Artificial Intelligence in Behavioral and Mental Health Care Market research report is the new statistical data source added by Healthcare Intelligence Markets. It uses several approaches for analyzing the data of target market such as primary and secondary research methodologies. It includes investigations based on historical records, current statistics, and futuristic developments. Artificial Intelligence In Behavioral And Mental Health Care Market is predicted to grow at a significant CAGR in the forecast period.

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Artificial Intelligence In Behavioral And Mental Health Care Market to Witness Astonishing Growth by 2026 Focusing on Leading Players AdvancedMD ,...

Friday, 12 June 2020, 3:38 pmPress Release: Phantom House

Wellington photographer Grant Sheehan has used artificialneural network technology to create photographic images thatvisualise an artificial intelligence (AI) dreamscape andthen published them in a book made of metal.

Thismassive new Kiwi project is a fusion of art and science.Does Ava Dream? has many different elements but atthe heart of it are the questions: what might an AI dreamof, and what might those dreams look like?

Sheehanattempts to answer these questions using photography, film,music, and cutting-edge publishing technology. The artworksof Does Ava Dream? are created using high-res patternimages, combined with artificial neural network photographyplug-ins, to illustrate how an AI's dream fragments mightlook on output.

Continuing the theme of AI androbotics, Sheehan has printed these images onto metal tocreate a singular metal book singular both in the sensethat it is remarkable and in the sense that there is onlyone of it.

Those interested can view the metal book atPtaka gallery in Porirua. It is accompanied by plus largedisplay versions of the dream images, plus a short filmshowing these images in motion. The music for this shortfilm, like the images, have been co-created by Sheehan andartificial neural network technology.

For those whocan't make it to the exhibition, Sheehan has also created amore traditional paper book about the project as a whole,called The Making Of Does Ava Dream? This is agorgeous hardback coffee-table book that is published in twoeditions, one of which comes with a signed metallic paperprint of one of the dream images.

Someimages are available for republication uponenquiry

More information about Does Ava Dream?at https://doesavadream.click/

Watchthe trailer for the metal book Does Ava Dream? here:https://www.youtube.com/watch?v=-sdNzxbrxXU

Findout more about the exhbition at Ptaka here: https://pataka.org.nz/whats/exhibitions/grant-sheehan-does-ava-dream/

Title: The Making of Does AvaDream?

Prices: $160.00 and $495.00

ISBN:9780994128560

Full colour landscape, 310 x 280mm, 70pages, June 2020

Binding: Hardcover with a dustjacket

Text: Satin low gloss

Images: Glosscoated

Each unit signed and numbered

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Metal Book Co-created By Human And Artificial Intelligence - Scoop.co.nz

In addition to the 57 percent who believe that advancements in artificial intelligence does indeed pose a threat, 43 percent disagree and believe it's not something to be worried about. Elaborating on this data further, the 57 percent who consider it a threat, approximately 16 percent of them doubled down even further and indicated they believe it's a 'very serious threat.'Rasmussen said that the number of respondents who consider AI a threat has risen three percent since November, stating hat younger voters are a bit more concerned about artificial intelligence than voters over fifty.

A 2017 Pew Research study showed that approximately 70 percent of Americans are not sure as to whether they should be concerned about the rise of robots and AI.The biggest giants on the planet such as Google, Apple and Amazon have continuously placed strong emphasis on the potential for these technologies to make our lives easier and improving efficiency, with other benefits.

Although artificial intelligence could potentially pose a threat to mankind, as of right now our understanding and research in artificial intelligence is not advanced enough to create machines capable of carrying out multiple tasks. AI, as of right now, helps to make peoples lives easier and improves efficiency in the work environment.

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Does AI Still Pose A Threat to Humanity? - AI Daily

June 9th, 2020 Sam Chambers Operations, Tech 1 comments

A drone has successfully inspected a 19.4 meter high oil tank onboard a Floating Production, Storage and Offloading vessel. The video shot by the drone was interpreted in real-time by an algorithm to detect cracks in the structure.

Scout Drone Inspection and class society DNV GL have been working together to develop an autonomous drone system to overcome the common challenges of tank inspections. For the customer, costs can run into hundreds of thousands of dollars as the tank is taken out of service for days to ventilate and construct scaffolding. The tanks are also tough work environments, with surveyors often having to climb or raft into hard to reach corners. Using a drone in combination with an algorithm to gather and analyse video footage can significantly reduce survey times and staging costs, while at the same time improving surveyor safety.

Weve been working with drone surveys since 2015, said Geir Fuglerud, director of ofshore classification at DNV GL Maritime. This latest test showcases the next step in automation, using AI to analyse live video. As class we are always working to take advantage of advances in technology to make our surveys more efficient and safer for surveyors, delivering the same quality while minimising our operational downtime for our customers.

