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As covid-19 disrupted the world in March, online retail giant Amazon struggled to respond to the sudden shift caused by the pandemic. Household items like bottled water and toilet paper, which never ran out of stock, suddenly became in short supply. One- and two-day deliveries were delayed for several days. Though Amazon CEO Jeff Bezos would go on to make $24 billion during the pandemic, initially, the company struggled with adjusting its logistics, transportation, supply chain, purchasing, and third-party seller processes to prioritize stocking and delivering higher-priority items.

Under normal circumstances, Amazons complicated logistics are mostly handled by artificial intelligence algorithms. Honed on billions of sales and deliveries, these systems accurately predict how much of each item will be sold, when to replenish stock at fulfillment centers, and how to bundle deliveries to minimize travel distances. But as the coronavirus pandemic crisis has changed our daily habits and life patterns, those predictions are no longer valid.

In the CPG [consumer packaged goods] industry, the consumer buying patterns during this pandemic has shifted immensely, Rajeev Sharma, SVP and global head of enterprise AI solutions & cognitive engineering at AI consultancy firm Pactera Edge, told Gizmodo. There is a tendency of panic buying of items in larger quantities and of different sizes and quantities. The [AI] models may have never seen such spikes in the past and hence would give less accurate outputs.

Artificial intelligence algorithms are behind many changes to our daily lives in the past decades. They keep spam out of our inboxes and violent content off social media, with mixed results. They fight fraud and money laundering in banks. They help investors make trade decisions and, terrifyingly, assist recruiters in reviewing job applications. And they do all of this millions of times per day, with high efficiencymost of the time. But they are prone to becoming unreliable when rare events like the covid-19 pandemic happen.

Among the many things the coronavirus outbreak has highlighted is how fragile our AI systems are. And as automation continues to become a bigger part of everything we do, we need new approaches to ensure our AI systems remain robust in face of black swan events that cause widespread disruptions.

Key to the commercial success of AI is advances in machine learning, a category of algorithms that develop their behavior by finding and exploiting patterns in very large sets of data. Machine learning and its more popular subset deep learning have been around for decades, but their use had previously been limited due to their intensive data and computational requirements. In the past decade, the abundance of data and advances in processor technology have enabled companies to use machine learning algorithms in new domains such as computer vision, speech recognition, and natural language processing.

When trained on huge data sets, machine learning algorithms often ferret out subtle correlations between data points that would have gone unnoticed to human analysts. These patterns enable them to make forecasts and predictions that are useful most of the time for their designated purpose, even if theyre not always logical. For instance, a machine-learning algorithm that predicts customer behavior might discover that people who eat out at restaurants more often are more likely to shop at a particular kind of grocery store, or maybe customers who shop online a lot are more likely to buy certain brands.

All of those correlations between different variables of the economy are ripe for use by machine learning models, which can leverage them to make better predictions. But those correlations can be ephemeral, and highly context-dependent, David Cox, IBM director at the MIT-IBM Watson AI Lab, told Gizmodo. What happens when the ground conditions change, as they just did globally when covid-19 hit? Customer behavior has radically changed, and many of those old correlations no longer hold. How often you eat out no longer predicts where youll buy groceries, because dramatically fewer people eat out.

As consumers change their habits, the intrinsic correlations between the myriad variables that define the behavior of a supply chain fall apart, and those old prediction models lose their relevance. This can result in depleted warehouses and delayed deliveries on a large scale, as Amazon and other companies have experienced. If your predictions are based on these correlations, without an understanding of the underlying causes and effects that drive those correlations, your predictions will be wrong, said Cox.

The same impact is visible in other areas, such as banking, where machine learning algorithms are tuned to detect and flag sudden changes to the spending habits of customers as possible signs of compromised accounts. According to Teradata, a provider of analytics and machine learning services, one of the companies using its platform to score high-risk transactions saw a fifteen-fold increase in mobile payments as consumers started spending more online and less in physical stores. (Teradata did not disclose the name of the company as a matter of policy.) Fraud-detection algorithms search for anomalies in customer behavior, and such sudden shifts can cause them to flag legitimate transactions as fraudulent. According to the firm, it was able to maintain the accuracy of its banking algorithms and adapt them to the sudden shifts caused by the lockdown.

