Modern agriculture depends on bees. In fact, our entire ecosystem, including the food we eat and the air we breathe, counts on pollinators. But the pollinator population is declining according to Sabiha Rumani Malik, the founder and executive president of The World Bee Project. But, in an intriguing collaboration with Oracle and by putting artificial intelligence, internet of things and big data to work on the problem, they hope to reverse the trend.

How Artificial Intelligence, IoT and Big Data Can Save The Bees

Why is the global bee population in decline?

According to an Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) report, pollinators are in danger. There are many reasons pollinators are being driven to extinction, including habitat destruction, urbanization, use of pesticides, pollution, fragmentation of natural flowering habitats, predators and parasites, and changing climate. However, until recently, with The World Bee Project's work, there hasn't been a global initiative to study bee populations or to research and attack the issue from a global perspective.

Why is it important to save the bees?

Did you know that bees, along with other pollinators, such as butterflies, are the reason plants can produce seeds and reproduce? According to the United States Department of Agriculture (USDA), 35 percent of food crops and three-quarters of the worlds flowering plants depend on bees and pollinators. In fact, in order to ensure the almond crop gets pollinated in California each year, most of the beehives in the United States are shipped to California to ensure it. In fact, bees help to pollinate 90% of the leading global crop types, including fruit trees, coffee, vanilla, and cotton plants. And, of course, healthy plants are critical in replenishing our oxygen supply thanks to photosynthesis.

If the pollinators aren't alive or healthy enough to do their job, our global crop production, food security, biodiversity, and clean air is in peril. Honeybees are the world's most important pollinators. As much as 40 percent of the global nutrient supply for humans depends on pollinators. Presently there are approximately 2 billion people who suffer deficiencies of micronutrients.

Our lives are intrinsically connected to the bees, Malik said.

Partnership to monitor global honeybee population

The World Bee Project is the first private globally coordinated organization to launch and be devoted to monitoring the global honey bee population. Since 2014, the organization has brought together scientists to study the global problem of bee decline to provide insight about the issue to farmers, governments, beekeepers, and other vested organizations.

In 2018, Oracle Cloud technology was brought into the work to better understand the worldwide decline in bee populations, and The World Bee Project Hive Network began.

How technology can save the bees

How could technology be used to save the bees? Technology can be leveraged to help save the bees in a similar way that it is applied to other innovative projects. First, by using internet-of-things sensors, including microphones and cameras that can see invasive predators and collect data from the bees and hives. Human ingenuity and innovations such as wireless technologies, robotics, and computer vision help deliver new insights and solutions to the issue. One of the key metrics of a hive's health is the sounds it produces. Critical to the data-gathering efforts is to "listen" to the hives to determine colony health, strength, and behavior as well as collect temperature, humidity, apiary weather conditions, and hive weight.

The sound and vision sensors can also detect hornets, which can be a threat to bee populations.

The data is then fed to the Oracle Cloud, where artificial intelligence (AI) algorithms get to work to analyze the data. The algorithms will look for patterns and try to predict behaviors of the hive, such as if it's preparing to swarm. The insights are then shared with beekeepers and conservationists so they can step in to try to protect the hives. Since it's a globally connected network, the algorithms can also learn more about differences in bee colonies in different areas of the world. Students, researchers, and even interested citizens can also interact with the data, work with it through the hive network's open API, and discuss it via chatbot.

For example, the sound and vision sensors can detect hornets, which can be a threat to bee populations. The sound from the wing flab or a hornet is different from those of bees, and the AI can pick this up automatically and alert beekeepers to the hornet threat.

Technology is making it easier for The World Bee Project to share real-time information and gather resources to help save the world's bee population. In fact, Malik shared, "Our partnership with Oracle Cloud is an extraordinary marriage between nature and technology." Technology is helping to multiply the impact of The World Bee Project Hive Network across the world and makes action to save the bees quicker and more effective.

Here you can see a short video showing the connected beehive in augmented reality during my interview with Sabiha Rumani Malik - pretty cool:

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How Artificial Intelligence, IoT And Big Data Can Save The Bees - Forbes

An Italian artificial intelligence (AI) company that specializes in natural language reading and semantics is using its AI tech to extract emotions and sentiment from 63,000 English-language social media posts on Twitter every 24 hours to create a semantic analysis of peoples feelings during COVID-19.

Expert System collects the data in the same time frame - 10 am EST (3 pm CET) on the same day of each week. The data is analyzed every 24 hours and interpreted by Sociometrica.The company applied the most frequently used hashtags related to coronavirus to analyze the data such as #coronalockdown, #covid19, #coronavirusuk, #stayathome, #stayhomesavelives, #coronaviruspandemic, #clapforourcarers, #isolationlife.