The drone, developed by Scout Drone Inspection, uses LiDAR to navigate inside the tank as GPS-reception is not available in the enclosed space. A LiDAR creates a 3-D map of the tank and all images and video is accurately geo-tagged with position data.

During the test, the drone was controlled by a pilot using the drones flight assistance functions, but as the technology matures it will be able to navigate more and more autonomously. DNV GL has been developing artificial intelligence to interpret videos to spot any cracks and eventually the camera and algorithm will be able to detect anomalies below the surface such as corrosion and structural deformations.

This is another important step towards autonomous drone inspections, said Nicolai Husteli, CEO of Scout Drone Inspection.

Up until now the process has been completely analogue but technology can address the urgent need to make the process more efficient and safer.

Altera Infrastructure hosted the test on its Petrojarl Varg FPSO. The video was livestreamed via Scout Drone Inspections cloud-system back to Altera Infrastructures headquarters in Trondheim, where the footage was monitored by engineers.

Sam Chambers

Starting out with the Informa Group in 2000 in Hong Kong, Sam Chambers became editor of Maritime Asia magazine as well as East Asia Editor for the worlds oldest newspaper, Lloyds List. In 2005 he pursued a freelance career and wrote for a variety of titles including taking on the role of Asia Editor at Seatrade magazine and China correspondent for Supply Chain Asia. His work has also appeared in The Economist, The New York Times, The Sunday Times and The International Herald Tribune.

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Artificial intelligence and algorithms bring drone inspection breakthrough - - Splash 247

London, June 03, 2020 (GLOBE NEWSWIRE) -- Artificial intelligence has emerged as the most potent technologies over the past few years, that is transitioning the landscape of almost all industry verticals. Although enterprise applications based on AI and machine learning (ML) are still in the nascent stages of development, they are gradually beginning to drive innovation strategies of the business.

In the supply chain and logistics industry, artificial intelligence is gaining rapid traction among industry stakeholders. Players operating in the supply chain and logistics industry are increasingly realizing the potential of AI to solve the complexities of running a global logistics network. Adoption of artificial intelligence in the supply chain is routing a new era or industrial transformation, allowing the companies to track their operations, enhance supply chain management productivity, augment business strategies, and engage with customers in digital world.

Theartificial intelligence in supply chain market is expected to grow at a CAGR of 45.3% from 2019 to 2027 to reach $21.8 billion by 2027. The growth in this market is mainly driven by rising awareness of artificial intelligence and big data & analytics and widening implementation of computer vision in both autonomous & semi-autonomous applications. In addition, consistent technological advancements in the supply chain industry, rising demand for AI-based business automation solutions, and evolving supply chain complementing growing industrial automation are further offering opportunities for vendors providing AI solutions in the supply chain industry. However, high deployment and operating costs and lack of infrastructure hinder the growth of the artificial intelligence in supply chain market.

In this study, the globalAI in supply chain market is segmented on the basis of component, application, technology, end user, and geography.

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Based on component, AI in supply chain market is broadly segmented into hardware, software, and services. The software segment commanded the largest share of the overall AI in supply chain market in 2019. This can be attributed to the increasing demand for AI-based platforms and solutions, as they offer supply chain visibility through software, which include inventory control, warehouse management, order procurement, and reverse logistics & tracking.

Based on technology, AI in supply chain market is broadly segmented into machine learning, computer vision, natural language processing, and context-aware computing. In 2019, the machine learning segment commanded the largest share of the overall AI in supply chain market. This growth can be attributed to the growing demand for AI-based intelligent solutions; increasing government initiatives; and the ability of AI solutions to efficiently handle and analyze big data and quickly scan, parse, and react to anomalies

Based on application, AI in supply chain market is broadly segmented into supply chain planning, warehouse management, fleet management, virtual assistant, risk management, inventory management, and planning & logistics. In 2019, the supply chain planning segment commanded the largest share of the overall AI in supply chain market. The growth of this segment can be attributed to the increasing demand for enhancing factory scheduling & production planning and the evolving agility and optimization of supply chain decision-making. In addition, digitizing existing processes and workflows to reinvent the supply chain planning model is also contributing to the growth of this segment.

Based on end user, artificial intelligence in supply chain market is broadly segmented into manufacturing, food & beverage, healthcare, automotive, aerospace, retail, and consumer packaged goods sectors. The retail sector commanded the largest share of the overall AI in supply chain market in 2019. This can be attributed to the increase in demand for consumer retail products.

