Robots have come a long way in their functionality, but there are still many sensing capabilities that can’t be achieved by these systems that compare to how humans interact with their environments.

To solve this issue, researchers at the National University of Singapore (NUS) have created a complex artificial brain system called NeuTouch that mimics human neural networks to provide neuromorphic processing for robotic systems. This should provide them with more sophisticated sensing functionality, including what’s needed to pick up, hold, and manipulate objects in a way that mimics human interactions.

The current problem with robotic systems is they depend on visual processing rather than the actual sense of touch that humans have to help us handle and manipulate objects, said Benjamin C.K. Tee, an assistant professor at NUS Materials Science and Engineering, who co-led the development of NeuTouch with Assistant Professor Harold Soh from NUS Computer Science.

“Robots need to have a sense of touch in order to interact better with humans, but robots today still cannot feel objects very well,” he told Design News. “Touch sensing allows robots to perceive objects based on their physical properties, e.g., surface texture, weight, and stiffness. Such tactile sensing capability augments the robot’s perception of the physical world with information beyond what standard vision and auditory modalities can provide.”

Building a Complete System

The new solution builds on technology Tee and fellow researchers created last year when they developed an artificial nervous system that can give robots and prosthetic devices a sense of touch on par with or even better than human skin.

This system, called Asynchronous Coded Electronic Skin (ACES), can detect touches more than 1,000 times faster than the human sensory nervous system, as well as identify the shape, texture, and hardness of objects 10 times faster than the blink of an eye, Design News reported at the time.

NeuTouch can process sensory data from ACES using neuromorphic technology, which is an area of computing that emulates the neural structure and operation of the human brain. To do this, researchers integrated Intel’s Loihi neuromorphic research chip into the system, Tee said.

By using ACES, NeuTouch can mimic the function of the fast-adapting (FA) mechano-receptors of a human fingertip, which captures dynamic pressure, or dynamic skin deformations, Tee said.

 “FA responses are crucial for dexterous manipulation tasks that require rapid detection of object slippage, object hardness, and local curvature,” he told Design News.

Testing for Results

To test the system, researchers fitted a robotic hand with ACES and used it to read braille, passing the tactile data to Loihi via the cloud to convert the micro bumps felt by the hand into a semantic meaning.

In these experiments, Loihi achieved over 92 percent accuracy in classifying the Braille letters, while using 20 times less power than a normal microprocessor.

In other tests, researchers demonstrated how they could improve the robot’s perception capabilities by combining both vision and touch data in a spiking neural network. They tasked a robot equipped with both artificial skin and vision sensors to classify various opaque containers containing differing amounts of liquid. They also tested the system’s ability to identify rotational slip, which is important for stable grasping.

In both tests, the spiking neural network that used both vision and touch data was able to classify objects and detect object slippage with 10 percent more accuracy than a system that used only vision.

Moreover, NeuTouch also could classify the sensory data while it was being accumulated, unlike the conventional approach where data is classified after it has been fully gathered.

The tests also demonstrated the efficiency of neuromorphic technology; Loihi processed the sensory data 21 percent faster than a top-performing graphics processing unit (GPU) while using more than 45 times less power.

Researchers published a paper on their work online and presented their findings at the Robotics: Science and Systems conference.

Applications and Post-COVID 19 Uses

Some applications for NeuTouch include integrating the system into robot grippers to detect slip, which is key to manipulating fragile objects safely and with stability, such as in factory or supply-chain settings, Tee told Design News.

“Accurate detection of slip will allow the robot controller to re-grasp the object and remedy poor initial grasp locations,” he told us. “This feature can be applied to develop more intelligent robots to take over mundane operations such as packing of items in warehouses, which robotic arms can easily adapt to unfamiliar items and apply the appropriate amount of strength to manipulate the items without slippage.”

The system also can be used to create autonomous robots “capable of deft manipulation in (unstructured) physical spaces, since the robots have the ability to feel and better perceive their surroundings,” he added.  

