As the COVID-19 coronavirus outbreak continues to spread across the globe, companies and researchers are looking to use artificial intelligence as a way of addressing the challenges of the virus. Here are just some of the projects using AI to address the coronavirus outbreak.

Using AI to find drugs that target the virus

A number of research projects are using AI to identify drugs that were developed to fight other diseases but which could now be repurposed to take on coronavirus. By studying the molecular setup of existing drugs with AI, companies want to identify which ones might disrupt the way COVID-19 works.

BenevolentAI, a London-based drug-discovery company, began turning its attentions towards the coronavirus problem in late January. The company's AI-powered knowledge graph can digest large volumes of scientific literature and biomedical research to find links between the genetic and biological properties of diseases and the composition and action of drugs.

EE: How to implement AI and machine learning (ZDNet special report) | Download the report as a PDF (TechRepublic)

The company had previously been focused on chronic disease, rather than infections, but was able to retool the system to work on COVID-19 by feeding it the latest research on the virus. "Because of the amount of data that's being produced about COVID-19 and the capabilities we have in being able to machine-read large amounts of documents at scale, we were able to adapt [the knowledge graph] so to take into account the kinds of concepts that are more important in biology, as well as the latest information about COVID-19 itself," says Olly Oechsle, lead software engineer at BenevolentAI.

While a large body of biomedical research has built up around chronic diseases over decades, COVID-19 only has a few months' worth of studies attached to it. But researchers can use the information that they have to track down other viruses with similar elements, see how they function, and then work out which drugs could be used to inhibit the virus.

"The infection process of COVID-19 was identified relatively early on. It was found that the virus binds to a particular protein on the surface of cells called ACE2. And what we could with do with our knowledge graph is to look at the processes surrounding that entry of the virus and its replication, rather than anything specific in COVID-19 itself. That allows us to look back a lot more at the literature that concerns different coronaviruses, including SARS, etc. and all of the kinds of biology that goes on in that process of viruses being taken in cells," Oechsle says.

The system suggested a number of compounds that could potentially have an effect on COVID-19 including, most promisingly, a drug called Baricitinib. The drug is already licensed to treat rheumatoid arthritis. The properties of Baricitinib mean that it could potentially slow down the process of the virus being taken up into cells and reduce its ability to infect lung cells. More research and human trials will be needed to see whether the drug has the effects AI predicts.

Shedding light on the structure of COVID-19

DeepMind, the AI arm of Google's parent company Alphabet, is using data on genomes to predict organisms' protein structure, potentially shedding light on which drugs could work against COVID-19.

DeepMind has released a deep-learning library calledAlphaFold, which uses neural networks to predict how the proteins that make up an organism curve or crinkle, based on their genome. Protein structures determine the shape of receptors in an organism's cells. Once you know what shape the receptor is, it becomes possible to work out which drugs could bind to them and disrupt vital processes within the cells: in the case of COVID-19, disrupting how it binds to human cells or slowing the rate it reproduces, for example.

Aftertraining up AlphaFold on large genomic datasets, which demonstrate the links between an organism's genome and how its proteins are shaped, DeepMind set AlphaFold to work on COVID-19's genome.

"We emphasise that these structure predictions have not been experimentally verified, but hope they may contribute to the scientific community's interrogation of how the virus functions, and serve as a hypothesis generation platform for future experimental work in developing therapeutics," DeepMind said. Or, to put it another way, DeepMind hasn't tested out AlphaFold's predictions outside of a computer, but it's putting the results out there in case researchers can use them to develop treatments for COVID-19.

Detecting the outbreak and spread of new diseases

Artificial-intelligence systems were thought to be among the first to detect that the coronavirus outbreak, back when it was still localised to the Chinese city of Wuhan, could become a full-on global pandemic.

It's thought that AI-driven HealthMap, which is affiliated with the Boston Children's Hospital,picked up the growing clusterof unexplained pneumonia cases shortly before human researchers, although it only ranked the outbreak's seriousness as 'medium'.

"We identified the earliest signs of the outbreak by mining in Chinese language and local news media -- WeChat, Weibo -- to highlight the fact that you could use these tools to basically uncover what's happening in a population," John Brownstein, professor of Harvard Medical School and chief innovation officer at Boston Children's Hospital, told the Stanford Institute for Human-Centered Artificial Intelligence's COVID-19 and AI virtual conference.