Expert Systems and Sociometrica analyze the sentiment of 63,000 social media posts each day to ... [+] determine the emotional state of the internet in response to COVID-19

Walt Mayo, CEO of Expert System Group, said that social media sentiment analysis shows that fear and anxiety around the Corona crisis and how it is unfolding and the efforts to combat it dominate communications.

We also have seen growing criticism of individual behavior that is considered irresponsible and goes against advice to follow social distancing and other recommendations to flatten the curve, added Mayo. But we also have seen growing expressions of gratitude toward health care workers and emerging signs of hope more broadly.

Mayo believes its important to monitor peoples sentiment changes because some of the success of the anti-virus strategy depends on the behavior of individuals. From the data, the general trend shows that fear is the most widespread emotion.

Mayo says that sentiment data two weeks ago in early April indicated that people were afraid because they wanted to return to their normal life; they were insisting on answers both regarding the progression of the pandemic and actions to combat the virus. Strong criticism was leveled at those who didnt respect safety distancing rules and other behavior [..] that would prevent the spread of the virus, said Mayo.

The days preceding Easter were a turning point, with more positive emotions correlated to a growing expression of action around the commitment to the fight against the virus and the courage of doctors and nurses working at the forefront of the fight and the confidence in science, said Mayo.

April 17, 2020, data showed positive emotions, including hope and love expressed towards health care personnel, showed a slight increase from 21.6% to 23.9% in 24 hours.

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This Artificial Intelligence Extracts Emotions And Shows What People Are Feeling - Forbes

Everybody poopsand after a few thousand years underground, these droppings often start to look the same. That stool-based similarity poses something of a puzzle for archaeologists investigating sites where dogs and humans once cohabited, as it isnt always easy to deduce which species left behind specific feces.

But as a team of researchers writes in the journal PeerJ, a newly developed artificial intelligence system may end these troubles once and for all. Called corpoIDan homage to coprolite, the formal term for fossilized fecesthe program is able to distinguish the subtle differences between ancient samples of human and canine excrement based on DNA data alone, reports David Grimm for Science magazine.

Applied to feces unearthed from sites around the world, the new method could help researchers unveil a trove of valuable information about a defecators diet, health, and perhapsif the excretion contains enough usable DNAidentity. But in places where domesticated dogs once roamed, canine and human DNA often end up mixed in the same fecal samples: Dogs are known to snack on peoples poop, and some humans have historically dined on canine meat.

Still, differences in the defecations do existespecially when considering the genetic information left behind by the microbiome, or the microbes that inhabit the guts of all animals. Because microbiomes vary from species to species (and even from individual to individual within a species), they can be useful tools in telling droppings apart.

To capitalize on these genetic differences, a team led by Maxime Borry of Germanys Max Planck Institute for the Science of Human History trained a computer to analyze the DNA in fossilized feces, comparing it to known samples of modern human and canine stool. The researchers then tested the programs performance on a set of 20 samples with known (or at least strongly suspected) species origins, including seven that only contained sediments.

The system was able to identify all of the sediments as uncertain, and it correctly classified seven other samples as either dog or human. But the final six appeared to stump the program.

Writing in the study, Borry and his colleagues suggest that the system may have struggled to identify microbiomes that didnt fall in line with modern human and canine samples. People who had recently eaten large quantities of dog meat, for instance, might have thrown the program for a loop. Alternatively, ancient dogs with unusual diets could have harbored gut microbes that differed vastly from their peers, or from modern samples.

There is not so much known about the microbiome of dogs, Borry tells Vices Becky Ferreira.

With more information on how diverse canine gut microbes can get, he says, the teams machine learning program may have a shot at performing better.

Ainara Sistiaga, a molecular geoarchaeologist at the University of Copenhagen who wasnt involved in the study, echoes this sentiment in an interview with Science, pointing out that the data used to train coproID came exclusively from dogs living in the modern Western world. It therefore represented just a small sliver of the riches found in canine feces.

CoproID also failed to determine the origins of highly degraded samples that contained only minimal microbial DNA. With these issues and others, there are definite issues that need to be resolved before the method can be used widely, Lisa-Marie Shillito, an archaeologist at Newcastle University who wasnt involved in the study, tells Michael Le Page of New Scientist.

With more tinkering, the method could reveal a great deal about the history of humans and dogs alikeincluding details about how the two species first became close companions, Melinda Zeder, an archaeozoologist at the Smithsonian Institutions National Museum of Natural History who wasnt involved in the study, tells Science.

