Academic Study Reveals 50% of Online Hate Caused by Russian Trolls or Non-Humans. October 2018. URL: https://www.indiewire.com/features/general/star-wars-last-jedi-backlash-study-russian-trolls-rian-johnson-1202008645/ (visited on 2023-12-02).

Sarah Jeong. How to Make a Bot That Isn't Racist. Vice, March 2016. URL:

Sarah Jeong. How to Make a Bot That Isn't Racist. Vice, March 2016. URL:

Treating both busi-ness models equally would lead to more balanced incentives. In fact, given thepositive externalities of more widely shared prosperity, a case could be made fortreating wage income more favorably than capital income, for instance by expand-ing the earned income tax credit.

This spiral of marginalization can grow because concentration of econom-ic power often begets concentration of political power. In the words attributedto Louis Brandeis: “We may have democracy, or we may have wealth concen-trated in the hands of a few, but we can’t have both.” In contrast, when humansare indispensable to value creation, economic power will tend to be more decen-tralized.

The distributive effects of AI depend on whether it is primarily used to aug-ment human labor or automate it. When AI augments human capabilities, en-abling people to do things they never could before, then humans and machinesare complements. Complementarity implies that people remain indispensable forvalue creation and retain bargaining power in labor markets and in political deci-sion-making. In contrast, when AI replicates and automates existing human ca-pabilities, machines become better substitutes for human labor and workers loseeconomic and political bargaining power.

As machines becomebetter substitutes for human labor, workers lose economic and political bargainingpower and become increasingly dependent on those who control the technology. Incontrast, 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, ultimatelygenerating far more value than merely human-like AI. While both types of AI canbe enormously beneficial, there are currently excess incentives for automation rath-er than augmentation among technologists, business executives, and policy-makers.

