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

(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

How Artificial Intelligence-backed technology is helping in the battle against COVID-19 – THE WEEK

COVID-19 has caused some irrevocable damages to the public healthcare systems in countries across the world. As scientists race against time to find the right vaccine and cure for the pandemic, technologists too have chipped in with solutions in the field of Artificial Intelligence (AI), offering assistance in testing, tracing and treatment.

A number of institutions and companies in India are coming up with indigenously developed, AI-backed technologies, which are likely to take the country and the world a step closer to tackling the virus. In an interview to THE WEEK, Prashant Warier, CEO and founder, Qure.ai, which is now deploying AI-powered solutions for COVID-19 management by using chest X-rays which are about 10 to 15 times cheaper than the RT-PCR tests, speaks about how technology is helping in the battle against the pandemic.

You use Artificial Intelligence to detect and triage COVID-19. Please elaborate on how it is done

COVID-19 ranges in severityfrom asymptomatic, mild to severe. The virus is known to mutate and change, making it extremely hard to treat and contain. Having all information at hand and having actionable insights is something that will aid healthcare providers in offering better care. Today, the usage of chest X-rays is being recommended by the American College of Radiology, the Royal College of Radiology and the Canadian Association of Radiologists to screen for COVID-19. Many hospitals across the globe have already implemented chest X-rays in their COVID-19 diagnosis and progression protocols.

The lungs of a COVID-19 patient is very different from that of a healthy person. That is where Qure.ais technology comes into the picture. In 2017, we developed an AI technology that could identify abnormal findings on a chest X-ray. Research shows that in COVID-19 patients, lungs will have consolidations and ground-glass opacities, all of which are detected and marked out using our algorithm. The solution can also quantify the volume of infection, automating a time-consuming step for the already resource-strapped physicians. This is ideal to monitor ICU patients to understand if their lung condition is improving or worsening on a daily basis.

Our solution has also found use in locations where there are not enough swab test kits. The chest X-ray is about 10 to 15 times cheaper than RT-PCR tests, widely available and also can be done in a few minutes, compared to several hours taken for the RT-PCR tests. As such, they have been adopted by various hospital groups in triaging who should be further tested for COVID-19. Unfortunately, radiologist interpretations of the chest X-rays might take hours, sometimes days. That is where Qure comes in, by automatically processing a chest X-ray in under a minute and identifying the COVID risk level of the patient.

What has been the role of Qure.ai in the field of tuberculosis over the past years?

Tuberculosis (TB) is one of the oldest and most infectious diseases of all times. While it is totally curable, it is still one of the top 10 causes of deaths worldwide. In India and some of the emerging economies, there is a huge dearth of radiologists to read chest X-rays that are used as a screening for TB. In India alone, 80 million chest X-rays are being acquired every year. There arent enough radiologists to read them within an acceptable timeline. Depending on the availability of radiology expertise, it can take anywhere between one to 14 days to get diagnostic reports. This leads to delayed diagnosis of the TB patient and further spread of the disease. This gap can be minimised by automating and classifying chest X-ray readings as normal or abnormal with a solution that is scalable and requires little manual intervention. This is precisely how Qures solution, trained on more than 2.5 million chest X-rays, supports the grassroot level health infrastructure. The tool automates reading chest X-rays and generates reports within seconds, thereby reducing the waiting time for TB confirmatory tests, from weeks to a couple of hours and allowing for treatments to be started on the same day. Today, we are present in more than 40 sites across some of the highest TB-burdened countries and have touched more than 200,000 lives.

Can Artificial Intelligence backed technology by Qure be applied in high population density areas of Mumbai such as slums?

Yes, Qure is already working in Maharashtra with Municipal Corporation of Greater Mumbai (MCGM). Qure has partnered with MCGM and other private entities and launched the first mobile COVID-19 testing unit earlier this month.

The COVID-19 bus, equipped with Qures AI and a host of other medical testing facilities like oxygen saturation and body temperature checks, mass screens individuals in the high-risk areas of Worli. Densely populated Worli-Koliwada, one of the first containment zones in Mumbai, is a region of specific interest since it is a high-risk area, comprising of slums and fisherman colonies. The COVID-19 screening bus enables close contacts of positive patients and other asymptomatic residents to be screened via X-rays on the spot, along with checks for fever and oxygen saturation levels. Following the qXR analysis, medium and high- risk candidates are immediately directed for the next steps in the pandemic management process.

In the last 30 days, we have screened more than 2,000 cases and around eight per cent have been instructed for further medical attention. More such screening buses are in the pipeline for other high-risk areas in the city.

What have been your observations so far, with regard to disease progression, given that your technology has been used by health systems in India and abroad?

The progression monitoring capability of qXR is currently being used in San Raffaele Hospital in Milan, Italy, and at the NHS with Royal Bolton Hospital in the UK. They have had very positive feedback for us since we are now supplementing their reports with the ability to give a quantified report, something which was previously rare for chest X-rays. This adds a lot of value in their workflow.