As dogs swapped the fleshy, protein-heavy diets of their wolfish ancestors for starchy human fare, their gut microbes were almost certainly taken along for the ride. Even thousands of years after the fact, feces could benchmark this transition.

Says Zeder, The ability to track this through time is really exciting.

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Artificial Intelligence Gives Researchers the Scoop on Ancient Poop - Smithsonian.com

SINGAPORE - At least two universities here are turning to technology to send students into a lockdown when they take online examinations, to prevent them from cheating.

At the Singapore Management University (SMU) and Singapore Institute of Technology (SIT), students web browsers are locked, such that they will not be able to access other websites or capture screenshots until they have completed the exam.

Before the exam, students will also have to take a short video of their location, such as the room and study table.

During the exam, they use a webcam to record themselves. An artificial intelligence algorithm will track their eye movement to determine where and what they are looking at, to deter cheating.

After the exam, only the course instructor can review video recordings and the results of the proctoring or invigilating session and video segments. Potential violations, if any, are then flagged.

Universities in Singapore have moved to online lessons and exams due to the coronavirus outbreak and circuit breaker measures.

Online exams at SMU have been running from April 13, and will end on Friday (April 24). SITs exam period is from April 27 to May 8.

In response to queries from The Straits Times, an SMU spokesman said on Thursday (April 23) that the university has been using this tool for online exams since two years ago, but on a much smaller scale.

At the time, it was "mainly to support students who were unable to take their tests on campus, such as due to illnesses or participation in overseas competitions".

"We have scaled it up this round to facilitate online undergraduate and postgraduate closed-book exams. More than 100 instructors have used the tool in the past week," said the SMU spokesman.

The Straits Times understands that so far students have had no issues with their webcams, and there have been no requests for equipment support.

Associate Professor Lieven Demeester, who teaches business modules at SMU, said he used the tool for the first time at the start of the month to conduct a 2 hour exam for 40 students.

"I gave them instructions in advance on what I wanted to see in their videos - my own view of what I think is a secure work environment. I asked them to show the underside of their tables and chairs, and they also had to film their pockets so I could make sure they didn't have anything in them," Prof Demeester told ST.

After the exam, he scrolled through the videos for all 40 students, which took him about 1 hours. There were a set of thumbnails, or pictures of students taken at various intervals, he added.

"I don't watch every second of every video. The programidentifies major changes and highlights parts where someone is moving or someone else comes into the frame, but these are rare occurrences."

He noted that the tool was an effective deterrent for cheating that also provides a mechanism to follow up on potential breaches.

"When the camera is on you, if you're looking away from the screen, its very visible. In a classroom, there's usually only one invigilator, and you can never be looking at every student all the time. But here, we can, even though it's after the fact," Prof Demeester said.

He noted that students looking away from their screens may not necessarily be trying to cheat.

"You'll have to look a bit closer at the video to see if it re-occurs or if there's a pattern (of them looking away). But I've been lucky, none of my students have shown such behaviour."

The SMU spokesman added that mock tests are created before the online exam for students to prepare themselves and their laptops in advance.

Follow-up IT support is also available to students who have difficulty with their set-up.

The spokesman noted that the tool "is by no means a guarantee against acts of cheating, just like in normal exams and graded assignments".

"Typically, instructors are mindful of this and will take special care to set questions that can minimise cheating and to spot suspicious similarities in answers for further investigation."

A National University of Singapore spokesman told ST the university has put in place measures, such as online proctoring, to preserve the integrity of online assessments.

"Students have been reminded of the serious consequences, including suspension or expulsion, if they are found responsible for any academic misconduct," the spokesman said.

Besides measures to prevent cheating during online exams, universities here also use software to check assignments that are submitted online.

ST understands that the Singapore University of Social Sciences, for instance, uses a plagiarism checker software to check online assignments.

The Singapore University of Technology and Design does not have any online exams for this term.

Excerpt from:
Coronavirus: Universities use artificial intelligence to deter cheating in online exams - straits times

CHARLOTTESVILLE, Va., April 22, 2020 /PRNewswire/ --Biovista, a privately-held AI / bioscience firm best known for drug repositioning, has identified four treatments two prescription drugs and two over-the-counter compounds to counter key symptoms of COVID-19.

"Drug AI helps find that needle in the haystack, and we are optimistic that we have found four of them to start," said Aris Persidis, Biovista's President and Co-Founder.

The first drug has the potential to reduce viral replication, limit inflammatory events, and help protect against acute lung injury. The second drug has the potential to reduce viral load, improving the primary ARDS component of the disease and reducing the cytokine storm in COVID-19. The two over-the-counter compounds would be adjunct treatments.These findings are presented in a White Paper which Biovista is making available upon request via email: covid-19whitepaper@biovista.com.

