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Spurred by China's gains in this area, other nations can unite to share expertise in order to expand AI's current capability and ensure that AI can replicate its role in helping China deal with the novel coronavirus pandemic.

March30, 20208 min read

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From its epicenter in China, the novel coronavirus has spread to infect 414,179 people and cause no less than 18,440 deaths in at least 160 countries across a three-month span from January 2020 till date. These figures are according to the World Health Organization (WHO) Situation report as of March 25th. Accompanying the tragic loss of life that the virus has caused is the impact to the global economy, which has reeled from the effects of the pandemic.

Due to the lockdown measures imposed by several governments, economic activity has slowed around the world, and the Organization for Economic Cooperation and Development (OECD) has stated that the global economy could be hit by its worst growth rate since 2009. The OECD have alerted that the growth rate could be as slow as 2.4%, potentially dragging many countries into recession. COVID-19 has, in a short period of time, emerged as one of the biggest challenges to face the 21st century world. Further complicating the response to this challenge are the grey areas surrounding the virus itself, in terms of its spread and how to treat it.

Related:We're In This Together: Business Resources, Offers, And More For MENA Entrepreneurs To Get Through The Coronavirus Pandemic

As research details emerge, the data pool grows exponentially, beyond the capacity of human intelligence alone to handle. Artificial intelligence (AI) is adept at identifying patterns from big data, and this piece will elucidate how it has become one of humanitys ace cards in handling this crisis. Using China as a case-study, Chinas success with AI as a crisis management tool demonstrates its utility, and justifies the financial investment the technology has required to evolve over the last few years.

Advancements in AI application such as natural language processing, speech recognition, data analytics, machine learning, deep learning, and others such as chatbots and facial recognition have not only been utilized for diagnosis but also for contact tracing and vaccine development. AI has no doubt aided the control of the COVID-19 pandemic and helped to curb its worst effects.

Related:Here's What Your Business Should Focus On As It Navigates The Coronavirus Pandemic

Spurred by Chinas gains in this area, other nations can unite to share expertise in order to expand AIs current capability and ensure that AI can replicate its role in helping China deal with the novel coronavirus pandemic. AI has been deployed in several ways so far, and the following are just seven of the ways in which AI has been applied as a measure to solve the pandemic:

1. DISEASE SURVEILLANCE AI With an infectious disease like COVID-19, surveillance is crucial. Human activity -especially migration- has been responsible for the spread of the virus around the world. Canada based BlueDot has leveraged machine learning and natural language processing to track, recognize, and report the spread of the virus quicker than the World Health Organization and the US Centre for Disease Control and Prevention (CDC). In the near and distant future, technology like this may be used to predict zoonotic infection risk to humans considering variables such as climate change and human activity. The combined analysis of personal, clinical, travel and social data including family history and lifestyle habits obtained from sources like social media would enable more accurate and precise predictions of individual risk profiles and healthcare results. While concerns may exist about the potential infringement to civil liberties of individuals, policy regulations that other AI applications have faced will ensure that this technology is used responsibly.

2. VIRTUAL HEALTHCARE ASSISTANTS (CHATBOTS) The number of COVID-19 cases has shown that healthcare systems and response measures can be overwhelmed. Canada-based Stallion.AI has leveraged its natural language processing capabilities to build a multi-lingual virtual healthcare agent that can answer questions related to COVID-19, provide reliable information and clear guidelines, recommend protection measures, check and monitor symptoms, and advise individuals whether they need hospital screening or self-isolation at their homes.

Related:The Coronavirus Pandemic Versus The Digital Economy: The Pitfalls And The Opportunities

3. DIAGNOSTIC AI Immediate diagnosis means that response measures such as quarantine can be employed quickly to curb further spread of the infection. An impediment to rapid diagnosis is the relative shortage of clinical expertise required to interpret diagnostic results due to the volume of cases. AI has improved diagnostic time in the COVID-19 crisis through technology such as that developed by LinkingMed, a Beijing-based oncology data platform and medical data analysis company. Pneumonia, a common complication of COVID-19 infection, can now be diagnosed from analysis of a CT scan in less than sixty seconds with accuracy as high as 92% and a recall rate of 97% on test data sets. This was made possible by an open-source AI model that analyzed CT images and not only identified lesions but also quantified in terms of number, volume and proportion. This platform, novel in China, was powered by Paddle Paddle, Baidus open-source deep learning platform.

4. FACIAL RECOGNITION AND FEVER DETECTOR AI Thermal cameras have been used for some time now for detecting people with fever. The drawback to the technology is the need for a human operator. Now, however, cameras possessing AI-based multisensory technology have been deployed in airports, hospitals, nursing homes, etc. The technology automatically detects individuals with fever and tracks their movements, recognize their faces, and detect whether the person is wearing a face mask.

5. INTELLIGENT DRONES & ROBOTS The public deployment of drones and robots has been accelerated due to the strict social distancing measures required to contain the virus spread. To ensure compliance, some drones are used to track individuals not using facemasks in public, while others are used to broadcast information to larger audiences and also disinfect public spaces. MicroMultiCopter, a Shenzhen-based technology company, has helped to lessen the virus transmission risk involved with city-wide transport of medical samples and quarantine materials through the deployment of their drones. Patient care, without risk to healthcare workers, has also benefited as robots are used for food and medication delivery. The role of room cleaning and sterilization of isolation wards has also been filled by robots. Catering-industry centred Pudu Technology have extended their reach to the healthcare sector by deploying their robots in over 40 hospitals for these purposes.

Related:How Managers Can Weather The Impact Of The Coronavirus Pandemic On Their Businesses

6. CURATIVE RESEARCH AI Part of what has troubled the scientific community is the absence of a definitive cure for the virus. AI can potentially be a game changer as companies such as the British startup, Exscienta, has shown. Earlier this year, they became the first company to present an AI designed drug molecule that has gone to human trials. A year is all it took the algorithm to develop the molecular structure compared with the five-year average time that it takes traditional research methods.

In the same vein, AI can lead the charge for the development of antibodies and vaccines for the novel coronavirus, either entirely designed from scratch or through drug repurposing. For instance, using its AlphaFold system, Googles AI company, DeepMind, is creating structure models of proteins that have been linked with the virus in a bid to aid the science worlds comprehension of the virus. Although the results have not been experimentally verified, it represents a step in the right direction.