According to the Bolton Critical care team leader: From my experience, the implementation of this innovative solution has been very positive, I am still getting our junior team used to looking at the additional information the software provides. The software has identified successfully a range of pathology from different patterns of consolidation to pneumothorax. I think a significant

benefit will be seen in the post COVID era when we return to our more usual range of clinical presentations. Im hoping this will improve the diagnostic accuracy when our junior doctors first assess patients, and should also improve the correct coding and identification of community-acquired pneumonia.

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How Artificial Intelligence-backed technology is helping in the battle against COVID-19 - THE WEEK

Artificial intelligence that mimics the brain needs sleep just like humans, study reveals – The Independent

Artificial intelligence designed to function like a human could require periods of rest similar to those needed by biological brains.

Researchers at Los Alamos National Laboratory in the US discovered that neural networks experienced benefits that were "the equivalent of a good night's rest" when exposed to an artificial analogue of sleep.

"We were fascinated by the prospect of training a neuromorphic processor in a manner analogous to how humans and other biological systems learn from their environment during childhood development," said Yijing Watkins, a computer scientist at Los Alamos.

Sharing the full story, not just the headlines

The discovery was made by the team of researchers while working on a form of artificial intelligence designed to mimic how humans learn to see.

The AI became unstable during long periods of unsupervised learning, as it attempted to classify objects using their dictionary definitions without having any prior examples to compare them to.

When exposed to a state that is similar to what a human brain experiences during sleep, the neural network's stability was restored.

Russia has launched a humanoid robot into space on a rocket bound for the International Space Station (ISS). The robot Fedor will spend 10 days aboard the ISS practising skills such as using tools to fix issues onboard. Russia's deputy prime minister Dmitry Rogozin has previously shared videos of Fedor handling and shooting guns at a firing range with deadly accuracy.

Dmitry Rogozin/Twitter

Google celebrates its 21st birthday on September 27. The The search engine was founded in September 1998 by two PhD students, Larry Page and Sergey Brin, in their dormitories at Californias Stanford University. Page and Brin chose the name google as it recalled the mathematic term 'googol', meaning 10 raised to the power of 100

Google

Chief engineer of LIFT aircraft Balazs Kerulo demonstrates the company's "Hexa" personal drone craft in Lago Vista, Texas on June 3 2019

Reuters

Microsoft announced Project Scarlett, the successor to the Xbox One, at E3 2019. The company said that the new console will be 4 times as powerful as the Xbox One and is slated for a release date of Christmas 2020

Getty

Apple has announced the new iPod Touch, the first new iPod in four years. The device will have the option of adding more storage, up to 256GB

Apple

Samsung will cancel orders of its Galaxy Fold phone at the end of May if the phone is not then ready for sale. The $2000 folding phone has been found to break easily with review copies being recalled after backlash

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Apple has cancelled its AirPower wireless charging mat, which was slated as a way to charge numerous apple products at once

AFP/Getty

India has claimed status as part of a "super league" of nations after shooting down a live satellite in a test of new missile technology

EPA

5G wireless internet is expected to launch in 2019, with the potential to reach speeds of 50mb/s

Getty

Uber has halted testing of driverless vehicles after a woman was killed by one of their cars in Tempe, Arizona. March 19 2018

Getty

A humanoid robot gestures during a demo at a stall in the Indian Machine Tools Expo, IMTEX/Tooltech 2017 held in Bangalore

Getty

A humanoid robot gestures during a demo at a stall in the Indian Machine Tools Expo, IMTEX/Tooltech 2017 held in Bangalore

Getty

Engineers test a four-metre-tall humanoid manned robot dubbed Method-2 in a lab of the Hankook Mirae Technology in Gunpo, south of Seoul, South Korea

Jung Yeon-Je/AFP/Getty

Engineers test a four-metre-tall humanoid manned robot dubbed Method-2 in a lab of the Hankook Mirae Technology in Gunpo, south of Seoul, South Korea

Jung Yeon-Je/AFP/Getty

The giant human-like robot bears a striking resemblance to the military robots starring in the movie 'Avatar' and is claimed as a world first by its creators from a South Korean robotic company

Jung Yeon-Je/AFP/Getty

Engineers test a four-metre-tall humanoid manned robot dubbed Method-2 in a lab of the Hankook Mirae Technology in Gunpo, south of Seoul, South Korea

Jung Yeon-Je/AFP/Getty

Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi

Rex

Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session

Rex

A test line of a new energy suspension railway resembling the giant panda is seen in Chengdu, Sichuan Province, China

Reuters

A test line of a new energy suspension railway, resembling a giant panda, is seen in Chengdu, Sichuan Province, China

Reuters

A concept car by Trumpchi from GAC Group is shown at the International Automobile Exhibition in Guangzhou, China

Rex

A Mirai fuel cell vehicle by Toyota is displayed at the International Automobile Exhibition in Guangzhou, China

Reuters

A visitor tries a Nissan VR experience at the International Automobile Exhibition in Guangzhou, China

Reuters

A man looks at an exhibit entitled 'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London

Getty

A new Israeli Da-Vinci unmanned aerial vehicle manufactured by Elbit Systems is displayed during the 4th International conference on Home Land Security and Cyber in the Israeli coastal city of Tel Aviv

Getty

Russia has launched a humanoid robot into space on a rocket bound for the International Space Station (ISS). The robot Fedor will spend 10 days aboard the ISS practising skills such as using tools to fix issues onboard. Russia's deputy prime minister Dmitry Rogozin has previously shared videos of Fedor handling and shooting guns at a firing range with deadly accuracy.