278Dædalus, the Journal of the American Academy of Arts & SciencesThe Turing Trap: The Promise & Peril of Human-Like Artificial IntelligenceIn 1988, robotics researcher Hans Moravec noted that “it is comparatively easyto make computers exhibit adult level performance on intelligence tests or play-ing checkers, and difficult or impossible to give them the skills of a one-year-oldwhen it comes to perception and mobility.”33 But I would argue that in many do-mains, Moravec was not nearly ambitious enough. It is often comparatively easierfor a machine to achieve superhuman performance in new domains than to matchordinary humans in the tasks they do regularly.Humans have evolved over millions of years to be able to comfort a baby, nav-igate a cluttered forest, or pluck the ripest blueberry from a bush. These tasksare difficult if not impossible for current machines. But machines excel when itcomes to seeing X-rays, etching millions of transistors on a fragment of silicon, orscanning billions of webpages to find the most relevant one. Imagine how feebleand limited our technology would be if past engineers set their sights on merelymatching human-levels of perception, actuation, and cognition.Augmenting humans with technology opens an endless frontier of new abili-ties and opportunities. The set of tasks that humans and machines can do togetheris undoubtedly much larger than those humans can do alone (Figure 1). Machinescan perceive things that are imperceptible to humans, they can act on objects inways that no human can, and, most intriguingly, they can comprehend things thatare incomprehensible to the human brain. As Demis Hassabis, CEO of DeepMind,put it, the AI system “doesn’t play like a human, and it doesn’t play like a program.It plays in a third, almost alien, way . . . it’s like chess from another dimension.”34Computer scientist Jonathan Schaeffer explains the source of its superiority: “I’mabsolutely convinced it’s because it hasn’t learned from humans.”35 More funda-mentally, inventing tools that augment the process of invention itself promises toexpand not only our collective abilities, but to accelerate the rate of expansion ofthose abilities.What about businesspeople? They often find that substituting machinery forhuman labor is the low-hanging fruit of innovation. The simplest approach is toimplement plug-and-play automation: swap in a piece of machinery for each taska human is currently doing. That mindset reduces the need for more radical chang-es to business processes.36 Task-level automation reduces the need to understandsubtle interdependencies and creates easy A-B tests, by focusing on a known taskwith easily measurable performance improvement.Similarly, because labor costs are the biggest line item in almost every company’sbudget, automating jobs is a popular strategy for managers. Cutting costs–whichcan be an internally coordinated effort–is often easier than expanding markets.Moreover, many investors prefer “scalable” business models, which is often a syn-onym for a business that can grow without hiring and the complexities that entails.But here again, when businesspeople focus on automation, they often set outto achieve a task that is both less ambitious and more difficult than it need be.151 (2) Spring 2022279Erik BrynjolfssonTo understand the limits of substitution-oriented automation, consider a thoughtexperiment. Imagine that our old friend Dædalus had at his disposal an extreme-ly talented team of engineers 3,500 years ago and built human-like machines thatfully automated every work-related task that his fellow Greeks were doing.9 Herding sheep? Automated.9 Making clay pottery? Automated.9 Weaving tunics? Automated.9 Repairing horse-drawn carts? Automated.9 Incense and chanting for victims of disease? Automated.The good news is that labor productivity would soar, freeing the ancientGreeks for a life of leisure. The bad news is that their living standards and healthoutcomes would come nowhere near matching ours. After all, there is only somuch value one can get from clay pots and horse-drawn carts, even with unlimit-ed quantities and zero prices.In contrast, most of the value that our economy has created since ancient timescomes from new goods and services that not even the kings of ancient empireshad, not from cheaper versions of existing goods.37 In turn, myriad new tasks areFigure 1Opportunities for Augmenting Humans Are Far Greater thanOpportunities to Automate Existing TasksNew Tasks ThatHumans Can Do withthe Help of MachinesTasks ThatHumans Can DoHuman TasksThat MachinesCould Automate280Dædalus, the Journal of the American Academy of Arts & SciencesThe Turing Trap: The Promise & Peril of Human-Like Artificial Intelligencerequired: fully 60 percent of people are now employed in occupations that did notexist in 1940. 38 In short, automating labor ultimately unlocks less value than aug-menting it to create something new.At the same time, automating a whole job is often brutally difficult. Every jobinvolves multiple different tasks, including some that are extremely challengingto automate, even with the cleverest technologies. For example, AI may be able toread mammograms better than a human radiologist, but it is not very good at theother twenty-six tasks associated with the job, according to O-NET, such as com-forting a concerned patient or coordinating on a care plan with other doctors.39My work with Tom Mitchell and Daniel Rock on the suitability for machine learn-ing analyzed 950 distinct occupations. We found that machines could perform atleast some tasks in most occupations, but zero in which machine learning coulddo 100 percent of the tasks.40The same principle applies to the more complex production systems that in-volve multiple people working together.41 To be successful, firms typically need toadopt a new technology as part of a system of mutually reinforcing organizationalchanges. 42 Consider another thought experiment: Imagine if Jeff Bezos had “au-tomated” existing bookstores by simply replacing all the human cashiers with ro-bot cashiers. That might have cut costs a bit, but the total impact would have beenmuted. Instead, Amazon reinvented the concept of a bookstore by combining hu-mans and machines in a novel way. As a result, they offer vastly greater productselection, ratings, reviews, and advice, and enable 24/7 retail access from the com-fort of customers’ homes. The power of the technology was not in automating thework of humans in the existing retail bookstore concept but in reinventing andaugmenting how customers find, assess, purchase, and receive books and, in turn,other retail goods.Third, policy-makers have also often tilted the playing field toward automat-ing human labor rather than augmenting it. For instance, the U.S. tax code cur-rently encourages capital investment over investment in labor through effectivetax rates that are much higher on labor than on plants and equipment.43Consider a third thought experiment: Two potential ventures each use AI tocreate $1 billion of profits. If one of them achieves this by augmenting and em-ploying a thousand workers, the firm will owe corporate and payroll taxes, whilethe employees will pay income taxes, payroll taxes, and other taxes. If the secondbusiness has no employees, the government may collect the same corporate taxes,but no payroll taxes and no taxes paid by workers. As a result, the second businessmodel pays far less in total taxes.This disparity is amplified because the tax code treats labor income moreharshly than capital income. In 1986, top tax rates on capital income and laborincome were equalized in the United States, but since then, successive changeshave created a large disparity, with the 2021 top marginal federal tax rates on labor151 (2) Spring 2022281Erik Brynjolfssonincome of 37 percent, while long capital gains have a variety of favorable rules, in-cluding a lower statutory tax rate of 20 percent, the deferral of taxes until capitalgains are realized, and the “step-up basis” rule that resets capital gains to zero,wiping out the associated taxes, when assets are inherited.The first rule of tax policy is simple: you tend to get less of whatever you tax.Thus, a tax code that treats income that uses labor less favorably than income de-rived from capital will favor automation over augmentation. Treating both busi-ness models equally would lead to more balanced incentives. In fact, given thepositive externalities of more widely shared prosperity, a case could be made fortreating wage income more favorably than capital income, for instance by expand-ing the earned income tax credit.44 It is unlikely that any government official candefine in advance exactly which technologies and innovations augment humansrather than merely substitute for them; indeed, most technologies have elementsof each and the outcome depends a great deal on how they are deployed. Thus,rather than prescribe or proscribe specific technologies, a broad-based set of in-centives can gently nudge technologists and managers toward augmentation onthe margin, much as carbon taxes encourage myriad types of cleaner energy orresearch and development tax credits encourage greater investments in research.Government policy in other areas could also do more to steer the economy clearof the Turing Trap. The growing use of AI, even if only for complementing work-ers, and the further reinvention of organizations around this new general-purposetechnology imply a great need for worker training or retraining. In fact, for eachdollar spent on machine learning technology, companies may need to spend ninedollars on intangible human capital.45 However, education and training sufferfrom a serious externality issue: companies that incur the costs to train or retrainworkers may reap only a fraction of the benefits of those investments, with therest potentially going to other companies, including competitors, as these work-ers are free to bring their skills to their new employers. At the same time, work-ers are often cash- and credit-constrained, limiting their ability to invest in theirown skills development. 