But the disruption was more fundamental in other areas such as computer vision systems, the algorithms used to detect objects and people in images.

Weve seen several changes in underlying data due to covid-19, which has had an impact on performances of individual AI models as well as end-to-end AI pipelines, said Atif Kureishy, VP of global emerging practices, artificial intelligence and deep learning for Teradata. As people start wearing masks due to the covid-19, we have seen performance decay as facial coverings introduce missed detections in our models.

Teradatas Retail Vision technology uses deep learning models trained on thousands of images to detect and localize people in the video streams of in-store cameras. With powerful and potentially ominous capabilities, the AI also analyzes the video for information such as peoples activities and emotions, and combines it with other data to provide new insights to retailers. The systems performance is closely tied to being able to locate faces in videos, but with most people wearing masks, the AIs performance has seen a dramatic performance drop.

In general, machine and deep learning give us very accurate-yet-shallow models that are very sensitive to changes, whether it is different environmental conditions or panic-driven purchasing behavior by banking customers, Kureishy said.

We humans can extract the underlying rules from the data we observe in the wild. We think in terms of causes and effects, and we apply our mental model of how the world works to understand and adapt to situations we havent seen before.

If you see a car drive off a bridge into the water, you dont need to have seen an accident like that before to predict how it will behave, Cox said. You know something (at least intuitively) about why things float, and you know things about what the car is made of and how it is put together, and you can reason that the car will probably float for a bit, but will eventually take on water and sink.

Machine learning algorithms, on the other hand, can fill the space between the things theyve already seen, but cant discover the underlying rules and causal models that govern their environment. They work fine as long as the new data is not too different from the old one, but as soon as their environment undergoes a radical change, they start to break.

Our machine learning and deep learning models tend to be great at interpolationworking with data that is similar to, but not quite the same as data weve seen beforebut they are often terrible at extrapolationmaking predictions from situations that are outside of their experience, Cox says.

The lack of causal models is an endemic problem in the machine learning community and causes errors regularly. This is what causes Teslas in self-driving mode to crash into concrete barriers and Amazons now-abandoned AI-powered hiring tool to penalize a job applicant for putting womens chess club captain in her resume.

A stark and painful example of AIs failure to understand context happened in March 2019, when a terrorist live-streamed the massacre of 51 people in New Zealand on Facebook. The social networks AI algorithm that moderates violent content failed to detect the gruesome video because it was shot in first-person perspective, and the algorithms had not been trained on similar content. It was taken down manually, and the company struggled to keep it off the platform as users reposted copies of it.

Major events like the global pandemic can have a much more detrimental effect because they trigger these weaknesses in a lot of automated systems, causing all sorts of failures at the same time.

It is imperative to understand that the AI/ML models trained on consumer behavior data are bound to suffer in terms of their accuracy of prediction and potency of recommendations under a black swan event like the pandemic, said Pacteras Sharma. This is because the AI/ML models may have never seen that kind of shifts in the features that are used to train them. Every AI platform engineer is fully aware of this.

This doesnt mean that the AI models are wrong or erroneous, Sharma pointed out, but implied that they need to be continuously trained on new data and scenarios. We also need to understand and address the limits of the AI systems we deploy in businesses and organizations.

Sharma described, for example, an AI that classifies credit applications as Good Credit or Bad Credit and passes on the rating to another automated system that approves or rejects applications. If owing to some situations (like this pandemic), there is a surge in the number of applicants with poor credentials, Sharma said, the models may have a challenge in their ability to rate with high accuracy.

As the worlds corporations increasingly turn to automated, AI-powered solutions for deciding the fate of their human clients, even when working as designed, these systems can have devastating implications for those applying for credit. In this case, however, the automated system would need to be explicitly adjusted to deal with the new rules, or the final decisions can be deferred to a human expert to prevent the organization from accruing high risk clients on its books.

Under the present circumstances of the pandemic, where model accuracy or recommendations no longer hold true, the downstream automated processes may need to be put through a speed breaker like a human-in-the-loop for added due diligence, he said.