"These compounds won't cure COVID-19, but appear able, based on their mechanism of action, to limit the damage," said Dr. Eftychia Lekka, Senior Investigator, Drug Discovery. "We continue to probe deeper and update our findings."

With a predictive success rate of over 77%, Biovista has significant experience in drug repositioning, on which many of the world's hopes for COVID therapeutics revolve today. "Our track record gives us confidence that we can find compounds to target COVID-19 and improve patient outcomes," said Dr. Persidis.

Looking at known and likely mechanisms of the SARS-CoV-2 virus, Biovista's AI platform has assessed to date over 13,000 approved drugs and pharmaceutically active compounds.

"Working from these initial findings, we are open to collaborators able to test these candidates and hypotheses in either virtual clinical trials using EMR and EHR data, or in models and trials," said Dr. Persidis.

To collaborate, please contact us atinfo@biovista.com.

About BiovistaBiovista's Project Prodigy AI develops novel hypotheses supported by detailed reasoning from the $2T-worth of publicly available research data. The objective is to solve complex healthcare problems, including:1. Finding new uses for existing drugs2. Finding existing drugs to target a disease of interest, as in this case with COVID-193.Assessing the safety of drugs entering the clinic and how best to mitigate adverse drug reactions

Biovista was recently named a top-20 company in AI drug development by Forbes ( https://bit.ly/2RWYhgE).

For more information, please visit Biovista's website:www.biovista.com

View original content to download multimedia:http://www.prnewswire.com/news-releases/artificial-intelligence-firm-biovista-repositions-four-promising-therapeutics-against-covid-19-301045096.html

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Artificial Intelligence firm Biovista repositions four promising therapeutics against COVID-19 - Yahoo Finance

By Maj William Giannetti, USAFR*

The DODs new artificial intelligence (AI) strategy is a treasure trove of ideas.1 Unveiled during a February 2019 press conference, it is (to put it mildly) an ambitious document, and its implications are far-reaching. In a departure from hard-coded garbage-in, garbage-out programs that burp out specific output, algorithm writers will craft code that learns on its own. Neural networks modeled after biological systems might one day roam the gray areas of human thought. With time and considerable training, AI will discern tanks from trucks or MiGs from run-of-the-mill airplanes.

Autonomous vehicles will transport troops to the frontlines, and someday pilotless aircraft might transport cargo and refuel fighters. Developmental Air Force AI already enables semiautonomous loyal wingmen, guided by pilots, to carry out preprogrammed missions from the relative safety of their cockpits.2 Later, faulty parts imbued with AI would speak out when their replacement comes due, making maintenance schedules more efficient and less costly. Military doctors might recommend an early biopsy after an AI-assisted ultrasound detects disease, thus improving prognoses so that all Americans might live longer, fuller lives.

Air Force generals presently envision a world where AI rapidly transforms data into knowledge that accurately informs a human-led decision-making process.3 We need our analysts to harmonize the data-to-decision quality at speed, said Air Force Director of Intelligence Lt Gen VeraLinn Dash Jamieson during an interview at Goodfellow AFB,Texas in 2017.We must build the next generation ISR enterprise capable of possessing decision advantage across the entire spec- trum of conflict.4

But to get there, developers require a preaccredited, flexible cloud to cultivate the AI strategys ideas, lest they die an untimely death on the policy vine. Another must is a secure DOD cloud that stores the considerable quantity of data that would fuel the nations AI and machine learning algorithms. Skeptics say the piles of servers and processors it would take cost billions. But a partnership between Lt Gen John N. T. ( Jack) Shanahans new Joint Artificial Intelligence Center ( JAIC) and a little-known Air Force cloud service called TITAN (Technology for Innovation and Testing on Accredited Networks) could bring value while making everyones AI dreams come true for a fraction of the cost. First, lets put cloud computing in context by looking at its costs and the role it plays in managing the Pentagons IT.

According to the Government Accountability Office, the federal government invests more than $90 billion annually in the development, implementation, and maintenance of IT infrastructure.5 To offset this cost, the Office of Management and Budget debuted its Cloud First Strategy, which mandated agencies pool IT resources in secure, efficient, and cost-effective ways.6 Cloud computing eliminates storing data on bare metal, stand-alone hard drives and shifts the burden to groupings of software and high-capacity storage servers. A clouds elasticity allows administrators to add (or subtract) storage and computing power while public and private user groups lend it scalability.