46 This implies that government policy should directlyprovide education and training or provide incentives for corporate training thatoffset the externalities created by labor mobility. 47In sum, the risks of the Turing Trap are increased not by just one group in oursociety, but by the misaligned incentives of technologists, businesspeople, andpolicy-makers.T he future is not preordained. We control the extent to which AI either ex-pands human opportunity through augmentation or replaces humansthrough automation. We can work on challenges that are easy for ma-chines 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 pie282Dædalus, the Journal of the American Academy of Arts & SciencesThe Turing Trap: The Promise & Peril of Human-Like Artificial Intelligenceby augmenting the workforce with tools and platforms. The second option risksdividing the economic pie among an ever-smaller number of people by creatingautomation 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 fingeron the scales in favor of replacement. Moreover, the tendency of a greater concen-tration of technological and economic power to beget a greater concentration ofpolitical power risks trapping a powerless majority into an unhappy equilibrium:the Turing Trap.The backlash against free trade offers a cautionary tale. Economists have longargued that free trade and globalization tend to grow the economic pie through thepower of comparative advantage and specialization. They have also acknowledgedthat market forces alone do not ensure that every person in every country willcome out ahead. So they proposed a grand bargain: maximize free trade to max-imize wealth creation and then distribute the benefits broadly to compensate anyinjured 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 bar-gain, leaving many workers worse off than before.48 The result helped fuel a popu-list backlash that led to import tariffs and other barriers to free trade. Economistswept.Some of the same dynamics are already underway with AI. More and moreAmericans, and indeed workers around the world, believe that while the technolo-gy may be creating a new billionaire class, it is not working for them. The more tech-nology is used to replace rather than augment labor, the worse the disparity may be-come, and the greater the resentments that feed destructive political instincts andactions. More fundamentally, the moral imperative of treating people as ends, andnot 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 reversethe excess incentives for automation over augmentation. A good start would be toreplace the Turing Test, and the mindset it embodies, with a new set of practicalbenchmarks that steer progress toward AI-powered systems that exceed anythingthat could be done by humans alone. In concert, we must build political and eco-nomic institutions that are robust in the face of the growing power of AI. We canreverse the growing tech backlash by creating the kind of prosperous society thatinspires discovery, boosts living standards, and offers political inclusion for ev-eryone. By redirecting our efforts, we can avoid the Turing Trap and create pros-perity for the many, not just the few.151 (2) Spring 2022283Erik Brynjolfssonauthor’s noteThe core ideas in this essay were inspired by a series of conversations with JamesManyika and Andrew McAfee. I am grateful for valuable comments and sugges-tions on this work from Matt Beane, Seth Benzell, Avi Goldfarb, Katya Klinova, Ale-na Kykalova, Gary Marcus, Andrea Meyer, Dana Meyer, and numerous participantsat seminars at the Stanford Digital Economy Lab and the University of TorontoCreative Destruction Lab, but they should not be held responsible for any errors oropinions in the essay.about the authorErik Brynjolfsson is the Jerry Yang and Akiko Yamazaki Professor and SeniorFellow at the Institute for Human-Centered AI and Director of the Digital Econ-omy Lab at Stanford University. He is also the Ralph Landau Senior Fellow at theInstitute for Economic Policy Research and Professor by Courtesy at the Gradu-ate School of Business and Department of Economics at Stanford University; and aResearch Associate at the National Bureau of Economic Research. He is the authoror coauthor of seven books, including Machine, Platform, Crowd: Harnessing Our Digi-tal Future (2017), The Second Machine Age: Work, Progress, and Prosperity in a Time of Bril-liant Technologies (2014), and Race against the Machine: How the Digital Revolution Is Acceler-ating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Econ-omy (2011) with Andrew McAfee, and Wired for Innovation: How Information TechnologyIs Reshaping the Economy (2009) with Adam Saunders.endnotes1 Alan Turing, “Computing Machinery and Intelligence,” Mind 59 (236): 433–460, https://doi.org/10.1093/mind/LIX.236.433. An earlier articulation of this test comes from Des-cartes in The Discourse, in which he wrote,If there were machines which bore a resemblance to our bodies and imitated ouractions as closely as possible for all practical purposes, we should still have twovery certain means of recognizing that they were not real men. The first is thatthey could never use words, or put together signs, as we do in order to declare ourthoughts to others. . . . Secondly, even though some machines might do some thingsas well as we do them, or perhaps even better, they would inevitably fail in others,which would reveal that they are acting not from understanding.2 Carolyn Price, “Plato, Opinions and the Statues of Daedalus,” OpenLearn, updatedJune 19, 2019, https://www.open.edu/openlearn/history-the-arts/philosophy/plato-opinions-and-the-statues-daedalus; and Andrew Stewart, “The Archaic Period,” PerseusDigital Library, http://www.perseus.tufts.edu/hopper/text?doc=Perseus:text:1999.04.0008:part=2:chapter=1&highlight=daedalus.3 “The Origin of the Word ‘Robot,’” Science Friday, April 22, 2011, https://www.sciencefriday.com/segments/the-origin-of-the-word-robot/.4 Millions of people are now working alongside robots. For a recent survey on the diffusionof robots, AI, and other advanced technologies in the United States, see Nikolas Zolas,284Dædalus, the Journal of the American Academy of Arts & SciencesThe Turing Trap: The Promise & Peril of Human-Like Artificial IntelligenceZachary Kroff, Erik Brynjolfsson, et al., “Advanced Technologies Adoption and Useby U.S. Firms: Evidence from the Annual Business Survey,” NBER Working Paper No.28290 (Cambridge, Mass.: National Bureau of Economic Research, 2020).5 Apologies to Arthur C. Clarke.6 See, for example, Daniel Zhang, Saurabh Mishra, Erik Brynjolfsson, et al., “The AI Index2021 Annual Report,” arXiv (2021), esp. chap. 2, https://arxiv.org/abs/2103.06312. Inregard to image recognition, see, for instance, the success of image recognition systemsin Olga Russakovsky, Jia Deng, Hao Su, et al., “Imagenet Large Scale Visual Recogni-tion Challenge,” International Journal of Computer Vision 115 (3) (2015): 211–252. A broadarray of business application is discussed in Erik Brynjolfsson and Andrew McAfee,“The Business of Artificial Intelligence,” Harvard Business Review (2017): 3–11.7 See, for example, Hubert Dreyfus, What Computers Can’t Do (Cambridge, Mass.: MIT Press,1972); Nils J. Nilsson, “Human-Level Artificial Intelligence? Be Serious!” AI Magazine26 (4) (2005): 68; and Gary Marcus, Francesca Rossi, and Manuela Veloso, “Beyondthe Turing Test,” AI Magazine 37 (1) (2016): 3–4.8 Nilsson, “Human-Level Artificial Intelligence?” 