Human epidemiologists at ProMed, an infectious-disease-reporting group, published their own alert just half an hour after HealthMap, and Brownstein also acknowledged the importance of human virologists in studying the spread of the outbreak.

"What we quickly realised was that as much it's easy to scrape the web to create a really detailed line list of cases around the world, you need an army of people, it can't just be done through machine learning and webscraping," he said. HealthMap also drew on the expertise of researchers from universities across the world, using "official and unofficial sources" to feed into theline list.

The data generated by HealthMap has been made public, to be combed through by scientists and researchers looking for links between the disease and certain populations, as well as containment measures. The data has already been combined with data on human movements, gleaned from Baidu,to see how population mobility and control measuresaffected the spread of the virus in China.

HealthMap has continued to track the spread of coronavirus throughout the outbreak, visualising itsspread across the world by time and location.

Spotting signs of a COVID-19 infection in medical images

Canadian startup DarwinAI has developed a neural network that can screen X-rays for signs of COVID-19 infection. While using swabs from patients is the default for testing for coronavirus, analysing chest X-rays could offer an alternative to hospitals that don't have enough staff or testing kits to process all their patients quickly.

DarwinAI released COVID-Net as an open-source system, and "the response has just been overwhelming", says DarwinAI CEO Sheldon Fernandez. More datasets of X-rays were contributed to train the system, which has now learnt from over 17,000 images, while researchers from Indonesia, Turkey, India and other countries are all now working on COVID-19. "Once you put it out there, you have 100 eyes on it very quickly, and they'll very quickly give you some low-hanging fruit on ways to make it better," Fernandez said.

The company is now working on turning COVID-Net from a technical implementation to a system that can be used by healthcare workers. It's also now developing a neural network for risk-stratifying patients that have contracted COVID-19 as a way of separating those with the virus who might be better suited to recovering at home in self-isolation, and those who would be better coming into hospital.

Monitoring how the virus and lockdown is affecting mental health

Johannes Eichstaedt, assistant professor in Stanford University's department of psychology, has been examining Twitter posts to estimate how COVID-19, and the changes that it's brought to the way we live our lives, is affecting our mental health.

Using AI-driven text analysis, Eichstaedt queried over two million tweets hashtagged with COVID-related terms during February and March, and combined it with other datasets on relevant factors including the number of cases, deaths, demographics and more, to illuminate the virus' effects on mental health.

The analysis showed that much of the COVID-19-related chat in urban areas was centred on adapting to living with, and preventing the spread of, the infection. Rural areas discussed adapting far less, which the psychologist attributed to the relative prevalence of the disease in urban areas compared to rural, meaning those in the country have had less exposure to the disease and its consequences.

SEE:Coronavirus: Business and technology in a pandemic

There are also differences in how the young and old are discussing COVID-19. "In older counties across the US, there's talk about Trump and the economic impact, whereas in young counties, it's much more problem-focused coping; the one language cluster that stand out there is that in counties that are younger, people talk about washing their hands," Eichstaedt said.

"We really need to measure the wellbeing impact of COVID-19, and we very quickly need to think about scalable mental healthcare and now is the time to mobilise resources to make that happen," Eichstaedt told the Stanford virtual conference.

Forecasting how coronavirus cases and deaths will spread across cities and why

Google-owned machine-learning community Kaggle is setting a number of COVID-19-related challenges to its members, includingforecasting the number of cases and fatalities by cityas a way of identifying exactly why some places are hit worse than others.

"The goal here isn't to build another epidemiological model there are lots of good epidemiological models out there. Actually, the reason we have launched this challenge is to encourage our community to play with the data and try and pick apart the factors that are driving difference in transmission rates across cities," Kaggle's CEO Anthony Goldbloom told the Stanford conference.

Currently, the community is working on a dataset of infections in 163 countries from two months of this year to develop models and interrogate the data for factors that predict spread.