7. INFORMATION VERIFICATION AI The uncertainty of the pandemic has unavoidably resulted in the propagation of myths on social media platforms. While no quantitative assessment has been done to evaluate how much misinformation is out there already, it is certainly a significant figure. Technology giants like Google and Facebook are battling to combat the waves of conspiracy theories, phishing, misinformation and malware. A search for coronavirus/COVID-19 yields an alert sign coupled with links to verified sources of information. YouTube, on the other hand, directly links users to the WHO and similar credible organizations for information. Videos that misinform are scoured for and taken down as soon as they are uploaded.

While the world continues to grapple with the effects of COVID-19, positives can be drawn from the expertise and bravery of healthcare workers, as well as the complementary efforts of AI technology to their endeavors in the above listed ways. As the AI world partners with other sectors for solutions, the light at the end of this tunnel shines brighter, creating the much-needed hope the world needs in these uncertain times.

Related:Work In The Time Of Coronavirus: Here's How You Can Do Your Job From Home (Like A Pro)

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How Artificial Intelligence Is Helping Fight The COVID-19 Pandemic - Entrepreneur

The coronavirus outbreak has rapidly accelerated the nations slow-moving effort to incorporate artificial intelligence into medical care, as hospitals grasp onto experimental technologies to relieve an unprecedented strain on their resources.

AI has become one of the first lines of defense in the pandemic. Hospitals are using it to help screen and triage patients and identify those most likely to develop severe symptoms. Theyre scanning faces to check temperatures and harnessing fitness tracker data, to zero on individual cases and potential clusters. They are also using AI to keep tabs on the virus in their own communities. They need to know who has the disease, who is likely to get it, and what supplies are going to run out tomorrow, two weeks from now, and farther down the road.

Just weeks ago, some of those efforts might have stirred a privacy backlash. Other AI tools were months from deployment because clinicians were still studying their impacts on patients. But as Covid-19 has snowballed into a global crisis, health cares normally methodical approach to new technology has been hijacked by demands that are plainly more pressing.

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Theres a crucial caveat: Its not clear if these AI tools are going to work. Many are based on drips of data, often from patients in China with severe disease. Those data might not be applicable to people in other places or with milder disease. Hospitals are testing models for Covid-19 care that were never intended to be used in such a scenario. Some AI systems could also be susceptible to overfitting, meaning that theyve modeled their training data so well that they have trouble analyzing new data which is coming in constantly as cases rise.

The uptake of new technologies is moving so fast that its hard to keep track of which AI tools are being deployed and how they are affecting care and hospital operations. STAT has developed a comprehensive guide to that work, broken down by how the tools are being used.

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This list focuses only on AI systems being used and developed to directly aid hospitals, clinicians, and patients. It doesnt cover the flurry of efforts to use AI to identify drug and vaccine candidates, or to track and forecast the spread of the virus.

This is one of the earliest and most common uses of AI. Hospitals have deployed an array of automated tools to allow patients to check their symptoms and get advice on what precautions to take and whether to seek care.

Some health systems, including Cleveland Clinic and OSF Healthcare of Illinois, have customized their own chatbots, while others are relying on symptom checkers built in partnership with Microsoft or startups such as Boston-based Buoy Health. Apple has also released its own Covid-19 screening system, created after consultation with the White House Coronavirus Task Force and public health authorities.

Developers code knowledge into those tools to deliver recommendations to patients. While nearly all of them are built using the CDCs guidelines, they vary widely in the questions they ask and the advice they deliver.

STAT reporters recently drilled eight different chatbots about the same set of symptoms. They produced confusing patchwork of responses. Some experts on AI have cautioned that these tools while well-intentioned are a poor substitute for a more detailed conversation with a clinician. And given the shifting knowledge-base surrounding Covid-19, these chatbots also require regular updates.

If you dont really know how good the tool is, its hard to understand if youre actually helping or hurting from a public health perspective.

Andrew Beam, artificial intelligence researcher

If you dont really know how good the tool is, its hard to understand if youre actually helping or hurting from a public health perspective, said Andrew Beam, an artificial intelligence researcher in the epidemiology department at Harvard T.H. Chan School of Public Health.

Clover, a San Francisco-based health insurance startup, is using an algorithm to identify its patients most at risk of contracting Covid-19 so that it can reach out to them proactively about potential symptoms and concerns. The algorithm uses three main sources of data: an existing algorithm the company uses to flag people at risk of hospital readmission, patients scores on a frailty index, and information on whether a patient has an existing condition puts them at a higher risk of dying from Covid-19.

AI could also be used to catch early symptoms of the illness in health care workers, who are at particularly high risk of contracting the virus. In San Francisco, researchers at the University of California are using wearable rings made by health tech company Oura to track health care workers vital signs for early indications of Covid-19. If those signs including elevated heart rate and increased temperature show up reliably on the rings, they could be fed into an algorithm that would give hospitals a heads up about workers who need to be isolated or receive medical care.

Covid-19 testing is currently done by taking a sample from a throat or nasal swab and then looking for tiny snippets of the genetic code of the virus. But given severe shortages of those tests in many parts of the country, some AI researchers believe that algorithms could be used as an alternative.

Theyre using chest images, captured via X-rays or computed tomography (CT) scans, to build AI models. Some systems aim simply to recognize Covid-19; others aim to distinguish, say, a case of Covid-19-induced pneumonia from a case caused by other viruses or bacteria. However, those models rely on patients to be scanned with imaging equipment, which creates a contamination risk.

Other efforts to detect Covid-19 are sourcing training data in creative ways including by collecting the sound of coughs. An effort called Cough for the Cure led by a group of San Francisco-based researchers and engineers is asking people who have tested either negative or positive for Covid-19 to upload audio samples of their cough. Theyre trying to train a model to tell the difference, though its not clear yet that a Covid-19 cough has unique features.

Among the most urgent questions facing hospitals right now: Which of their Covid-19 patients are going to get worse, and how quickly will that happen? Researchers are racing to develop and validate predictive models that can answer those questions as rapidly as possible.

The latest algorithm comes from researchers at New York University, Columbia University, and two hospitals in Wenzou, China. In an article published in a computer science journal on Monday, the researchers reported that they had developed a model to predict whether patients would go on to develop acute respiratory distress syndrome or ARDS, a potentially deadly accumulation of fluid in the lungs. The researchers trained their model using data from 53 Covid-19 patients who were admitted to the Wenzhou hospitals. They found that the model was between 70% and 80% accurate in predicting whether the patients developed ARDS.