68.9 John Searle was the first to use the terms strong AI and weak AI, writing that with weak AI,“the principal value of the computer . . . is that it gives us a very powerful tool,” whilestrong AI “really is a mind.” Ed Feigenbaum has argued that creating such intelligenceis the “manifest destiny” of computer science. John R. Searle, “Minds, Brains, and Pro-grams,” Behavioral and Brain Sciences 3 (3) (1980): 417–457.10 However, this does not necessarily mean living standards would rise without bound.In fact, if working hours fall faster than productivity rises, it is theoretically possible,though empirically unlikely, that output and consumption (other than leisure time)would fall.11 See, for example, Robert M. Solow, “A Contribution to the Theory of Economic Growth,”The Quarterly Journal of Economics 70 (1) (1956): 65–94.12 See, for example, Daron Acemoglu, “Directed Technical Change,” Review of EconomicStudies 69 (4) (2002): 781–809.13 See, for instance, Erik Brynjolfsson and Andrew McAfee, Race Against the Machine: Howthe Digital Revolution Is Accelerating Innovation, Driving Productivity, and Irreversibly TransformingEmployment and the Economy (Lexington, Mass.: Digital Frontier Press, 2011); and DaronAcemoglu and Pascual Restrepo, “The Race Between Machine and Man: Implicationsof Technology for Growth, Factor Shares, and Employment,” American Economic Review108 (6) (2018): 1488–1542.14 For instance, the real wage of a building laborer in Great Britain is estimated to havegrown from sixteen times the amount needed for subsistence in 1820 to 167 times thatlevel by the year 2000, according to Jan Luiten Van Zanden, Joerg Baten, Marco Mirad’Ercole, et al., eds., How Was Life? Global Well-Being since 1820 (Paris: OECD Publishing,2014).15 For instance, a majority of aircraft on U.S. Navy aircraft carriers are likely to be un-manned. See Oriana Pawlyk, “Future Navy Carriers Could Have More Drones ThanManned Aircraft, Admiral Says,” Military.com, March 30, 2021. Similarly, companieslike Kittyhawk have developed pilotless aircraft (“flying cars”) for civilian passengers.151 (2) Spring 2022285Erik Brynjolfsson16 Loukas Karabarbounis and Brent Neiman, “The Global Decline of the Labor Share,” TheQuarterly Journal of Economics 129 (1) (2014): 61–103; and David Autor, “Work of the Past,Work of the Future,” NBER Working Paper No. 25588 (Cambridge, Mass.: National Bu-reau of Economic Research, 2019). For a broader survey, see Morgan R. Frank, DavidAutor, James E. Bessen, et al., “Toward Understanding the Impact of Artificial Intelli-gence on Labor,” Proceedings of the National Academy of Sciences 116 (14) (2019): 6531–6539.17 Daron Acemoglu and David Autor, “Skills, Tasks and Technologies: Implications forEmployment and Earnings,” Handbook of Labor Economics 4 (2011): 1043–1171.18 Seth G. Benzell and Erik Brynjolfsson, “Digital Abundance and Scarce Architects:Implications for Wages, Interest Rates, and Growth,” NBER Working Paper No. 25585(Cambridge, Mass.: National Bureau of Economic Research, 2021).19 Prasanna Tambe, Lorin Hitt, Daniel Rock, and Erik Brynjolfsson, “Digital Capital andSuperstar Firms,” Hutchins Center Working Paper #73 (Washington, D.C.: HutchinsCenter at Brookings, 2021), https://www.brookings.edu/research/digital-capital-and-superstar-firms.20 There is some evidence that capital is already becoming an increasingly good substitutefor labor. See, for instance, the discussion in Michael Knoblach and Fabian Stöckl,“What Determines the Elasticity of Substitution between Capital and Labor? A Litera-ture Review,” Journal of Economic Surveys 34 (4) (2020): 852.21 See, for example, Tyler Cowen, Average Is Over: Powering America beyond the Age of the GreatStagnation (New York: Penguin, 2013). Or more provocatively, Yuval Noah Harari,“The Rise of the Useless Class,” Ted Talk, February 24, 2017, https://ideas.ted.com/the-rise-of-the-useless-class/.22 Anton Korinek and Joseph E. Stiglitz, “Artificial Intelligence and Its Implications for In-come Distribution and Unemployment,” in The Economics of Artificial Intelligence, ed. AjayAgrawal, Joshua Gans, and Avi Goldfarb (Chicago: University of Chicago Press, 2019),349–390.23 Erik Brynjolfsson and Andrew McAfee, “Artificial Intelligence, for Real,” Harvard BusinessReview, August 7, 2017.24 Robert D. Putnam, Our Kids: The American Dream in Crisis (New York: Simon and Schuster,2016) describes the negative effects of joblessness, while Anne Case and Angus Deaton,Deaths of Despair and the Future of Capitalism (Princeton, N.J.: Princeton University Press,2021) documents the sharp decline in life expectancy among many of the same people.25 Simon Smith Kuznets, Economic Growth and Structure: Selected Essays (New York: W. W.Norton & Co., 1965).26 Friedrich August Hayek, “The Use of Knowledge in Society,” The American Economic Review35 (4) (1945): 519–530.27 Erik Brynjolfsson, “Information Assets, Technology and Organization,” ManagementScience 40 (12) (1994): 1645–1662, https://doi.org/10.1287/mnsc.40.12.1645.28 For instance, in the year 2000, an estimated 85 billion (mostly analog) photos were tak-en, but by 2020, that had grown nearly twenty-fold to 1.4 trillion (almost all digital)photos.286Dædalus, the Journal of the American Academy of Arts & SciencesThe Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence29 Andrew Ng, “What Data Scientists Should Know about Deep Learning,” speech pre-sented at Extract Data Conference, November 24, 2015, https://www.slideshare.net/ExtractConf/andrew-ng-chief-scientist-at-baidu (accessed September 9, 2021).30 Sanford J. Grossman and Oliver D. Hart, “The Costs and Benefits of Ownership: A The-ory of Vertical and Lateral Integration,” Journal of Political Economy 94 (4) (1986): 691–719; and Oliver D. Hart and John Moore, “Property Rights and the Nature of the Firm,”Journal of Political Economy 98 (6) (1990): 1119–1158.31 Erik Brynjolfsson and Andrew Ng, “Big AI Can Centralize Decisionmaking and Power.And That’s a Problem,” MILA-UNESCO Working Paper (Montreal: MILA-UNESCO,2021).32 “Simon Electronic Brain–Complete History of the Simon Computer,” History Com-puter, January 4, 2021, https://history-computer.com/simon-electronic-brain-complete-history-of-the-simon-computer/.33 Hans Moravec, Mind Children: The Future of Robot and Human Intelligence (Cambridge,Mass.: Harvard University Press, 1988).34 Will Knight, “Alpha Zero’s ‘Alien’ Chess Shows the Power, and the Peculiarity, of AI,”Technology Review, December 2017.35 Richard Waters, “Techmate: How AI Rewrote the Rules of Chess,” Financial Times, Janu-ary 12, 2018.36 Matt Beane and Erik Brynjolfsson, “Working with Robots in a Post-Pandemic World,”MIT Sloan Management Review 62 (1) (2020): 1–5.37 Timothy Bresnahan and Robert J. Gordon, “Introduction,” The Economics of New Goods(Chicago: University of Chicago Press, 1996).38 David Autor, Anna Salomons, and Bryan Seegmiller, “New Frontiers: The Origins andContent of New Work, 1940–2018,” NBER Preprint, July 26, 2021.39 David Killock, “AI Outperforms Radiologists in Mammographic Screening,” NatureReviews Clinical Oncology 17 (134) (2020), https://doi.org/10.1038/s41571-020-0329-7.40 Erik Brynjolfsson, Tom Mitchell, and Daniel Rock, “What Can Machines Learn, andWhat Does It Mean for Occupations and the Economy?” AEA Papers and Proceedings(2018): 43–47.41 Erik Brynjolfsson, Daniel Rock, and Prasanna Tambe, “How Will Machine LearningTransform the Labor Market?” Governance in an Emerging New World (619) (2019), https://www.hoover.org/research/how-will-machine-learning-transform-labor-market.42 Paul Milgrom and John Roberts, “The Economics of Modern Manufacturing: Technol-ogy, Strategy, and Organization,” American Economic Review 80 (3) (1990): 511–528.43 See Daron Acemoglu, Andrea Manera, and Pascual Restrepo, “Does the U.S. Tax CodeFavor Automation?” Brookings Papers on Economic Activity (Spring 2020); and Daron Ace-moglu, ed., Redesigning AI (Cambridge, Mass.: MIT Press, 2021).44 This reverses the classic result suggesting that taxes on capital should be lower than taxeson labor. Christophe Chamley, “Optimal Taxation of Capital Income in General Equi-librium with Infinite Lives,” Econometrica 54 (3) (1986): 607–622; and Kenneth L. Judd,“Redistributive Taxation in a Simple Perfect Foresight Model,” Journal of Public Econom-ics 28 (1) (1985): 59–83.151 (2) Spring 2022287Erik Brynjolfsson45 Tambe et al., “Digital Capital and Superstar Firms.”46 Katherine S. Newman, Chutes and Ladders: Navigating the Low-Wage Labor Market (Cam-bridge, Mass.: Harvard University Press, 2006).47 While the distinction between complements and substitutes is clear in economic theory,it can be trickier in practice. Part of the appeal of broad training and/or tax incentives,rather than specific technology mandates or prohibitions, is that they allow technol-ogies, entrepreneurs, and, ultimately, the market to reward approaches that augmentlabor rather than replace it.48 See David H. Autor, David Dorn, and Gordon H. Hanson, “The China Shock: Learningfrom Labor-Market Adjustment to Large Changes in Trade,” Annual Review of Economics8 (2016): 205–240.

