The UK government has enlisted Palantir, a US big data firm founded by Peter Thiel, and Faculty, a startup that specializes in professional data science strategy, software and skills training to combat the spread of COVID-19. While this may mobilize concern about privacy issues, it should be noted that collecting big data including private health data from the general population is necessary for governments to make informed decisions on how to halt the spread of COVID-19, discern which members of society are most vulnerable, and learn which treatment options are the most effective.
Palantir understands there is cause for concern with user privacy which is why they released an outline of their best practices for using data during a crisis. Palantir stated: “Knowing how to competently apply data science to the right set of problems will serve as a critical asset for augmenting and enhancing comprehensive strategies to battle this public health crisis”, this is undeniably true.
They also stated the following which is an acknowledgment of the precarious risk society embarks upon accessing this type of big data sharing: “Rich data sources often inspire unanticipated — even rogue — analyses. Establish and enforce collective ground rules on how the data should be used and who should have what levels of access to, and use of, data. Misuse of data can result in public mistrust in institutions. Even the most well-meaning of problem solvers sometimes are blinded to the risks of the solutions they create.”
What type of data is the UK government collecting? Currently, the appropriate data which is needed to tackle the COVID-19 challenge. As reported by Guardian, current anonymized data includes gender, protected health information, Covid-19 test results, the contents of people’s calls to the National Health Service (NHS), health advice line 111 and clinical information about those in intensive care.
While data privacy should be anonymized so it can never be traced to a specific individual, we need this data for machine learning systems to analyze. Deep learning systems use this type of big data to identify patterns, and data points which are overlooked by humans. Something as trivial as gender, may reveal important insights, an example could be that diabetic men are a more vulnerable segment of the population than diabetic women. Certain treatment options may work better for different age groups, genders, genetic backgrounds, etc.
Instead of being vilified we should give the UK government the benefit of the doubt. This type of data collecting, and data sharing efforts by all facets of the health care system, is something that should be maintained for the long-term. This could serve us in the future for both fighting future pandemics, as well as regular health issues, cancer, and other physiological ailments.
Currently, the project uses a “pseudo NHS number” to cross-match large datasets, including a master patient index, an existing NHS resource that uses “social marketing data” to segment the British population into different “types” at the household level. While it remains to be seen if this is the most effective data distribution method, we do have concerns with some aspects of the data collection process.
Currently, phone location data is being collected. While narrowing down the data to a postal code may be appropriate, it is unnecessary to directly pinpoint the exact source of the phone call, as this information cannot be anonymized or randomized. This could cause sick individuals to fear using the phoneline which may result in unnecessary deaths from those who most need help.
British citizens should be alarmed regarding the phone location datapoint which is unnecessary to effectively train a deep learning algorithm but can be directly used to track an individual.
Should the UK government continue in its path to collect this type of big data, and fix the issues outlined above, as well as other privacy/user rights issues of which we are unaware of, it may be appropriate for the UK to enlist the assistance of the European Union to gather similar datapoints from their respective populations. After all, how deep learning works is the more data that is collected, the more effective the algorithm. This would go a long way in bridging nations after the closure of international borders.
We urge careful analysis, and the assistance of a non-profit entity to ensure that the UK government does not abuse the information it gathers. Nonetheless, it should be acknowledged that this is an important step towards fighting COVID-19.
Intel AI Powered Virtual Assistant Mobilized to Assist Reopening of Military Museum
A Canadian museum is safely reopening from its pandemic closure with the help of a virtual
The Ontario Regiment Museum houses North America’s largest collection of operational military vehicles, many dating back to the 1940s. The collection allows the public to experience a piece of history, both at the museum and through the historical films in which the vehicles often appear.
At the start of the pandemic in early 2020, CloudConstable began working with the museum to design Master Corporal Lana as an AI virtual assistant who would greet
Before Lana’s deployment, COVID-19 closed the museum to the public. But with over 120 military vehicles that need constant servicing and driving, the museum needed its volunteers to continue their essential maintenance and operations work at the site.
“The Ontario Regiment Museum is one of the few museums in the world with such a large and diverse collection of operating military vehicles, which help people experience history in a very real way. Regular maintenance is crucial, even during the worst of the pandemic, which is why we turned to CloudConstable and Intel to help build an autonomous solution,” said Jeremy Blowers, executive director of the Ontario Regiment Museum.
