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ARLINGTON, VA, May 19, 2020 (GLOBE NEWSWIRE) -- The artificial intelligence industry is rapidly growing, providing an opportunity to enhance government data security in the United States. Enabled Intelligence, Inc. and SourceAmerica have recently partnered to expand competitive integrative employment for professionals with disabilities.

Enabled Intelligence is an artificial intelligence company that provides highly secure and accurate data labeling services. SourceAmerica is a national nonprofit organization committed to providing employment opportunities for people with disabilities through its network of more than 600 community-based nonprofit agencies across the country. Together, they will work to recruit and train highly capable people with disabilities to join Enabled Intelligences growing tech workforce.

Enabled Intelligence is expanding its workforce to meet the U.S. governments rapidly increasing demand for secure high-quality data labeling to support artificial intelligence technology development. The Department of Defense, intelligence agencies and other federal programs are increasingly deploying emerging artificial intelligence technologies and accurately labeled data to train those systems. Enabled Intelligences workforce of highly-trained U.S. based employees provide the subject matter expertise and secure systems able to handle the government's most sensitive data.

We are honored to be working with SourceAmerica as we expand our integrated team including professionals with disabilities. People with disabilities are often overlooked as a resource but they are invaluable to us in their commitment to service and excellent labeling skills, explained Peter Kant, CEO of Enabled Intelligence.

SourceAmerica is pleased to partner with Enabled Intelligence to make an impact in the artificial intelligence industry, said Vince Loose, president and CEO of SourceAmerica. Professionals with disabilities will bring unique insights and talents to this relationship with Enabled Intelligence and their federal and commercial customers who are looking to enhance their capabilities in this area.

About Enabled Intelligence, Inc.Enabled Intelligence is a small company based in Arlington, Virginia providing sensitive and classified data labeling services for government and other critical artificial intelligence applications. The company is hyper focused on labeling accuracy and security employing a competitive integrated team of professionals including veterans, people with disabilities and other subject matter experts. Visitwww.enabledintelligence.net to learn more.

About SourceAmericaEstablished in 1974, SourceAmerica creates employment opportunities for a skilled and dedicated workforce of people with disabilities. SourceAmerica is the vital link between the federal government and private sector organizations that procure the products and services provided by this exceptional workforce via a network of more than 600 community-based nonprofits. Headquartered in Vienna, Virginia, SourceAmerica provides its nonprofit agency network with business development, contract management, legislative and regulatory assistance, communications and public relations materials, information technology support, engineering and technical assistance, and extensive professional training needed for successful nonprofit management. Visit SourceAmerica.org to learn more, or follow them onFacebook(@SourceAmerica),Twitter(@SourceAmericaUS) andLinkedIn(@SourceAmerica).

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Enabled Intelligence, Inc. and SourceAmerica announce partnership to expand high-tech employment opportunities for people with disabilities -...

Dr D Narayana, Professor, AIML, D Arpna, S Peeyushi, S Samiksha, V Sanjay and P Sneha, Researchers, Great Learning discuss use of AI, ML which can boost process by identifying drugs having efficacy against COVID-19, bridging gap between thousands of repurposed drugs

COVID-19 pandemic has spread far and wide and has been different from the other pandemics of the last few decades. In India, where the first case was reported on January 30, 2020, till date there have been 1,01,139 confirmed cases of COVID-19 with 3,163 deaths. Many countries worldwide, including India are under lockdown to avoid the spread of the disease. However, to fight the disease effectively, the need of the hour is vaccines to combat the virus.

There are two broad categories of vaccines prophylactic and therapeutic. While prophylactic vaccines make a person immune to the virus, therapeutic ones are for making body fight against the virus which has already entered the body.

Many prophylactic vaccines are under trial world over but chances of those being mass produced and reaching India early on seem to be low. In India, due to low hospitals to population ratio, the focus should be on therapeutic vaccines to reduce the number of cases of hospitalisation.

Amongst therapeutic vaccines, repurposed drugs (using existing drugs for other diseases) should be our first line of attack against the pandemic. Other reasons for the focus on repurposed drugs are low chances of adverse reaction to the host (human) body as those drugs are already being used for treating other conditions. Also, these drugs can be used immediately and hence save many lives.

Repurposed drugs are being identified to interrupt or block different stages of the virus life cycle. Virus life cycle ranges from host cell penetration to self-replication inside the cell to exocytosis of new virions from the host cell.

For ease of understanding, we have categorised the repurposed drugs identified world over, as per the stage of the virus life cycle at which those are effective.