Most of the community's models have been producing feature-importance plots to show which elements may be contributing to the differences in cases and fatalities. So far, said Goldbloom, latitude and longitude are showing up as having a bearing on COVID-19 spread. The next generation of machine-learning-driven feature-importance plots will tease out the real reasons for geographical variances.

"It's not the country that is the reason that transmission rates are different in different countries; rather, it's the policies in that country, or it's the cultural norms around hugging and kissing, or it's the temperature. We expect that as people iterate on their models, they'll bring in more granular datasets and we'll start to see these variable-importance plots becoming much more interesting and starting to pick apart the most important factors driving differences in transmission rates across different cities. This is one to watch," Goldbloom added.

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AI and the coronavirus fight: How artificial intelligence is taking on COVID-19 - ZDNet

This article is part of our latest Artificial Intelligence special report, which focuses on how the technology continues to evolve and affect our lives.

Steve Jobs once described personal computing as a bicycle for the mind.

His idea that computers can be used as intelligence amplifiers that offer an important boost for human creativity is now being given an immediate test in the face of the coronavirus.

In March, a group of artificial intelligence research groups and the National Library of Medicine announced that they had organized the worlds scientific research papers about the virus so the documents, more than 44,000 articles, could be explored in new ways using a machine-learning program designed to help scientists see patterns and find relationships to aid research.

This is a chance for artificial intelligence, said Oren Etzioni, the chief executive of the Allen Institute for Artificial Intelligence, a nonprofit research laboratory that was founded in 2014 by Paul Allen, the Microsoft co-founder.

There has long been a dream of using A.I. to help with scientific discovery, and now the question is, can we do that?

The new advances in software applications that process human language lie at the heart of a long-running debate over whether computer technologies such as artificial intelligence will enhance or even begin to substitute for human creativity.

The programs are in effect artificial intelligence Swiss Army knives that can be repurposed for a host of different practical applications, ranging from writing articles, books and poetry to composing music, language translation and scientific discovery.

In addition to raising questions about whether machines will be able to think creatively, the software has touched off a wave of experimentation and has also raised questions about new challenges to intellectual property laws and concerns about whether they might be misused for spam, disinformation and fraud.

The Allen Institute program, Semantic Scholar, began in 2015. It is an early example of this new class of software that uses machine-learning techniques to extract meaning from and identify connections between scientific papers, helping researchers more quickly gain in-depth understanding.

Since then, there has been a rapid set of advances based on new language process techniques leading a variety of technology firms and research groups to introduce competing programs known as language models, each more powerful than the next.

What has been in effect an A.I. arms race reached a high point in February, when Microsoft introduced Turing-NLG (natural language generation), named after the British mathematician and computing pioneer Alan Turing. The machine-learning behemoth consists of 17 billion parameters, or weights, which are numbers that are arrived at after the program was trained on an immense library of human-written texts, effectively more than all the written material available on the internet.

As a result, significant claims have been made for the capability of language models, including the ability to write plausible-sounding sentences and paragraphs, as well as draw and paint and hold a believable conversation with a human.

Where weve seen the most interesting applications has really been in the creative space, said Ashley Pilipiszyn, a technical director at OpenAI, an independent research group based in San Francisco that was founded as a nonprofit research organization to develop socially beneficial artificial intelligence-based technology and later established a for-profit corporation.

Early last year, the group announced a language model called GPT-2 (generative pretrained transformer), but initially did not release it publicly, saying it was concerned about potential misuse in creating disinformation. But near the end of the year, the program was made widely available.

Everyone has innate creative capabilities, she said, and this is a tool that helps push those boundaries even further.

Hector Postigo, an associate professor at the Klein College of Media and Communication at Temple University, began experimenting with GPT-2 shortly after it was released. His first idea was to train the program to automatically write a simple policy statement about ethics policies for A.I. systems.

After fine-tuning GPT-2 with a large collection of human-written articles, position papers, and laws collected in 2019 on A.I., big data and algorithms, he seeded the program with a single sentence: Algorithmic decision-making can pose dangers to human rights.

The program created a short essay that began, Decision systems that assume predictability about human behavior can be prone to error. These are the errors of a data-driven society. It concluded, Recognizing these issues will ensure that we are able to use the tools that humanity has entrusted to us to address the most pressing rights and security challenges of our time.