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Based on geography, the global artificial intelligence in supply chain market is categorized into five major geographies, namely, North America, Europe, Asia Pacific, Latin America, and Middle East & Africa. In 2019, North America commanded for the largest share of the global artificial intelligence in supply chain market, followed by Europe, Asia-Pacific, Latin America, and the Middle East & Africa. The large share of the North American region is attributed to the presence of developed economies focusing on enhancing the existing solutions in the supply chain space, and the existence of major players in this market along with a high willingness to adopt advanced technologies.

On the other hand, the Asia-Pacific region is projected to grow at the fastest CAGR during the forecast period. The high growth rate is attributed to rapidly developing economies in the region; presence of young and tech-savvy population in this region; and growing proliferation of internet of things (IoT); rising disposable income; increasing acceptance of modern technologies across several industries including automotive, manufacturing, and retail; and broadening implementation of computer vision technology in numerous applications. Furthermore, the growing adoption of AI-based solutions and services among supply chain operations, increasing digitalization in the region, and improving connectivity infrastructure are also playing a significant role in the growth of this market in the region.

The globalAI in supply chain market is fragmented in nature and is characterized by the presence of several companies competing for the market share. Some of the leading companies in the artificial intelligence in supply chain market are from the core technology background. These include IBM Corporation (U.S.), Microsoft Corporation (U.S.), Google LLC (U.S.), and Amazon.com, Inc. (U.S.). These companies are leading the market owing to their strong brand recognition, diverse product portfolio, strong distribution & sales network, and strong organic & inorganic growth strategies. The other key players in the global artificial intelligence in supply chain market are Intel Corporation (U.S.), Nvidia Corporation (U.S.), Oracle Corporation (U.S.), Samsung (South Korea), LLamasoft, Inc. (U.S.), SAP SE (Germany), General Electric (U.S.), Deutsche Post DHL Group (Germany), Xilinx, Inc. (U.S.), Micron Technology, Inc. (U.S.), FedEx Corporation (U.S.), ClearMetal, Inc. (U.S.), Dassault Systmes (France), and JDA Software Group, Inc. (U.S.), among others.

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Artificial Intelligence (AI) in Supply Chain Market is projected to reach $21.8 billion by 2027, Growing at a CAGR of 45.3% from 2019- Meticulous...

As a young dentist, Dr. David Gane learned quickly that photographic imaging records were a terrific tool for his dental practice in many ways. Within 10 years he segued into his own imaging software company, and today hes the CEO of Apertyx Imaging.

In this discussion, he and Dr. Pamela Maragliano-Muniz agree that April was a challenging month for dentists and dental companies. But theyre encouraged by recent data from the Health Policy Institute and the American Dental Association that indicates dentistry is beginning to bounce back successfully and will continue to do so. Surveys from the HPI also show that most patients want to return to their dentists as soon as they're able.

Dr. Maragliano Muniz notes that to continue to be successful, dentists are investing more in themselves. She and Dr. Gane believe that an efficient imaging system is an excellent tool for dentists to spend their money on. Dr. Gane explains the advantages of Apteryx.

Editor's note:To viewDentistryIQ's full coverage of the COVID-19 pandemic, including original news articles and video interviews with dental thought leaders,visit theDentistryIQCOVID-19 Resource Center.

Pamela Maragliano-Muniz, DMD,is the chief editor ofDentistryIQ.Based in Salem, Massachusetts, Dr. Maragliano-Muniz began her clinical career as a dental hygienist. She went on to attend Tufts University School of Dental Medicine, where she earned her doctorate in dental medicine. She then attended the University of California, Los Angeles, School of Dental Medicine, where she became board-certified in prosthodontics. Dr. Maragliano-Munizowns a private practice, Salem Dental Arts, and lectures on a variety of clinical topics.

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Coronavirus impact on dental practices: Imaging software capabilities and artificial intelligence (Video) - Dentistry IQ

Researchstore.biz has recently announced a research report Global Artificial Intelligence in Big Data Analytics and IoT Market 2020 by Company, Regions, Type and Application, Forecast to 2025 which elaborates the industry coverage, current market competitive status, and market outlook and forecast by 2025. It evaluates global Artificial Intelligence in Big Data Analytics and IoT market size, product sales volume, value, as well as market dynamics such as opportunities, challenges, threats, and issues the market is facing. It categorizes the global market by key players, product types, applications, and regions, etc. The report shows the competitive landscape, future trends, volume, manufacturing cost, and investment strategy. Comprehensive analysis of consumption, market share, and growth rate of each application is offered.

Some of the key players operating in this market include: Amazon , NVIDIA Corporation , Microsoft Corporation , Google Inc. , Infineon Technologies AG , IBM Corporation , Apple Inc. , Intel Corporation , CISCO Systems Inc. , Veros Systems Inc.