Virus entry blockers like camostat mesylate, a protease inhibitor, shown to inhibit TMPRSS2 (used for cleaving spike protein during virus entry). Its clinical trial for COVID-19 was started on April 3, 2020 (the drug is already licensed in Japan and South Korea for pancreatitis). The antimalarial drug, Hydroxychloroquine, can increase the endosomal pH required for virus-cell fusion and hence can potentially block the viral infection whereas another antimalarial drug, chloroquine phosphate, can target ACE2 cells. However, the study on these anti-malarial drugs in France led to no improvement in patients.

Virus replication blockers like Remedesivir and Favipiravir can interfere with RdRP (RNA dependent RNA polymerase) which is a viral generated protein responsible for intracellular sub-genomic RNA production. On May 1, 2020, Remedesivir was granted Emergency use Drug authorisation by US FDA whereas Favipiravir is emerging as one of the top drugs being recommended by CSIR (Council of Scientific and Industrial research), India. Ivermectin, a drug to treat broad spectrum parasitic infections, was studied by Australian researchers in-vitro and it was found that the drug was able to stop the virus replication. However, questions are being raised on toxicity of the dosage required.

Cytokine storm cytokine storms occur in viral infections when a large number of cytokines are produced. It is associated with multi-organ failure, which is frequently fatal. During infection from SARS-CoV-2, this cytokine storm is associated with increased levels of interleukins IL 2-2, IL-7 and other cytokines. A multi-centre, randomised controlled trial of tocilizumab (IL-6 receptor blockade, licensed for cytokine release syndrome), has been approved in patients with COVID-19 pneumonia and elevated IL-6 in China.

Potential natural drugs

Recently Indian governments CTRI (an arm of the Indian Council of Medical Research), has provided approval to conduct a randomised multicentre interventional clinical trial of a repurposed ayurvedic drug named as Zingivir-H. This drug, developed by Pankajakasthuri Herbal Research Foundation, an ayurvedic organisation from Kerala, is part of clinical practice for nearly 15 years for viral fever, acute viral bronchitis and contagious fever. It has been found to not have any side effects as per in-vitro experiments carried out at Rajiv Gandhi Centre for Biotechnology. It has seven ingredients including herbomineral and these ingredients are part of scientific manuscript. Additionally, studies have been carried out to check the efficacy of 64 naturally occurring flavonoids. Hesperidin, herbacetin, rhoifolin and pectolinarin were found to efficiently block the enzymatic activity of SARS-CoV 3CLpro.

As per latest statistics, the trial count of most popular allopathic drugs are as follows:

All the above drugs have been identified / shortlisted by researchers all over the world by using pre-existing drug repositories. Those drug repositories have been filtered / scanned to identify the ones with high affinity for virus proteins and hence leading to interruption of key activities of the virus during its life cycle.

Few open source repositories are : ReDO database which is maintained by AntiCancer Fund, Excelra Repurposed Drugs Database, CAS antiviral drugs dataset, DrugBank Database, the database of commercially available compounds for virtual screening known as ZINC, PubChem and ChEMBL dataset etc.

Sifting through thousands of these drug repos and coming up with the most effective drugs in itself is a time-consuming process. Artificial intelligence(AI) and machine learning(ML) can serve as a booster for this search by narrowing down the most effective drugs amongst the lot which can be further studied by specialists of the pharmacology field.

One of the examples of usage of AI for identifying suitable drugs in-silico are: Deep learning-based models to predict binding affinities based on chemical sequences (SMILES) and amino acid sequences (FASTA) of a target protein. Drugs like Atazanavir, Remedesivir, Kaletra, Rapamycin and tiotropium bromide were identified as potential inhibitors of the SARS-CoV2 virus (Of these ramdesivir has recently been approved by US FDA).

In addition, many drugs under trial can be critically examined for adverse outcomes using these techniques. An approach known as PrOCTOR has been used to predict side effects of under-trial drugs using Random Forest and Principal component analysis.

The drug discovery landscape, discussed here, shows that repurposed drugs is the fastest way to bring COVID-19 treatment to the general population. In addition to already approved repurposed drugs, there is a need for identifying more repurposed drugs. AI and ML can boost this process by quickly identifying drugs having efficacy against COVID-19 and hence bridge the gap between thousands of repurposed drugs, laboratory /clinical testing and final drug authorisation.

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Artificial intelligence as COVID-19 drug discovery booster - Express Healthcare

From chatbots to Amazon Alexa, artificial intelligence has become a normal part of everyday life that we now take for granted. But now in the middle of the coronavirus pandemic, it is being used to save lives.