Mr. Postigo said the new generation of tools would transform the way people create as authors.

We already use autocomplete all the time, he said. The cat is already out of the bag.

Since his first experiment, he has trained GPT-2 to compose classical music and write poetry and rap lyrics.

That poses the question of whether the programs are genuinely creative. And if they are able to create works of art that are indistinguishable from human works, will they devalue those created by humans?

A.I. researchers who have worked in the field for decades said that it was important to realize that the programs were simply assistive and that they were not creating artistic works or making other intellectual achievements independently.

The early signs are that the new tools will be quickly embraced. The Semantic Scholar coronavirus webpage was viewed more than 100,000 times in the first three days it was available, Dr. Etzioni said. Researchers at Google Health, Johns Hopkins University, the Mayo Clinic, the University of Notre Dame, Hewlett Packard Labs and IBM Research are using the service, among others.

Jerry Kaplan, an artificial-intelligence researcher who was involved with two of Silicon Valleys first A.I. companies, Symantec and Teknowledge during the 1980s, pointed out that the new language modeling software was actually just a new type of database retrieval technology, rather than an advance toward any kind of thinking machine.

Creativity is still entirely on the human side, he said. All this particular tool is doing is making it possible to get insights that would otherwise take years of study.

Although that may be true, philosophers have begun to wonder whether these new tools will permanently change human creativity.

Brian Smith, a philosopher and a professor of artificial intelligence at the University of Toronto, noted that although students are still taught how to do long division by hand, calculators now are universally used for the task.

We once used rooms full of human computers to do these tasks manually, he said, noting that nobody would want to return to that era.

In the future, however, it is possible that these new tools will begin to take over much of what we consider creative tasks such as writing, composing and other artistic ventures.

What we have to decide is, what is at the heart of our humanity that is worth preserving, he said.

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You Cant Spell Creative Without A.I. - The New York Times

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Twenty-five years after the adoption of the Beijing Declaration and Platform for Action, significant gender bias in existing social norms remains. For example, as recently as February 2020, the Indian Supreme Court had to remind the Indian government that its arguments for denying women command positions in the Army were based on stereotypes. And gender bias is not merely a male problem: a recent UNDP report entitled Tackling Social Norms found that about 90% of people (both men and women) hold some bias against women.

Gender bias and various forms of discrimination against women and girls pervades all spheres of life. Womens equal access to science and information technology is no exception. While the challenges posed by the digital divide and under-representation of women in STEM (science, technology, engineering and mathematics) continue, artificial intelligence (AI) and automation are throwing newer challenges to achieving substantive gender equality in the era of the Fourth Industrial Revolution.

If AI and automation are not developed and applied in a gender-responsive way, they are likely to reproduce and reinforce existing gender stereotypes and discriminatory social norms. In fact, this may already be happening (un)consciously. Let us consider a few examples:

Despite the potential for such gender bias, the growing crop of AI standards do not adequately integrate a gender perspective. For example, the Montreal Declaration for the Responsible Development of Artificial Intelligence does not make an explicit reference to integrating a gender perspective, while the AI4Peoples Ethical Framework for a Good AI Society mentions diversity/gender only once. Both the OECD Council Recommendation on AI and the G20 AI Principles stress the importance of AI contributing to reducing gender inequality, but provide no details on how this could be achieved.

The Responsible Machine Learning Principles do embrace bias evaluation as one of the principles. This siloed approach of embracing gender is also adopted by companies like Google and Microsoft, whose AI Principles underscore the need to avoid creating or reinforcing unfair bias and to treat all people fairly, respectively. Companies related to AI and automation should adopt a gender-response approach across all principles to overcome inherent gender bias. Google should, for example, embed a gender perspective in assessing which new technologies are socially beneficial or how AI systems are built and tested for safety.

What should be done to address the gender bias in AI and automation? The gender framework for the UN Guiding Principles on Business and Human Rights could provide practical guidance to states, companies and other actors. The framework involves a three-step cycle: gender-responsive assessment, gender-transformative measures and gender-transformative remedies. The assessment should be able to respond to differentiated, intersectional, and disproportionate adverse impacts on womens human rights. The consequent measures and remedies should be transformative in that they should be capable of bringing change to patriarchal norms, unequal power relations. and gender stereotyping.