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Customer-Based Analysis:

For the customer-based market, the report classifies market maker data to better comprehend who the customers are, their purchasing patterns and behavior. Segments of the global Artificial Intelligence in Big Data Analytics and IoT market are analyzed on the basis of market share, production, consumption, revenue, CAGR, market size, and more factors. The market report provides all data with easily absorbable information to assist every businessmans future innovation and move the business forward.

Market research supported product sort includes: Machine Learning, Deep Learning Platform, Voice Recognition, Artificial Neural Network, Others

Market research supported application coverage: Smart Machine, Self Driving Vehicles, Cyber Security Intelligence, Others

Region-Wise Analysis of the Market as follows:

Geographic penetration also shows the market potential, market risk, industry trends, and opportunities. Reportedly, the global Artificial Intelligence in Big Data Analytics and IoT market region is dominating this industry in the forthcoming year. On a regional basis, the global market can be segmented into: North America (United States, Canada and Mexico), Europe (Germany, France, UK, Russia and Italy), Asia-Pacific (China, Japan, Korea, India and Southeast Asia), South America (Brazil, Argentina, etc.), Middle East& Africa (Saudi Arabia, Egypt, Nigeria and South Africa)

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Table of Contents:

Chapter 1, Scope of The Report: Market introduction, research objectives, market research methodology, data source

Chapter 2, Executive Summary: Market overview, consumption, segment by type, consumption by type, segment by application, consumption by application

Chapter 3, Global Artificial Intelligence in Big Data Analytics and IoT By Company: Sales market share, revenue, sale price, manufacturing base distribution, sales area, type, concentration rate analysis, competition landscape analysis, new products, and potential entrants, mergers & acquisitions, expansion

Chapter 4, 5, 6, 7, 8, By Regions: Consumption growth, consumption by countries, value by countries, consumption by type, consumption by application, consumption by regions, value by regions

Chapter 9, Market Drivers, Challenges and Trends: Market drivers and impact, growing demand from key regions, growing demand from key applications and potential industries, market challenges and impact, market trends

Chapter 10, Marketing, Distributors, and Customer: Sales channel, direct channels, indirect channels, distributors, and customer

Chapter 11, Global Market Forecast: Consumption forecast, consumption forecast, forecast by regions, value forecast by regions, market forecast

Chapter 12, Key Players Analysis: Company information, sales, revenue, price, and gross margin, business overview, latest developments, company information

Chapter 13, Research Findings and Conclusion

Customization of the Report:This report can be customized to meet the clients requirements. Please connect with our sales team ([emailprotected]), who will ensure that you get a report that suits your needs. You can also get in touch with our executives on +1-201-465-4211 to share your research requirements.

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Global Artificial Intelligence in Big Data Analytics and IoT Market 2020 Industry Analysis, Key Players, Type and Application, Regions, Forecast to...

Editors Note: This is an excerpt from a policy roundtable Artificial Intelligence and International Security from our sister publication, the Texas National Security Review. Be sure to check out the full roundtable.

Artificial intelligence (AI) has recently become a focus of efforts to maintain and enhance U.S. military, political, and economic competitiveness. The Defense Departments 2018 strategy for AI, released not long after the creation of a new Joint Artificial Intelligence Center, proposes to accelerate the adoption of AI by fostering a culture of experimentation and calculated risk taking, an approach drawn from the broader National Defense Strategy. But what kinds of calculated risks might AI entail? The AI strategy has almost nothing to say about the risks incurred by the increased development and use of AI. On the contrary, the strategy proposes using AI to reduce risks, including those to both deployed forces and civilians.

While acknowledging the possibility that AI might be used in ways that reduce some risks, this brief essay outlines some of the risks that come with the increased development and deployment of AI, and what might be done to reduce those risks. At the outset, it must be acknowledged that the risks associated with AI cannot be reliably calculated. Instead, they are emergent properties arising from the arbitrary complexity of information systems. Nonetheless, history provides some guidance on the kinds of risks that are likely to arise, and how they might be mitigated. I argue that, perhaps counter-intuitively, using AI to manage and reduce risks will require the development of uniquely human and social capabilities.

A Brief History of AI, From Automation to Symbiosis

The Department of Defense strategy for AI contains at least two related but distinct conceptions of AI. The first focuses on mimesis that is, designing machines that can mimic human work. The strategy document defines mimesis as the ability of machines to perform tasks that normally require human intelligence for example, recognizing patterns, learning from experience, drawing conclusions, making predictions, or taking action. A somewhat distinct approach to AI focuses on what some have called human-machine symbiosis, wherein humans and machines work closely together, leveraging their distinctive kinds of intelligence to transform work processes and organization. This vision can also be found in the AI strategy, which aims to use AI-enabled information, tools, and systems to empower, not replace, those who serve.