Dmitry Rogozin/Twitter

Google celebrates its 21st birthday on September 27. The The search engine was founded in September 1998 by two PhD students, Larry Page and Sergey Brin, in their dormitories at Californias Stanford University. Page and Brin chose the name google as it recalled the mathematic term 'googol', meaning 10 raised to the power of 100

Google

Chief engineer of LIFT aircraft Balazs Kerulo demonstrates the company's "Hexa" personal drone craft in Lago Vista, Texas on June 3 2019

Reuters

Microsoft announced Project Scarlett, the successor to the Xbox One, at E3 2019. The company said that the new console will be 4 times as powerful as the Xbox One and is slated for a release date of Christmas 2020

Getty

Apple has announced the new iPod Touch, the first new iPod in four years. The device will have the option of adding more storage, up to 256GB

Apple

Samsung will cancel orders of its Galaxy Fold phone at the end of May if the phone is not then ready for sale. The $2000 folding phone has been found to break easily with review copies being recalled after backlash

PA

Apple has cancelled its AirPower wireless charging mat, which was slated as a way to charge numerous apple products at once

AFP/Getty

India has claimed status as part of a "super league" of nations after shooting down a live satellite in a test of new missile technology

EPA

5G wireless internet is expected to launch in 2019, with the potential to reach speeds of 50mb/s

Getty

Uber has halted testing of driverless vehicles after a woman was killed by one of their cars in Tempe, Arizona. March 19 2018

Getty

A humanoid robot gestures during a demo at a stall in the Indian Machine Tools Expo, IMTEX/Tooltech 2017 held in Bangalore

Getty

A humanoid robot gestures during a demo at a stall in the Indian Machine Tools Expo, IMTEX/Tooltech 2017 held in Bangalore

Getty

Engineers test a four-metre-tall humanoid manned robot dubbed Method-2 in a lab of the Hankook Mirae Technology in Gunpo, south of Seoul, South Korea

Jung Yeon-Je/AFP/Getty

Engineers test a four-metre-tall humanoid manned robot dubbed Method-2 in a lab of the Hankook Mirae Technology in Gunpo, south of Seoul, South Korea

Jung Yeon-Je/AFP/Getty

The giant human-like robot bears a striking resemblance to the military robots starring in the movie 'Avatar' and is claimed as a world first by its creators from a South Korean robotic company

Jung Yeon-Je/AFP/Getty

Engineers test a four-metre-tall humanoid manned robot dubbed Method-2 in a lab of the Hankook Mirae Technology in Gunpo, south of Seoul, South Korea

Jung Yeon-Je/AFP/Getty

Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi

Rex

Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session

Rex

A test line of a new energy suspension railway resembling the giant panda is seen in Chengdu, Sichuan Province, China

Reuters

A test line of a new energy suspension railway, resembling a giant panda, is seen in Chengdu, Sichuan Province, China

Reuters

A concept car by Trumpchi from GAC Group is shown at the International Automobile Exhibition in Guangzhou, China

Rex

A Mirai fuel cell vehicle by Toyota is displayed at the International Automobile Exhibition in Guangzhou, China

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Artificial intelligence that mimics the brain needs sleep just like humans, study reveals - The Independent

Artificial intelligence harnessed to enforce face mask use on public transport and beyond – Verdict

Artificial intelligence (AI) that can detect if a person is wearing a face mask or similar protective face covering has been launched for deployment in public and private locations.

Developed by Aerialtronics, a subsidiary of French civilian drone manufacturer Drone Volt, the AI is compatible with all forms of cameras, including surveillance cameras and those equipped in drones.

It is designed to identify faces and classify them as either wearing or not wearing a mask, and can provide real-time notifications to the operator of people who are not wearing a suitable protective face covering.

The technology is intended to be used in areas where a face mask is required, such as in transit zones such as airports, and on public transport, as well as in spaces such as construction sites, shopping centres and sports venues, to assist security with the enforcement of mask wearing policies.

The development of the artificial intelligence technology comes as many countries and private organisations are mandating the wearing of face masks in areas where the concentration of people is high, as part of ongoing efforts to limit the spread of the coronavirus beyond lockdown.

In the UK, for example, face masks will become mandatory to be worn on public transport from 15 June, while over 50 countries have now made the wearing of such face coverings mandatory in public spaces.

However, enforcing the wearing of face masks is immensely challenging for police and other security, giving this artificial intelligence product particular promise.

The objective of this solution is to simplify the mission of security services through effective real-time monitoring of faces with or without masks, said Olivier Gualdoni, chairman and CEO of Drone Volt.

