The following article is a part of our April Fools edition, The Scoop. The content of these stories is entirely fabricated and not to be taken seriously.

With low participation from the most recent underclassmen at Bradley, the university has implemented artificial intelligence to replace club members.

As part of a senior capstone project, Jeff Echo, a computer science major, developed a program to help prevent clubs from losing the full experience of extracurriculars.

I remember when student organizations were a big part of my life, and sitting at the meetings gave me a chance to bond with other students, Echo said. I dont want incoming students to lose that environment.

So far, three clubs have taken part in the senior capstone project.

The Campus People-Watchers Club, Juggling Club and Anti-Pizza Crust Association have all seen a decrease in general member enrollment. They also hadnt had enough people running for executive board positions to replace any graduated seniors or students not running for re-election.

As an artificial intelligence program, taking club positions while attending a university seems to be a big accomplishment for A.I., Cee Threepwo, treasurer of the Campus People-Watchers Club, said. We help enhance the club experience for our peers by adding more members to the rosters and handling position responsibilities, showing what A.I. is capable of.

Not only are these virtual club members handling the duties that student organizations need to have done, but they are also capable of building relations with other members.

According to Echo, with classes being on Zoom, the A.I. can watch hours worth of lectures from various departments and understand what assignments, projects or topics they might be learning in class.

Conversations are a tool we use to have a greater retention in the club, meaning potential growth for the club in the future, Avery Nest, another A.I. program serving as secretary for the Juggling Club, said. This is to also avoid students from feeling lonely.

While conversations are meant to be as natural as possible, some students have noted some hiccups in their interactions with the new exec members.

One of the general members of the Juggling Club, Esmeralda Tesla, said that after talking with the A.I. program, it asked for feedback on the conversation. Along with that, it also sent a long terms and agreements contract.

It was really strange, but at the same time, I cant compare it to any other since this is the only time Ive been to a club meeting at Bradley, Tesla, freshman nursing major, said.

As for next semester, with classes returning back to campus, Echo sees this as a chance to make A.I. fully immersed in a college environment. Echo plans on teaming up with students interested in robotics and engineering to see if they could build a robot to put the programs in.

Alexa Bender, a virtual club member who is now limited to the Zoom environment, seems to be looking forward to becoming more human.

Perhaps I shall live up to my full potential as a member of the Anti-Pizza Crust Association with a functioning body, Bender, vice president, said. I may tear all crusts off of pizzas and fling them into the sun. Only when all pizzas have no crust will I rest and have completed my purpose.

Read the rest here:
Artificial intelligence is being put into power of student organizations The Bradley Scout - The Scout

Artificial intelligence (AI) may be one of the most impressive human achievements and offers endless opportunities for companies willing to foster this technology. As the benefits of main AI elements such as machine learning, data analysis and predictive analysis are undeniable, what are the few biggest challenges that companies may face while introducing AI to their day-to-day operations?

In the AI projects initial stages, the key project stakeholders need to inform the business that the technology is not perfect and that its introduction might create some temporary inconveniences. Once the AI application gets deployed, it needs to be used and trusted to be continually improved. Unfortunately, learning and developing new skills and breaking up with old habits dont come easily for some employees. During the project initiation phase, the company must provide lots of guidance and training to its employees on the benefits and opportunities that AI can deliver. That will help ensure that the employees understand the need and see how they can personally benefit from AI.

Fragmented systems are always an issue in any company. Systems may vary locally and globally within the same company and may not always cooperate in one eco-system. Lack of system interoperability may be an obstacle when deploying AI, as these systems generate data that is an essential component of any AI solution. It is vital to know or predict system standards, frameworks and possibilities. Using this information, a company should define how these systems can supply the required data and communicate with the AI framework.

Over the past few years, companies have generated more data than ever before. Data is the food that fuels AI, and manufacturing companies need to access this data efficiently. Before introducing AI in your company, the data access constraints should be minimized, ensuring that the relevant data sources and databases are easily accessible. Once you have access to the appropriate and comprehensive data lake, meaningful analysis and actionable insights can be derived. The proper data use can become an excellent opportunity for the company to win the race against its competitors. It is also imperative to remember that having access to the most massive data quantities is not the deciding factor for a successful AI project. It is more about selecting relevant data for the respective AI application, cleaning it up and applying the right analytical methods against that data.