States, companies and other actors can take several concrete steps. First, women should be active participantsrather than mere passive beneficiariesin creating AI and automation. Women and their experiences should be adequately integrated in all steps related to design, development and application of AI and automation. In addition to proactively hiring more women at all levels, AI and automation companies should engage gender experts and womens organisations from the outset in conducting human rights due diligence.

Second, the data that informs algorithms, AI and automation should be sex-disaggregated, otherwise the experiences of women will not inform these technological tools and in turn might continue to internalise existing gender biases against women. Moreover, even data related to women should be guarded against any inherent gender bias.

Third, states, companies and universities should plan for and invest in building capacity of women to achieve smooth transition to AI and automation. This would require vocational/technical training at both education and work levels.

Fourth, AI and automation should be designed to overcome gender discrimination and patriarchal social norms. In other words, these technologies should be employed to address challenges faced by women such as unpaid care work, gender pay gap, cyber bullying, gender-based violence and sexual harassment, trafficking, breach of sexual and reproductive rights, and under-representation in leadership positions. Similarly, the power of AI and automation should be employed to enhance womens access to finance, higher education and flexible work opportunities.

Fifth, special steps should be taken to make women aware of their human rights and the impact of AI and automation on their rights. Similar measures are needed to ensure that remedial mechanismsboth judicial and non-judicialare responsive to gender bias, discrimination, patriarchal power structures, and asymmetries of information and resources.

Sixth, states and companies should keep in mind the intersectional dimensions of gender discrimination, otherwise their responses, despite good intentions, will fall short of using AI and automation to accomplish gender equality. Low-income women, single mothers, women of colour, migrant women, women with disability, and non-heterosexual women all may be affected differently by AI and automation and would have differentiated needs or expectations.

Finally, all standards related to AI and automation should integrate a gender perspective in a holistic manner, rather than treating gender as merely a bias issue to be managed.

Technologies are rarely gender neutral in practice. If AI and automation continue to ignore womens experiences or to leave women behind, everyone will be worse off.

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Addressing the gender bias in artificial intelligence and automation - OpenGlobalRights

Artificial intelligence (AI) refers to software-based systems that use data inputs to make decisions on their own. Machine learning is an application of AI that gives computer systems the ability to learn and improve from data without being explicitly programmed.

2019 saw financial institutions explore a broad-range of possible AI use cases in both customer-facing and back-office processes, increasing budgets, headcounts, and partnerships. 2020 will see increased focus on breaking out the marketing story from actual business impact to place bigger bets in fewer areas. This will help banks scale proven AI across the enterprise to forge competitive advantage.

Artificial intelligence will re-invigorate digital money management, helping incumbents drip-feed highly personalised spending tips to build trust and engagement in the absence of in-person interaction. Features like predictive insights around cashflow shortfalls, alerts on upcoming bill payments, and various what if scenarios when trying on different financial products give customers transparency around their options and the risks they face. This service will render as an always-on, in-your-pocket, and predictive advisor.

AI-enhanced customer relationship management (CRM) will help digital banks optimise product recommendations to rival the conversion rates of best-in-class online retailers. These product suggestions wont render as sales, but rather valuable advice received, such as a pre-approved loan before a cash shortfall or an option to remortgage to fund home improvements. This will help incumbents build customer advocacy and trust as new entrants vie for attention.

AI-powered onboarding, when combined with voice and facial recognition technologies, will help incumbents make themselves much easier to do business with, especially at the initial point of conversion but also thereafter at each moment of authentication. AI will offer particular support through Know Your Customer (KYC) processes, helping incumbents keep pace with new entrants. Standard Bank in South Africa, for example, used WorkFusions AI capabilities to reduce the customer onboarding time from 20 days to just five minutes.

Banks heavy compliance burden will continue to drive AI. Last year, large global banks such as OCBC Bank, Commonwealth Bank, Wells Fargo, and HSBC made big investments in areas such as automated data management, reporting, anti-money laundering (AML), compliance, automated regulation interpretation, and mapping. Increasingly partnering with artificial intelligence-enabled regtech firms will help incumbents reduce operational risk and enhance reporting quality.