Moving forward, researchers plan to continue their work to develop the artificial skin for applications in the logistics and food manufacturing industries where there is a high demand for robotic automation, Tee told Design News.

This type of functionality will especially become more critical in a post-COVID 19 world for creating applications that avoid human contact by letting robots do the work, he said.

The post Artificial Brain Gives Robots Unprecedented Sensing Capabilities appeared first on Artificial Intelligence.

Tech giants Apple and Google, in an attempt to combat the spread of the deadly pandemic coronavirus, are working on a contract-tracing tool.

Stronger privacy protections are also on the feature cards as the upcoming Covid-19 contract-tracing tool’s developer version will be launched soon.

How will Covid-19 contract-tracing tool work?

Apple, in a ‘Frequently Asked Questions’ document, explains how the tool will help the authorities and governments around the world in contract-tracing efforts to combat Covid-19.

The tool, according to the FAQ, is a “two-phase exposure notification solution.”

The tool uses Bluetooth technology on respective smartphone which once enabled, will let the user’s device send out beacons that includes a string of random numbers.

The numbers have nothing to do with the users’ identity, and will change every 10-20 minutes for security reasons.

All the beacons will be collected in the system as a list after the tracks on every phone perform this function.

“At least once per day, the system will download a list of beacons that have been verified as belonging to people confirmed as positive for COVID-19 from the relevant public health authority. Each device will check the list of beacons it has recorded against the list downloaded from the server. If there is a match between the beacons stored on the device and the positive diagnosis list, the user may be notified and advised on steps to take next,” the FAQ read.

How will the tool on your device?

First, the users will be required to download the public health apps issued by their country’s government.

After installing the apps, open and accept all the terms and condition before the program is activated.

The technology becomes functional ones you agree to the terms and conditions.

However, to use the technology or not rests with you.

“The choice to use this technology rests with the user, and he or she can turn it off at anytime by uninstalling the contact tracing application or turning off exposure notification in Settings,” the document states.

While this was the first phase of the tool, in the second phase, which will be available in the months to come, the technology will be available at the operating system level.

However, an app will not be required in the second phase of the contract-tracing technology.

The document states: “If a match is detected, the user will be notified, and if the user has not already downloaded an official app they will be prompted to download an official app and advised on next steps. Only public health authorities will have access to this technology and their apps must meet specific criteria around privacy, security, and data control.”

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“While it may feel counterintuitive to look backward in order to move forward in the face of significant economic events,” Payrix Chief Risk and Compliance Officer Billi Jo Wright told PYMNTS, “applying the proper data mining, data analytics tools and action plans to your business’ wealth of transactional data may provide a roadmap through adverse situations.” Learn how Wright mines data to power Payrix and its clients in Black Swan, a special report exclusively from PYMNTS.

The following is an excerpt from Black Swan, contributed by Payrix Chief Risk and Compliance Officer Billi Jo Wright.

How Learning From History and Digging Into Data Helps Steer Through Adverse Economic Environments

In the midst of a black swan event — and certainly in the early days of understanding what the United States and the world face amid COVID-19 — the downturn often feels sudden and unexpected. Looking surface-level at the defining black swan events of the past few decades – from acts of terrorism to the bursting of a major industry bubble to a global viral pandemic – you’d no doubt see few similarities in the events that came before.

But for businesses scrambling to reforecast and plan for potential economic developments (McKinsey is currently outlining three potential scenarios, ranging from a quick recovery to a global slowdown to a full recession), there are rich insights that lie within historical transactional data.

There are a number of key factors and fields that can be scrutinized and analyzed to enable a business to more successfully steer through uncertain times. A holistic view of payments data includes analysis at an industry and vertical level, taking into account geographic regions, company sizes, chargeback trends, and transactional clusters and patterns before, during and after an economic event. That information can then be used to proactively prepare or stabilize your portfolio.

Highlighted below are two key focuses for navigating and preparing for uncertainty.