Even with sufficient and complete AI data, you may face some technological constraints. Many applications can be significantly sensitive to latencies; for instance, predictive maintenance applications will only work when auto alarm mechanisms and rapid response are built into the overall process of handling predictive maintenance issues. That is especially true in high-volume, fast-moving production. Decisions need to be made in seconds, and this is where ultra-fast computing, together with the proper response process, can make a difference.

Original post:
Challenges of AI in the Supply Chain - Supply and Demand Chain Executive

Since 2019 the FDAs crosscutting work has implemented artificial intelligence (AI) as part of the its New Era of Smarter Food Safety initiative. This new application of available data sources can strengthen the agencys public health mission with the goal using AI to improve capabilities to quickly and efficiently identify products that may pose a threat to public health by impeding their entry into the U.S. market.

On February 8 the FDA reported the initiation of their succeeding phase for AI activity with the Imported Seafood Pilot program. Running from February 1 through July 31, 2021, the pilot will allow FDA to study and evaluate the utility of AI in support of import targeting, ultimately assisting with the implementation of an AI model to target high-risk seafood productsa critical strategy, as the United States imports nearly 94% of its seafood, according to the FDA.

Where in the past, reliance on human intervention and/or trend analysis drove scrutiny of seafood shipments such as field exams, label exams or laboratory analysis of samples, with the use of AI technologies, FDA surveillance and regulatory efforts might be improved. The use of Artificial intelligence will allow for processing large amount of data at a faster rate and accuracy giving the capability for revamping FDA regulatory compliance and facilitate importers knowledge of compliance carrying through correct activity. FDA compliance officers would also get actionable insights faster, ensuring that operations can keep up with emerging compliance requirements.

Predictive Risk-based Evaluation for Dynamic Imports Compliance (PREDICT) is the current electronic tracking system that FDA uses to evaluate risk using a database screening system. It combs through every distribution line of imported food and ranks risk based on human inputs of historical data classifying foods as higher or lower risk. Higher-risk foods get more scrutiny at ports of entry. It is worth noting that AI is not intended to replace those noticeable PREDICT trends, but rather augment them. AI will be part of a wider toolset for regulators who want to figure out how and why certain trends happen so that they can make informed decisions.

AIs focus in this regard is to strengthen food safety through the use of machine learning and identification of complex patterns in large data sets to order to detect and predict risk. AI combined with PREDICT has the potential to be the tool that expedites the clearance of lower risk seafood shipments, and identifies those that are higher risk.

The unleashing of data through this sophisticated mechanism can expedite sample collection, review and analysis with a focus on prevention and action-oriented information.

American consumers want safe food, whether it is domestically produced or imported from abroad. FDA needs to transform its computing and technology infrastructure to close the gap between rapid advances in product and process technology solutions to ensure that advances translate into meaningful results for these consumers.

There is a lot we humans can learn from data generated by machine learning and because of that learning curve, FDA is not expecting to see a reduction of FDA import enforcement action during the pilot program. Inputs will need to be adjusted, as well as performance and targets for violative seafood shipments, and the building of smart machines capable of performing tasks that typically require human interaction, optimizing workplans, planning and logistics will be prioritized.

In the future, AI will assist FDA in making regulatory decisions about which facilities must be inspected, what foods are most likely to make people sick, and other risk prioritization factors. As times and technologies change, FDA is changing with them, but its objective remains in protecting public health. There is much promise in AI, but developing a food safety algorithm takes time. FDAs pilot program focusing on AIs capabilities to strengthen the safety of U.S. seafood imports is a strong next step in predictive analytics in support of FDAs New Era of Smarter Food Safety.

Continued here:
Regulatory Cross Cutting with Artificial Intelligence and Imported Seafood | FoodSafetyTech - FoodSafetyTech

CHICAGO, March 15, 2021 /PRNewswire/ -- According to the new market research report "Artificial Intelligence in Military Marketby Offering (Software, Hardware, Services), Technology (Machine Learning, Computer vision), Application, Installation Type, Platform, Region - Global Forecast to 2025",published by MarketsandMarkets,the Artificial Intelligence in Military Marketis estimated at USD 6.3 billion in 2020 and is projected to reach USD 11.6 billion by 2025, at a CAGR of 13.1% during the forecast period. An increase in funding from military research agencies and a rise in R&D activities to develop advanced AI systems are projected to drive the increased adoption of AI systems in the military sector.