Of course, mimesis and symbiosis are not mutually exclusive. Mimesis may be understood as a means to symbiosis, as suggested by the Defense Departments proposal to augment the capabilities of our personnel by offloading tedious cognitive or physical tasks. But symbiosis is arguably the more revolutionary of the two concepts and also, I argue, the key to understanding the risks associated with AI.

Both approaches to AI are quite old. Machines have been taking over tasks that otherwise require human intelligence for decades, if not centuries. In 1950, mathematician Alan Turing proposed that a machine can be said to think if it can persuasively imitate human behavior, and later in the decade computer engineers designed machines that could learn. By 1959, one researcher concluded that a computer can be programmed so that it will learn to play a better game of checkers than can be played by the person who wrote the program.

Meanwhile, others were beginning to advance a more interactive approach to machine intelligence. This vision was perhaps most prominently articulated by J.C.R. Licklider, a psychologist studying human-computer interactions. In a 1960 paper on Man-Computer Symbiosis, Licklider chose to avoid argument with (other) enthusiasts for artificial intelligence by conceding dominance in the distant future of cerebration to machines alone. However, he continued: There will nevertheless be a fairly long interim during which the main intellectual advances will be made by men and computers working together in intimate association.

Notions of symbiosis were influenced by experience with computers for the Semi-Automatic Ground Environment (SAGE), which gathered information from early warning radars and coordinated a nationwide air defense system. Just as the Defense Department aims to use AI to keep pace with rapidly changing threats, SAGE was designed to counter the prospect of increasingly swift attacks on the United States, specifically low-flying bombers that could evade radar detection until they were very close to their targets.

Unlike other computers of the 1950s, the SAGE computers could respond instantly to inputs by human operators. For example, operators could use a light gun to select an aircraft on the screen, thereby gathering information about the airplanes identification, speed, and direction. SAGE became the model for command-and-control systems throughout the U.S. military, including the Ballistic Missile Early Warning System, which was designed to counter an even faster-moving threat: intercontinental ballistic missiles, which could deliver their payload around the globe in just half an hour. We can still see the SAGE model today in systems such as the Patriot missile defense system, which is designed to destroy short-range missiles those arriving with just a few minutes of notice.

SAGE also inspired a new and more interactive approach to computing, not just within the Defense Department, but throughout the computing industry. Licklider advanced this vision after he became director of the Defense Departments Information Processing Technologies Office, within the Advanced Research Projects Agency, in 1962. Under Lickliders direction, the office funded a wide range of research projects that transformed how people would interact with computers, such as graphical user interfaces and computer networking that eventually led to the Internet.

The technologies of symbiosis have contributed to competitiveness not primarily by replacing people, but by enabling new kinds of analysis and operations. Interactive information and communications technologies have reshaped military operations, enabling more rapid coordination and changes in plans. They have also enabled new modes of commerce. And they created new opportunities for soft power as technologies such as personal computers, smart phones, and the Internet became more widely available around the world, where they were often seen as evidence of American progress.

Mimesis and symbiosis come with somewhat distinct opportunities and risks. The focus on machines mimicking human behavior has prompted anxieties about, for example, whether the results produced by machine reasoning should be trusted more than results derived from human reasoning. Such concerns have spurred work on explainable AI wherein machine outputs are accompanied by humanly comprehensible explanations for those outputs.

By contrast, symbiosis calls attention to the promises and risks of more intimate and complex entanglements of humans and machines. Achieving an optimal symbiosis requires more than well-designed technology. It also requires continual reflection upon and revision of the models that govern human-machine interactions. Humans use models to design AI algorithms and to select and construct the data used to train such systems. Human designers also inscribe models of use assumptions about the competencies and preferences of users, and the physical and organizational contexts of use into the technologies they create. Thus, like a film script, technical objects define a framework of action together with the actors and the space in which they are supposed to act. Scripts do not completely determine action, but they configure relationships between humans, organizations, and machines in ways that constrain and shape user behavior. Unfortunately, these interactively complex sociotechnical systems often exhibit emergent behavior that is contrary to the intentions of designers and users.

Competitive Advantages and Risks

Because models cannot adequately predict all of the possible outcomes of complex sociotechnical systems, increased reliance on intelligent machines leads to at least four kinds of risks: The models for how machines gather and process information, and the models of human-machine interaction, can both be inadvertently flawed or deliberately manipulated in ways not intended by designers. Examples of each of these kinds of risks can be found in past experiences with smart machines.