CloudConstable relied on the Intel RealSense team’s insight that Lana’s existing and unique capabilities — already built on the Intel RealSense Depth Camera and using the Intel® Distribution of OpenVINO™ toolkit for accelerated machine vision inferences — could be extended for a more comprehensive and safer COVID-19 screening solution. Adding an Intel® NUC 9 Pro with Intel Active Management Technology, as part of the Intel vPro® platform, the team reworked Lana to take temperatures via thermal scans and ask a series of questions to assess COVID-19 risk and exposure. Since June, Lana has provided an enhanced, fully automated and touchless screening process so volunteers can continue to do their important work with the vehicles.
“Intel RealSense technology is used to develop products that enrich people’s lives by enabling machines and devices to perceive the world in 3D. CloudConstable leverages Intel’s technology to help create a state-of-the-art natural voice and vision interface with touchless, self-service COVID-19 screening,” said Joel Hagberg, head of product management and marketing, Intel’s RealSense group.
With the Ontario Regiment Museum now preparing to reopen to the public, CloudConstable, along with Intel, is now working to bring the new COVID-19 protection capabilities into the original concept for Lana as a greeter for visitors. Lana will greet visitors, provide contactless check-in, scan temperatures and ensure the museum adheres to visitor limits and other COVID-19 health protection protocols. Eventually, she’ll even thank them for coming and help visitors keep in touch with all the latest activities at the museum.
U.S. National Institutes of Health Turns to AI for Fight Against COVID-19
The National Institutes of Health has turned to artificial intelligence (AI) for diagnosis, treatment, and monitoring of COVID-19 through the creation of the Medical Imaging and Data Resource Center (MIDRC).
What is the MIDRC?
The MIDRC consists of multiple institutions working together, led by the National Institute of Biomedical Imaging and Bioengineering (NIBIB), which is part of NIH. The collaboration aims to develop new technologies that will help physicians detect the virus early and create personalized therapies for patients.
Bruce J. Tromberg, Ph.D., is Director of the NIBIB.
“This program is particularly exciting because it will give us new ways to rapidly turn scientific findings into practical imaging tools that benefit COVID-19 patients,” Tromberg said. “It unites leaders in medical imaging and artificial intelligence from academia, professional societies, industry, and government to take on this important challenge.”
One of the ways experts assess the severity of a COVID-19 case is by looking at the features of infected lungs and hearts on medical images. This can also help predict how a patient will respond to treatment and improve the overall outcomes.
The big challenge surrounding this method is that it’s difficult to rapidly and accurately identify these signatures and evaluate the information, especially when there are other clinical symptoms and tests.
Machine Learning Algorithms
The MIDRC aims to develop and implement new and effective diagnostics. One of these will be machine learning algorithms, which solve some of those issues. Machine learning algorithms can help physicians optimize treatment after accurately and rapidly assessing the disease.
Guoying Liu, Ph.D., is the NIBIB scientific program lead on the new approach.
“This effort will gather a large repository of COVID-19 chest images,” Liu explained, “allowing researchers to evaluate both lung and cardiac tissue data, ask critical research questions, and develop predictive COVID-19 imagining signatures that can be delivered to healthcare providers.”
Krishna Kandarpa, M.D., Ph.D., is director of research sciences and strategic directions at NIBIB.
“This major initiative responds to the international imagining community’s expressed unmet need for a secure technological network to enable the development and ethical application of artificial intelligence to make the best medical decisions for COVID-19 patients,” Kandarpa said. “Eventually, the approaches developed could benefit other conditions as well.”
Some of the other major names on this project include Maryellen L. Giger, Ph.D., who is taking the lead. She is Professor of Radiology, Committee on Medical Physics at the University of Chicago. Co-investigators include Etta Pisano, MD, and Michael Tikin, MS, from the American College of Radiology (ACR), Curtis Langlotz, MD, Ph.D., and Adam Flanders, MD, from the Radiological Society of North America (RSNA), and Paul Kinahan, Ph.D., from the American Association of Physicists in Medicine (AAPM).