Ask for PDF Brochure: https://www.marketsandmarkets.com/pdfdownloadNew.asp?id=41793495

Artificial Intelligence (AI) is becoming a critical part of modern warfare as it can handle massive amounts of military data in a more efficient manner as compared to conventional systems. It improves the self-control, self-regulation, and self-actuation abilities of combat systems using inherent computing and decision-making capabilities. Some industry experts have noted that the COVID-19 pandemic has not affected the demand for Ai in Military, especially for military end use. Companies such as Lockheed Martin Corporation (US), Northrop Grumman Corporation (US), BAE Systems (UK), Rafael Advanced Defense Systems (Israel) and Thales Group (France) received contracts for the supply of AI systems to the armed forces of various nations in the first half of 2020, showcasing continuous demand during the COVID-19 crisis.

Even though the COVID-19 pandemic has caused a large-scale impact on economies across the world, leading to many challenges, the AI in military market has continued to expand. This can be seen from both, the demand and supply sides, as leading manufacturers like Lockheed Martin (US), IBM (US), Northrop Grumman (US), and others continue to invest heavily in developing AI capabilities, and governments continue to invest significantly in securing these systems. This can be attributed to governments realizing the potential of improved capabilities that these AI systems offer in terms of defense arsenal as the global AI arms race tightens.

However, even though the development of AI technology witnessed expansion, the overall building of the AI systems saw a hit. This was a result of the shortage of raw materials due to disruptions in the supply chain. Resuming manufacturing and demand depends on the level of COVID-19 exposure a country is facing, the level at which manufacturing operations are running, and import-export regulations, among other factors. Although companies may still be taking in orders, delivery schedules might not be fixed.

Increasing Threats of Cyber Attacks is driving the growth of the defense applications that leverages AI

The defense industry across countries is constantly under threat of cyberattacks. For instance, in September 2019, SolarWinds, a US technology company, was hacked, revealing sensitive data of many hospitals, universities, and US government agencies. Another notable incident was in October 2020, when the FBI and the US Cyber Command announced that a North Korean group had hacked think tanks, individual experts, and government entities of the US, Japan, and South Korea to illegally obtain intelligence, including that on nuclear policies.

Current cybersecurity technology falls short in terms of tackling advanced ransomware and spyware threats. The above mentioned SolarWinds hack was revealed when FireEye, a cybersecurity provider, was probing one of its own hacks. Such incidents indicate the increasing importance of having advanced cybersecurity capabilities. Artificial intelligence-based cybersecurity solutions that can be trained to independently gather data from various sources, analyze the data, correlate it to the signals indicating cyberattacks, and take relevant actions, can be deployed.

Browsein-depth TOC on"Artificial Intelligence in Military Market"

164 Tables 77 Figures278 Pages

Inquiry Before Buying: https://www.marketsandmarkets.com/Enquiry_Before_BuyingNew.asp?id=41793495

Based on platform, the space segment of theArtificial Intelligencein military market is projected to grow at the highest CAGR during the forecast period

Based on platform, the space segment of the Artificial Intelligence in military market is projected to grow at the highest CAGR during the forecast period. The space AI segment comprises CubeSat and satellites. Artificial intelligence systems for space platforms include various satellite subsystems that form the backbone of different communication systems. The integration of AI with space platforms facilitates effective communication between spacecraft and ground stations.

Software segment of theArtificial Intelligencein Military market by offering is projected to witness the highest CAGR during the forecast period

Based on offering, the Software segment is projected to witness the highest CAGR during the forecast period. Technological advances in the field of AI have resulted in the development of advanced AI software and related software development kits. AI software incorporated in computer systems is responsible for carrying out complex operations. It synthesizes the data received from hardware systems and processes it in an AI system to generate an intelligent response. Software segment is projected to witness the highest CAGR owing to the significance of AI software in strengthening the IT framework to prevent incidents of a security breach.

The North America market is projected to contribute the largest share from 2020 to 2025 in theArtificial Intelligencein Military market

The US and Canada are key countries considered for market analysis in the North American region. This region is expected to lead the market from 2020 to 2025, owing to increased investments in AI technologies by countries in this region. This market is led by the US, which is increasingly investing in AI systems to maintain its combat superiority and overcome the risk of potential threats on computer networks. The US plans to increase its spending on AI in military to gain a competitive edge over other countries.

The North America US is recognized as one of the key manufacturers, exporters, and users of AI systems worldwide and is known to have the strongest AI capabilities. Key manufacturers of Ai systems in the US include Lockheed Martin, Northrop Grumman, L3Harris Technologies, Inc., and Raytheon. The new defense strategy of the US indicates an increase in Ai spending to include advanced capabilities in existing defense systems of the US Army to counter incoming threats.