At Stanford, researchers are trying to validate an off-the-shelf AI tool to see if it can help identify which hospitalized patients may soon need to be transferred to the ICU. The model, built by the electronic health records vendor Epic, analyzes patients data and assigns them a score based on how sick they are and how likely they are to need escalated care. Stanford researchers are trying to validate the model which was trained on data from patients hospitalized for other conditions in dozens of Covid-19 patients. If it works, Stanford plans to use it as a decision-support tool in its network of hospitals and clinics.

Similar efforts are underway around the globe. In a paper posted to a preprint server that has not yet been peer-reviewed, researchers in Wuhan, China, reported that they had built models to try to predict which patients with mild Covid-19 would ultimately deteriorate. They trained their algorithms using data from 133 patients who were admitted to a hospital in Wuhan at the height of its outbreak earlier this year. And in Israel, the countrys largest hospital has deployed an AI model developed by the Israeli company EarlySense, which aims to predict which Covid-19 patients may experience respiratory failure or sepsis within the next six to eight hours.

AI is also helping to answer pressing questions about when hospitals might run out of beds, ventilators, and other resources. Definitive Healthcare and Esri, which makes mapping and spatial analytics software, have built a tool that measures hospital bed capacity across the U.S. It tracks the location and number of licensed beds and intensive care (ICU) beds, and shows the average utilization rate.

Using a flu surge model created by the CDC, Qventus is working with health systems around the country to predict when they will reach their breaking point. It has published a data visualization tracking how several metrics will change from week to week, including the number of patients on ventilators and in ICUs.

Its current projection: At peak, there will be a shortage of 9.100 ICU beds and 115,000 beds used for routine care.

To focus in-person resources on the sickest patients, many hospitals are deploying AI-driven technologies designed to monitor patients with Covid-19 and chronic conditions that require careful management. Some of these tools simply track symptoms and vital signs, and make limited use of AI. But others are designed to pull out trends in data to predict when patients are heading toward a potential crisis.

Mayo Clinic and the University of Pittsburgh Medical Center are working with Eko, the maker of a digital stethoscope and mobile EKG technology whose products can flag dangerous heart rhythm abnormalities and symptoms of Covid-19. Mayo is also teaming up with another mobile EKG company, AliveCor, to identify patients at risk of a potentially deadly heart problem associated with the use of hydroxychloroquine, a drug being evaluated for use in Covid-19.

Many developers of remote monitoring tools are scrambling to deploy them after the Food and Drug Administration published a new policy indicating it will not object to minor modifications in the use or functionality of approved products during the outbreak. That covers products such as electronic thermometers, pulse oximeters, and products designed to monitor blood pressure and respiration.

Among them is Biofourmis, a Boston-based company that developed a wearable that uses AI to flag physiological changes associated with the infection. Its product is being used to monitor Covid-19 patients in Hong Kong and three hospitals in the U.S. Current Health, which makes a similar technology, said orders from hospitals jumped 50% in a five-day span after the coronavirus began to spread widely in the U.S.

Several companies are exploring the use of AI-powered temperature monitors to remotely detect people with fevers and block them from entering public spaces. Tampa General Hospital in Florida recently implemented a screening system that includes thermal-scanning face cameras made by Orlando-based company Care.ai. The cameras look for fevers, sweating, and discoloration. In Singapore, the nations health tech agency recently partnered with a startup called KroniKare to pilot the use of a similar device at its headquarters and at St. Andrews Community Hospital.

As experimental therapies are increasingly tested in Covid-19 patients, monitoring how theyre faring on those drugs may be the next frontier for AI systems.

A model could be trained to analyze the lung scans of patients enrolled in drug studies and determine whether those images show potential signs of improvement. That could be helpful for researchers and clinicians desperate for signal on whether a treatment is working. Its not clear yet, however, whether imaging is the most appropriate way to measure response to drugs that are being tried for the first time on patients.

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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STAT's guide to how hospitals are using AI to fight Covid-19 - STAT

New drug therapies for a range of chronic diseases. Defenses against various cyber attacks. Technologies to make cities work smarter. Weather and wildfire forecasts that boost safety and reduce risk. And commercial efforts to monetize so-called deepfakes.

What do all these disparate efforts have in common? Theyre some of the solutions that the worlds most promising artificial intelligence startups are pursuing.

Data research firm CB Insights released its much-anticipated fourth annual list of the top 100 AI startups earlier this month. The New York-based company has become one of the go-to sources for emerging technology trends, especially in the startup scene.

About 10 years ago, it developed its own algorithm to assess the health of private companies using publicly-available information and non-traditional signals (think social media sentiment, for example) thanks to more than $1 million in grants from the National Science Foundation.

It uses that algorithm-generated data from what it calls a companys Mosaic scorepulling together information on market trends, money, and momentumalong with other details ranging from patent activity to the latest news analysis to identify the best of the best.

Our final list of companies is a mix of startups at various stages of R&D and product commercialization, said Deepashri Varadharajanis, a lead analyst at CB Insights, during a recent presentation on the most prominent trends among the 2020 AI 100 startups.

About 10 companies on the list are among the worlds most valuable AI startups. For instance, theres San Francisco-based Faire, which has raised at least $266 million since it was founded just three years ago. The company offers a wholesale marketplace that uses machine learning to match local retailers with goods that are predicted to sell well in their specific location.

Another startup valued at more than $1 billion, referred to as a unicorn in venture capital speak, is Butterfly Network, a company on the East Coast that has figured out a way to turn a smartphone phone into an ultrasound machine. Backed by $350 million in private investments, Butterfly Network uses AI to power the platforms diagnostics. A more modestly funded San Francisco startup called Eko is doing something similar for stethoscopes.

In fact, there are more than a dozen AI healthcare startups on this years AI 100 list, representing the most companies of any industry on the list. In total, investors poured about $4 billion into AI healthcare startups last year, according to CB Insights, out of a record $26.6 billion raised by all private AI companies in 2019. Since 2014, more than 4,300 AI startups in 80 countries have raised about $83 billion.

One of the most intensive areas remains drug discovery, where companies unleash algorithms to screen potential drug candidates at an unprecedented speed and breadth that was impossible just a few years ago. It has led to the discovery of a new antibiotic to fight superbugs. Theres even a chance AI could help fight the coronavirus pandemic.

There are several AI drug discovery startups among the AI 100: San Francisco-based Atomwise claims its deep convolutional neural network, AtomNet, screens more than 100 million compounds each day. Cyclica is an AI drug discovery company in Toronto that just announced it would apply its platform to identify and develop novel cannabinoid-inspired drugs for neuropsychiatric conditions such as bipolar disorder and anxiety.