As artificial intelligence becomes more embedded into all areas of customers lives, concerns around the black box driving decisions will grow, with more demands for explainable AI. As it is, customers with little or no digital footprint are less visible to applications that rely on data to profile people and assess risk. Traditional banks credit risk algorithms often disproportionately exclude black and Hispanic groups in the US as well as women, because these groups have historically earned less over their lifetimes.

In 2020, senior management will be held directly accountable for the decisions of AI-enabled algorithms. This will drive increased focus on data quality to feed the algorithms and perhaps limits to the use of the most dynamic machine learning because of their regulatory opacity.

This is an edited extract from the Banking & Payments Predictions 2020 Thematic Research report produced by GlobalData Thematic Research.

GlobalData is this websites parent business intelligence company.

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Banking and payments predictions 2020: Artificial intelligence - Verdict

The Artificial Intelligence (AI) tool has been shown to accurately predict which patients that have been newly infected with the COVID-19 virus would go on to develop severe respiratory disease.

Named SARS-CoV-2, the new novel coronavirus, as of March 30, had infected 735,560 patients worldwide. According to the World Health Organization, the illness has caused more than 34,830 deaths to date, more often among older patients with underlying health conditions.

The study, published in the journalComputers, Materials & Continua, was led by NYU Grossman School of Medicine and the Courant Institute of Mathematical Sciences at New York University, in partnership with Wenzhou Central Hospital and Cangnan Peoples Hospital, both in Wenzhou, China.

The study has revealed the best indicators of future severity and found that they were not as expected.

Corresponding author Megan Coffee, clinical assistant professor in the Division of Infectious Disease & Immunology at NYU Grossman School of Medicine, said: While work remains to further validate our model, it holds promise as another tool to predict the patients most vulnerable to the virus, but only in support of physicians hard-won clinical experience in treating viral infections.

Our goal was to design and deploy a decision-support tool using AI capabilities mostly predictive analytics to flag future clinical coronavirus severity, says co-author Anasse Bari, PhD, a clinical assistant professor in Computer Science at the Courant institute. We hope that the tool, when fully developed, will be useful to physicians as they assess which moderately ill patients really need beds, and who can safely go home, with hospital resources stretched thin.

For the study, demographic, laboratory, and radiological findings were collected from 53 patients as each tested positive in January 2020 for COVID-19 at the two Chinese hospitals. In a minority of patients, severe symptoms developed with a week, including pneumonia.

The researchers wanted to find out whether AI techniques could help to accurately predict which patients with the virus would go on to develop Acute Respiratory Distress Syndrome or ARDS, the fluid build-up in the lungs that can be fatal in the elderly.

To do this they designed computer models that make decisions based on the data fed into them, with programmes getting smarter the more data they consider. Specifically, the current study used decision trees that track series of decisions between options, and that model the potential consequences of choices at each step in a pathway.

The AI tool found that changes in three features levels of the liver enzyme alanine aminotransferase (ALT), reported myalgia, and haemoglobin levels were most accurately predictive of subsequent, severe disease. Together with other factors, the team reported being able to predict risk of ARDS with up to 80% accuracy.

ALT levels, which rise dramatically as diseases like hepatitis damage the liver, were only a bit higher in patients with COVID-19, but still featured prominently in prediction of severity. In addition, deep muscle aches (myalgia) were also more commonplace and have been linked by past research to higher general inflammation in the body.

Lastly, higher levels of haemoglobin, the iron-containing protein that enables blood cells to carry oxygen to bodily tissues, were also linked to later respiratory distress. Could this be explained by other factors, like unreported smoking of tobacco, which has long been linked to increased haemoglobin levels?

Of the 33 patients at Wenzhou Central Hospital interviewed on smoking status, the two who reported having smoked, also reported that they had quit.

Limitations of the study, say the authors, included the relatively small data set and the limited clinical severity of disease in the population studied.

I will be paying more attention in my clinical practice to our data points, watching patients closer if they for instance complain of severe myalgia, adds Coffee. Its exciting to be able to share data with the field in real time when it can be useful. In all past epidemics, journal papers only published well after the infections had waned.

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How Artificial Intelligence is helping the fight against COVID-19 - Health Europa