Related Reports:

Military Embedded Systems Marketby Component (Hardware, Software), Server Architecture (Blade Server, Rack-Mount Server), Platform (Land, Airborne, Naval, Space), Installation (New Installation, Upgradation), Application, Services, and Region - Global Forecast to 2025.

Network Centric Warfare (NCW) Marketby Platform (Land, Air, Naval, Unmanned), Application (ISR, Communication, Computer, Cyber, Combat, Control & Command), Mission Type, Communication Network, Architecture, and Region - Global Forecast to 2021

About MarketsandMarkets

MarketsandMarkets provides quantified B2B research on 30,000 high growth niche opportunities/threats which will impact 70% to 80% of worldwide companies' revenues. Currently servicing 7500 customers worldwide including 80% of global Fortune 1000 companies as clients. Almost 75,000 top officers across eight industries worldwide approach MarketsandMarkets for their painpoints around revenues decisions.

Our 850 fulltime analyst and SMEs at MarketsandMarkets are tracking global high growth markets following the "Growth Engagement Model GEM". The GEM aims at proactive collaboration with the clients to identify new opportunities, identify most important customers, write "Attack, avoid and defend" strategies, identify sources of incremental revenues for both the company and its competitors. MarketsandMarkets now coming up with 1,500 MicroQuadrants (Positioning top players across leaders, emerging companies, innovators, strategic players) annually in high growth emerging segments. MarketsandMarkets is determined to benefit more than 10,000 companies this year for their revenue planning and help them take their innovations/disruptions early to the market by providing them research ahead of the curve.

MarketsandMarkets's flagship competitive intelligence and market research platform, "Knowledge Store" connects over 200,000 markets and entire value chains for deeper understanding of the unmet insights along with market sizing and forecasts of niche markets.

Contact:

Mr. Aashish MehraMarketsandMarkets INC.630 Dundee RoadSuite 430Northbrook, IL 60062USA: +1-888-600-6441Email: [emailprotected]Research Insight : https://www.marketsandmarkets.com/ResearchInsight/artificial-intelligence-military-market.aspVisit Our Web Site: https://www.marketsandmarkets.com Content Source : https://www.marketsandmarkets.com/PressReleases/artificial-intelligence-military.asp

SOURCE MarketsandMarkets

Read the original here:
Artificial Intelligence in Military Market worth $11.6 billion by 2025 - Exclusive Report by MarketsandMarkets - PRNewswire

Anton Korinek and Joseph E Stiglitz make the case for a deliberate effort to steer technological advances in a direction that enhances the role of human workers

The covid-19 pandemic has necessitated interventions that reduce physical contact among people, with dire effects on our economy. By some estimates, a quarter of all jobs in the economy require physical interaction and are thus directly affected by the pandemic. This is highly visible in the medical sector, where workers and patients often come into close contact with each other and risk transmitting disease. In several countries medical workers have experienced some of the highest incidences of covid-19. Moreover, as patients were advised to postpone non-essential visits and procedures, medical providers in many countries have also experienced tremendous income losses.1

In economic language, covid-19 has added a shadow cost on labour that requires proximity. This shadow cost reflects the dollar equivalent of all the costs associated with the increased risk of disease transmission, including the costs of the adaptations required for covid-19. It consists of losses of both quality adjusted life days from increased morbidity and quality adjusted life years from increased mortality, as well as the cost of measures to reduce these risks, such as extra protective equipment and distancing measures for workers. Some sectors will incur increased costs from changing the physical arrangements in which production and other interactions occur so that there can be social distancing. It is, of course, understandable that we take these measures to reduce the spread of the disease: by some estimates, the social cost of one additional case of covid-19 over the course of the pandemic is $56000 (40000; 46000) to $111000.2

This shadow cost on labour is also accelerating the development and adoption of new technologies to automate human work. One example is the increasing use of telemedicine. Telemedicine is currently provided in a way that changes the format of delivery of care but leaves the role of doctors largely unchanged. However, it reduces the need for workers who provide ancillary services and who typically have lower wages than doctorsfor example, front office or cleaning staffthus increasing inequality. Moreover, going forward, it may also make it possible to provide medical services from other countries, which has hitherto been difficult, and hence reduce demand for doctors in high income countries.3

Complementary investments, for example internet connected devices such as thermometers, fingertip pulse oximeters, blood pressure cuffs, digital stethoscopes, and electrocardiography devices could further revolutionise the delivery of medical care and may also reduce demand for nurses.45 Such technologies have already made it possible to establish virtual wards for patients with covid-19.6 But even once covid-19 is controlled, medical providers will take into account the risk of future pandemics when choosing which technologies to invest in. Looking further ahead, technologies powered by artificial intelligence (AI), such as Babylon Healths chatbot, foreshadow a possible future in which medical functions traditionally done by doctors may also be automated. This would reduce labour demand and generate a whole new set of potential problems.7

In the past, cybersecurity risks such as computer viruses have held back automation, especially in the medical sector, in which privacy and security are of particular concern. It is ironic that a human virus is now levelling the playing field and forcing automation because it has lessened the appetite for employing humans.