Distilling Insights Within Historical Payment Data

To determine the potential business impact, look at past payment volumes to understand how an event could impact your business, monitor trends in chargebacks or refunds to advise merchants on what to expect, and consider isolating merchants and payment volume in affected areas. Margin-sensitive or event-contextual verticals will inform where to prioritize retention versus acquisition efforts and where to find resourcing during tight times.

Analyze Concentration Risk

The risk of amplified losses that may come from having a large portion of processing volume in a particular vertical is significant. A diversified portfolio requires ongoing and proactive analysis to minimize concentration risk in an economic shutdown. In the current environment, we can expect to see industries like nonprofit, travel (hotels, airlines and cruises), events and service (bars, restaurants and salons/spas) in despair.

While it may feel counterintuitive to look backward in order to move forward in the face of significant economic events, applying the proper data mining, data analytics tools and action plans to your business’ wealth of transactional data may provide a roadmap through adverse situations.

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TROY, N.Y. — A researcher from Rensselaer Polytechnic Institute is suggesting that the coronavirus will peak in the Capital Region in the second half of May or early June.

Malik Magdon-Ismail tailored a robust machine learning model that can predict pandemic impact even in smaller cities, with 75% of the population in the Capital Region in New York remaining at home, the COVID-19 pandemic will peak locally in the second half of May. If the rate of people staying home drops to 50%, it will peak in early June, according to the model.

Magdon-Ismail said he tailored the models he is developing to work with sparse data points, like those available during the early phase in a pandemic or in smaller cities, which ordinarily make trend-spotting difficult.

“There are no simple, robust, general tools that, for example, officials in Albany could use to make projections,” said Magdon-Ismail, a professor of computer science, and expert in machine learning, data mining, and pattern recognition. “These models show that the projections vary enormously from one city to another. This knowledge could relieve some of the uncertainty that is around in developing policy.”

Using county data available through the New York State Department of Health and Mental Hygiene, Magdon-Ismail said he has developed models that can predict local aspects of the pandemic such as the rate of infections over time, the infectious force of the pandemic, the rate at which mild infections become serious, and estimates for asymptomatic infections. The research model is ongoing work and, given the time-sensitive nature of the work, earlier versions have been released on the arXiv preprint server, which is moderated but not peer-reviewed.

His model for the Capital Region — which incorporates the data from Albany, Rensselaer, Saratoga, and Schenectady counties up to April 10 — uses a total at-risk population of 855,000 to estimate that daily confirmed infections will peak at 1,490 on June 8 with 50% staying at home, or 750 on May 28 with 75% staying at home. The number of infections would total 58,000 or 29,000, respectively. Confirmed infections as of April 10 are approximately 1,000 and the model estimates 14,000 asymptomatic cases at that time, according to a news release.

“The machine gives you the model that best fits the data, but it turns out the best is usually a very fragile principle. There are lots of different models, lots of different explanations that are essentially as good,” Magdon-Ismail said in the news release. “To make the output robust, we consider the collection of models that have near-optimal levels of consistency with the data. I find a variety of models that fit the data, and then I use all of those models together to predict.”

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Artificial intelligence (AI) remains one of the main features of most European countries’ strategies, even during these times of the COVID-19 emergency. AI can in fact not only improve health care systems but also be a fundamental tool to analyze data to fight and prevent pandemics.

While there is little doubt about the benefits that that can be drawn, there are also increasing concerns about how to effectively address the risks associated with the usage of AI systems. Such concerns include, among others, data privacy risks – AI may easily be used to de-anonymize individuals’ data etc… (see this previous bite on this point) ̶ and also potential breaches of other fundamental rights, including freedom of expression, non-discrimination, human dignity, etc.

There has been a demand for a common approach to address such concerns, in order to give citizens and corporations enough trust in using (and investing in) AI systems, while also avoiding the market fragmentation that would limit the scale of development throughout Europe.