IBMs Cox believes if we manage to integrate our own understanding of the world into AI systems, they will be able to handle black swan events like the covid-19 outbreak.

We must build systems that actually model the causal structure of the world, so that they are able to cope with a rapidly changing world and solve problems in more flexible ways, he said.

MIT-IBM Watson AI Lab, where Cox works, has been working on neurosymbolic systems that bring together deep learning with classic, symbolic AI techniques. In symbolic AI, human programmers explicitly specify the rules and details of the systems behavior instead of training it on data. Symbolic AI was dominant before the rise of deep learning and is better suited for environments where the rules are clearcut. On the other hand, it lacks the ability of deep learning systems to deal with unstructured data such as images and text documents.

The combination of symbolic AI and machine learning has helped create systems that can learn from the world, but also use logic and reasoning to solve problems, Cox said.

IBMs neurosymbolic AI is still in the research and experimentation stage. The company is testing it in several domains, including banking.

Teradatas Kureishy pointed to another problem that is plaguing the AI community: labeled data. Most machine learning systems are supervised, which means before they can perform their functions, they need to be trained on huge amounts of data annotated by humans. As conditions change, the machine learning models need new labeled data to adjust themselves to new situations.

Kureishy suggested that the use of active learning can, to a degree, help address the problem. In active learning models, human operators are constantly monitoring the performance of machine learning algorithms and provide them with new labeled data in areas where their performance starts to degrade. These active learning activities require both human-in-the-loop and alarms for human intervention to choose what data needs to be relabeled, based on quality constraints, Kureishy said.

But as automated systems continue to expand, human efforts fail to meet the growing demand for labeled data. The rise of data-hungry deep learning systems has given birth to a multibillion-dollar data-labeling industry, often powered by digital sweatshops with underpaid workers in poor countries. And the industry still struggles to create enough annotated data to keep machine learning models up to date. We will need deep learning systems that can learn from new data with little or no help from humans.

As supervised learning models are more common in the enterprise, they need to be data-efficient so that they can adapt much faster to changing behaviors, Kureishy said. If we keep relying on humans to provide labeled data, AI adaptation to novel situations will always be bounded by how fast humans can provide those labels.

Deep learning models that need little or no manually labeled data is an active area of AI research. In last years AAAI Conference, deep learning pioneer Yann LeCun discussed progress in self-supervised learning, a type of deep learning algorithm that, like a child, can explore the world by itself without being specifically instructed on every single detail.

I think self-supervised learning is the future. This is whats going to allow our AI systems to go to the next level, perhaps learn enough background knowledge about the world by observation, so that some sort of common sense may emerge, LeCun said in his speech at the conference.

But as is the norm in the AI industry, it takes yearsif not decadesbefore such efforts become commercially viable products. In the meantime, we need to acknowledge and embrace the power and limits of current AI.

These are not your static IT systems, Sharma says. Enterprise AI solutions are never done. They need constant re-training. They are living, breathing engines sitting in the infrastructure. It would be wrong to assume that you build an AI platform and walk away.

Ben Dickson is a software engineer, tech analyst, and the founder of TechTalks.

Link:
How the Coronavirus Pandemic Is Breaking Artificial Intelligence and How to Fix It - Gizmodo

Currently ethics for AI focuses too much on high-level principles. Using AI to deal with crises would mean anticipating problems before they happen and building safety and reliability into it. Plus, ethics should be part of how AI is built and used, not an add-on or afterthought. Researchers and engineers need to think through the implications of what they build.

By Will HeavenJess Whittlestone at the Leverhulme Centre for the Future of Intelligence at the University of Cambridge and her colleagues published a comment piece in Nature Machine Intelligence this week arguing that if artificial intelligence is going to help in a crisis, we need a new, faster way of doing AI ethics, which they call ethics for urgency. For Whittlestone, this means anticipating problems before they happen, finding better ways

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Artificial intelligence needs an update on ethics to be able to help humanity in times of crisis - Economic Times

Theres still a lot thats not known about the novel coronavirus SARS-CoV-2 and COVID-19, the disease it causes. What leads some people to have mild symptoms and others to end up in the hospital? Do masks help stop the spread? What are the economic and political implications of the pandemic?