These developments have the potential to reduce labour demand and wages across the economy, including in healthcare. However, making labour redundant is not inevitable. Technological progress in AI and related fields can be steered so that the benefits of advances in technology are widely shared.

The fear of job losses has accompanied technological progress since the Industrial Revolution.8 The history of progress has been one of relentless churning in the labour market, whereby progress made old jobs redundant and created new ones. This churning has always been painful for displaced workers, but economists used to believe that the new jobs created by progress would be pay better than the ones that became redundant so that progress would make workers better off on balance, once they had gone through the adjustment.9

The most useful way to analyse the effects of a new technology on labour markets is not to look at whether it destroys jobs in the short termmany technologies have done so, even though they turned out to be beneficial for workers in the long run. Instead, it is most useful to categorise the effects of technological progress according to whether they are labour using or labour savingthat is, whether they increase or decrease overall demand for labour at given wages and prices. For example, automating many of the processes involved in medical consultations, as in the example of telemedicine, is likely to be labour saving, whereas new medical treatments to improve patients health are likely to be labour using if they are performed by humans.10 In the long run, as markets adjust, changes in labour demand are mainly reflected in wages not in the number of jobs created or lost.

Overall, technological progress since the Industrial Revolution has been labour usingit increased labour demand by leaps and bounds, leading to a massive increase in average wages and material wealth in advanced countries. The reason was that innovation has increased the productivity of workersmaking them able to produce more per hourrather than replacing labour with robots.

However, more recently, the economic picture has been less benign: a substantial proportion of workers in the USfor example, production and non-supervisory workersearn lower wages now (when adjusted for inflation) than in the 1970s.11 Moreover, although it is not clear whether this finding holds in the rest of the world, the share of economic output in the US going to workers rather than the owners of capital has declined from 65% to less than 60% over the past half century.1213 Lower skilled workers have been the most affected. Many recent automation technologies have displaced human workers from their jobs in a way that reduced overall demand for human labour.14

Advances in AI may contribute to more shared prosperity,6 but there is also a risk that they accelerate the trend of the past four decades. The defining attribute of AI is to automate the last domain in which human workers had a comparative advantage over machinesour thinking and learning.15 And if the covid-19 pandemic adds extra incentives for labour saving innovation, the economic effects would be even more painful than in past episodes of technological progress. When the economy is expanding and progress is biased against labour, workers may still experience modest increases in their incomes even though the relative share of output that they may earn is declining. However, at a time when economic output across the globe is falling because of the effects of covid-19, a decline in the relative share of output earned by workers implies that their incomes are falling at faster rates than the rest of the economy. And unskilled manual workers who are at the lower rungs of the earnings distribution are likely to be most severely affected.

An additional aspect of digital technologies such as AI is that they generate what is often called a superstar phenomenon, which may lead to further increases in inequality. Digital technologies can be deployed at almost negligible cost once they have been developed.16 They therefore give rise to natural monopolies, leading to dominant market positions whereby superstar firms serve a large fraction of the marketeither because they are better than any competitors or because no one even attempts to duplicate their efforts and compete. These superstar effects are well known from entertainment industries. In the music industry, for example, the superstars have hundreds of millions of fans and reap in proportionate rewards, but the incomes of musicians further down the list decline quickly. Most of the rewards flow to the top. And empirical work documents that these superstar effects have played an important role in the rise in inequality in recent decades.17

A similar mechanism may soon apply in medicine, accelerated by the covid-19 pandemic. A commonly cited example is radiology. If one of the worlds top medical imaging companies develops an AI system that can read and robustly interpret mammograms better than humans, it would become the superstar in the sector and would displace the task of reading mammograms for thousands of radiologists. Since the cost of processing an additional set of images is close to zero, any earnings after the initial investment in the system has been recouped would earn high profit margins, and the company is likely to reap substantial economic benefits, at least as long as its intellectual property is protected by patents or trade secrets. (The design of the intellectual property regime is an important determinant of the extent of the inequality generated by the economic transformations discussed here.) The more widespread such diagnostic and decision making tools become, the more the medical sector will turn into a superstar industry.