And then theres OWKIN out of New York City, a startup that uses a type of machine learning called federated learning. Backed by Google, the companys AI platform helps train algorithms without sharing the necessary patient data required to provide the sort of valuable insights researchers need for designing new drugs or even selecting the right populations for clinical trials.

Privacy and data security are the focus of a number of AI cybersecurity startups, as hackers attempt to leverage artificial intelligence to launch sophisticated attacks while also trying to fool the AI-powered systems rapidly coming online.

I think this is an interesting field because its a bit of a cat and mouse game, noted Varadharajanis. As your cyber defenses get smarter, your cyber attacks get even smarter, and so its a constant game of whos going to match the other in terms of tech capabilities.

Few AI cybersecurity startups match Silicon Valley-based SentinelOne in terms of private capital. The company has raised more than $400 million, with a valuation of $1.1 billion following a $200 million Series E earlier this year. The companys platform automates whats called endpoint security, referring to laptops, phones, and other devices at the end of a centralized network.

Fellow AI 100 cybersecurity companies include Blue Hexagon, which protects the edge of the network against malware, and Abnormal Security, which stops targeted email attacks, both out of San Francisco. Just down the coast in Los Angeles is Obsidian Security, a startup offering cybersecurity for cloud services.

Deepfakes of videos and other types of AI-manipulated media where faces or voices are synthesized in order to fool viewers or listeners has been a different type of ongoing cybersecurity risk. However, some firms are swapping malicious intent for benign marketing and entertainment purposes.

Now anyone can be a supermodel thanks to Superpersonal, a London-based AI startup that has figured out a way to seamlessly swap a users face onto a fashionista modeling the latest threads on the catwalk. The most obvious use case is for shoppers to see how they will look in a particular outfit before taking the plunge on a plunging neckline.

Another British company called Synthesia helps users create videos where a talking head will deliver a customized speech or even talk in a different language. The startups claim to fame was releasing a campaign video for the NGO Malaria Must Die showing soccer star David Becham speak in nine different languages.

Theres also a Seattle-based company, Wellsaid Labs, which uses AI to produce voice-over narration where users can choose from a library of digital voices with human pitch, emphasis, and intonation. Because every narrator sounds just a little bit smarter with a British accent.

Speaking of smarter: A handful of AI 100 startups are helping create the smart city of the future, where a digital web of sensors, devices, and cloud-based analytics ensure that nobody is ever stuck in traffic again or without an umbrella at the wrong time. At least thats the dream.

A couple of them are directly connected to Google subsidiary Sidewalk Labs, which focuses on tech solutions to improve urban design. A company called Replica was spun out just last year. Its sort of SimCity for urban planning. The San Francisco startup uses location data from mobile phones to understand how people behave and travel throughout a typical day in the city. Those insights can then help city governments, for example, make better decisions about infrastructure development.

Denver-area startup AMP Robotics gets into the nitty gritty details of recycling by training robots on how to recycle trash, since humans have largely failed to do the job. The U.S. Environmental Protection Agency estimates that only about 30 percent of waste is recycled.

Some people might complain that weather forecasters dont even do that well when trying to predict the weather. An Israeli AI startup, ClimaCell, claims it can forecast rain block by block. While the company taps the usual satellite and ground-based sources to create weather models, it has developed algorithms to analyze how precipitation and other conditions affect signals in cellular networks. By analyzing changes in microwave signals between cellular towers, the platform can predict the type and intensity of the precipitation down to street level.

And those are just some of the highlights of what some of the worlds most promising AI startups are doing.

You have companies optimizing mining operations, warehouse logistics, insurance, workflows, and even working on bringing AI solutions to designing printed circuit boards, Varadharajanis said. So a lot of creative ways in which companies are applying AI to solve different issues in different industries.

Image Credit: Butterfly Network

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The Top 100 AI Startups Out There Now, and What They're Working On - Singularity Hub

MADRID, SPAIN - MARCH 28: Health personnel are seen outside the emergency entrance of the Severo ... [+] Ochoa Hospital on March 28, 2020 in Madrid, Spain. Spain plans to continue its quarantine measures at least through April 11. The Coronavirus (COVID-19) pandemic has spread to many countries across the world, claiming over 20,000 lives and infecting hundreds of thousands more. (Photo by Carlos Alvarez/Getty Images)

AI (Artificial Intelligence) has a long history, going back to the 1950s when the computer industry started. Its interesting to note that much of the innovation came from government programs, not private industry.This was all about how to leverage technologies to fight the Cold War and put a man on the moon.

The impact of these program would certainly be far-reaching.They would lead to the creation of the Internet and the PC revolution.

So fast forward to today: Could the COVID-19 pandemic have a similar impact? Might it be our generations Space Race?

I think so. And of course, its just not the US this time. This is about a worldwide effort.

Wide-scale availability of data will be key.The White House Office of Science and Technology has formed the Covid-19 Open Research Dataset, which has over 24,000 papers and is constantly being updated.This includes the support of the National Library of Medicine (NLM), National Institutes of Health (NIH), Microsoft and the Allen Institute for Artificial Intelligence.

This database helps scientists and doctors create personalized, curated lists of articles that might help them, and allows data scientists to apply text mining to sift through this prohibitive volume of information efficiently with state-of-the-art AI methods, said Noah Giansiracusa, who is the Assistant Professor at Bentley University.

Yet there needs to be an organized effort to galvanize AI experts to action.The good news is that there are already groups emerging.For example, there is the C3.ai Digital Transformation Institute, which is a new consortium of research universities, C3.ai (a top AI company) and Microsoft.The organization will be focused on using AI to fight pandemics.

There are even competitions being setup to stir innovation.One is Kaggles COVID-19 Open Research Dataset Challenge, which is a collaboration with the NIH and White House.This will be about leveraging Kaggles 4+ million community of data scientists.The first contest was to help provide better forecasts of the spread of COVID-19 across the world.

Next, the Decentralized Artificial Intelligence Alliance is putting together Covidathon, an AI hackathon to fight the pandemic coordinated by SingularityNET and Ocean Protocol.The organization has more than 50 companies, labs and nonprofits.

And then there is MIT Solve, which is a marketplace for social impact innovation.It has established the Global Health Security & Pandemics Challenge.In fact, a member of this organization, Ada Health, has developed an AI-powered COVID-19 personalized screening test.

AI tools and infrastructure services can be costly.This is especially the case for models that target complex areas like medical research.