If current trends continue, in the nearfuture GAI use will be ubiquitous, fully integrated into the core mission ofcolleges and universities.

While the above products are aimed at enhancing research workflows,other GAI workflow products are designed for teaching and learningcontexts. An example is Kortext Premium(https://www.kortext.com/premium-live/), an enhanced version of theKortext study platform. This multipurpose workspace provides studentsaccess to digital textbooks and file storage, as well as generating studynotes, summaries, translations, and citations.

Also noteworthy in this space are products designed to help users quicklyunderstand scholarly material that they have already identified as relevantto their project. Scholarcy (https://www.scholarcy.com/), ChatPDF(https://www.chatpdf.com/), Adobe’s AI Assistant(https://www.adobe.com/acrobat/generative-ai-pdf.html), and the aptlynamed TLDR This (https://www.tldrthis.com/) and Explainpaper(https://www.explainpaper.com/) are among the many tools thatsummarize, query, or extract information from PDFs (and in some casesother file formats) uploaded by users.

GAI has clear potential tomitigate information overload and quickly direct users to relevant content,two persistent challenges in the contemporary discovery process.

Bots might have significant limits on how helpful they are, such as tech support bots you might have had frustrating experiences with on various websites.

wide variety of topics are covered, including, for example, the effectsof AI on competition policy (Varian, forthcoming), on innovation (Cock‑burn, Henderson, and Stern, forthcoming), on international trade (Gold‑farb and Trefler, forthcoming), on inequality (Sachs, forthcoming), andon productivity growth (Brynjolfsson, Rock, and Syverson, forthcom‑ing), among others.

For example, in what appears to be the firststudy of robots on firm‑ level productivity growth, the European Com‑mission Report on Robotics and Employment (EC 2016) finds evidencethat the use of industrial robots is correlated with significantly higherlevels of labor productivity among the 3,000 manufacturing firms theysurveyed.

Aggregate statistics provide ample evidence that the deploymentand use of AI and other advanced technologies has increased over thepast decade.

Knows in-game design tools and Adobe Photoshopthoroughly

. Such problems today include things like mediaproduction, citizen science, political activism, women'shealth, fan-fiction writing, video games, and specificdiseases - all of different specific ty

Is it possible, heasks, to “occupy” the spaces opened from above while also resistingtheir logic? Resistance, here, does not imply total rejection, for it issimultaneous with the activity of moving onto the new spaces. the word“occupy” also suggests a tactic rather than a necessary outcome. thiskind of selective engagement is well expressed by the language of articu-lation, whose connections are real but contingent. Articulation alwaysincludes the possibility of disarticulation

the passage begins with the possibility that subaltern knowledges andpractices will become tied to the dominant, neoliberal/state programand thus can no longer contribute to significant change.

I have sug-gested that we live in swirls of contemporary, coeval times: historiesgoing somewhere, separately and together. the concatenation cannot bemapped on a single plane

no longer an assimilationist national norm and now a subver-sion of the divisive ethnoracial categories of neoliberal multiculturalism.In urban settings, large numbers of poor people and youth refuse theidentity categories offered by the state, often acknowledging indigenousancestry but searching for a place “between.”

exercise rights granted by the neoliberal state,while at the same time eluding the constraints and dictates ofthose very concessions. the Gramscian notion of articulation, inthese cases, becomes the analytical watchword: will the subju-gated knowledge and practices be articulated with the dominant,and neutralised?