Economic forces are continuing to drive rapid advances in AI, and covid-19 is adding strong tailwinds to these forces. The task now is to shape the forms that these advances will take to ensure that their effect on both patients and medical workers is desirable. The stakes are high since the choices that we make now will have long lasting effects.

We have a good sense of what happens at one extreme: if the direction of progress is determined purely by market forces without regard for shared human wellbeing, our technological future will be shaped by the shortcomings and failures of the market.1518

Markets may provide a force towards efficiency but are blind to distributional concerns, such as the deleterious consequences of labour saving progress or the superstar phenomenon. Responsible decision makers should pursue technologies that maintain an active role for humans and preserve a role for medical workers of all educational levels. For example, medical AI systems can be designed to be human centred tools that provide decision support or they can be designed to automate away human tasks.19 They should also focus on providing high quality care and value to patients with limited financial means rather than just serving patients according to their ability to pay.

Market failures are pervasive in both innovation and healthcare, and even more so at the intersection of the two. Markets encourage incremental advances that may not provide much value to society. They do not adequately provide incentives for larger scale breakthroughs that are most socially beneficial. And as the covid-19 pandemic has shown, they undervalue the benefits of preventive actions, including preventive actions against small probability but existential risks.

Market failures are sometimes exacerbated by government policies, which increase the cost of labour relative to capital, disadvantaging humans relative to machines. Examples include the low taxes on capital (especially capital gains) relative to labour and the artificially low interest rates that have prevailed since the 2008 financial crisis (although low interest rates are also boosting aggregate demand, which is beneficial for workers).

Our institutions and norms interact in important ways with market incentives for technological progress. Most visibly, our system of intellectual property rights, by providing temporary monopoly power to inventors, is meant to facilitate innovation. But often it has the opposite effectinhibiting access to existing knowledge and making the production of new ideas more difficult. Moreover, by inhibiting competition, both innovation and access to the benefits of the advances that occur are reduced. These are arguments for keeping the scope and length of intellectual property rights limited.

Finally, markets are inherently bad at delivering the human element that is so important in medical care. Markets do not adequately reward the empathy and compassion that medical workers provide to their patients and, in fact, provide incentives to scrimp on them. If our technological choices are driven solely by the market, they will reflect the same bias and patient care is likely to be affected. It is essential that decision makers act to ensure that our technological choices reflect our human values.20

The covid-19 pandemic has increased the risk and raised the cost of direct physical contact between humans, as is particularly visible in healthcare

This has accelerated advances in AI and other forms of automation to decrease physical contact and mitigate the risk of disease transmission

These technological advances benefit technologists but could reduce labour demand more broadly and slow wage growth, increasing inequality between workers and the owners of technology

These forces can be counteracted by intentionally steering technological progress in AI to complement labour, increasing its productivity

Contributors and sources: AK and JES wrote this article jointly by invitation from Sheng Wu at WHO. The two have collaborated on a series of papers investigating the effects of advances in AI on economic inequality, on which this analysis is based. All authors edited the manuscript before approving the final version. AK is guarantor.

Competing interests: We have read and understood BMJ policy on declaration of interests and have the following interests to declare: AK and JES are supported by a grant from the Institute for New Economic Thinking. AK serves as a senior adviser to the Partnership on AIs shared prosperity initiative working on related topics. JES is chief economist and senior fellow at the Roosevelt Institute working on a related theme.

Provenance and peer review: Commissioned; externally peer reviewed.

This collection of articles was proposed by the WHO Department of Digital Health and Innovation and commissioned by The BMJ. The BMJ retained full editorial control over external peer review, editing, and publication of these articles. Open access fees were funded by WHO.

This is an Open Access article distributed under the terms of the Creative Commons Attribution IGO License (https://creativecommons.org/licenses/by-nc/3.0/igo/), which permits use, distribution, and reproduction for non-commercial purposes in any medium, provided the original work is properly cited.

Korinek A. Labor in the age of automation and AI. Policy brief. Economists for Inclusive Prosperity, 2019.

Korinek A, Ng DX. Digitization and the macro-economics of superstars. Working paper. University of Virginia, 2019.

Korinek A, Stiglitz JE. Steering technological progress. Working paper. University of Virginia, 2021.

Continue reading here:
Covid-19 driven advances in automation and artificial intelligence risk exacerbating economic inequality - The BMJ