First, changing circumstances can render the models used to develop machine intelligence irrelevant. Thus, those models and the associated algorithms need constant maintenance and updating. For example, what is now the Patriot missile defense system was initially designed for air defense but was rapidly redesigned and deployed to Saudi Arabia and Israel to defend against short-range missiles during the 1991 Gulf War. As an air defense system it ran for just a few hours at a time, but as a missile defense system it ran for days without rebooting. In these new operating conditions, a timing error in the software became evident. On Feb. 25, 1991, this error caused the system to miss a missile that struck a U.S. Army barracks in Dhahran, Saudi Arabia, killing 28 American soldiers. A software patch to fix the error arrived in Dhahran a day too late.

Second, the models upon which machines are designed to operate can be exploited for deceptive purposes. Consider, for example, Operation Igloo White, an effort to gather intelligence on and stop the movement of North Vietnamese supplies and troops in the late 1960s and early 1970s. The operation dropped sensors throughout the jungle, such as microphones, to detect voices and truck vibrations, as well as devices that could detect the ammonia odors from urine. These sensors sent signals to overflying aircraft, which in turn sent them to a SAGE-like surveillance center that could dispatch bombers. However, the program was a very expensive failure. One reason is that the sensors were susceptible to spoofing. For example, the North Vietnamese could send empty trucks to an area to send false intelligence about troop movements, or use animals to trigger urine sensors.

Third, intelligent machines may be used to create scripts that enact narrowly instrumental forms of rationality, thereby undermining broader strategic objectives. For example, unpiloted aerial vehicle operators are tasked with using grainy video footage, electronic signals, and assumptions about what constitutes suspicious behavior to identify and then kill threatening actors, while minimizing collateral damage. Operators following this script have, at times, assumed that a group of men with guns was planning an attack, when in fact they were on their way to a wedding in a region where celebratory gun firing is customary, and that families praying at dawn were jihadists rather than simply observant Muslims. While it may be tempting to dub these mistakes operator errors, this would be too simple. Such operators are enrolled in a deeply flawed script one that presumes that technology can be used to correctly identify threats across vast geographic, cultural, and interpersonal distances, and that the increased risk of killing innocent civilians is worth the increased protection offered to U.S. combatants. Operators cannot be expected to make perfectly reliable judgments across such distances, and it is unlikely that simply deploying the more precise technology that AI enthusiasts promise can bridge the very distances that remote systems were made to maintain. In an era where soft power is inextricable from military power, such potentially dehumanizing uses of information technology are not only ethically problematic, they are also likely to generate ill will and blowback.

Finally, the scripts that configure relationships between humans and intelligent machines may ultimately encourage humans to behave in machine-like ways that can be manipulated by others. This is perhaps most evident in the growing use of social bots and new social media to influence the behavior of citizens and voters. Bots can easily mimic humans on social media, in part because those technologies have already scripted the behavior of users, who must interact through liking, following, tagging, and so on. While influence operations exploit the cognitive biases shared by all humans, such as a tendency to interpret evidence in ways that confirm pre-existing beliefs, users who have developed machine-like habits reactively liking, following, and otherwise interacting without reflection are all the more easily manipulated. Remaining competitive in an age of AI-mediated disinformation requires the development of more deliberative and reflective modes of human-machine interaction.

Conclusion

Achieving military, economic, and political competitiveness in an age of AI will entail designing machines in ways that encourage humans to maintain and cultivate uniquely human kinds of intelligence, such as empathy, self-reflection, and outside-the-box thinking. It will also require continual maintenance of intelligent systems to ensure that the models used to create machine intelligence are not out of date. Models structure perception, thinking, and learning, whether by humans or machines. But the ability to question and re-evaluate these assumptions is the prerogative and the responsibility of the human, not the machine.

Rebecca Slayton is an associate professor in the Science & Technology Studies Department and the Judith Reppy Institute of Peace and Conflict Studies, both at Cornell University. She is currently working on a book about the history of cyber security expertise.

Image: Flickr (Image by Steve Jurvetson)

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The Promise and Risks of Artificial Intelligence: A Brief History - War on the Rocks

Artificial intelligence has become its own sub-industry in health care, driving the development of products designed to detect diseases earlier, improve diagnostic accuracy, and discover more effective treatments. One recent report projected spending on health care AI in the United States will rise to $6.6 billion in 2021, an 11-fold increase from 2014.

The Covid-19 pandemic underscores the importance of the technology in medicine: In the last few months, hospitals have used AI to create coronavirus chatbots, predict the decline of Covid-19 patients, and diagnose the disease from lung scans.