With this in mind, the European Commission recently published its White Paper on Artificial Intelligence, which is aligned with the key principles set out in the Guidelines on Trustworthy AI published by the EU High-Level Expert Group, namely human agency and oversight, technical robustness and safety, privacy and data governance, transparency and accountability, diversity, non-discrimination and fairness, societal and environmental wellbeing.

In addition to some improvements to the liability regime (such improvements are separately addressed in our TMT Bites), the EU Commission proposes to opt for a risk-based approach, to make proportional regulatory intervention in order to address mainly “high-risk” AI applications. Such high risks are identified where both the relevant sector (e.g. health care) and the intended use involve significant risks.

According to the EU Commission, AI regulations should be based on the following main requirements:

1. Training data – Datasets and their usage should meet the standards set out in the applicable EU safety rules, in addition to the existing provisions set out in the GDPR and in the Law Enforcement Directive. There should also be ad hoc AI training data provisions. For instance, AI systems should operate with sufficiently broad data sets to cover all scenarios needed to avoid dangerous situations, thus avoiding unnecessary risks. This includes also taking reasonable measures to avoid discrimination, e.g. where applicable, adequate gender and ethnical coverage.
2. Record-keeping – Adequate measures should be taken to avoid the so-called “black box effect”. Accordingly, records should be kept on the data sets used to train and test the AI systems, as well as the main characteristics. There should also be clear documentation on the programming, training and processes used to build and validate the AI systems. In certain cases, the data themselves should also be kept, although this may entail additional storage costs.
3. Information – AI systems should be transparent. Information on the use of AI systems should be provided, also including information on the capabilities, limitations and expected level of accuracy. This also implies a proactive approach, e.g. informing individuals when they are interacting with AI systems, while all information needs to be concise and understandable.
4. Robustness – Many AI technologies are unpredictable and difficult to control, even ex-post. There should be an ex-ante assessment of risks, as well as assessments to check that AI systems operate accurately during the whole of their life-cycle phase, with reproducible outcomes. Furthermore, AI systems should also adequately deal with errors, with processes to handle and correct them. Additional regulations should also be drawn up to ensure resilience against attacks and attempts to manipulate the data or the algorithms.
5. Human oversight – There should be adequate involvement by human beings, in addition to what is already established by the GDPR for automated decision making. Depending upon the circumstances, the human oversight should intervene prior to the output to be produced, or afterwards and/or throughout the whole learning and output process. This will depend upon the type of system and its usage: for instance, an automated driverless car should have a safety button or similar device in order to allow a human to take control under certain circumstances; it should also provide for an interruption of operations when certain sensors are not operating in a reliable way.

Other requirements may be set for other specific systems, including remote biometric identification, which allows identification at a distance and in a public space of individuals through a set of biometric identifiers (e.g. fingerprints, facial image, etc.) which are compared to other data stored in database(s). Additional requirements may be set, whatever sector is involved, in order to ensure that any such processing is justified, proportionate and subject to adequate safeguards.

The Commission further highlighted that, in order to make future regulations effective, there should be a level playing field, and accordingly any such requirement should be applied to all those that provide AI products or services in the EU, thus including non-EU companies.

The detailed implementation of the above requirements is yet to be determined, including the frameworks for testing and certification.

Do you agree with the above requirements? We would be interested in hearing your views.

The post The EU White Paper on Artificial Intelligence: the five requirements appeared first on Artificial Intelligence.

In only a few weeks, the COVID-19 pandemic has completely disrupted our normal way of life. With many businesses shutting their doors or transitioning to a work-from-home system, adaptability to a constantly changing situation will prove key for the survival of organizations large and small. Despite everything that is going on, however, the pandemic is also spurring new innovations, particularly in the world of artificial intelligence. Here are several important ways AI is already making a difference in improving public health and safety as the world adapts to a new normal.

1. AI predicts the spread of disease

One of the biggest challenges with this coronavirus (and the COVID-19 disease it subsequenly causes) has been how quickly it can spread. While social-distancing measures and the closure of high-risk facilities are viewed as the best way to control the spread, many areas have been slow to enact such measures because they don’t have an accurate perception of their risk.