This tool contributes to both the health challenges involved in the fight against viruses such as the coronavirus and to the prevention of occupational risks.

As with any technology that involves identifying faces, the installation of such a technology is likely to be met with privacy concerns.

In a bid to alleviate this, Aerialtronics has included an optional face blur to protect users faces from being recorded. However, in large-scale environments or spaces where enforcement is through after-the-fact fines, this will not be an option.

Read more: NHS trials AI system to predict coronavirus ventilator demand

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Artificial intelligence harnessed to enforce face mask use on public transport and beyond - Verdict

Chinese Debates on the Military Utility of Artificial Intelligence – War on the Rocks

The Chinese military believes it is losing a high-stakes competition with the United States and Russia to lead the world in artificial intelligence (AI). In articles like, The Quiet Rise of an Artificial Intelligence Arms Race (), Chinese military authors point to a quote from Russian President Vladimir Putin, that whoever leads in AI will rule the world. As evidence of the U.S. militarys ambition to dominate in this field, they cite findings about AI in future warfare from the U.S. National Security Commission on Artificial Intelligence, calls for the United States to ally with other nations against Chinese AI development, the Department of Defense AI strategy, and the establishment of the Pentagons Joint Artificial Intelligence Center. In 2017, China was among the first nations to advance a national-level AI development strategy that broadly addressed AIs role in economic development.

The Chinese military, however, has been opaque about its AI strategy and intentions. Undoubtedly, Chinese military officials understand they must compete with the United States by adapting quickly to changes in warfare brought about by AI and autonomous systems. An examination of the ongoing debate within the ranks of Chinas Peoples Liberation Army (PLA) about the transformation of warfare by AI what they call intelligentized warfare () reveals that this new form of warfare is an extension of existing Chinese strategy and operational concepts.

AI may allow China to realize its long-standing, information-centric military strategies. The American military tends to focus on how AI can enable lethal attacks against opposing forces. Chinese strategists tend to argue that AI technologies should be used kinetically and non-kinetically to dominate information systems and networks, to effectively paralyze an opponents joint force. Information warfare and information control are at the heart of the PLAs approach to warfare and AI. Countering Chinas strategy will require a defensive and offensive use of new AI technologies. In a future confrontation, the U.S. military will need to employ AI and autonomous capabilities to enable and defend its information system-of-systems while simultaneously using AI technologies to attack Chinas information-centric strategy and capabilities.

Chinas Established Military Strategy Will Be Enhanced by AI

The PLAs overarching strategy for defeating the U.S. military, or any foreign adversary, is to dominate in a system-of-systems confrontation. This method of warfighting focuses on creating disruption or paralysis across an enemy system-of-systems versus emphasizing the attrition of forces. First, the PLA will attempt to crash the adversarys information networks using kinetic and non-kinetic means. The Chinese military believes that information is the critical element that binds and enables a larger military system-of-systems. Second, the PLA intends to eliminate individual elements of a now-disaggregated enemy force with long-range precision fires. This Chinese military doctrine has been described as systems confrontation, but that short-hand does not accurately capture the potential for a cascade of compounding effects within a complex system-of-systems and the resulting paralyzing outcomes. AI may provide a critical means to that end.

American assessments of military AI often focus on the second step coordinated lethal attacks using autonomous systems against opposing forces. Drones and other autonomous systems are certainly under development in the PLA. However, the Chinese focus is currently on developing AI technology, methods, and tactics to precisely target key elements within an enemy system-of-systems. The objective is to paralyze the adversary, and goes well beyond merely throwing sand in the gears of the enemy joint force. If successful, the large-scale attrition of forces may not even be necessary.

The use of AI in a system-of-systems confrontation conforms with and enables existing Chinese military doctrine on informationized warfare. The PLA believes that the center of gravity in modern military operations has shifted from concentrations of forces to information systems-of-systems everything from target detection to communication to information processing to command of action. Modern military information systems-of-systems are vast, complex, and in the future will likely be managed by AI. Therefore, it follows that they can only be analyzed in real-time and attacked using AI.

The PLAs objective is to use AI algorithms, machine learning, human-machine teaming, and autonomous systems collaboratively to paralyze its adversaries. The ultimate goal for the Chinese military appears to be cognitive advantage the ability to adapt ones system-of-systems faster than ones adversary. The Chinese seek to use AI to deliver precise effects to immobilize their adversary while defending their own system-of-systems. Any Chinese military challenger would be wise to understand the implications of how future AI capabilities may be employed to realize Chinese goals in system-of-systems confrontation.

An Intelligentized Form of War

The English-language version of Chinas 2019 Defense White Paper observes a change in modern warfare: War is evolving in form towards informationized warfare, and intelligent warfare is on the horizon. A translation of the Chinese-language version, however, reveals that the change is not about moving toward informationized warfare, it is about an evolution in informationized warfare: The form of war is accelerating toward an informationized warfare evolution, there are indications intellgentized warfare is emerging (). The Chinese form of war () speaks to the changing character of war; an assessment of the objective basis that will drive the conduct of present and future warfare. The information age had yielded informationized warfare () forming the basis for PLA development since the early-2000s. Chinese military leaders now believe that informationized war is evolving and intelligentized warfare will become the prevailing form of war.