The DOD went all in with the cloud, investing $2.7 billion between 201518. Its subordinate organizations operate an estimated 500 clouds, and as of 2019, the Pentagon racked up 88 cloud investments out of 2,735 for IT overall.7 The sheer number of clouds managed by multiple vendors poses a growing administrative headache. The Joint Enterprise Defense Infrastructure ( JEDI) initiative, with its estimated $10 billion price tag over 10 years, seemed to be the cure. Businesses from across the tech community flocked to Washington with their proposals. Microsoft, a decades-long mainstay of government IT, recommended Azure for JEDI. Amazon Web Services (AWS), a relative upstart, offered its seemingly infinite storage capacity.

AWS was a favorite to win because it gained the governments confidence in storing sensitive information and programs.8 Engineers from the private and government sectors use AWS SageMaker to create machine-learning algorithms with drag-and-drop ease. Clouds would consolidate under JEDIs umbrella and lessen confusion as the department transferred its oldest legacies into it.9 One set of tools and standards for AI (or other software development for that matter) affords engineers a shared environment to discover information and create algorithms.

Traditional computer firms, like IBM and other Silicon Valley players like Oracle, have lodged complaints. They claimed awarding JEDI to a single company unfairly stifles competition and makes the militarys cloud especially vulnerable to Russian and Chinese cyberattacks.10 The arguments soon intensified and took a more personal turn. President Donald Trumps feud with Amazon CEO Jeff Bezos spilled into public view, and in a surprise ruling Microsoft was granted the huge contract. In its appeal to a U.S. federal court, Amazon says political influence tipped the Pentagons decision, and that procurements should be administered objectively.11

Meanwhile, as corporate and government lawyers do battle, an average-looking industrial building sits tucked into the scrub pines and dogwood trees of Fort Belvoir, Virginia. Inside one of its air-cooled rooms, chilled to 65 degrees Fahrenheit, are dozens of repurposed computer servers quietly whirring away. To the average onlooker, the sight might seem unimpressive, but this is TITAN, a government-owned, contractor-operated cloud worth $18 milliona veritable shoestring compared to JEDI. The US Air Forces ISR Innovations Directorate founded TITAN in 2016. It is funded entirely by Headquarters Air Forces ISR chief information officer and maintained by a handful of defense workers.

TITAN is unique because it is a hybrid cloud, a place where engineers rapidly prototype and deploy their software or custom applications. At 7.6 petabytes, it is modestly sized and ideal for the JAICs specialized work. To the layman, a petabyte might not seem like much, but its a very sizable chunk of data. Back in 2013, the Air Forces Distributed Common Ground System was processing 1.3 petabytes per month, which equates to about 1,000 hours of full-motion video per day.12 By comparison, in 2014, Facebooks massive 1.2 billion user base was generating four new petabytes of content per day.13

A hybrid cloud combines the best of private and public clouds. Public clouds combination of hardware, software, and storage services are managed by a third party while private clouds are sequestered from the public and protected by a firewall. Combining public services with private clouds and the data center as a hybrid is the new definition of corporate computing, says Judith Hurwitz of Hurwitz and Associates, an IT consulting firm. Not all companies that use some public and some private cloud services have a hybrid cloud. Rather, a hybrid cloud is an environment where the private and public services are used together to create value.14

Top cloud competitors AWS and Microsoft Azure offer a combination of physical and virtual suites, too. They bill their customers on a monthly pay-as- you-go basis. While AWS typically charges customers by the hour, Microsoft Azure and its Machine Learning Service charge by the minute. The attraction to AWS stems from its unalloyed computing power. Depending upon the customer, it can increase scale to thousands of machines and weave neural nets that far exceed TITANs limit. Azure, on the other hand, is less hardware intensive. Customers can have as many virtual machines as they like. Simple to use cookie-cutter software loads make start-up easy. And both firms enable the fast-paced development-to-operations (DevOps) culture that pervades software development and AI today.

But AWS and Microsoft Azure create vendor lock-in, which eventually commits (or locks) customers into using their specific proprietary tools indefinitely. Not TITAN. Its value to the JAIC comes from its agnostic nature, where users are in control. They can choose either the Microsoft or Linux operating systems for DevOps, at no monthly or daily expense, and with zero strings attached.

And, unlike the typical government 1990s-style data center where IT support occurs in-house (or, on-premises), TITAN is managed off-premises. Its servers are separate from the Pentagon but kept secure and effectively reachable by all its customers. TITANs almost two dozen customer agencies can access 430 data feeds via virtual machines worldwide and develop custom software without purchasing additional equipment.