In Israel, however, an AI-powered survey system developed by the Weizmann Institute of Science aims to better predict outbreaks so authorities can proactively enact measures that will mitigate the virus’s spread. The system uses a questionnaire focusing on key issues like health symptoms and isolation practices, then matches responses with a location-based algorithm. AI analysis can then identify potential hotspots in advance, which can help local authorities enact measures that will slow down the virus.

2. AI helps support centers

With COVID-19 constantly dominating headlines, it should come as no surprise that hospitals and health organizations are getting more inquiries than ever from patients worried that they might have the coronavirus.

Virtual assistants have already alleviated the workloads of customer support professionals in other industries, and now, similar tools specifically designed to address questions related to COVID-19 are being introduced. These AI tools can be embedded directly into healthcare apps and websites.

One example of this is Hyro, a free COVID-19 virtual assistant that is being offered to healthcare organizations to help them manage the uptick in calls and questions. By answering frequently asked questions about the coronavirus, triaging symptoms and delivering information from verified sources like the WHO and CDC, such AI tools can help reduce the burden on healthcare workers who are already being stretched thin by pandemic conditions.

3. AI fights the spread of misinformation

An unfortunate issue that has popped up in the wake of the COVID-19 pandemic is the rapid spread of misinformation online. From downplaying the risks posed by the virus to false text messages warning of mandatory quarantine orders, this can further fuel panic during what is already a scary time.

Many social media platforms use human content moderators to check for harmful posts, but with more employees being required to work from home or stop working altogether, AI is becoming increasingly important in combating misinformation. Though the lack of human supervision means an increased risk for mistakes, it could also spur new improvements for these machine-learning tools.

As one example of this, The Verge’s Jacob Kastrenakes explains, “YouTube will rely more on AI to moderate videos during the coronavirus pandemic, since many of its human reviewers are being sent home to limit the spread of the virus. This means videos may be taken down from the site purely because they’re flagged by AI as potentially violating a policy, whereas the videos might normally get routed to a human reviewer to confirm that they should be taken down.”

4. AI identifies sick patients

As noted by the Guardian, one of the biggest challenges in containing the spread of COVID-19 is the fact that many patients experience symptoms most similar to a mild cold. Some are entirely asymptomatic. Because of this, many people who could spread the virus to others may continue to go out in public rather than self-quarantining.

While testing can be slow, AI is already stepping up to the challenge. As reported by The Next Web, several AI tools have already been developed to identify patients with COVID-19 and deliver treatment that keeps healthcare professionals safe.

In China, a computer-vision algorithm was developed to scan people’s temperatures in public locations and flag anyone with even a slight fever. Another AI algorithm helps doctors more accurately discern between coronavirus and typical pneumonia patients. In Washington State, robots have even been used to provide remote treatment and communication to keep the disease from spreading from patients to doctors.

The future surrounding the COVID-19 pandemic is rife with uncertainty. There is no telling how long social isolation measures and other precautions will need to remain in place to mitigate the spread of the disease, or what the overall impact of such actions will be.

While AI may not have all the answers, it is clear that continuing innovation in this field will help — and already is helping — to make the world a safer place during these troubling times. By helping slow the spread of the virus and improving conditions for healthcare workers, these tech developments could very well save lives now and in the future.

The post 4 Ways AI Is Making the World a Safer Place appeared first on Artificial Intelligence.

The COVID-19 pandemic has shifted the top concerns of enterprise CIOs, who are now prioritizing their spending on areas such as cybersecurity, public cloud, infrastructure and AI/ML.

That’s according to a new survey from Adobe, who teamed up with Fortune to measure “How A Remote Workforce Is Shifting CIO Priorities.”

“Amid the uncertainty of COVID-19, the role of the CIO has become even more critical as this group works quickly to minimize disruption to their business while also moving to a remote workforce,” a spokesperson for Adobe told Virtualization & Cloud Review.

