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According to a recent study, humans and artificial intelligence systems can perform better when both of them work together to tackle problems. The research was done by Eric Horvitz Chief scientist Microsoft, Ece Kamar the Microsoft Research principal researcher, and Bryan Wilder, a student at Harvard University and Microsoft Research intern.

It seems that Eric Horvitz first published the research paper. He was hired as Microsoft principal researcher back in the year 1993 and the company named him Microsoft Chief Scientist officer during March. He led the companys research programs from the year 2017 to 2020. The research paper was published earlier this month and it studies the performance of artificial intelligence teams and humans operating together on two PC vision projects namely breast cancer metastasis recognition and Galaxy categorization. With this proposed approach, the artificial intelligence (AI) model evaluates which tasks humans can perform best and what type of tasks AI systems can handle better.

In this approach, the learning procedure is developed to merge human contributions and machine predictions. The artificial intelligence systems work to tackle problems that can be difficult for humans while humans focus on solving issues that can be tough for AI systems to figure out. Basically, AI system predictions generated with lower accuracy levels are routed to human teams in this system. According to researchers, combined training of human and artificial intelligence systems can enhance the galaxy classification model for us. It can improve the performance of Galaxy Zoo with a 21 to 73% decrease in loss. This system can also deliver an up to 20% better performance for CAMELYON16.

The research paper states that the performance of machine learning in segregation overcomes the circumstances where human skills can add integral context, although human teams have their own restrictions including systematic biases. Researchers stated in the paper that they have developed methods focused on training the AI learning model to supplement human strengths. It also accounts for the expense of inquiring an expert. Human and AI system teamwork can take various forms but the researchers focused on settings where machines would decide which instances required human absorption and then merging human and machine judgments.

Horvitz, during the year 2007, worked on a policy to examine when human assistants should interfere in consumer conversations with computerized receptionist systems. The researchers also stated in the paper, Learning to Complement Humans, that they see opportunities of studying extra aspects of human-machine cooperation across various settings. While studying a different type of teamwork, Open Artificial Intelligence research experts have looked at machine assistants operating together in games such as hide and seek, and Quake 3.

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Read next: Researchers Developed An Artificial Intelligence System That Can Transform Brain Signals Into Words

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Humans And Artificial Intelligence Systems Perform Better Together: Microsoft Chief Scientist Eric Horvitz - Digital Information World

Among the myriad thematic exchange traded funds investors have to consider, artificial intelligence products are numerous and some are catching on with investors.

Count the ROBO Global Artificial Intelligence ETF THNQ, +0.40% as the latest member of the artificial intelligence ETF fray. HNQ, which debuted earlier this week, comes from a good gene pool as its stablemate,the Robo Global Robotics and Automation Index ETF ROBO, -0.32%, was the original and remains one of the largest robotics ETFs.

That's relevant because artificial intelligence and robotics are themes that frequently intersect with each other. Home to 72 stocks, the new THNQ follows the ROBO Global Artificial Intelligence Index.

Adding to the case for A.I., even with a new product such as THNQ, is that the technology has hundreds, if not thousands, of applications supporting its growth.

Companies developing AV technology are mainly relying on machine learning or deep learning, or both, according to IHS Markit. A major difference between machine learning and deep learning is that, while deep learning can automatically discover the feature to be used for classification in unsupervised exercises, machine learning requires these features to be labeled manually with more rigid rulesets. In contrast to machine learning, deep learning requires significant computing power and training data to deliver more accurate results.

Like its family ROBO, THNQ offers wide reach with exposure to 11 sub-groups. Those include big data, cloud computing, cognitive computing, e-commerce and other consumer angles and factory automation, among others. Of course, semiconductors are part of the THNQ fold, too.

The exploding use of AI is ushering in a new era of semiconductor architectures and computing platforms that can handle the accelerated processing requirements of an AI-driven world, according to ROBO Global. To tackle the challenge, semiconductor companies are creating new, more advanced AI chip engines using a whole new range of materials, equipment, and design methodologies.

While THNQ is a new ETF, investors may do well to not focus on that rather focus on the fact the AI boom is in its nascent stages.

Historically, the stock market tends to under-appreciate the scale of opportunity enjoyed by leading providers of new technologies during this phase of development, notes THNQ's issuer. This fact creates a remarkable opportunity for investors who understand the scope of the AI revolution, and who take action at a time when AI is disrupting industry as we know it and forcing us to rethink the world around us.

The new ETF charges 0.68% per year, or $68 on a $10,000 investment. That's inline with rival funds.

2020 Benzinga.com. Benzinga does not provide investment advice. All rights reserved.

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A New Way To Think About Artificial Intelligence With This ETF - MarketWatch

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