But AI companies have stepped upthat is, by eliminating their fees:

DarwinAI's COVID-19 neural network

Patient care is an area where AI could be essential.An example of this is Biofourmis.In a two-week period, this startup created a remote monitoring system that has a biosensor for a patients arm and an AI application to help with the diagnosis.In other words, this can help reduce infection rates for doctors and medical support personnel.Keep in mind thatin Chinaabout 29% of COVID-19 deaths were healthcare workers.

Another promising innovation to help patients is from Vital. The founders are Aaron Patzer, who is the creator of Mint.com, and Justin Schrager, an ER doc.Their company uses AI and NLP (Natural Language Processing) to manage overloaded hospitals.

Vital is now devoting all its resources to create C19check.com.The app, which was built in a partnership with Emory Department of Emergency Medicine's Health DesignED Center and the Emory Office of Critical Event Preparedness and Response, provides guidance to the public for self-triage before going to the hospital.So far, its been used by 400,000 people.

And here are some other interesting patient care innovations:

While drug discovery has made many advances over the years, the process can still be slow and onerous.But AI can help out.

For example, a startup that is using AI to accelerate drug development is Gero Pte. It has used the technology to better isolate compounds for COVID-19 by testing treatments that are already used in humans.

Mapping the virus genome has seemed to happen very quickly since the outbreak, said Vadim Tabakman, who is the Director of technical evangelism at Nintex.Leveraging that information with Machine Learning to explore different scenarios and learn from those results could be a game changer in finding a set of drugs to fight this type of outbreak.Since the world is more connected than ever, having different researchers, hospitals and countries, providing data into the datasets that get processed, could also speed up the results tremendously.

Tom (@ttaulli) is the author of Artificial Intelligence Basics: A Non-Technical Introduction and The Robotic Process Automation Handbook: A Guide to Implementing RPA Systems.

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AI (Artificial Intelligence) Companies That Are Combating The COVID-19 Pandemic - Forbes

Artificial intelligence (AI) has the potential to help us tackle the pressing issues raised by the COVID-19 pandemic. It is not the technology itself, though, that will make the difference but rather the knowledge and creativity of the humans who use it.

Indeed, the COVID-19 crisis will likely expose some of the key shortfalls of AI. Machine learning, the current form of AI, works by identifying patterns in historical training data. When used wisely, AI has the potential to exceed humans not only through speed but also by detecting patterns in that training data that humans have overlooked.

However, AI systems need a lot of data, with relevant examples in that data, in order to find these patterns. Machine learning also implicitly assumes that conditions today are the same as the conditions represented in the training data. In other words, AI systems implicitly assume that what has worked in the past will still work in the future.

A new strain of Coronavirus, COVID 19, is spreading around the world, causing deaths and major disruption to the global economy.

Responding to this crisis requires global cooperation among governments, international organizations and the business community, which is at the centre of the World Economic Forums mission as the International Organization for Public-Private Cooperation.

The Forum has created the COVID Action Platform, a global platform to convene the business community for collective action, protect peoples livelihoods and facilitate business continuity, and mobilize support for the COVID-19 response. The platform is created with the support of the World Health Organization and is open to all businesses and industry groups, as well as other stakeholders, aiming to integrate and inform joint action.

As an organization, the Forum has a track record of supporting efforts to contain epidemics. In 2017, at our Annual Meeting, the Coalition for Epidemic Preparedness Innovations (CEPI) was launched bringing together experts from government, business, health, academia and civil society to accelerate the development of vaccines. CEPI is currently supporting the race to develop a vaccine against this strand of the coronavirus.

What does this have to do with the current crisis? We are facing unprecedented times. Our situation is jarringly different from that of just a few weeks ago. Some of what we need to try today will have never been tried before. Similarly, what has worked in the past may very well not work today.

Humans are not that different from AI in these limitations, which partly explains why our current situation is so daunting. Without previous examples to draw on, we cannot know for sure the best course of action. Our traditional assumptions about cause and effect may no longer hold true.

Humans have an advantage over AI, though. We are able to learn lessons from one setting and apply them to novel situations, drawing on our abstract knowledge to make best guesses on what might work or what might happen. AI systems, in contrast, have to learn from scratch whenever the setting or task changes even slightly.

The COVID-19 crisis, therefore, will highlight something that has always been true about AI: it is a tool, and the value of its use in any situation is determined by the humans who design it and use it. In the current crisis, human action and innovation will be particularly critical in leveraging the power of what AI can do.

One approach to the novel situation problem is to gather new training data under current conditions. For both human decision-makers and AI systems alike, each new piece of information about our current situation is particularly valuable in informing our decisions going forward. The more effective we are at sharing information, the more quickly our situation is no longer novel and we can begin to see a path forward.

Projects such as the COVID-19 Open Research Dataset, which provides the text of over 24,000 research papers, the COVID-net open-access neural network, which is working to collaboratively develop a system to identify COVID-19 in lung scans, and an initiative asking individuals to donate their anonymized data, represent important efforts by humans to pool data so that AI systems can then sift through this information to identify patterns.

Global spread of COVID-19

Image: World Economic Forum

A second approach is to use human knowledge and creativity to undertake the abstraction that the AI systems cannot do. Humans can discern between places where algorithms are likely to fail and situations in which historical training data is likely still relevant to address critical and timely issues, at least until more current data becomes available.

Such systems might include algorithms that predict the spread of the virus using data from previous pandemics or tools that help job seekers identify opportunities that match their skillsets. Even though the particular nature of COVID-19 is unique and many of the fundamental rules of the labour market are not operating, it is still possible to identify valuable, although perhaps carefully circumscribed, avenues for applying AI tools.

Efforts to leverage AI tools in the time of COVID-19 will be most effective when they involve the input and collaboration of humans in several different roles. The data scientists who code AI systems play an important role because they know what AI can do and, just as importantly, what it cant. We also need domain experts who understand the nature of the problem and can identify where past training data might still be relevant today. Finally, we need out-of-the-box thinkers who push us to move beyond our assumptions and can see surprising connections.

Toronto-based startup Bluedot is an example of such a collaboration. In December it was one of the first to identify the emergence of a new outbreak in China. Its system relies on the vision of its founder, who believed that predicting outbreaks was possible, and combines the power several different AI tools with the knowledge of epidemiologists who identified where and how to look for evidence of emerging diseases. These epidemiologists also verify the results at the end.

Reinventing the rules is different from breaking the rules, though. As we work to address our current needs, we must also keep our eye on the long-term consequences. All of the humans involved in developing AI systems need to maintain ethical standards and consider possible unintended consequences of the technologies they create. While our current crisis is very pressing, we cannot sacrifice our fundamental principles to address it.