Its rapid advancement is already changing practices in image-based specialties such as radiology and pathology, and the Food and Drug Administration has approved dozens of AI products to help diagnose eye diseases, bone fractures, heart problems, and other conditions. So much is happening that it can be hard for health professionals, patients, and even regulators to keep up, especially since the concepts and language of AI are new for many people.

The use of AI in health care also poses new risks. Biased algorithms could perpetuate discrimination along racial and economic lines, and lead to the adoption of inadequately vetted products that drive up costs without benefiting patients. Understanding these risks and weighing them against the potential benefits requires a deeper understanding of AI itself.

Its for these reasons that we created STACI: the STAT Terminal for Artificial Computer Intelligence. She will walk you through the key concepts and history of AI, explain the terminology, and break down its various uses in health care. (This interactive is best experienced on screens larger than a smartphones.)

Remember, AI is only as good as the data fed into it. So if STACI gets something wrong, blame the humans behind it, not the AI!

This is part of a yearlong series of articles exploring the use of artificial intelligence in health care that is partly funded by a grant from the Commonwealth Fund.

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Meet STACI: your interactive guide to advances of AI in health care - STAT

(Photo by Engin Akyurt/Unsplash)

Private and public entities around the world, particularly in the health care and governance sectors, are developing and deploying a range of artificial intelligence (AI) systems in emergency response to COVID-19. Some of these systems work to track and predict its spread; others support medical response or help maintain social control. Indeed, AI systems can reduce strain on overwhelmed health care systems; help save lives by quickly diagnosing patients, and assessing health declines or progress; and limit the viruss spread.

But theres a problem: The algorithms driving these systems are human creations, and as such, they are subject to biases that can deepen societal inequities and pose risks to businesses and society more broadly. In this article, we look at data on the pandemic, share two recent applications of AI, and suggest a number of ways nonprofit and business leaders can help ensure that they develop, manage, and use transformative AI equitably and responsibly.

Using techinical frameworks, such as machine learning, AI systems use algorithms to make inferences from data about people. This includes demographic attributes, preferences, and likely future behaviors. To effectively serve a range of populations, AI systems must learn to make associations based on massive amounts of data that accurately reflect information across identities. However, the data they rely on is often rife with social and cultural biases. Data might not exist for certain populations, may exist but be poor quality for certain groups, and/or reflect inequities in society. As a result, algorithms can make inaccurate predictions and perpetuate social stereotypes and biases.

Unfortunately, much of the data about COVID-19 that the US Center for Disease Control and Prevention (CDC) and others are collecting and tracking is incomplete and biased. COVID-19 infection rates, for example, have been subject to a vast undercount, by a factor of 50 or more. Medical data is reflecting only a subset of the populationin many cases, the affluent, white communities who have ready access to limited tests and expensive medical procedures. But there are other important data gaps too:

Some of the AI systems created to support COVID-19 medical response help diagnose and detect COVID-19 through basic online screening or analyzing chest images. Others, such as the forthcoming version of eCART, can help predict COVID-specific outcomes and inform clinical decisions. This is particularly useful for medical volunteers without pulmonary training, who must assess patients conditions and decide who needs help first. AI tech may also prove helpful in the search for a COVID-19 vaccine and other therapies.

However, the data gaps we mentioned earlier have major implications for medical AI systems and AI-enhanced vaccine trials. People react differently to viruses, vaccines, and treatments, as previous outbreaks like SARS and Ebola have illustrated. Data available on COVID-19 outside the United States, for example, shows that men and women face different fatality rates, and a recent research paper found that women patients admitted to the Wuhan Union Hospital had higher levels of COVID-19 antibodies than men. Given systemic inequities that worsen health outcomes for certain racial and ethnic groups, its equally important to understand COVID-19 health outcomes for different identities, as well as the intersectional implications.

Algorithms that dont account for existing inequities risk making inaccurate predictionsor worse. In 2019, a study found that the widely used Optum algorithm, which used health-care spending as a proxy to measure need, was biased against black Americans. It didnt account for discrimination or lack of access, both of which lead to lower spending on health care by black Americans. Amid the COVID-19 crisis, AI systems that inform limited-resource allocations (such as who to put on a ventilator) must be careful not to inadvertently prioritize certain identities over others. While developers aim to make algorithms race-blind by excluding race as a metric, this can ignore or hide rather than preventdiscrimination. For example, algorithms that inform clinical decisions may use proxies such as preexisting conditions. Diabetes is a preexisting condition linked to higher rates of COVID-19, and it has a higher incidence for black Americans. If an algorithm uses preexisting conditions but is blind to race, it can result in disproportionately prioritizing white Americans over black Americans.