AI, for example, is at the heart of the NHS track and trace app, which is being trialled in the Isle of Wight before a nationwide rollout. Users of the service input their symptoms into a smartphone, then an algorithm looks at who theyve had contact with and alerts them to the potential risks of catching or spreading the virus.

For Chris Ford, manager of the Smith & Williamson Artificial Intelligence fund, this is a pivotal moment for AI, especially as we are now willing to share our data with the government for the greater good. He argues that the Covid-19 crisis has accelerated the cultural acceptance of AIs role in our lives, from the sudden and widespread use of telemedicine to the use of computers for speedy diagnosis and the search for a vaccine. Theres a renewed focus and vigour that has been absent before in how we approach AI, he says.

But there are misunderstandings about what AI is. Defined by Stanford University as the science and engineering of making intelligent machines, it is now seeping into so many aspects of our lives that a complete definition it is hard to pin down. There is also confusion whether it is good for us, with negative perceptions of "robots taking human jobs" balanced by medical breakthroughs such as discovering new antibiotics and robotic surgery.

Robotics and automation are boom areas of AI the iShares Automation and Robotics ETF (RBOT) has over $2 billion in assets but they are not the game in town, says S&W's Ford. Not all robotics have artificial intelligence, and not all AI platforms are robotic, he says. For investors its been relatively easy to ride the trend by backing big tech firms like Microsoft (MSFT), Amazon (AMZN), Apple (AAPL) and Google parent company Alphabet (GOOGL), which have invested billions in AI in its many forms.

Many of the pioneers in AI are not on the radar of retail investors, but their work will have a profound impact on our lives. One such area is autonomous and semi-autonomous vehicles, which Google and Tesla (TSLA) are backing to be the next game-changing technology. With 1.3 million people losing their lives in traffic accidents worldwide every year, 90% of which are down to human error, there is clearly scope for technology to drive better than us. AI has come a long way in recent years in the field of image recognition, which teaches cars how to assess and react to certain hazards.

Image recognition was arguably the most impactful first-wave application of AI technology, argues Xuesong Zhao, manager of the Polar Capital Automation and Artificial Intelligence fund. Tom Riley, co-manager of the Neutral-ratedAxa Framlington Robotech fund agrees, saying that vision systems have come on leaps and bounds recently. He holds JapansKeyence (6861), which develops manufactures automation sensors and vision systems used in the automotive industry. As the dominant player in the machine vision market, the company has a narrow moat from Morningstar analysts.

Modern cars already have some element of AI, particularly in hazard awareness and automatic parking, but Riley says drivers are not yetready for the full hands-off, eyes-off autonomous driving experience. Still, S&W's Ford argues that fully autonomous vehicles may become mainstream sooner than we think, say five to 10 years time, rather than 20.

Some of AIs most high-profile wins to date have been in the medical sphere, and that is where many fund managers are focused. Robots are now routinely used alongside surgeons and Nasdaq-listed Intuitive Surgical (ISRG) makes Da Vinci robots that perform millions of surgical operations every year. The company is the fourth largest holding in the Axa fund.. Axas Riley has positioned around 20% of the fund into the healthcare sector because he thinks it provides useful diversification away from the tech giants.

Ford also owns US firm iRhythm (IRTC), which uses an AI platform to warn people that they are at risk of cardiacarrhythmia, irregular heart movements that can potentially be fatal. He cites this as an example of AI's strength in capturing large amounts of real-time data and improving how it interprets the information.

Away from robotic surgery and self-driving cars, where else do fund managers see future opportunities? Polar CapitalsXuesong thinks natural language processing (NLP) is likely to be the next growth area for AI, although not without its challenges. He thinks that teaching computers to read and analyse documents would be truly transformational in many industries. He cites legal, financial and insurance companies as some of the biggest beneficiaries of this trend in the coming years. For example, complex fraud trials often involve millions of documents having a computer to sift through them would speed up the legal proceedings and keep costs down.

Ford, meanwhile, thinks industries such as mining and oil, which have so far been late adopters of AI, could start to change, and also expects greater use of AI in education. That trend could be accelerated by the Covid-19 crisis, where schools and universities have been forced to go virtual in the lockdown. AI, then, could be a natural next step for students to work semi-independently with tailored curriculums.

AI is only as good as the data on which it stands, Ford says. And with younger people less reticent to share their data than older tech users, AI is only going to improve in the coming years.

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Artificial Intelligence in the Covid Frontline - Morningstar

(Getty Images/istockphoto / Peshkova)

The debate on contact-tracing highlights the urgency of tackling unregulated technologies like artificial intelligence (AI). With a strong democracy and reputation for first-class research, Switzerland has the potential to be at the forefront of shaping ethical AI.