The key takeaway is this: Despite the hype, there are many ways that humans in which still surpass the capabilities of AI. The stunning advances that AI has made in recent years are not an inherent quality of the technology, but rather a testament to the humans who have been incredibly creative in how they use a tool that is mathematically and computationally complex and yet at its foundation still quite simple and limited.

As we seek to move rapidly to address our current problems, therefore, we need to continue to draw on this human creativity from all corners, not just the technology experts but also those with knowledge of the settings, as well as those who challenge our assumptions and see new connections. It is this human collaboration that will enable AI to be the powerful tool for good that it has the potential to be.

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Matissa Hollister, Assistant Professor of Organizational Behaviour, McGill University

The views expressed in this article are those of the author alone and not the World Economic Forum.

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COVID-19: AI can help - but the right human input is key - World Economic Forum

Given the outsized hold Artificial Intelligence (AI) technology has acquired on public imagination of late, it comes as no surprise that many are wondering whatAI can do for the public health crisis wrought by the COVID-19 coronavirus.

A casual search of AI and COVID-19 already returns a plethora of news stories, many of them speculative. While AI technology is not ready to help with the magical discovery of a new vaccine, there are important ways it can assist in this fight.

Controlling epidemics is, in large part, based on laborious contact tracing and using that information to predict the spread. We live in a time in which we constantly leave digital footprints through our daily life and interactions. These massive troves of data can be analyzed with AI technologies for detection, contact tracing and to find infection clusters, spread patterns and identify high-risk patients.

There is some evidence that AI techniques analyzing news feeds and social media data were not too far behind humans in originally detecting the COVID-19 outbreak in Wuhan. China seems not only to have used existing digital traces but also enforced additional ones; for example, citizens in Nanjing are required to register their presence in subway trains and many shops by scanning QR codes with their cellphones. Singapore, lauded for its effective containment of the virus without widespread lockdowns, has used public cameras to trace the interaction patterns of the infected, and even introduced a crowd-sourced app for voluntary contact tracing. In the U.S., research efforts are underway to mine body temperature and heart-rate data from wearables for early detection of COVID-19 infection.

It is widely feared that COVID-19 cases, at their peak, will overwhelm medical infrastructure in many cities. Evidence from Hubei, China, and Lombardy, Italy, do indeed support this fear. One way to alleviate this situation is to adopt novel methods of remotely providing medical help. The basic infrastructure for telemedicine has existed for a long time but has been a hard-sell from consumer, provider and regulatory points of view until now. Already, the U.S. has waived regulations to allow doctors to practice across state boundaries; the Department of Health and Human Services (HHS) has also announced that it will not levy penalties on medical providers using certain virtual communication tools, such as Skype and FaceTime, to connect with patients.

AI technologies certainly can help as a force-multiplier here, as front-line medical decision support tools for patient-provider matching, triage and even in faster diagnosis. For example, the Chinese company Alibaba claims rapid diagnostic image analytics for chest CT scans; China also has leveraged robots in disinfection of public spaces. Remote tele-presence robots increasingly could be leveraged to bring virtual movement and solace to people in forced medical quarantines.

AI technologies already have been enablers of, and defenders against, fake news. In the context of this pandemic, our incomplete knowledge coupled with angst has led to an infodemic of unreliable/fake information about coping with the outbreak, often spread by well-meaning (if gullible) people. AI technologies certainly can be of help here, both in flagging stories of questionable lineage and pointing to more trusted information sources.

AI also can be used to distill COVID-19-related information. A prominent example here is a White House Office of Science and Technology Policy-supported effort to use natural language-processing technologies to mine the stream of research papers relevant to the COVID-19 virus, with the aim of helping scientists quickly gain insight and spot trends within the research. There is some hope that such distillation can help in vaccine discovery efforts, too.

Suppression by social distancing has emerged as the most promising way to stem the tide of infection. It is clear, however, that social distancing like sticking to a healthy diet runs very much counter to our natural impulses. Short of draconian state enforcement, what can we do to increase the chances that people follow the best practices? One way AI can help here is via micro-targeted behavioral nudges. Like it or not, AI technologies already harvest vast troves of user profiles via our digital footprints and weaponize those for targeted ads. The same technologies can be readily rejiggered for subliminal micro-targeted social distancing messages that could include distracting us from cabin fever. There already is some evidence that mild nudging can even reduce the sharing of misinformation.

Lockdowns and social distancing measures are affecting the education of millions of schoolchildren. Tutoring services assisted by AI technologies can help significantly when students are stuck at home. China reportedly has relied on the help of online AI-based tutoring companies such as Squirrel AI to engage some of its millions of schoolchildren in lockdown.

And while self-driving cars remain a distant dream, delivering essential goods to people via deserted streets certainly could be within reach. Depending on how long shelter-at-home continues, we might rely increasingly on such technologies to transport critical personnel and goods.

Some of these potential uses of AI are controversial, as they infringe on privacy and civil liberties or reflect the very type of applications that the AI ethics community has resisted. Do we really want our personal AI assistants to start nudging us subliminally? Should we support increased cellphone tracking for infection control? It will be interesting to see to what extent society is willing to adopt them.

Indeed, our readiness to try almost anything to fight this unprecedented viral war is opening an inadvertent window into how we might handle the worries surrounding an AI-enabled future. Ideas such as universal basic income (UBI) in the presence of widespread technological unemployment, or concerns about diminished privacy thanks to widespread AI-based surveillance all are coming to the fore.

China has mobilized state resources to feed its quarantined population and used extensive cellphone tracking to analyze the spread of the virus. Israel is reportedly using cellphone tracking to ensure quarantines, as did Taiwan. The U.S. is considering UBI-like ideas e.g., providing thousand-dollar checks to many adults effectively unemployed during the pandemic and is reportedly mulling cellphone-based tracking to get people to follow social distancing guidelines.

Once such practices are adopted, they will no longer just be theoretical constructs. Some or all of them will become part of our society beyond this war on the virus, just as many Great Depression-era programs became part of our social fabric. The possibility that our choices in this time of crisis can change our society in crucial ways is raising alarms and calls for circumspection. Yet, to what extent civil society is likely to pause for circumspection at the height of this execution imperative remains to be seen.

Subbarao Kambhampati, PhD, is a professor of computer science at Arizona State University and chief AI officer for AI Foundation, which focuses on the responsible development of AI technologies. He served as president and is now past-president of the Association for the Advancement of Artificial Intelligence and was a founding board member of Partnership on AI. He can be followed on Twitter@rao2z.