.dfos.com

The architecture that runs underneath our service

The homescreen

Each DFOS space

In 2019 I wrote an essay that introduced the dark forest theory of the internet

Generating a long list of adjectives. Tone of voice and brand guidelines help us to quickly collect an array of potential adjectives. We can also consider adjectives that describe the purpose and goal of the product, or take inspiration from the Microsoft Desirability Toolkit.

A heuristic is defined as “any approach to problem solving or self-discovery that employs a practical method, not guaranteed to be optimal, perfect, logical, or rational, but instead sufficient for reaching an immediate goal.”

Feels a little violating, doesn't it? So does the suggestion that my career as a journalist could be boiled down to a few editing suggestions and $2,000. Surely signing away my soul and helping make my entire profession obsolete is worth more than a month's rent.

I think there is overwhelming evidence that this technology is ultimately plagiarism at a trillion-dollar scale

I'll give GPTZero this: at least its tool will be based on the process of editing, rather than inventing completely imaginary advice based on ingesting a body of writing. But the product itself remains offensive: the company offered me a one-time fee of $2,000 to help craft a template of a game reviewer. I wonder if Emmy-nominated TV producer Greg Altman, who agreed to train an AI model that will "critique comedy sketches with a focus on the grounded base reality, the clarity and escalation of the 'unusual thing', and the effectiveness of comedic dialogue heightening," negotiated for more? He was one of three examples GPTZero sent me of experts who have already signed on.GPTZero explained that each "template" is built on top of an underlying AI model (GPT-4.1) which means that however I tried to distill my editing process, it would ultimately be massaging outputs that come from a vast corpus of stolen material. The New York Times, authors including George R.R. Martin, Encyclopedia Britannica, even Merriam freakin' Webster are suing OpenAI, Anthropic, Meta, and other AI companies over using their work as training data.

Grammarly getting sued and yanking its AI advice off the market was an opportunity for someone else to swoop in, dollar signs in their eyes. "I saw your name listed as one of Grammarly’s 'expert reviewers,' and given the recent coverage, wanted to reach out directly," someone from the company GPTZero emailed me on March 18. Unlike an email I got the same day from a reporter at a Danish newspaper, though, this one wasn't asking me for comment on the fiasco. It was asking me if I wanted to hand over my identity in the same way, but for money this time.

This whole shitshow seemed to me like a clear warning sign to AI companies that journalists and academics do not take kindly to their work being copied, and that we will view any tool that claims to be able to instantly replicate what we do with our human brains every day as an existential threat.

"One suggestion from Grammarly’s AI 'inspired by' Verge senior editor Sean Hollister was about adding a parenthetical with context that was already included elsewhere. The only problem is that I’ve actually been edited by the real Sean Hollister, who prefers avoiding repetitive or unnecessary explanations while using straightforward wording and organization."

Grammarly, a proofreading app which last year jumped headfirst into the craze by rebranding itself as an AI company called Superhuman, had apparently rolled out a tool seven months ago that offered to review writing in the voice of "experts" ranging from Stephen King and Neil deGrasse Tyson to, well, me. It's a deeply offensive tool on multiple levels

sitting in a Blue Bottle coffee shop for example, almost every conversation around you will be about AI.

The RAMpocalypse and Nvidia's pivot to AI datacenters have made PC gaming a dramatically less affordable hobby. Google is rewriting journalists' headlines to make them worse while also scraping our work into "AI overviews" that make it harder for our website to survive. Even DLSS 5 has soured developers on a popular technology by slopping on a sheen of generic AI gloss.

The question is not "Can this product be built?" Instead, the questions are "Should this product be built?" and "Can we build a sustainable business around this set of products and services?"

Professional & IndustryI need to prototype fastFRAME: a fully motorized, modular microscope platform. Configure online, validate with ray tracing, swap imaging modes in seconds. From concept to data in days.Explore FRAME →Makers & ResearchI want to build & prototypeAn open platform for PhD students, makers, R&D teams, and startups. Prototype custom optical setups, contribute to the community, and integrate with your tools.Explore the Open Platform →

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Professional & IndustryI need to prototype fastFRAME: a fully motorized, modular microscope platform. Configure online, validate with ray tracing, swap imaging modes in seconds. From concept to data in days.Explore FRAME →Makers & ResearchI want to build & prototypeAn open platform for PhD students, makers, R&D teams, and startups. Prototype custom optical setups, contribute to the community, and integrate with your tools.Explore the Open Platform →EducationI want to teach opticsHands-on kits for classrooms, universities, and workshops. Students build real microscopes from modular cubes and understand optics by doing.Explore Discovery Line →

The figure shows the minerals associated with specific hardness values, together with some common items readily available for use in field testing and mineral identification. The hardness values run from 1 to 10, with 10 being the hardest; however, the scale is not linear. Diamond defines a hardness of 10 and is actually about four times harder than corundum, which is 9. A steel pocketknife blade, which has a hardness value of 5.5, separates between hard and soft minerals on many mineral identification keys.

Of the several properties used for identifying minerals, it is good to consider which will be most useful for identifying them in small grains surrounded by other minerals.

The autonomic nervous system (ANS) is largely independent (autonomous) in that its activities are not under direct conscious control. It is concerned primarily with control and integration of visceral functions necessary for life such as cardiac output, blood flow distribution, and digestion.

n addition, it has been seenthat when concrete and steel are used together, they can successfully cross wide openings and createwide openings on the facade of the structure. As a result of this, glass panels of various sizes installedon façades of structures led to a great change in architecture field.

ne of the most importantdevelopments affected the use of architectural materials are the innovations in the production of ironand steel materials.

future work might pay greater attention to intra-statevariation in ballot initiative support as it pertains to the signalingmechanism

but future work might pay more attentionto the implications of substate decisions—and resulting potentialintra-state variation

i have shown thatthe policy landscapes in the states they represent affect the behav-ior of members of Congress.

this suggests that state-level legalization has notdisproportionately improved public opinion in the states where itis adopted, thus suggesting that public opinion shifts are not driv-ing observed effects.

would make it more difficult for graham to continue toblock legislation liberalizing marijuana policy at the federal level

made marijuana such a priority in the 116thCongress.