What is Artificial Intelligence (AI)? "Artificial intelligence is either the best or the worst thing ever to happen to humanity," the prominent scientist, Stephen Hawking, who died in 2018, once said.

An expert group set up by the European Commission presented a draft ofethics guidelinesexternal linkfor trustworthy AI at the end of 2018, but as of yet there is no agreed global strategy for defining common principles, which would include rules on transparency, privacy protection, fairness, and justice.

Thanks to its unique features a strong democracy, its position of neutrality, and world-class research Switzerland is well positioned to play a leading role in shaping the future of AI that adheres toethical standards. The Swiss government recognizes the importance of AI to move the country forward, and with that in mind, has been involved in discussions at the international level.

What is AI?

There is no single accepted definition of Artificial Intelligence. Often, it's divided into two categories, Artificial General Intelligence (AGI) which strives to closely replicate human behaviour while Narrow Artificial Intelligence focuses on single tasks, such as face recognition, automated translations and content recommendations, such as videos on YouTube.

However, on the domestic front, the debate has just begun, albeit in earnest as Switzerland and other nations are confronted with privacy concerns surrounding the use of new technologieslike contact-tracing apps, whether they use AI or not, to stop the spread of Covid-19.

The European initiative the Pan-European Privacy-Preserving Proximity Tracing initiative PEPP-PT advocated a centralized data approach that raised concern about its transparency and governance. However, it was derailed when a number of nations, including Switzerland, decided in favour of a decentralized and privacy-enhancing system, called DP-3T (Decentralized Privacy-Preserving Proximity Tracing). The final straw for PEPP-PT was when Germany decided to exit as well.

"Europe has engaged in a vigorous and lively debate over the merits of the centralized and decentralized approach to proximity tracing. This debate has been very beneficial as it made the issues aware to a broad population and demonstrated the high level of concern with which these apps are being designed and constructed. People will use the contact-tracing app only if they feel that they don't have to sacrifice their privacy to get out of isolation," said Jim Larus. Larus is Dean of the School of Computer and Communication Sciences (IC) at EPFL Lausanne and a member of the group that initially started the DP3T effort at EPFL.

According to a recent survey, nearly two-thirds of Swiss citizens said they were in favour of contact tracing. The DP-3T app is currently being tested on a trial basis, while waiting for the definition of the legal conditions for its widespread use, as decided by the Swiss parliament.However, the debate highlights the urgency of answering questions surrounding ethics and governance of unregulated technologies.

+ Read more about the controversial Swiss app

The "Swiss way"

Artificial intelligence was included for the first time in the Swiss government's strategy to create the right conditions to accelerate the digital transformation of society.

Last December, a working group delivered its report to the Federal Council (executive body) called the "Challenges of Artificial Intelligence". The report stated that Switzerland was ready to exploit the potential of AI, but the authors decided not to specifically highlight the ethical issues and social dimension of AI, focusing instead on various AI use cases and the arising challenges.

"In Switzerland, the central government does not impose an overarching ethical vision for AI. It would be incompatible with our democratic traditions if the government prescribed this top-down," Daniel Egloff, Head of Innovation of the State Secretariat for Education, Research and Innovation (SERI) told swissinfo.ch. Egloff added that absolute ethical principles are difficult to establish since they could change from one technological context to another. "An ethical vision for AI is emerging in consultations among national and international stakeholders, including the public, and the government is taking an active role in this debate," he added.

Seen in a larger context, the government insists it is very involved internationally when it comes to discussions on ethics and human rights. Ambassador Thomas Schneider, Director of International Affairs at the Federal Office of Communications (OFCOM), told swissinfo.ch that Switzerland in this regard "is one of the most active countries in the Council of Europe, in the United Nations and other fora". He also added that it's OFCOM's and the Foreign Ministry's ambition to turn Geneva into a global centre of technology governance.

Just another buzzword?

How is it possible then to define what's ethical or unethical when it comes to technology? According to Pascal Kaufmann, neuroscientist and founder of theMindfire Foundationexternal linkfor human-centric AI, the concept of ethics applied to AI is just another buzzword: "There is a lot of confusion on the meaning of AI. What many call 'AI' has little to do with Intelligence and much more with brute force computing. That's why it makes little sense to talk about ethical AI. In order to be ethical, I suggest to hurry up and create AI for the people rather than for autocratic governments or for large tech companies.Inventing ethical policies doesn't get us anywhere and will not help us create AI.''