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Enlisting AI in our war on coronavirus: Potential and pitfalls | TheHill - The Hill

The newly-formed C3.ai Digital Transformation Institute has an open call for proposals to mitigate the COVID-19 epidemic using artificial intelligence and machine learning.

With the coronavirus impacting most of the world, the medical community is hard at work trying to come up with some type of magic bullet that will stop the pandemic from propagating. Can artificial intelligence (AI) and machine learning (ML) help nurture a solution? That's what Microsoft and a host of top universities are hoping.

In a blog post published last week, Microsoft detailed the creation of the C3.ai Digital Transformation Institute (C3.ai DTI), a consortium of scientists, researchers, innovators, and executives from the academic and corporate worlds whose mission it is to push AI to achieve social and economic benefits. As such, C3.ai DTI will sponsor and fund scientists and researchers to spur the digital transformation of business, government, and society.

Created by Microsoft, AI software provider C3.ai, and several leading universities, C3.ai DTI already has the first task on its agenda--to harness the power of AI to combat the coronavirus.

SEE:Coronavirus: Critical IT policies and tools every business needs(TechRepublic Premium)

Known as "AI Techniques to Mitigate Pandemic," C3.ai DTI's first call for research proposals is asking scholars, developers, and researchers to "embrace the challenge of abating COVID-19 and advance the knowledge, science, and technologies for mitigating future pandemics using AI." Researchers are free to develop their own topics in response to this subject, but the consortium outlined 10 different areas open for consideration:

"We are collecting a massive amount of data about MERS, SARS, and now COVID-19," Condoleezza Rice, former US Secretary of State, said in the blog post. "We have a unique opportunity before us to apply the new sciences of AI and digital transformation to learn from these data how we can better manage these phenomena and avert the worst outcomes for humanity."

This first call is currently open with a deadline of May 1, 2020. Interested participants can check the C3.ai DTI website to learn about the process and find out how to submit their proposals. Selected proposals will be announced by June 1, 2020.

The group will fund as much as $5.8 million in awards for this first call, with cash awards ranging from $100,000 to $500,000 each. Recipients will also receive cloud computing, supercomputing, data access, and AI software resources and technical support provided by Microsoft and C3.ai. Specifically, those with successful proposals will get unlimited use of the C3 AI Suite, access to the Microsoft Azure cloud platform, and access to the Blue Waters supercomputer at the National Center for Super Computing Applicationsat the University of Illinois Urbana-Champaign (UIUC).

To fund the institute, C3.ai will provide $57,250,000 over the first five years of operation. C3.ai and Microsoft will contribute an additional $310 million, which includes use of the C3 AI Suite and Microsoft Azure. The universities involved in the consortium include the UIUC; the University of California, Berkeley; Princeton University; the University of Chicago; the Massachusetts Institute of Technology; and Carnegie Mellon University.

Beyond funding successful research proposals, Microsoft said that C3.ai DTI will generate new ideas for the use of AI and ML through ongoing research, visiting professors and research scholars, and faculty and scholars in residence, many of whom will come from the member universities.

More specifically, the group will focus its research on AI, ML, Internet of Things, Big Data Analytics, human factors, organizational behavior, ethics, and public policy. This research will examine new business models, develop ways for creating change within organizations, analyze methods to protect privacy, and ramp up the conversations around the ethics and public policy of AI.

"In these difficult times, we need--now more than ever--to join our forces with scholars, innovators, and industry experts to propose solutions to complex problems," Gwenalle Avice-Huet, Executive Vice President of ENGIE, said. "I am convinced that digital, data science, and AI are a key answer. The C3.ai Digital Transformation Institute is a perfect example of what we can do together to make the world better."

Learn the latest news and best practices about data science, big data analytics, and artificial intelligence. Delivered Mondays

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Microsoft teams up with leading universities to tackle coronavirus pandemic using AI - TechRepublic

A new reinforcement-learning algorithm has learned to optimize the placement of components on a computer chip to make it more efficient and less power-hungry.

3D Tetris: Chip placement, also known as chip floor planning, is a complex three-dimensional design problem. It requires the careful configuration of hundreds, sometimes thousands, of components across multiple layers in a constrained area. Traditionally, engineers will manually design configurations that minimize the amount of wire used between components as a proxy for efficiency. They then use electronic design automation software to simulate and verify their performance, which can take up to 30 hours for a single floor plan.

Time lag: Because of the time investment put into each chip design, chips are traditionally supposed to last between two and five years. But as machine-learning algorithms have rapidly advanced, the need for new chip architectures has also accelerated. In recent years, several algorithms for optimizing chip floor planning have sought to speed up the design process, but theyve been limited in their ability to optimize across multiple goals, including the chips power draw, computational performance, and area.

Intelligent design: In response to these challenges, Google researchers Anna Goldie and Azalia Mirhoseini took a new approach: reinforcement learning. Reinforcement-learning algorithms use positive and negative feedback to learn complicated tasks. So the researchers designed whats known as a reward function to punish and reward the algorithm according to the performance of its designs. The algorithm then produced tens to hundreds of thousands of new designs, each within a fraction of a second, and evaluated them using the reward function. Over time, it converged on a final strategy for placing chip components in an optimal way.

Validation: After checking the designs with the electronic design automation software, the researchers found that many of the algorithms floor plans performed better than those designed by human engineers. It also taught its human counterparts some new tricks, the researchers said.

Production line: Throughout the field's history, progress in AI has been tightly interlinked with progress in chip design. The hope is this algorithm will speed up the chip design process and lead to a new generation of improved architectures, in turn accelerating AI advancement.

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Google is using AI to design chips that will accelerate AI - MIT Technology Review

Reinforcement learning algorithms may be the next best thing since sliced bread for engineers looking to improve chip placement.

Researchers from Google have created a new algorithm that has learned how to optimize the placement of the components in a computer chip, so as to make it more efficient and less power-hungry.

SEE ALSO: WILL AI AND GENERATIVE DESIGN STEAL OUR ENGINEERING JOBS?

Typically, engineers can spend up to 30 hours configuring a single floor plan of chip placement, or chip floor planning. This complicated 3D design problem requires the configuration of hundreds, or even thousands, of components across a number of layers in a constrained area. Engineers will manually design configurations to minimize the number of wires used between components as a proxy for efficiency.

Because this is time-consuming, these chips are designed to only last between two and five years. However, as machine-learning algorithms keep improving year upon year, a need for new chip architectures has also arisen.