The 2019 forecast of an evolution in the form of war toward intelligentization is very similar to where the PLA found itself in the early 2000s. The 2002 Chinese Defense White Paper stated, The form of war is developing in the direction of informationization. What happened in the wake of that top-level proclamation was a vigorous discussion among Chinese military writers parsing out the transformation of warfare by information and modern information technology. That debate and the dialectical back-and-forth ultimately formed the foundations of Chinese informationized warfare theory and doctrine. It bore out informationized warfare patterns of operations () and basic guiding thought () informationized warfare theory that placed information control at the center of PLA operational concepts. As with those explorations of informationized warfare in the early 2000s, Chinese military authors are currently discussing the changes that intelligentized warfare will bring to the PLA.

The Chinese military is not a hive mind. Debates within the Chinese military are robust and often contrarian. Western analysts must distinguish between guidance from central leadership and what may be dissent or consensus in a debate. The pages of the PLAs official newspaper, the PLA Daily () and the PLAs official web site provide a venue for the discussion of emerging phenomenon on any number of topics, including intelligentized warfare. While far from authoritative guidance, the publication of research, opinions, and proposals in official media comes with the tacit endorsement of the Chinese military establishment. The PLA Daily newspapers Military Forum () offers monographs by select military authors and provides insights into the consensus that may be emerging within the PLA over intelligentized warfare.

Since the Defense White Paper was published in mid-2019, authoritative commentaries have appeared with increasing frequency in official PLA media discussing informationized warfare, intelligentized warfare, unmanned systems, autonomous decision-making, and cognitive warfare. In articles like, Seize the Commanding Heights of Artificial Intelligence Technology Development (), authors discuss the development of this new type of intelligentized warfare, forecast new types of intelligentized technologies and operational concepts, and ultimately seek to propel PLA thinking ahead of counterparts in the United States. These commentaries demonstrate a trajectory of PLA military thinking that integrates AI with Chinese thinking on informationized warfare and the PLAs information-centric military strategy.

Six Principles of Chinas Intelligentized Warfare

There is a significant amount of overlap among Chinese, Russian, and Western militaries on the topic of military AI. For example, all seem to recognize that autonomous swarms of unmanned platforms may generate advantages in terms of cost, scale, dispersion, and adaptation, enabling lethal saturation attacks. Articles like Intelligentized Warfare, Where are the Constants? (, ) (January 2020), remind readers that AI may have changed the character of war, but the nature of war endures war is still a violent action taken to a political end with humans central to the endeavor. Several Chinese authors emphasize that humans will still plan, organize and initiate wars. While man-machine teaming may enhance human cognition and action, Chinese analysts are directed to guard against the anthropomorphization of weapons and the weaponization of humans and always place humans in the dominant role.

However, there are a number of unique perspectives emerging from the Chinese debate over AI that may merit attention by Western analysts. Most appear to reflect the premise that AI will enable and evolve existing Chinese warfare theory and doctrine.

First, PLA strategists believe that intelligentized warfare is an evolution of informationized warfare. Several authors point out that intelligentized warfare is essentially highly evolved informationized warfare system-of-systems confrontation reliant on information moving through digital systems and networks. How to Integrate the Mechanization, Informationization and Intelligentization of Weapons and Equipment (, , ) (October 2019) acknowledges that informationization and intelligentization are inextricably linked. But articles like this have begun to categorize intelligentization as an independent, aspirational stage of future military development.

Next, Chinese military authors argue that ubiquitous networks will enable systems-of-systems warfare. In articles such as, Picturing a New Combat System () (June 2019) they refer to the emergence of ubiquitous networks () that will shorten the distance between perception, judgement, decision-making, and action. The Chinese concept of ubiquitous networks appears similar to the U.S. Defense Advanced Research Projects Agencys (DARPA) Mosaic Warfare concept of system-agnostic, flexible, and rapidly configurable networks. The PLA has been focused on military systems-of-systems development for well over 15 years. Ubiquitous networks and AI may give the PLA its own version of mosaic warfare with Chinese characteristics.

Chinese strategists also contend that AI enables command and operational design. For example, How Unmanned Combat can Change the Form of War () (August 2019) highlights an underappreciated feature of Chinese military theory the way in which command and control is exercised through operational design and pre-conflict campaign planning. There are misperceptions that the PLA has a Soviet-style centralized command and control system the premise being that if PLA conventional forces or autonomous systems are cut off from centralized control, they either will be unable to function or will make haphazard decisions. However, the PLA believes that command and control can be built-in to system design and operational plans to mitigate threats of either human or machine errors in combat. Consistent with its Marxist-Leninist intellectual heritage, the PLA conceptualizes war as a scientific process that can be deconstructed, allowing for calculated outcomes. AI and machine-learning may provide the Chinese military with the algorithms and tools it believes it needs to determine end results and develop invulnerable systems-of-systems, operational capabilities and military plans.