Portability is a plus, too, because administrators can log in almost anywhere to diagnose problems, upload software patches, make updates themselves, or automate the tasks. As an added benefit, like AWS and Microsoft Azure, TITAN has authorities to operate on the DODs unclassified and classified systems, an essential requirement for clouds, according to former Deputy Secretary Patrick Shanahans Cloud Executive Steering Group.15 With a flexible, preaccredited cloud that provides developers value and relative cost savings to the taxpayer, the JAICs choice is clear. A TITAN partnership will help the Pentagon discover the AI advances of tomorrow to improve Americas security and quality of life today.

Disclaimer: The views and opinions expressed or implied in the Journal are those of the authors and should not be construed as carrying the official sanction of the Department of Defense, Air Force, Air Education and Training Command, Air University, or other agencies or departments of the US government.

*About the author: Major Giannetti (MS, St. Josephs University) is a reserve officer assigned to the Headquarters Air Force staff at the Pentagon.

Thisarticlewas originally published in theAir and Space Power JournalVolume 34, Issue 1, Spring 2020 (PDF).

Notes:

1. Summary of the 2018 Department of Defense Artificial Intelligence Strategy: Harnessing AI to Advance Our Security and Prosperity, 12 February 2019, https://media.defense.gov/.

2. Andrew Liptak, Skyborg Could Let F-35 and F-15 Fighter Jets Control Their Own Companion Drones, Verge, 22 May 2019, https://www.theverge.com/.

3. Air Superiority 2030 Flight Plan: Enterprise Capability Collaboration Team, May 2016, https:// http://www.af.mil/.

4. Oriana Pawlyk, China Leaving US Behind on Artificial Intelligence: Air Force General, Military.com, 30 July 2018, https://www.military.com/.

5. US Government Accountability Office (GAO), Cloud Computing: Agencies Have In- creased Usage and Realized Benefits, but Cost and Savings Need to Be Better Tracked, Report to Congressional Requesters, April 2019, https://www.gao.gov/.

6. Federal Cloud Computing Strategy, From Cloud First to Cloud Smart, accessed 24 Oc- tober 2019, https://cloud.cio.gov/.

7. DoD Cloud Update, Deputy Secretary of Defense, 22 June 2018, https://federalnews network.com/. See also GAO, Cloud Computing: Agencies Have Increased Usage and Realized Benefits, but Cost and Savings Need to Be Better Tracked, Report to Congressional Requesters, April 2019, https://www.gao.gov/.

8. Frank Konkel, Tech Firms Ask Congress to Intervene on Pentagon JEDI Contract, Next- Gov, 30 April 2018, https://www.nextgov.com/.

9. Jack Moore, Here Are 10 of the Oldest IT Systems in the Federal Government, NextGov, 25 May 2016, https://www.nextgov.com/.

10. GAO, Decision in the Matter of International Business Machines Corporation, 11 De- cember 2018, https://www.gao.gov/. See also GAO, Decision in the Matter of Oracle America, Inc., 14 November 2018, https://www.gao.gov/.

11. Aaron Gregg and Jay Greene, Fierce Backlash Against Amazon Paved the Way For Microsofts Stunning Pentagon Cloud Win, Washington Post, 30 October 2019, https://www .washingtonpost.com/. See also Jared Serbu, Amazon to Protest DoDs JEDI Cloud Contract, Federal News Network, 14 November 2019, https://federalnewsnetwork.com/.

12. Marc V. Schanz, ISR After Afghanistan, Air Force Magazine, January 2013, http://www .airforcemag.com/.

13. Janet Wiener and Nathan Bronson, Facebooks Top Open Data Problems, Facebook Re- search, 22 October 2014, https://research.fb.com/.

14. Judith Hurwitz et al., What is Hybrid Cloud Computing? Dummies, https://www .dummies.com/.

15.DOD,AcceleratingEnterpriseCloudAdoption,15February2018,https://dod.defense.gov/.

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Quiet Giant: The TITAN Cloud And The Future Of DOD Artificial Intelligence Analysis - Eurasia Review

April 22, 2020 - With artificial intelligence coming up more and more frequently in every area of everyday lifefrom transportation, grocery shopping to Alexa and Siri it might be natural assume that the technology would be helpful in addressing the coronavirus.

That at least so far hasnt turned out to be true, said Alex Engler, a Brookings Institution fellow of government studies.

There have been small pockets of COVID-19 relief in which AI has been helpful at the margins, such as in assisting the construction of some disease spread models.

And there are hypothetical projects where it could be helpful, like to improve the accuracy of COVID-19 testing in conjunction with CT scans and in modeling the protein structure of the virus.

But mostly, the media has been rife with misleading or inaccurate claims Engler called them snake oil about the ability of AI to address the crisis.