Facing these challenges, Google researchers Anna Goldie and Azalia Mirhoseini, have looked into reinforcement learning. These types of algorithms use positive and negative feedback in order to learn new and complicated tasks. Thus, the algorithm is either "rewarded" or "punished" depending on how well it learns a task. Following this, it then creates tens to hundreds of thousands of new designs. Ultimately, it creates an optimal strategy on how to place these chip components.

After their tests, the researchers checked their designs with the electronic design automation software and discovered that their method's floor planning was much more effective than the ones human engineers designed. Moreover, the system was able to teach its human workers a new trick or two.

Progress in AI has been largely interlinked with progress is computer chip design. The researchers' hope is that their new algorithm will assist in speeding up the chip design process and pave the way for new and improved architectures, which would ultimately accelerate AI.

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Google Researchers Create AI-ception with an AI Chip That Speeds Up AI - Interesting Engineering

A paper coauthored by over 112 researchers across 160 data and social science teams found that AI and statistical models, when used to predict six life outcomes for children, parents, and households, werent very accurate even when trained on 13,000 data points from over 4,000 families. They assert that the work is a cautionary tale on the use of predictive modeling, especially in the criminal justice system and social support programs.

Heres a setting where we have hundreds of participants and a rich data set, and even the best AI results are still not accurate, said study co-lead author Matt Salganik, a professor of sociology at Princeton and interim director of the Center for Information Technology Policy at the Woodrow Wilson School of Public and International Affairs. These results show us that machine learning isnt magic; there are clearly other factors at play when it comes to predicting the life course.

The study, which was published this week in the journal Proceedings of the National Academy of Sciences, is the fruit of the Fragile Families Challenge, a multi-year collaboration that sought to recruit researchers to complete a predictive task by predicting the same outcomes using the same data. Over 457 groups applied, of which 160 were selected to participate, and their predictions were evaluated with an error metric that assessed their ability to predict held-out data (i.e., data held by the organizer and not available to the participants).

The Challenge was an outgrowth of the Fragile Families Study (formerly Fragile Families and Child Wellbeing Study) based at Princeton, Columbia University, and the University of Michigan, which has been studying a cohort of about 5,000 children born in 20 large American cities between 1998 and 2000. Its designed to oversample births to unmarried couples in those cities, and to address four questions of interest to researchers and policymakers:

When we began, I really didnt know what a mass collaboration was, but I knew it would be a good idea to introduce our data to a new group of researchers: data scientists, said Sara McLanahan, the William S. Tod Professor of Sociology and Public Affairs at Princeton. The results were eye-opening.

The Fragile Families Study data set consists of modules, each of which is made up of roughly 10 sections, where each section includes questions about a topic asked of the childrens parents, caregivers, teachers, and the children themselves. For example, a mother who recently gave birth might be asked about relationships with extended kin, government programs, and marriage attitudes, while a 9-year-old child might be asked about parental supervision, sibling relationships, and school. In addition to the surveys, the corpus contains the results of in-home assessments, including psychometric testing, biometric measurements, and observations of neighborhoods and homes.

The goal of the Challenge was to predict the social outcomes of children aged 15 years, which encompasses 1,617 variables. From the variables, six were selected to be the focus:

Contributing researchers were provided anonymized background data from 4,242 families and 12,942 variables about each family, as well as training data incorporating the six outcomes for half of the families. Once the Challenge was completed, all 160 submissions were scored using the holdout data.

In the end, even the best of the over 3,000 models submitted which often used complex AI methods and had access to thousands of predictor variables werent spot on. In fact, they were only marginally better than linear regression and logistic regression, which dont rely on any form of machine learning.

Either luck plays a major role in peoples lives, or our theories as social scientists are missing some important variable, added McLanahan. Its too early at this point to know for sure.

Measured by the coefficient of determination, or the correlation of the best models predictions with the ground truth data, material hardship i.e., whether 15-year-old childrens parents suffered financial issues was .23, or 23% accuracy. GPA predictions were 0.19 (19%), while grit, eviction, job training, and layoffs were 0.06 (6%), 0.05 (5%), and 0.03 (3%), respectively.

The results raise questions about the relative performance of complex machine-learning models compared with simple benchmark models. In the Challenge, the simple benchmark model with only a few predictors was only slightly worse than the most accurate submission, and it actually outperformed many of the submissions, concluded the studys coauthors. Therefore, before using complex predictive models, we recommend that policymakers determine whether the achievable level of predictive accuracy is appropriate for the setting where the predictions will be used, whether complex models are more accurate than simple models or domain experts in their setting, and whether possible improvement in predictive performance is worth the additional costs to create, test, and understand the more complex model.

The research team is currently applying for grants to continue studies in this area, and theyve also published 12 of the teams results in a special issue of a journal called Socius, a new open-access journal from the American Sociological Association. In order to support additional research, all the submissions to the Challenge including the code, predictions, and narrative explanations will be made publicly available.

The Challenge isnt the first to expose the predictive shortcomings of AI and machine learning models. The Partnership on AI, a nonprofit coalition committed to the responsible use of AI, concluded in its first-ever report last year that algorithms are unfit to automate the pre-trial bail process or label some people as high-risk and detain them. The use of algorithms in decision making for judges has been known to produce race-based unfair results that are more likely to label African-American inmates as at risk of recidivism.

Its well-understood that AI has a bias problem. For instance, word embedding, a common algorithmic training technique that involves linking words to vectors, unavoidably picks up and at worst amplifies prejudices implicit in source text and dialogue. A recent study by the National Institute of Standards and Technology (NIST) found that many facial recognition systems misidentify people of color more often than Caucasian faces. And Amazons internal recruitment tool which was trained on resumes submitted over a 10-year period was reportedly scrapped because it showed bias against women.

A number of solutions have been proposed, from algorithmic tools to services that detect bias by crowdsourcing large training data sets.

In June 2019, working with experts in AI fairness, Microsoft revised and expanded the data sets it uses to train Face API, a Microsoft Azure API that provides algorithms for detecting, recognizing, and analyzing human faces in images. Last May, Facebook announced Fairness Flow, which automatically sends a warning if an algorithm is making an unfair judgment about a person based on their race, gender, or age. Google recently released the What-If Tool, a bias-detecting feature of the TensorBoard web dashboard for its TensorFlow machine learning framework. Not to be outdone, IBM last fall released AI Fairness 360, a cloud-based, fully automated suite that continually provides [insights] into how AI systems are making their decisions and recommends adjustments such as algorithmic tweaks or counterbalancing data that might lessen the impact of prejudice.

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Researchers find AI is bad at predicting GPA, grit, eviction, job training, layoffs, and material hardship - VentureBeat