Fourth, there is an expectation that AI will enable new operational concepts. Intelligentized Warfare, Where are the Changes? (, ) (January 2020) is the companion piece to Intelligentized Warfare, Where are the Constants? cited above. In this article, the authors identify several future patterns of operations Chinese operational concepts including autonomous swarm attrition warfare (), autonomous dormant assault warfare () autonomous cross-domain mobile warfare () and autonomous cognitive control warfare (). Swarm attrition warfare capitalizes on decentralized operations and coordinated saturation attacks by large numbers of autonomous, unmanned systems. Dormant assault warfare portends surprise attacks at key points or against critical adversary capabilities by autonomous platforms programmed to lie in wait until activated. Autonomous cross-domain mobile warfare envisions an autonomous force that is highly mobile and can strike on a large-scale and at long-ranges. This last concept presumably borrows from the U.S. Armys Multi-Domain Operations concept.

Autonomous cognitive control warfare is not explicitly described in the January 2020 article. However, articles like Cognitive Warfare: Dominating the Intelligence Age (: ) (March 2020) describe a shift in opposing military centers of gravity toward the cognitive domain. Chinese military authors are fond of invoking the U.S.-originated OODA loop (observe, orient, decide, act). These Chinese authors observe that decision-making is the bottleneck in the OODA loop. Future autonomous systems, they say, will compete for cognitive advantage and thus decision advantage enabling faster cycling of military action to dominate an adversary in parallel operations drawing from the U.S.-originated parallel warfare concept.

Fifth, AI offers the PLA the ability to precisely release kinetic energy and paralyze an opponents system-of-systems. A Chinese state-media (Xinhua) article, Military Intelligentization is Profoundly Affecting Future Operations () was posted to the Chinese Ministry of National Defense website in September 2019. The article observes that in intelligentized warfare cognitive-centric warfare AI and autonomous systems will allow for precise energy release, either dispersed across a system-of-systems or concentrated on a critical node to impose highly persistent paralysis on an adversary. Battlefield advantage will go to the force that can dominate the cognitive domain perceiving, adapting and acting faster than an opponent to impose or reverse system-of-systems paralysis.

Finally, Chinese military analysts recognize that the PLA must accelerate progress on AI. The military scholars cited here uniformly believe that China is well behind the United States in the development of military AI. The emphasis on AI in the Defense Departments Third-Offset Strategy, or even Russias progress in unmanned and autonomous systems, are often cited to illustrate the PLAs lagging progress, adding a sense of urgency to the need for military AI development in China.

The PLA Overtaking on the Curve

AI is a clear priority for the Chinese military. The PLA has adopted a strategy of overtaking on the curve (), catching up with and passing the United States and Russia by metaphorically turning more tightly in corners as trends in science and technology change direction. There will be myriad innovations that change the trajectory of AI technology in the coming years. Each one will be a potential opportunity for the PLA to close the technology gap with the United States and allow the Chinese military to realize its information-centric military strategy.

According to recent Western studies of the AI industry, the United States appears to be in the lead with China rapidly closing the gap. Assessing military competition in AI is more difficult. The U.S. militarys Fiscal Year 2021 budget proposed $841 million in direct spending on AI (0.1 percent of the $705 billion proposal), but that fails to capture how AI is being integrated throughout different weapons systems budgets. Chinese defense spending is even more opaque. However, a significant number of Chinese military institutions do appear to be working diligently on AI innovation.

Assessing how the Chinese and U.S. militaries compare in terms of AI is made even more difficult by the fact that innovation and technical progress on AI has been driven by industry for civil applications. Armed forces will continue to capitalize on the dual-use nature of big data processing and AI algorithms that increase industrial efficiencies and enable commercial autonomous systems.

In the competition to lead in AI, China enjoys the advantage of scale. The Chinese government is accelerating the development of AI technology using entire cities as laboratories. In early April 2020, in the wake of the COVID-19 outbreak, Chinese President Xi Jinping visited Hangzhou and called for Chinese cities to become smarter. Hangzhou is just one of several Chinese smart cities experimenting with the use of AI for city-wide management and security. Ultimately, the PLA will capitalize heavily on Chinas cutting edge progress in civil and commercial AI technology, much of it in cooperation with U.S. and other foreign industries. Accounting for these different development models directed by government or left to private industry may be critical in forecasting outcomes in a military AI arms race.

The Big Picture

The Chinese military, in its own self-assessment, falls well short of a globally present U.S. military that can project an overwhelming joint force anywhere in the world with cutting-edge military technology and a wealth of combat experience. President Xi has mandated that the Chinese military be fully modernized by 2035 and a world-class military on par with the United States military by 2050.

Chinas strategy for the use of AI technology is evolving from their interpretation of the character of war and is ultimately an extension of the PLAs informationized warfare concepts. While there is certainly overlap with U.S. military thinking on the use of AI, Chinese military scholars appear to be reaching different conclusions. U.S. thinking tends to emphasize the role of AI in enhancing firepower- and maneuver-centric strategies. The PLA, on the other hand, is advancing AI concepts that enhance its information-centric military strategies.