CMO by Adobe, which produces advice, guidance, data and research to senior business leaders, conducted a mid-March survey of more than 200 CIOs to gauge shifting attitudes subsequent to a pre-pandemic January post about CIO priorities.

Although security was (and always is) a top-of-mind concern in both survey efforts, cloud computing is now getting more attention in a remote-work world. However, while most organizations use the cloud, that usage may be increasing in the new world order.

“Public cloud, infrastructure, and artificial intelligence/machine learning (AI/ML) also will receive financial boosts in many organizations, but will only result in increased headcount in less than 25 percent of organizations,” Adobe said after noting the cybersecurity concerns of CIOs.

“Indeed, nearly all CIOs (90 percent) surveyed said they use a public cloud service for at least some of their data, but most is housed on-premises. Only one in three organizations store half or more of their data in a public cloud.”

Other top takeaways from the report as listed by Adobe include:

• COVID-19 Response: Most organizations feel they are set up to work remotely (84 percent of all organizations and 94 percent of smaller organizations), but challenges for CIOs include communication (53 percent) and shortfalls in technology tools (20 percent). Half of the firms are still actively hiring, but 47 percent anticipate an impact on their hiring cycle.
• Women Still Underrepresented: Female team members represent a minority of CIOs’ direct reports (slightly over 1 in 4). There is a higher proportion of female team members in smaller organizations and in healthcare organizations.
• Focus on Cyber Security Investments: 7 in 10 organizations anticipate increased financial investments in this area. Public cloud, infrastructure and AI/machine learning will also receive financial boosts in many organizations.
• AI Still Fairly New: Only 50 percent of organizations use AI in one or more projects (and only 25 percent of SMBs), with CIOs noting they leverage AI for IT and customer support the most. More than 90 percent of those that have implemented AI have only done so in the past year. Top challenges faced when implementing AI are around data and funding/talent.

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Cnvrg.io, a data science startup headquartered in Jerusalem and New York, today released a community version of its machine learning automation platform designed to help enterprises manage and scale AI. CEO Yochay Ettun says the release was motivated in part by the influx of social distancing and remote work stemming from the COVID-19 pandemic.

“The release of cnvrg.io CORE is our contribution to the strong data science community responsible for advancing AI innovation,” said Ettun. “CORE’s release marks a new vision for the data science field. As data scientists, we built CORE to fill the need that so many data scientists require, to focus less on infrastructure and more on what they do best — algorithms.”

CORE facilitates machine learning workflow management with end-to-end AI model tracking and monitoring. Its built-in cluster orchestration supports hybrid cloud and multi-cloud configurations, and its compute querying and autoscaling — which can be fine-tuned from a dashboard — ensure that every available resource is fully utilized.

CORE can be installed on-premises or in a cloud environment directly from Cnvrg.io’s website. Developers can connect data sources to it to build and automatically retrain machine learning models; run machine learning experiments at scale to ensure reproducibility; and deploy to production with any framework or programming language.

There’s no shortage of orchestration platforms in the over $1.5 billion global machine learning market. Amazon recently rolled out SageMaker Studio, an extension of its SageMaker platform that automatically collects all code and project folders for machine learning in one place. Google offers its own solution in Cloud AutoML, which supports tasks like classification, sentiment analysis, and entity extraction, as well as a range of file formats, including native and scanned PDFs. Not to be outdone, Microsoft recently introduced enhancements to Azure Machine Learning, its service that enables users to architect predictive models, classifiers, and recommender systems for cloud-hosted and on-premises apps, and IBM has a comparable product in Watson Studio AutoAI.

But two-year-old Cnvrg.io, which is backed by Jerusalem Venture Partners and private investors Kevin Bermeister and Prashant Malik, has managed to raise $8 million in venture capital to date and attract customers that include Nvidia, Sisense, NetApp, Lightricks, and Wargaming.net.

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While we focus on vaccines, anti-virals and respirators in the fight against COVID-19, there’s another type of technology that gets less attention, but may be even more important in lessening the impact of the pandemic—information technology.