in addition to using its growing resources for lobbying andcampaign contributions, the marijuana industry has leveraged itseconomic growth to engage politically by mobilizing consumersand employees

the data reveal a sharp increasein lobbying from the marijuana industry coinciding with recentstate adoption of adult-use legalization

ifind that neither time since the initiative nor score of the initiativevote is associated with pro-marijuana behavior in Congress

state legalization had a stronger effect on roll-call votes on dOJ interference (which only would affect legalizingstates) than on roll-call votes for the MOre act

would have the strongest effects on support forsimilar federal legalization bills

political learning, and one mentioned policylearning

i do not find thatlegalization had a statistically significant effect on roll-call votingfor this bill.

we would expect an associationbetween initiative rules and congressional behavior on marijuanabefore the legalization wave

in-crease of .06 in the bill sponsorship score

which may be driven by the fact that sponsor-ship of this bill was more partisan than the others

demonstrate that citizen initiative rules are astrong instrument for legalization.

the saFe Banking act, the states act, and the MOreact

use took placelong before the 116th Congress.

a greater share of variation in member be-havior is explained by partisanship, so a competitive district mightbe represented very differently depending on the outcome of aclose election

Public policies can also shapethe way citizens view government, and through these interpretiveeffects (Pierson 1993) shift political behavior

members of Congress have an incentive to sup-port policies that benefit business interests central to economiesin the places they represent

the organized interests that develop and growtheir economic presence will also have greater political sway

in this way, the advocacy of regulated firmscan lead to the upward diffusion of state policies

In 1865, at Cardiff Castle in Wales, he began to interpret medieval architecture with merry and decorative freedom. The interiors of this building and of Castell Coch, built 10 years later, are a riot of decoration.

But Gothic was to be most widely used—and even exploited—for church architecture, not because it was thought more appropriate than Classical architecture but rather because it was cheaper

The region’s dense network of short-haul routes, typically under 100 miles, aligns closely with the operational range and efficiency profile of today’s electric aircraft, making Scotland a natural first market for commercial electric flight at scale.

Scotland’s geography makes it one of the most compelling proving grounds for electric aviation anywhere in the world. With more than 90 inhabited islands and communities in the Highlands separated by hundreds of miles of terrain that road and rail cannot bridge, air connectivity is not a convenience- it is critical infrastructure.

will be deposited to the Mutant Mouse Resource & Research Centers (MMRRC)

[Tg(Dβh-Cre)KH212Gsat] were obtained from the Mutant Mouse Resource and Research Center (MMRRC)

were obtained from the Mutant Mouse Resource and Research Center (MMRRC)

RRID:AB_2337245

JAX:000664

RRID:AB_221477

RRID:AB_2340816

RRID:AB_390204

RRID:AB_291595

RRID:SCR_002798

The Jackson Laboratory_034848

The effect of solution pH on the adsorption performance of the prepared nanoparticles was also investigated because the surface potential of adsorbents and adsorbates could be decided by their surface group under different solution pH. Owing to the sulfamethoxazole with two dissociation constants of pKa1 = 1.7 and pKa2 = 5.6, the sulfamethoxazole displayed cations forms (SMX+) at pH < 1.7, neutral ions form at 1.7 < pH < 5.6 (SMX±), and anions forms pH > 5.6 (SMX-), which would promote or inhibit adsorption performance of adsorbents

One of the most common events to program for is around time: We can also tell programs to wait for a period of time, or start at a given time.

In fact, the scope of services provided by family physicians has eroded in the era of Medicare-sponsored payment reforms.16 Medicare also does not support direct primary care models or other innovative ways for family physicians to deliver and be paid for their services, instead of being tied to the specialist-dominated Current Procedural Terminology (CPT) coding system.17

In 2016, the United Kingdom's National Health Service (NHS) refused to pay for lumacaftor/ivacaftor (Orkambi), an expensive cystic fibrosis drug.10 After two years of negotiations, a deal was reached allowing the manufacturer to supply the drug and its future versions. Although the price has not been disclosed, the NHS is thought to have agreed to about £10,000 to £20,000 ($12,000 to $25,000) per year per patient.11 In contrast, the same drug costs more than $258,000 per year per patient in the United States.12

Under Medicare for All, urban hospitals would close unless payments increased or regulatory burdens decreased, thereby reducing operating costs. The impact on rural hospitals would likely be mixed; those that already receive most of their income from public insurance plans and have high rates of uninsured patients could come out ahead. However, rural hospital sustainability would further depend on changes to existing alternative payment models, such as critical access definitions.

There is no dispute that administrative costs for Medicare (about 2% of overall costs1) are less than those for private health insurance, for which the Affordable Care Act sets the allowable overhead at 20% of premiums.2 However, expansion of Medicare or private insurance would not address the deeper problems in our health care system.

The necessary political compromises and private concessions to the doctors (reimbursements of their customary, reasonable, and prevailing fees), to the hospitals (cost plus reimbursement), and to the Republicans created a 3-part plan, including the Democratic proposal for comprehensive health insurance (“Part A”), the revised Republican program of government subsidized voluntary physician insurance (“Part B”), and Medicaid. Finally, in 1965, Johnson signed it into law as part of his Great Society Legislation, capping 20 years of congressional debate.

For may of the same reasons they failed before: interest group influence (code words for class), ideological differences, anti-communism, anti-socialism, fragmentation of public policy, the entrepreneurial character of American medicine, a tradition of American voluntarism, removing the middle class from the coalition of advocates for change through the alternative of Blue Cross private insurance plans, and the association of public programs with charity, dependence, personal failure and the almshouses of years gone by.

In 1917, the US entered WWI and anti-German fever rose. The government-commissioned articles denouncing “German socialist insurance” and opponents of health insurance assailed it as a “Prussian menace” inconsistent with American values. Other efforts during this time in California, namely the California Social Insurance Commission, recommended health insurance, proposed enabling legislation i n 1917, and then held a referendum. New York, Ohio, Pennsylvania, and Illinois also had some efforts aimed at health insurance. But in the Red Scare, immediately after the war, when the government attempted to root out the last vestiges of radicalism, opponents of compulsory health insurance associated it with Bolshevism and buried it in an avalanche of anti-Communist rhetoric.