New technologies related to artificial intelligence, machine learning, and autonomous systems may provide the PLA with necessary tools to realize its long-standing goals of controlling the information domain, manipulating perception, and paralyzing adversary decision-making. In developing strategies to counter Chinese military capabilities, the Pentagon should pay close attention to the PLAs evolving warfighting concepts and views on AI in future combat.

Michael Dahm is a senior researcher at the Johns Hopkins University Applied Physics Laboratory (APL) and retired U.S. Navy intelligence officer. Mr. Dahms perspectives presented in War on the Rocks are his own and do not necessarily reflect those of APL or its sponsors.

Image: China Military

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UWA researchers to predict suicide risk with artificial intelligence – University News: The University of Western Australia

Researchers from The University of Western Australia are developing a system using artificial intelligence that can predict the likelihood of suicide in individuals most at risk, and assist with clinical decision-making.

The research team from UWA and the Mental Health Service at Armadale Kalamunda Group has been awarded a Mental Health Research Fund Grant by the East Metropolitan Health Service to create a system that will improve the existing risk assessment framework used by healthcare providers.

The current methodology to predict an individuals risk of suicide or self-harm is highly subjective and can be influenced by the assessors experience, recent negative outcomes and other biases.

Through the development of a computer-based system using artificial intelligence algorithms, the researchers will be able to determine the factors in actual behaviour which are better predictors of risk.

The system would be developed as a companion tool to be used alongside the current risk assessment models and protocols.

Professor Mohammed Bennamoun from UWAs Department of Computer Science and Software Engineering said a system that could successfully improve prediction of self-harm or suicide would have worldwide implications.

It will also be particularly useful in places where there are fewer mental health specialists available to carry out the risk analysis, Professor Bennamoun said.

Dr Dharjinder Rooprai, Head of Psychiatry and Consultant Psychiatrist at the Armadale Kalamunda Group, said a high proportion of individuals who went on to commit suicide often suffered with a mental illness, including depression.

An artificial intelligence system that can be employed by healthcare providers to assist their patients risk of suicide or self-harm assessment would have a profoundly positive impact on their patients care journey, Dr Rooprai said.

The researchers are also considering facial recognition as a tool to identify an individuals mental state and predict the likelihood of adverse outcomes.

Professor Bennamoun said that while, for example, facial recognition was increasingly used for tracking and surveillance, artificial intelligence overall would have a significant impact on humans in new ways.

The capability of facial recognition to identify patients at risk of suicide would be revolutionary in the field of psychiatry and would have implications far beyond risk prediction, he said.

Simone Hewett (UWA Media and PR Manager) 08 6488 3229 / 0432 637 716

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Artificial Intelligence as a Service Market Research, Dependability And Innovations In Technology – Cole of Duty

The report on the Artificial Intelligence as a Service Market provides a birds eye view of the current proceeding within the Artificial Intelligence as a Service market. Further, the report also takes into account the impact of the novel COVID-19 pandemic on the Artificial Intelligence as a Service market and offers a clear assessment of the projected market fluctuations during the forecast period. The different factors that are likely to impact the overall dynamics of the Artificial Intelligence as a Service market over the forecast period (2020-2026) including the current trends, growth opportunities, restraining factors, and more are discussed in detail in the market study.

The Report Titled on Artificial Intelligence as a Service Market (6 Year Forecast 2020-2026) includes Outline, Classification, Price, Industry Value, Cost and Gross Profit. Artificial Intelligence as a Service Market report enhanced on worldwide competition by topmost prime manufactures like (IBM, Google, Amazon Web Services, Microsoft, Salesforce, FICO, SAS Institute, Intel, SAP, IRIS AI, Bigml, H2o.AI, Absolutdata, Fuzzy.AI, Vital AI, Rainbird Technologies, Craft.AI, Sift Science, Mighty.AI, Cognitive Scale, Centurysoft, Yottamine Analytics, Datarobot, Meya.AI, etc.,) which providing information such as Shipments, Company Profiles, Gross and Gross Merging, Revenue (Million USD), Product Picture and Specification, Capacity, Production and contact information.

Target Audience of the Artificial Intelligence as a Service Market: Key Consulting Companies & Advisors, Large, medium-sized, and small enterprises, Venture capitalists, Value-Added Resellers (VARs), Manufacturers, , Third-party knowledge providers, Equipment Suppliers/ Buyers, Industry Investors/Investment Bankers, Research Professionals, Emerging Companies, Service Providers.

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VisionQuest Biomedical and The University of New Mexico combine artificial intelligence and infrared imaging to diagnose early signs of diabetic foot…

VisionQuest Biomedical and the University of New Mexico School of Medicine have been awarded a three-year US$3 million grant from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), part of the National Institutes of Health (NIH), to complete the clinical validation of a new technology to detect early signs of diabetic peripheral neuropathy (DPN), also known as diabetic foot.