Engler spoke about the claim that AI and thermal sensors could sense the presence of COVID-19 in individuals as an example of misinformation in the media.

He sighed when fellow panelist Michelle Richardson, director of the Data and Privacy Project at the Center for Democracy and Technology, referenced an article claiming that AI is being used to tell whether or not citizens are wearing a facemasks.

All currently impossible, and probably unethical, said Engler. For issues such as contact tracing, its still going to take an army of contact tracers, said Brookings Institution fellow Nicol Turner Lee.

The panel then turned to contact tracing and its hot new cousin: Proximity tracing.

Panelists Engler and Richardson debated the feasibility of Apple and Googles proposed proximity tracing applications, in which the spread of disease isnt tracked by individual locations but by the Bluetooth activation on devices that come within a certain distance of each other.

Individuals tested positive for the virus can then send an alert to every device that came into contact with that of the infected persons and tell them that they may have been exposed to the coronavirus.

Engler laid out the barriers: for proximity tracking to work: Americans would have to update their operating system, download an app, and consent to its privacy policy.

Then he laid out the numbers: 81 percent of Americans own smartphones, according to Pew.

Of those, how many would download the app? And then of those, how many people will report that they were sick? And then that number would need to be squared because it takes two to transmit, Engler said.

Richardson identified more practical problems, such as the utility of the model on asymptomatics.

But Engler pushed back against that notion. Proximity tracing would be helpful for asymptomatic because it would give them a good idea of whether of not they are silent carriers and would inform them on how careful they should be in their interactions.

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Artificial Intelligence Not Very Helpful in Addressing the Coronavirus, Say Experts on Brookings Panel - BroadbandBreakfast.com

The corporate affairs ministry is betting on artificial intelligence and data analytics as key elements in the fight against the menace of shell companies as it works to put in place an ecosystem that will have "zero tolerance" for non-compliance with regulations. Continuing efforts to have a robust corporate governance system and ensure high level of compliance, the ministry is also in the process of having an advanced MCA 21 portal.

The portal is used for submission of requisite filings under the companies law and is also a repository of data on corporates in the country. Corporate Affairs Secretary Injeti Srinivas told that once the third version of MCA 21 becomes fully operational, the portal would make it "almost impossible for a shell company to survive."

Generally, shell companies are those which are not complying with regulations and many such entities are allegedly used for money laundering and other illegal activities. Noting that the third version of the portal might be fully operational in a year from now, the secretary said the ecosystem would have zero tolerance for non-compliance.

"Surveillance with respect to compliance will be on auto pilot mode with artificial intelligence (AI) and data analytics," he said. MCA 21 system was started in 2006 and currently, the second version is operational.

There are nearly 12 lakh active companies in the country. Active companies are those that are in compliance with various regulatory requirements under the Companies Act.

Over the past two to three years, the ministry has been deregistering the names of companies from official records for prolonged non-compliance.

"From the trend I see, after 4.25 lakh shell companies having got struck off, the numbers getting added each year is reducing. This is a clear indication that the earlier scenario of shell companies openly indulging in accommodation entries has become a matter of past," Srinivas said. Along with weeding out shell companies, the KYC (Know Your Client) drive for directors and companies has encouraged greater compliance.

"Now, more and more companies are becoming compliant. Compliance levels in terms of filings has crossed 80 per cent. The latest fresh start scheme for companies and settlement scheme for LLPs (Limted Liability Partnerships) are expected to further improve compliance levels... it should soon cross 90 per cent," he noted.

At the end of February, there were around 19,89,777 registered companies in the country. Out of them, 7,44,014 companies were closed, 41,974 entities were in the process of being struck-off and 2,170 were assigned dormant status, as per data compiled by the ministry. According to the ministry, there were 11,95,045 active companies as on February 29.

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Govt bets on artificial intelligence, data analytics to weed out shell cos - Economic Times

Developing an effective vaccine for the current pandemic as well as treatment options for COVID-19 patients is easier said than done. The drug discovery and development processes are by no means a walk through the park, even after a potential lead is identified, countless hurdles still need to be overcome before any drug makes it to the public.

Traditional new drug development is an expensive process and is comprised of a discovery phase that includes target-based drug screening and optimization, among other processes to identify candidates to advance towards further development. Subsequent drug development involves drug combination design with these candidates, and clinical trials. Unfortunately, success rates are very low, said Dean Ho of the National University of Singapore. Ho and his collaborator, Professor Xianting Ding of Shanghai Jiao Tong University, have turned to AI to solve this problem.