As researchers try to address many of these questions, many of which will not have a simple yes or no answer, people are also trying to figure out how to keep themselves and their families safe. But between the 24-hour news cycle, hundreds of preprint research articles, and guidelines that vary between regional, state, and federal governments, how can people best navigate through such vast amounts of information?

Using insights from the field of natural language processing and artificial intelligence, computer scientist Dan Roth and the Cognitive Computation Group are developing an online platform to help users find relevant and trustworthy information about the novel coronavirus. As part of a broader effort by his group to develop tools for navigating information pollution, this platform is devoted to identifying the numerous perspectives that a single query might have, showing the evidence that supports each perspective and organizing results, along with each sources trustworthiness, so users can better understand what is known, by whom, and why.

Creating these types of automated platforms represents a huge challenge for researchers in the field of natural language processing and machine learning because of the complexity of human language and communication. Language is ambiguous. Every word, depending on context, could mean completely different things, says Roth. And language is variable. Everything you want to say, you can say in different ways. To automate this process, we have to get around these two key difficulties, and this is where the challenge is coming from.

Thanks to numerous conceptual and theoretical advances, the Cognitive Computational Groups fundamental research in natural language understanding has allowed them to apply their research insights and to develop automated systems that can better understand the contents of human language, such as what is being written about in a news article or scientific paper. Roth and his team have been working on issues related to information pollution for many years and are now applying what theyve learned to information about the novel coronavirus.

Information pollution comes in many forms, including biases, misinformation, and disinformation, and because of the sheer volume of information the process of sorting fact from fiction needs automated support. Its very easy to publish information, says Roth, adding that while organizations like FactCheck.org, a project of Penns Annenberg Public Policy Center, manually verify the validity of many claims, theres not enough human power to fact check every claim being posted on the Internet.

And fact checking alone isnt enough to address all of the problems of information pollution, says Ph.D. student Sihao Chen. Take the question of whether people should wear face masks: The answer to that question has changed dramatically in the past couple months, and the reason for that change is multi-faceted, he says. You couldnt find an objective truth attached to that specific question, and the answer to that question is context-dependent. Fact checking alone doesnt solve this problem because theres no single answer. This is why the team says that identifying various perspectives along with evidence that supports them is important.

To help address both of these hurdles, the COVID-19 search platform visualizes results that include a sources level of trustworthiness while also highlighting different perspectives. This is different from how online search engines display information, where top results are based on popularity and keyword match and where its not easy to see how the arguments in articles compare to one another. On this platform, however, instead of displaying articles on an individual basis, they are organized based on the claims they make.

Search engines make a point not to touch the information and not to give suggestions and organize this material, says Roth. The redundancy of information by itself is quite often misleading and leads to bias, since people tend to think that seeing something many times makes it more correct. Here, if there are 500 articles that are saying the same thing, we cluster them together and say, All these articles are quoting the same sources, so just focus on one of them. Then, these other articles are interviewing other people and making different claims, so you can sample from different clusters.

When visiting the website, users can enter a question, claim, or topic into the search bar, and results are grouped together based on the similarity of perspectives. Since everything is set up to be automated, the researchers are eager to share this first iteration of the platform with the community so they can improve the language-processing models. Its a community effort, says Roth, adding that their platform was designed to be transparent and open source so that they can easily collaborate with others.

Chen hopes that their efforts support both the users who are interested in sorting through COVID-19 information pollution as well as fellow researchers in the field of natural language processing. We want to help everyone whos interested in reading news like this, and at the same time we want to build better techniques to accommodate that need, says Chen.

Dan Roth is the Eduardo D. Glandt Distinguished Professor in the Department of Computer and Information Science in the School of Engineering and Applied Science at the University of Pennsylvania.

The online search platform is available on the Penn Information Pollution project website.

Additional information and resources on COVID-19 are available at https://coronavirus.upenn.edu/.