Given that we’ve been hearing more and more about the importance of widespread testing, we’re probably less surprised at this than we might have been a few weeks ago. It’s becoming increasingly clear that knowledge is actually one of the most important tools we have. The good news is that we have people working all the angles on this, from artificial intelligence data mining to genetic sequencing.

Why is this so important? It can get a little bit abstract, but the all-important testing strategy demonstrates exactly why knowledge is power.

Current evidence suggests that a certain number of people will get the novel coronavirus and be entirely asymptomatic. Some will never even know they had it. Meanwhile, others will get COVID-19 and have mild symptoms—not severe enough to go to the hospital. And, after they recover, they won’t know for sure what they had. Maybe it was just the flu.

Working under the assumption that people develop an immunity after they get it (likely, but not proven yet), these people, after the contagion period ended, would no longer be at risk of either getting or spreading it. That would leave them free to go back to work or, better yet, volunteer at a hospital.

Without testing, though, we have no way of knowing if they ever had it. And since, right now, we’re mainly limiting testing to people who have symptoms, we’re nowhere close to finding out who doesn’t have it at this point—a number that’s just as valuable as the number of active cases.

“Public health works best when we can get as precise as possible,” explains Steven Hoffman, director of the global strategy lab and professor of global health law and political science at York University. “So, as long as we’re in the current situation where we don’t have the full testing capacity, we’re stuck with more blunt tools like closing schools and asking everyone to remain at home except for essential trips outside. Those are effective for slowing the spread of the virus and buying us time but not, ultimately, for getting society back to normal.”

By now, most people understand that “buying time” through social distancing is important so that hospitals aren’t overwhelmed but, as Hoffman explains, it’s also about getting us to a point where we’ve beefed up our public health agencies so they’re ready to do mass-testing. The cuts to the Ontario Public Health budget under the Ford government means we started out at a disadvantage. And without mass “surveillance testing,” the official numbers we see reported are always out of date. It takes five days for symptoms to show up, then several more days for test results, so the confirmed case numbers we’re looking at could be 10 days post-transmission.

Fortunately, in other areas, we’re ahead of the curve, thanks to academic research into artificial intelligence that can mine data from social media and other sources to track outbreaks and hotspots before we get official confirmation of a positive test. Researchers at University of Guelph developed a system for doing this with avian flu and it’s now being applied to COVID-19. The idea, roughly, is to look for patterns and early signals, which includes everything from trade routes linked to disease transmission to people describing or complaining about symptoms on social media.

“So, we’re working with AI algorithms, the robots, and, so far, we have collected three million tweets related to COVID-19,” explains Rozita Dara, a professor in computer science at the University of Guelph. “Only two per cent of them have geo-location data but two per cent of three million is still a lot. So we are very excited to look at that and see if we can see patterns.”

In the United Kingdom, where mass surveillance testing hasn’t begun yet either, a team of doctors and scientists at King’s College London, Guys and St. Thomas hospitals have developed the COVID Symptom Tracker , an app that allows people to report their symptoms daily, whether they’re sick or not. From there, the researchers can aggregate and analyze data to identify clusters and researchers can better understand symptomology. For example, it’s still unclear if diarrhea and/or losing your sense of smell are early warning signs, but both are definitely on the radar. Apps like this might help determine how relevant they are.

The post How artificial intelligence data mining can help us fight COVID-19 appeared first on Artificial Intelligence.

The Chinese strategy of mass surveillance has, may be, achieved one of the greatest tangible results of the COVID-19 threat so far.

Singapore, Israel and China, though pressing the delicate button of the data protection regulation, have implemented strategies of controlled and monitored containment through advanced digital systems, such as drones and data mining tools, supported by applications installed in the mobile devices of the population, in order to track the population movements in quarantine times. 

Tracking the path of the virus that makes its way from man to man is perhaps one of the keys to solving the intricate puzzle of the pandemic management, a management that requires fully quantitative methods of analysis and prevention, as well as the application of mathematical and statistical models that, however, if not supported by the right amount of data to be taken into analysis, cannot be correctly implemented and risk to provide partially (or totally) wrong answers. 