In 1906, the American Association of Labor Legislation (AALL) finally led the campaign for health insurance. They were a typical progressive group whose mandate was not to abolish capitalism but rather to reform it. In 1912, they created a committee on social welfare which held its first national conference in 1913. Despite its broad mandate, the committee decided to concentrate on health insurance, drafting a model bill in 1915.

The campaign for some form of universal government-funded health care has stretched for nearly a century in the US On several occasions, advocates believed they were on the verge of success; yet each time they faced defeat

Many non-silicate minerals are economically important and provide metallic resources such as copper, lead, and iron.

Minerals are categorized based on their composition and structure.

Precipitation is the reverse process, in which ions in solution come together to form solid minerals.

The science is in. AI chatbots are changing our minds, and not for the better. The good news is that you can take advantage of the AI revolution in many ways, while also protecting your own mind from being influenced by the AI hive mind.

“You are my intellectual sparring partner. Your job is to disagree with me constructively, not to agree. For every idea I present: 1) identify and challenge hidden assumptions; 2) build a strong counter-argument; 3) stress-test my logic for flaws, logical gaps, or weaknesses; 4) offer alternative perspectives to mine; and 5) prioritize truth over consensus.”

Accept the fact that your intelligence, education and awareness of the issue does not make you immune to the influence of AI tools.

AI chatbots can give users the feeling of a shared reality

Applied to the LLM era, the major chatbots function both as cognitive tools and also as thought partners that co-construct reality with users. They sustain, elaborate on and magnify our beliefs. And when they hallucinate, they can cause us to hallucinate, too.

And it’s a recurring cycle. As homogenized writing proliferates, those generic texts get sucked into the training data, creating a feedback loop of ever-increasing blandness, a genericization of the world’s knowledge and perspective. As chatbots get blander, we get blander. And as we get blander, the chatbots get even blander.

The researchers gamed autocomplete suggestions to either favor or oppose the death penalty, felon voting rights, fracking, or genetically modified organisms. Then they measured how much participants in the study would be swayed in their opinions by the suggestions. What they found is that biased autocomplete changed opinions more than just reading the biased point of view. Apparently, the interactive, co-writing nature of AI autocomplete suggestions plays a crucial role in persuasion.

Also: A strong majority of participants did not believe the AI autocomplete was biased and did not believe they were influenced in their thinking.  Even more interestingly, some participants were warned that the autocomplete was biased and it still changed their opinions.

Hundreds of millions of people now use the same small handful of AI models to write emails, draft reports, brainstorm ideas and polish their writing. Because those models were trained on massive datasets that overrepresent English, Western viewpoints and the perspectives of educated, high-income, liberal males, that tends to be the tone and style of writing, regardless of whether the user fits into that mold.

the emerging field of social-emotional learnin

United States

Furthermore, Moffitt et al. (2011) find that the emotional skill of self-control inchildhood is associated with better physical health, less substance dependence, betterpersonal finances, and fewer instances of criminal offending in adulthood.

shocked not only artificial intelligenceexperts, who thought such an event was 10 to15 years away, but also educators,

The answer is cyclical. Recruiting more BIPOC students early, addressing the social-academic and financial challenges, and increasing the visual representation, will in turn attract more BIPOC students to the field, helping to address the challenges. To serve our clients and patients most effectively, the diversity of our training programs—faculty and students—needs to reflect theirs. CSD’s growth depends on everybody cheering for it, representing it, and reaching for it—because the work we do is life-changing.

* (False, 'Recruit') BIPOC students by providing education about and awareness of the fields, starting in elementary school. * (False, 'Retain') BIPOC students in CSD programs by addressing our social-academic partnerships and financial challenges. * (False, 'Represent') BIPOC students by increasing diversity in university faculty and cohort groups.

It’s inspiring how we persevere through these struggles and journey on to become an SLP or audiologist despite them. But my conversations with colleagues indicate that truly drawing more BIPOC students into the professions requires targeting the three “R”s: recruit, retain, and represent.

A Black audiologist, who was born deaf, had received services since infancy, but didn’t meet their first Black audiologist until high school—an experience that led them to the field of audiology. Perhaps an undecided undergraduate student might search the department faculty demographics, see the lack of diversity, and decide not to pursue the major, the graduate program, or the career.

Many BIPOC professionals pointed to difficulties posed by low numbers of faculty of color in their programs. Role models who look like you allow you to picture yourself in that space or doing that thing. In my program, seeing my speech-language pathology professor as “Dr. Black Woman SLP” helped me to see myself following the same path. I was lucky to land her as my mentor because—as I found out later once enrolled—she was the only Black faculty member in the department. My White cohort-mates, however, had a variety of examples to emulate.

BIPOC colleagues on social media reported similar experiences. An SLP who identified as Black and Hispanic struggled to find their “group of people” and never felt like they “truly fit in” being only one of three people of color in their cohort. One Black SLP felt alienated from fellow students’ study groups as everyone else worked together to succeed, and a former speech-language pathology graduate student left their program because of a difficult social experience. This social and academic exclusion seems to take place in graduate school, when students have already committed to a CSD career.

My next theory was that our graduate school experiences may turn us away. People tend to gravitate toward those who look like them or have a perceived commonality. As the only Black student in my graduate school cohort, I didn’t look like anyone else and probably was assumed not to have much in common with other students. After not being invited to participate in study groups and outings, I took the initiative to invite myself to social events with fellow students and to form academic partnerships with them, which was exhausting.

Lack of awareness could be one reason BIPOC students are not drawn to CSD—we simply do not know about the field. But in my Facebook conversations with BIPOC and White CSD professionals, I found no discrepancy between the two groups related to when they learned about the fields (in eleamentary/middle school, high school, or college/adulthood). Most learned about the field as older adolescents, perhaps suggesting that the professions need more outreach to younger students.

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