Visit link:
Navigating 'information pollution' with the help of artificial intelligence - Penn: Office of University Communications

There are more signs that Ohio once again is in play for the presidential election. The Columbus Dispatch reports that The Lincoln Project super PAC "is aligning with another group, Republican Voters Against Trump, for what they are calling 'Operation Grant,' a nod to Ohio native Ulysses S. Grant. That alliance's plan kicks off with a Lincoln Project advertisement attacking Trump's response to the COVID-19 pandemic that will air on broadcast and cable television from Friday through Monday in Columbus, Cleveland, Akron and Canton." The paper says the efforts "also will include a ground campaign that has had to move onto the web during the pandemic," according to John Weaver, a co-founder of the Lincoln Project and former top political adviser to former Ohio Gov. John Kasich. Weaver said the groups have 20,000 volunteers in Ohio and are planning a town hall meeting for next week. Meanwhile, the New York Times reports that two prominent polls of Ohio last month "showed the presidential race in a statistical tie. Turnout in the Ohio primary elections in April was higher for Democrats than Republicans for the first time in a dozen years, evidence of enthusiasm in the Democratic base. And the Trump campaign recently booked $18.4 million in fall TV ads in Ohio, more than in any state besides Florida a sign that (President Donald) Trump is on the defensive in a state that until recently seemed locked down for Republicans."

MLive Media Group in Michigan this week announced it will transfer production of its eight newspapers to Cleveland and close its printing facility outside Grand Rapids, Mich. The media company's eight newspapers currently printed at the production facility in Walker, Mich., will instead be printed in Cleveland beginning Oct. 5, said Tim Gruber, president and chief revenue officer of MLive Media Group. The newspapers will be printed at the same facility that prints The Plain Dealer. It's a case of corporate efficiencies, as Cleveland.com and MLive Media Group are owned by Advance Local.

Ohio's a great place to live if (when times are normal) you enjoy a good bar, according to Esquire. The magazine's new list of the best bars in America has no less than four Ohio spots: The Happy Dog and the Spotted Owl, both in Cleveland, plus Wdka Bar in Cincinnati and Law Bird in Columbus. Esquire calls The Happy Dog "a rock 'n' roll bar to its bones, with vinyl booths, Christmas lights, a no-bullshit beer list, and mics already set up for any ragged busker who's brave or drunk enough to climb onstage." The Spotted Owl won over Esquire's writer, who notes, "I was staring at a paper wheel that looked like a scrap of Ouija board. The wheel had words on it: bitter, potent, fruity, tropical, etc. Instead of ordering from a cocktail menu, I was instructed to select my desired mood (I went with relax) and a range of flavors (I went with umami and ginger) from this wheel. The bartender would then conjure something for me to drink. I figured this was all some sort of gimmick until I tasted my cocktail, which had been made with gin, lime, and a pho syrup yes, the Vietnamese soup. It was absurdly delicious, and it was then I decided the Spotted Owl is a next-wave mystic temple of cocktailing."

Grooming might not be all that high on your list of priorities during the pandemic, but if you're a man with a beard, you might want to check out this piece from The New York Times that offers tips for getting your bear under control and takes note of a product favored by Cavaliers center Andre Drummond. That product is a Kuschelbr, a heated beard-straightening brush made by Masc by Jeff Chastain. It has heated teeth that emerge from a heated plate, a compact version of the full-size hair-straightening brushes marketed to women. The Times notes that Drummond made a video of himself straightening his beard with it.

You also can follow me on Twitter for more news about business and Northeast Ohio.

Read more here:
Artificial intelligence data centers take on greater importance in facing the very real threat of COVID-19 - Crain's Cleveland Business