In the same way, in Italy there are some technological proposals, that would require both technical-operational and regulatory screening, in view of what could, for some, be seen as a potential violation of the privacy rights, though their purposes would be the exclusive safeguard of the community.

«Singula», How It Works 

One of the most accredited (perhaps the most accredited) at the moment, for simplicity of use and functionality of the process (in fact, you only need to download and identify yourself through your fiscal code), is the proposal of the Milan-based team led by the economist and innovation expert Giuseppe Signorelli, of Sicilian origin, who immediately saw digitalisation as the solution to this complex plot of limiting the spread of contagion. 

The problem is of quantitative kind, «If you do not track the virus through the collection of individual data, the timeline of the containment strategy implemented by our country will be much longer», Signorelli reports. 

The application is called «Singula» (from the Latin «Individual») and works as described from here on.

The first step, as anticipated, is the personal identification through the individual fiscal code. 

From the moment of activation, the application makes a recording of the user’s position every exact 30 minutes using the smartphone GPS, and saves the positioning data in an internal database, accessible only through the owner’s smartphone. 

The data is not shared with anyone, except with the express consent of the owner.

The application records the 48 daily positions (one every 30 minutes) and stores them for a period of time of 14 days (incubation time, even latent, of COVID-19). Every day, the data for the fifteenth day prior is deleted. 

All positioning data are accessible from the owner’s smartphone and can be requested by the Civil Protection Department everytime: 

– It is suspected that the owner had contacts with a virus carrier, in order to see, by cross-referencing the owner’s data with the data of the carrier, whether the two may actually had contacts; 

– A new carrier is discovered, in order to track this carrier’s movements in the previous days and find, through crossreferencing his movement data with the movement data of other users, potential other infected persons, thus “drawing” the viral path. 

The strategy is clearly aimed at isolating the virus by isolating its carriers, and thus reducing the timeline of the containment policy implemented by our government. 

If, hypothetically, it were decided to increase the number of swabs, it would be logical to use the positioning data thus detected and start the swabs from the areas where carriers of the virus have passed or have spent most of their time, thus increasing the probability of finding, through the new swabs, new infected persons and isolate them. 

«The effectiveness of the system is obviously directly proportional to the number of people using the application», Signorelli reports.

Ideally, if the application were mandatory for every Italian, it would be possible to have the amount of data to potentially isolate the virus almost completely and reduce the infection exponentially. 

2.0 Geo-localisation Database

Through the application it is possible to access a map that geo-localizes the so-called «red zones» («hotspots»), i.e. the areas where a certain number (more or less large, depending on the size of the bubble) of contagions had occurred, on a local and national scale. 

The data of the zones at risk are put into the application database by the application management team, which draws information from public sources, or directly from the Civil Protection Department for a more accurate report. 

The purpose is clear, allow the population to know which areas of their city are most affected, and thus measure their choices regarding their «exits for necessity» in terms of risk of contagion.  

The idea is that red zones should be discarded by those who were thinking to go there to carry out their necessary activities, preferring instead zones with lower levels of risk. 

By scrolling down the page, in the same section, is reported the daily COVID-19 bulletin from the Civil Protection

3.0 «Red Zones» Database 

Other feature of the application is the reporting of certified information on the virus spread on a global scale. 

Graphical and numerical reports are presented, as well as updated statistics on the spread, the mortality and the recovery from COVID-19. 

The data source is the daily monitoring report from the Johns Hopkins University, Baltimore (USA). 

4.0 Global Analysis COVID-19 (Source J.H.U.)

Lastly, among the application settings, is reported the privacy policy and are described the data encryption mechanisms for the user maximum safety. 

The same page also lists the regional emergency numbers activated specifically for COVID-19 and provides the possibility to report an hotspot, even a restricted area at risk of contagion, or any information that may be useful to the Civil Protection Department to limit the spread.

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