While some firms adhere to rigorous testingconducting large validation studies prior to releasing products, for examplenot all firms are thorough. Further, the decision-making processes of most AI algorithms are not transparent. This opens the door to inaccurate or discriminatory predictions for certain demographics, and thus poses immense risks to the individuals and practitioners using them.

Another recent application of AI is contact tracing, or tracking people who have come into contact with the virus to help contain it. By tracking user information such as health and location, and using AI-powered facial recognition, these tools can enforce social distancing and inform citizens of contact with positive cases. In China, users are assigned a coronavirus score, which impacts their access to public transportation, work, and school. And US government officials have begun raising the possibility of mass surveillance, collecting anonymized, aggregate user location data from tech giants like Facebook and Google to map the spread of COVID-19.

But surveillance tools have ethical implicationsagain, particularly for marginalized populations. Using AI to decide who leaves their home could lead to a form of COVID-19 redlining, subjecting certain communities to greater enforcements. This calls to mind another AI model that results in higher surveillance of poor communities of color: predictive policing. In the United States, risk-assessment algorithms use criminal history information, but dont take into account deep-rooted racial bias in the policing system, that black Americans are arrested more often for smaller crimes and that neighborhoods with high concentration of black Americans are more heavily patrolled. Black Americans end up overrepresented in the data, which then links to racially biased policing outcomes. Similarly, communities impacted by proposed surveillance systems would likely be poorer communities of color harder hit by COVID-19 for a variety of reasons linked to historical inequities and discrimination.

It is not clear how or how long government agencies or other entities will use these types of AI tools. In China, tracking could stick around after the crisis, allowing Beijing authorities to monitor religious minorities, political dissidents, and other marginalized communities with a history of being over-surveilled. And although data collection in the United States will initially be anonymized and aggregated, theres potential for misuse and de-anonymization in the future.

Various AI systems are proving incredibly valuable to tackling the pandemic, and others hold immense promise. But leaders must take care to develop, manage, and use this technology responsibly and equitably; the risks of discrimination and deepening inequality are simply unacceptable. Here are five actions to take now:

1. Demand transparency and explanation of the AI system. First and foremost, leaders need to hold themselves accountable. Particularly with AI systems targeting medical response, its important that decision makers understand which groups are represented in the datasets and what the quality of that data is across different groups. Tools such as Datasheets for Datasets are useful for tracking information on dataset creators; the composition, sampling, and labeling process; and intended uses. Leaders whose organizations develop AI systems should also ask questions like: Whose opinions, priorities, and expertise are included in development, and whose are left out?

2. Join and promote multidisciplinary ethics working groups or councils to inform response to COVID-19. This is already happening in Germany and can provide useful insights into how to respond to COVID-19, including using AI. Working groups are a way to bring together social scientists, philosophers, community leaders, and technical teams to discuss potential bias concerns and fairness tradeoffs, as well as solutions.

3. Build partnerships to fill health-data gaps in ways that protect and empower local communities. Nonprofits and universities are especially well-positioned to work with disenfranchised communities and form community research partnerships. In San Francisco, for example, a coalition of citywide Latinx organizations partnered with UCSF to form a COVID-19 task force. The coalition launched a project that tested nearly 3,000 residents in predominantly Latinx neighborhoods to better understand how the virus spreads. The task force and its local volunteers integrated concerns of community members and provided extensive support services to people who tested positive.

4. Advance research and innovation while emphasizing diversity and inclusion. Only a handful of tech companies and elite university labs develop most large-scale AI systems, and developers tend to be white, affluent, technically oriented, and male. Given that AI isnt neutral and that technologies are a product of the context in which they are created, these systems often fail to meet the needs of different communities. Research initiatives like the recently launched Digital Transformation Institute, a collaborative effort to bring together tech companies and US research universities to fight COVID-19, must emphasize inclusion and justice (alongside innovation and efficiency), and prioritize multi-disciplinary and diverse teams. They can and should take advantage of tools like an AI Fairness Checklist in designing solutions.

5. Resist the urge to prioritize efficiency at the cost of justice and equity. Leaders should rise to the challenge of not compromising justice and equity. In some cases, the question is not how best to develop or deploy an AI system, but whether the AI system should be built or used at all.

As the pandemic continues to severely impact individuals, communities, and economies, nonprofit and business leaders must respond quicklybut not at the cost of heightening discrimination and inequality in the communities hardest hit by the pandemic. AI can help us improve medical response and minimize the spread of COVID-19, but using it wisely requires equity-fluent leadership and a long-term view. As Prashant Warier, CEO and co-founder of the AI company Qure.ai, put it, Once people start using our algorithms, they never stop.

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The Problem With COVID-19 Artificial Intelligence Solutions and How to Fix Them - Stanford Social Innovation Review