PALM BEACH, Florida, July 16, 2020 /PRNewswire/ -- The global Artificial Intelligence (AI) -based Drone Software market size is expected to continue its rapid growth through the next five years, according to several reports. A Research And Markets reportsaid that: "Digital industries are now implementing AI in their devices to improve in their fields across the globe. Application of AI in drone is one such advancement which has brought a revolutionary change in the operations of the industries. AI enables storing and managing the data in bulk which enables the drones to give better performance. The application of AI can enable the drones to function as per the user's command and with longer distance coverage. In addition, AI integrated drone enables the industries to keep a bird-eye view of the land for vigilance & mapping purpose. The increased income levels have brought up new demands that have resulted in increasing supply of goods. Manufacturers are bringing in new features by implementing AI in their devices such as mobiles so as to make more appeal for the consumers to buy. So, the adoption in smartphones and increasing demands in aerial and drone services has made manufacturers to implement AI in drones across the globe. The drones are being in use over various s sectors such as agriculture, military and defense, media and entertainment, and others. Hence it is expected that AI-integrated drones will have significant growth in the near future. Active tech companies in the markets this week include Plymouth Rock Technologies Inc. (CSE: PRT) (OTCQB: PLRTF), Draganfly Inc. (OTCQB: DFLYF) (CSE: DFLY), Drone Delivery Canada Corp. (OTCQX: TAKOF) (TSX-V: FLT.V), Kratos Defense & Security Solutions, Inc. (NASDAQ: KTOS), AgEagle Aerial Systems, Inc. (NYSE: UAVS).

The global AI in Drone market is geographically analyzed into North America, Europe, Asia-Pacific, and Rest of the World. Asia-Pacific is the hub of drone manufacturers due to which, the demand for advanced technologies is expected to increase in the region. North America leads the market due to the presence of numerous key players in the region followed by Europe which has a few key players to dominate the market.

Plymouth Rock Technologies Inc. (CSE: PRT) (OTCQB: PLRTF) BREAKING NEWS: PLYMOUTH ROCK TECHNOLOGIES FORMS STRATEGIC ALLIANCE WITH HUMMINGBIRD DRONES TO FIGHT WILDFIRE THREATS - Plymouth Rock Technologies ("Plymouth Rock", "PRT", or the "Company"), a leader in the development of cutting-edge threat detection technologies, is pleased to announce a strategic alliance with Hummingbird Drones ("Hummingbird") fire AI. (Artificial Intelligence) for wildfire analysis from PRT's fleet of drones.

Fire AI.is a division of Hummingbird Drones,an infrared service provider in Canada, and has been used as their in-house hotspot detection platform for wildfires for the past three years.

"Live actionable data is precisely what the PRT unmanned aviation platforms were designed to deliver," stated Carl Cagliarini, Chief Strategy Officer of PRT. "This partnership is a further step in our mission centric focus. To date, commercially adapted Drones have used Wi-Fi frequencies with a limited range, usually under 2-3 miles. The X1 has both short-range capabilities, along with an optional military-grade system that enables high bandwidth data feeds up to 60 miles. These capabilities combined with best in class artificial intelligence applications such as fire AI. will deliver essential data when moments matter".

In the pursuit of providing the highest quality of intelligence, Hummingbird developed a wildfire-focused, data analytics software known as fire AI.. Bringing fire AI. to the public provides the global community with the highest quality of wildfire data analytics. Maximizingthe potential of infrared data sets,fire AI.specializes inhigh resolution hotspot maps, providingpreciselocationaldatafor fire crews in pursuit of heat. These aerial maps provide fire managers with higher levels of confidence and fire crews with more effective,accurate data to extinguish and efficiently reallocate resources.

"We believe that the analytic capability of fire AI. combined with the overall capabilities of the Plymouth Rock UAS platform will prove itself as a formidable tool", stated Robert Atwood CEO and Founder at Hummingbird Drones Inc.

Due to the vast data analysis combined with data download constraints of almost all UAS platforms, fire AI. is currently a post-processing service, where the ground is scanned and footage data is removed from the drone and uploaded to the fire AI. portal, which after process delivers fast data analytics results, to the fire management authorities. This service has been an invaluable tool in helping incident commanders and fire crews tackle blazes more effectively. The incorporation of the fire AI. into the X1 and XV platform will involve using this tried and tested method, whilst also utilizing PRT's high speed VPN data capabilities that will enable a connection directly to fire AI. servers to get analytics to the fire fighters as close to real time as possible.