Peter Soliz, founder and chief technology officer of VisionQuest, says, Our patented technology for detecting early signs of peripheral neuropathy will fundamentally change how physicians manage this severe complication of diabetes. This system will complement our already successful EyeStar system for the detection of diabetic retinopathy and will allow us to screen for multiple diabetes complications in one visit.

Mark Burge, deputy director of the University of New Mexicos Clinical & Translational Science Center (CTSC) and co-principal investigator on this project, adds, A simple test that can be performed by the primary care physician in the clinic and which is highly sensitive and specific does not currently exist. This device will fill an important gap in providing comprehensive care to individuals diagnosed with diabetes.

Diabetes affects 34.2 million people in the USA, or 10.5 percent of the population. The Foundation for Peripheral Neuropathy estimates that over 70% of people diagnosed with diabetes have developed DPN, a painful complication of diabetes that leads to loss of sensation, foot ulcers, and nearly 54,000 amputations per year.

Current screening methods cannot reliably detect the early stages of DPN, when preventative care can improve outcomes. VisionQuests fully automated, noninvasivesystem analyses real-time thermal video of changing temperatures on the bottom of the foot to produce highly sensitive and consistent measurements of blood flow that can be used for diagnosis in primary-care clinics and do not require interpretation. The device and technique were awarded a US patent in 2014.

Through this NIDDK grant, VisionQuest will complete the clinical validation needed to pursue clearance by the US Food and Drug Administration (FDA) to bring the device to market in the USA.

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VisionQuest Biomedical and The University of New Mexico combine artificial intelligence and infrared imaging to diagnose early signs of diabetic foot...

MegaCryption 6.5.0: Optimized Compression and Authentication – AiThority

MegaCryption, specifically designed for z/OS environments, provides organizations with encryption, decryption, compression, and file management capabilities

Advanced Software Products Group (ASPG, Inc.) is pleased to announce the latest release of MegaCryption 6.5.0. Specifically designed for z/OS environments, MegaCryption is a comprehensive enterprise and mainframe cryptography toolkit providing organizations with encryption, decryption, compression, and file management capabilities.

Enhancements to MegaCryptions compression features have been further developed through a zEDC (zEnterprise Data Compression) feature which can now be utilized by the OpenPGP and Zip/Unzip utilities, providing accelerated compression capabilities on z/OS systems and reducing organization processing times. A newly added Enhanced Compression Mode (ECM) feature also provides users with optimized file compression.

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An AES-GCM utility has been added, considered one of the most secure ciphers, providing MegaCryption users with an additional form of authenticated encryption and data integrity to their daily security operations.

The addition of IMS cryptography provides IMS users with robust cryptography that will appropriately protect mainframe database services containing classified or sensitive information. MegaCryptions capabilities satisfy both industry and federal data security compliance policies.

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In-keeping with ASPGs proactive approach to regular product developments, general performance enhancements have also been made to the MegaCryption tool providing a seamless end-user experience.

As cybersecurity threats continue to rise, MegaCryption provides an affordable and effective solution for users looking to take a proactive approach to data security. Complementary products within the MegaCryption range are also available for IDMS, DB2, Unix and Linux, and Windows environments. With a wide range of tools, MegaCryption is well suited to satisfy a wide variety of environments.

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MegaCryption 6.5.0: Optimized Compression and Authentication - AiThority

Watch | What is crypto-jacking? – The Hindu

A cryptocurrency is a digital asset stored on computerised databases. These digital coins are recorded in digital ledgers using strong cryptography to keep them secure.

The ledgers are distributed globally, and each transaction made using cryptocurrencies are codified as blocks. And multiple blocks linking each other forms a blockchain on the distributed ledger.

There are estimated to be more than 47 million cryptocurrency users around the world.

These cryptocurrencies are created through a process called mining. To mine digital coins, miners need to use high-end processors that will consume a lot of electricity.

These minted digital assets are decentralised, unlike physical cash that is regulated by each countrys central bank. The ownership of these digital assets is cryptographically coded, and the blockchain system enables transfer of ownership.

Also read: Notes on a digital currency plan, made in China

But, to ensure it is used only by one entity, the distributed ledger accepts transaction performed by the first user, rejecting all other blocks. This way, the same cryptocurrency cant be used by two different entities, making a fool-proof financial system.

However, there are other ways in which a security breach can happen in this world of cryptocurrency. Crypto-jacking is what some digital coin miners do to illegally gain access to many computers. The miners stealthily drop malware in an unsuspecting users pc.

Once installed, the crypto mining code runs surreptitiously and turns devices into cryptocurrency-mining botnets. The mined digital assets are then stored in digital ledgers with unique codes.

Unlike most other types of malware, crypto-jacking scripts do not use the victims data. But they drain the CPUs resources, which slows down the system, increases electricity usage, and causes irreparable damage to the hardware.

Hackers tend to prefer anonymous cryptocurrencies like Monero and Zcash, over the more popular Bitcoin as it is harder to track illegal activity back to them on these platforms.

The practice of crypto-jacking is currently on the rise as the price of the asset is falling, according to Palo Alto Networks. So, to reduce costs associated with mining, hackers resort to crypto-jacking.

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