For rapidly spreading pathogens with unpredictable clinical courses, [such as the current SARS-CoV-2 outbreak], this process takes too long, even with the assistance of emerging technologies, added Ho.

Even re-purposing known drugs for combination therapies can be quite challenging as choosing the right combination as well as dosage precludes the optimization of treatment outcomes, said Ho. This also limits how many drugs can be simultaneously explored, as conventional drug screening protocols cannot cope with the large data pools that get generated as a result. Given this challenge, traditional new drug development and traditional repurposing are inherently sub-optimal, he added.

In a recent paper published in Advanced Therapeutics, the team developed an AI-based platform called Project IDentif. AI that was shown to quickly screen and identify viable combination therapies for the past, present, and future infections.

Using traditional re-purposing to address SARS or MERS would be very challenging due to the aforementioned issues, said Ding. However, with a platform like IDentif.AI, combination therapy optimisation could be accomplished within days. IDentif.AI is a disease-agnostic platform. As such, it does not have to be reprogrammed, and can be immediately deployed against any novel or established pathogen.

According to the team, the core importance of IDentif.AI is that it simultaneously reconciles the optimal drugs and doses against virtually any disease model from the aforementioned extraordinarily large drug/dose parameter spaces. When good drugs are given at the wrong dose, there may be no treatment efficacy at all, said Ho. At the same time, drug dosing may also have a role in determining which drugs belong in a combination in the first place. Therefore, simultaneously pinpointing the right drugs and doses is absolutely essential.

To run a search, a small set of pre-designed combinations of drugs is given to provide a sample of the drug-dose parameter space. Imagine filling up an entire room with tiny marbles, with each marble representing a possible drug-dose combination. Our job is to find ranked list of best to worst marbles from a room filled with billions of them. This pre-designed set of combinations doesnt pinpoint every single one of them, but at least samples enough of the space to guide us to where the best one is and tells us the drugs/dosages of that optimal combination, explained Ding.

After this first set of experiments is done and the full drug-dose space is essentially mapped out for us, IDentif.AI operates off the concept that drugs and doses (inputs) are related to treatment outcomes (e.g., antiviral activity, drug toxicity) using a smooth quadratic surface (resembling a smooth mountain with one peak), added Ho. This surface is calibrated and mapped out by these set of unique initial experiments such as preventing a virus from infecting a healthy cell or shrinking a tumour (maximizing efficacy), or preventing healthy cell death (minimizing toxicity), etc.).

The map is therefore unique to every study, using different drugs and disease models, and can represent a population of cells, animals, people, or even a single patient, says Ding. To develop a population-optimized regimen, we can take biological samples pooled from a population of patients. This pooled sample can be run against a standardized cell infection model and within days, a combination will be derived. The surface map will be based on the viral/infected cell population represented by a large population of patients.

For a personalized case, if there is a patient with a high viral load, we can run the test using only their own sample, and within days, we can develop a regimen just for that patient, and the surface map will be represented by only their own sample.

And not every single drug combination needs to be screened, as once the team runs a threshold number of experiments, the map can be created and used to guide the team through the rankings of best to worst combinations based on optimal inhibition of infection and minimal toxicity.

What is really neat about IDentif.AI its ability to interrogate such as huge drug-dose space, which has already directly led to successful clinical outcomes and other indications, said Ho. As proof of concept, the team was able to identify an effective combination therapy that successfully inhibited A549 lung cell infection by the vesicular stomatitis virus (VSV) within three days of project. This compared to the months or even years that conventional drug discovery searches require, which are still only capable of exploring a tiny chemical space with poor clinical outcomes, demonstrates this technologys critical importance.

The reality is that the world will be confronted with challenges such as COVID-19 again, said Ho. We simply dont have the time or resources to wait for vaccines or antibody therapy every time. Lessons learned from COVID-19 have shown us that we cannot continue to relinquish valuable time in identifying optimal repurposed combinations. This will not solve the problem and will lead to drug shortages when some could have in fact been used correctly if a systematic optimization process was conducted.

IDentif.AI is also being adapted for additional pathogens such as Dengue fever and even the possibility of a SARS-CoV-2 mutation. If it mutates to a stage where a novel combination will be needed, IDentif.AI will be prepared to rapidly respond, said Ho.

Our aim is to give Project IDentif.AI to the world so that the next epidemic can potentially be contained or prevented using rapidly optimized drug repurposing. Implementing IDentif.AI is remarkable, rapid, and economical. As such, our work has involved healthcare economics, global health security, and surveillance experts to help us develop strategies to scale this towards widespread use on a cost-neutral basis.

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Recruiting artificial intelligence in battle against COVID-19 and future pandemics - Guardian