The fire AI. capability will be a standard configuration on all firefighting X1 and XV platforms for immediate benefit. This will include PRT assets deployed within the USA and Australia.Read this and more news for PRT at: https://www.plyrotech.com/news/

Other recent developments in the tech industry include:

Draganfly Inc. (OTCQB: DFLYF) (CSE: DFLY) an award-winning, industry-leading manufacturer within the commercial Unmanned Aerial Vehicle ("UAV"), Remotely Piloted Aircraft Systems ("RPAS"), and Unmanned Vehicle Systems ("UVS") sectors, recently announced that John M. Mitnick, former General Counsel of the U.S. Department of Homeland Security ("DHS") and Raytheon senior executive, was elected to the Board of Directors of Draganfly at the Company's annual general meeting on June 18, 2020. All of the matters submitted to shareholders for approval, as set out in the Company's management information circular, were approved by the requisite majority of votes cast at the annual general meeting of shareholders.

Drone Delivery Canada Corp. (OTCQX: TAKOF) (TSX-V: FLT.V) recently announcedthat on June 26th, 2020 it successfully completed Phase Two of its AED (Automated External Defibrillator) On The Fly project with Peel Region Paramedics and Sunnybrook Centre for Prehospital Medicine. Building on the success of Phase One of the study, the Company was able to demonstrate ease of use of its AED drone solution when provided to community responders in a simulated cardiac arrest scenario. The testing further validates that usingDDC's proprietary drone delivery platform with cargo drop functionality to deliver rapid first responder technology via drone may reduce response time to cardiac arrest patients in the field while being utilized by lay responders.

On June 26th, 2019, the Company had announced a 100% successful Phase One of the project. Phase Two utilized the Sparrow, with the new cargo drop capability and a new audio announcement system, to drop an AED where a designated lay bystander would then retrieve the AED and apply it to a simulated cardiac arrest patient in a rural environment. Multiple pairs of lay bystanders and simulated cardiac arrest patients in multiple locations were used to test the AED drone solution. Response time to drop, retrieve and apply an AED, and physiological and psychological human factors in a stressful situation were measured during the testing.

Kratos Defense & Security Solutions, Inc. (NASDAQ: KTOS) a leading National Security Solutions provider, recently announced that it has recently received approximately $30 million in contract awards for Command, Control, Computing, Communication, Combat, Intelligence, Surveillance and Reconnaissance (C5ISR) Systems, focused primarily on missile defense related combat systems. Kratos is an industry leader in the rapid development, demonstration and fielding of affordable leading technology products and solutions in support of the United States and its allies' national security missions. Kratos C5ISR Modular Systems Business is an industry leader in manufacturing, producing and delivering C5ISR Systems for Missile, Radar, High Power Directed Energy, Ballistic Missile Defense, Unmanned Aerial Vehicle, Chemical, Biological, Radiation, Nuclear and High Explosive (CBRNE) and other programs and applications. Work under these recent program awards will be performed at secure Kratos manufacturing and production facilities. The majority of the performance under these contract awards will be completed over the next 24 months. Due to customer, competitive and other considerations, no additional information will be provided related to these U.S. National Security related program awards.

AgEagle Aerial Systems, Inc. (NYSE: UAVS) J. Michael Drozd, new Chief Executive Officer ofthe company, an industry leading provider of unmanned aerial vehicles and advanced aerial imagery, data collection and analytics solutions, recently issued a letter to the Company's shareholders commenting on the Company's vision, defined growth strategy and key developments which have occurred since he assumed the helm of AgEagle on May 18, 2020. Drozd stated:

"I'd like to begin by sharing how pleased and privileged I am to have been selected by the Board to help lead AgEagle through its next critical phase of innovation and evolution. Since my first day on the job, I have immersed myself in meeting with our talented team; fully understanding the depth and capabilities of our software development and manufacturing operations; carefully evaluating our core strengths and many market opportunities; and attaining meaningful clarity into the dynamic, high growth company we are actively engaged in building. This has been and will undoubtedly remain an exciting and ongoing process.

"After an extensive evaluation process, I firmly believe that AgEagle has what it takes to become one of the leading, most trusted commercial drone technology, services and solutions providers globally. To achieve that aim, we are committing to a highly focused growth strategy centered on three primary industry sectors: U.S.-based drone hardware and subcomponent design, manufacturing, assembling and testing; Drone package delivery services; and Hemp cultivation registration, oversight, compliance, reporting and data analytics software solutions for government and commercial customers."

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