Anna Jobin, a postdoc at the Health Ethics and Policy Lab at the ETH Zurich, doesn't see it the same way. Based on her research, she believes that ethical considerations should be part of the development of AI: "We cannot treat AI as purely technological and add some ethics at the end, but ethical and social aspects need to be included in the discussion from the beginning." Because AI's impact on our daily lives will only grow, Jobin thinks that citizens need to be engaged in debates on new technologies that use AI and that decisions about AI should include civil society. However, she also recognizes the limits of listing ethical principles if there is a lack of ethical governance.

For Peter Seele, professor of Business Ethics at USI, the University of Italian-speaking Switzerland, the key to resolving these issues is to place business, ethics, and law on an equal footing. "Businesses are attracted by regulations. They need a legal framework to prosper. Good laws that align business and ethics create the ideal environment for all actors," he said. The challenge is to find a balance between the three pillars.

Artificial intelligence is being used to developrobots and drones that can explore dangerous places beyond the reach of humans and animals.

See in other languages: 4 See in other languages: 4 Languages: 4

The perfect combination

Even if the Swiss approach mainly relies on self-regulation, Seele argues that establishing a legal framework would give a significant impulse to the economy and society.

If Switzerland were to take a lead role in defining ethical standards, its political system based on direct democracy and democratically controlled cooperatives could play a central role in laying the foundation for the democratization of AI and the personal data economy. As the Swiss Academy of Engineering Sciences SATWsuggested in a whitepaper at the end of 2019, the model for that could be the SwissMIDATAexternal link, a nonprofit cooperative that ensures citizens' sovereignty over the use of their data, acting as a trustee for data collection. Owners of a data account can become members of MIDATA, participating in the democratic governance of the cooperative. They can also allow selective access to their personal data for clinical studies and medical research purposes.

The emergence of an open data ecosystem fostering the participation of civil society is raising awareness of the implications of the use of personal data, especially for health reasons, as in the case of the contact-tracing app. Even if it's argued that the favoured decentralized system does a better job preserving fundamental rights than a centralized approach, there are concerns about susceptibility to cyber attacks.

The creation of a legal basis for AI could ignite a public debate on the validity and ethics of digital systems.

Frida Polli is a neuroscientist and co-founder of pymetrics, an AI-based job matching platform based in the United States.

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Ethical artificial intelligence: Could Switzerland take the lead? - swissinfo.ch

Artificialintelligence (AI) comprises a type of computer science that develops entities,such as software programs, that can intelligently perform tasks or makedecisions.1 The development and use of AI in health care is not new;the first ideas that created the foundation of AI were documented in 1956, andautomated clinical tools that were developed between the 1970s and 1990s arenow in routine use. These tools, such as the automated interpretation ofelectrocardiograms, may seem simple, but are considered AI.

Today,AI is being harnessed to help with big problems in medicine such asprocessing and interpreting large amounts of data in research and in clinicalsettings, including reading imaging or results from broad genetic-testingpanels.1 In oncology, AI is not yet being used broadly, but its useis being studied in several areas.

Screeningand Diagnosis

Thereare several AI platforms approved by the US Food and Drug Administration (FDA)to assist in the evaluation of medical imaging, including for identifyingsuspicious lesions that may be cancer.2 Some platforms help tovisualize and manipulate images from magnetic resonance imaging (MRI) orcomputed tomography (CT) and flag suspicious areas. For example, there are severalAI platforms for evaluating mammography images and, in some cases, help todiagnose breast abnormalities. There is also an AI platform that helps toanalyze lung nodules in individuals who are being screened for lung cancer.1,3

AI isalso being studied in other areas of cancer screening and diagnosis. Indermatology, skin lesions are biopsied based on a dermatologists or primarycare providers assessment of the appearance of the lesion.1 Studiesare evaluating the use of AI to either supplement or replace the work of theclinician, with the ultimate goal of making the overall process moreefficient.

Big Data

Astechnology has improved, we now have the ability to create a vast amount ofdata. This highlights a challenge individuals have limited capabilities toassess large chunks of data and identify meaningful patterns. AI is beingdeveloped and used to help mine these data for important findings, process andcondense the information the data represent, and look for meaningful patterns.

Such toolswould be useful in the research setting, as scientists look for novel targetsfor new anticancer therapies or to further their understanding of underlyingdisease processes. AI would also be useful in the clinical setting, especiallynow that electronic health records are being used and real-world data are beinggenerated from patients.

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Artificial Intelligence in Cancer: How Is It Used in Practice? - Cancer Therapy Advisor