Through collaborations between the ACR, RSNA, and AAPM, the MIDRC will work toward rapid collection, analysis, and dissemination of imagining and other clinical data.
While many believe that the adoption of AI for pandemic-related solutions is long overdue, the National Institutes of Health’s new MIDRC is a step in that direction. It is only a matter of time before AI plays a major role in the detection, response, and eventual prevention of global pandemic causing viruses.
Supply Chains after Covid-19: How Autonomous Solutions are Changing the Game
Early measures by the material handling industry to curb the coronavirus pandemic saw border and plant closures all over the world. While for machine and vehicle manufacturers in eastern Europe and China production is in full swing again, the rest of Europe, North America and other western countries are struggling to get back to their pre-Covid-19 production strength.
Restrictions in freight transport across Europe are still very noticeable and are causing bottlenecks in supply chains. The strict stay-at-home-orders imposed in most European countries to contain the pandemic have had and are having a major impact on industrial production as the personnel are simply missing on site.
Security measures like keeping minimum distance or wearing masks are proving to be an organizational challenge for many production facilities around the world. In order to be able to comply with the safety requirements, in many premises only half of the workforce is allowed on-site, or the production line is divided into shifts. This in turn is restricting the flow of goods. Even when components exist, they stockpile, and cannot be integrated due to a lack of staff or time for those on reduced activity.
After the crisis, the industry will face new challenges. There is already speculation about a trend moving away from globalization towards regionalization. It is not necessarily the sourcing of production that could be affected by a possible regionalization, but rather warehouse management. Regardless of restricted supply chains, access to material inventory is essential for every production line. As a lesson-learned from the Covid-19 crisis, we could see a move from large central warehouses to smaller regional warehouses.
The automotive industry, for instance, was hit hard by supply shortages due to restrictions stemming from the pandemic. Automotive OEMs and their suppliers have long and complex supply chains with many steps in the production process. After the experienced bottlenecks, their follow-up measures might include a diversification of suppliers, as well as the decentralization of inventories in order to maintain agility in case of a crisis.
This presupposes digitalization of warehouse management: if existing stockpiling data is used rationally, transparency in the entire supply chain can easily be created. This would mean everyone involved could use existing data to optimize their processes. This requires intelligent warehouse management systems (WMS) and intelligent solutions for material handling to work hand-in-hand.
Automated guided vehicles (AGVs) are not a novelty in in-house material handling processes but their evolution could hold the key to the industry’s future. Since their introduction, technologies in autonomous vehicles have developed rapidly, enabling the transport of people in complex environments. Bringing this level of intelligence to industrial vehicles hails the next era of logistics automation: new AGV generations accessing complex outdoor environments are a real game changer and could potentially become more attractive after the Covid-19 crisis. As these vehicles become increasingly deployed in dynamic environments without infrastructure, these technologies have quickly migrated from manufacturing applications to supporting warehousing for manufacturing and distribution.
The process automation in supply chains – part of the so-called Industry 4.0 – will play a significant role. It could allow companies to keep or even reduce overall logistics operational costs, and eventually maintain a minimal operational flow even in times of crisis.
Rethinking the industrial supply chain: intelligence is key
The autonomous tow tractor TractEasy by autonomous technology leader EasyMile is a perfect example of this new generation. It masters the automation of outdoor and intralogistics processes on factory premises, logistics centers and airports. The company is currently demonstrating the maturity of these autonomous tow tractors at automotive supplier Peugeot Société Anonyme (PSA)’s manufacturing plant in Sochaux, France. Operated by GEODIS, PSA is using the tractor to find opportunities to optimize costs in the flows on its site.
The impact of the ongoing crisis has revealed the fragility of existing supply chains. Companies are reassessing large and complex procurement networks. Ultimately, the Covid -19 pandemic is putting supply chains to the test, but global supply chains should be prepared for crises as part of risk management anyway. The sheer number of natural disasters in recent years has meant that the international supply chains have been repeatedly overhauled. From this point of view, the Covid-19 crisis is an example of unpredictability that supply chains have to adapt to in order to develop.
What is certain is that the industry is on an upward trend toward more sustainable and stable industrial ecosystems. Automation is a concept that will play a major role in these future considerations, from manufacturers to logistic operators across the globe.
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