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Will COVID-19 Create a Big Moment for AI and Machine Learning? – Dice Insights

Posted: March 29, 2020 at 2:45 pm

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COVID-19 will change how the majority of us live and work, at least in the short term. Its also creating a challenge for tech companies such as Facebook, Twitter and Google that ordinarily rely on lots and lots of human labor to moderate content. Are A.I. and machine learning advanced enough to help these firms handle the disruption?

First, its worth noting that, although Facebook has instituted a sweeping work-from-home policy in order to protect its workers (along with Googleand a rising number of other firms), it initially required its contractors who moderate content to continue to come into the office. That situation only changed after protests,according toThe Intercept.

Now, Facebook is paying those contractors while they sit at home, since the nature of their work (scanning peoples posts for content that violates Facebooks terms of service) is extremely privacy-sensitive. Heres Facebooks statement:

For both our full-time employees and contract workforce there is some work that cannot be done from home due to safety, privacy and legal reasons. We have taken precautions to protect our workers by cutting down the number of people in any given office, implementing recommended work from home globally, physically spreading people out at any given office and doing additional cleaning. Given the rapidly evolving public health concerns, we are taking additional steps to protect our teams and will be working with our partners over the course of this week to send all contract workers who perform content review home, until further notice. Well ensure that all workers are paid during this time.

Facebook, Twitter, Reddit, and other companies are in the same proverbial boat: Theres an increasing need to police their respective platforms, if only to eliminate fake news about COVID-19, but the workers who handle such tasks cant necessarily do so from home, especially on their personal laptops. The potential solution? Artificial intelligence (A.I.) and machine-learning algorithms meant to scan questionable content and make a decision about whether to eliminate it.

HeresGoogles statement on the matter, via its YouTube Creator Blog.

Our Community Guidelines enforcement today is based on a combination of people and technology: Machine learning helps detect potentially harmful content and then sends it to human reviewers for assessment. As a result of the new measures were taking, we will temporarily start relying more on technology to help with some of the work normally done by reviewers. This means automated systems will start removing some content without human review, so we can continue to act quickly to remove violative content and protect our ecosystem, while we have workplace protections in place.

To be fair, the tech industry has been heading in this direction for some time. Relying on armies of human beings to read through every piece of content on the web is expensive, time-consuming, and prone to error. But A.I. and machine learning are still nascent, despite the hype. Google itself, in the aforementioned blog posting, pointed out how its automated systems may flag the wrong videos. Facebook is also receiving criticism that its automated anti-spam system is whacking the wrong posts, including those thatoffer vital information on the spread of COVID-19.

If the COVID-19 crisis drags on, though, more companies will no doubt turn to automation as a potential solution to disruptions in their workflow and other processes. That will force a steep learning curve; again and again, the rollout of A.I. platforms has demonstrated that, while the potential of the technology is there, implementation is often a rough and expensive processjust look at Google Duplex.

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Nonetheless, an aggressive embrace of A.I. will also create more opportunities for those technologists who have mastered A.I. and machine-learning skills of any sort; these folks may find themselves tasked with figuring out how to automate core processes in order to keep businesses running.

Before the virus emerged, BurningGlass (which analyzes millions of job postings from across the U.S.), estimated that jobs that involve A.I. would grow 40.1 percent over the next decade. That percentage could rise even higher if the crisis fundamentally alters how people across the world live and work. (The median salary for these positions is $105,007; for those with a PhD, it drifts up to $112,300.)

If youre trapped at home and have some time to learn a little bit more about A.I., it could be worth your time to explore online learning resources. For instance, theres aGooglecrash coursein machine learning. Hacker Noonalso offers an interesting breakdown ofmachine learningandartificial intelligence.Then theres Bloombergs Foundations of Machine Learning,a free online coursethat teaches advanced concepts such as optimization and kernel methods.

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Will COVID-19 Create a Big Moment for AI and Machine Learning? - Dice Insights

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March 29th, 2020 at 2:45 pm

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Self-driving truck boss: ‘Supervised machine learning doesnt live up to the hype. It isnt C-3PO, its sophisticated pattern matching’ – The Register

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Roundup Let's get cracking with some machine-learning news.

Starksy Robotics is no more: Self-driving truck startup Starsky Robotics has shut down after running out of money and failing to raise more funds.

CEO Stefan Seltz-Axmacher bid a touching farewell to his upstart, founded in 2016, in a Medium post this month. He was upfront and honest about why Starsky failed: Supervised machine learning doesnt live up to the hype, he declared. It isnt actual artificial intelligence akin to C-3PO, its a sophisticated pattern-matching tool.

Neural networks only learn to pick up on certain patterns after they are faced with millions of training examples. But driving is unpredictable, and the same route can differ day to day, depending on the weather or traffic conditions. Trying to model every scenario is not only impossible but expensive.

In fact, the better your model, the harder it is to find robust data sets of novel edge cases. Additionally, the better your model, the more accurate the data you need to improve it, Seltz-Axmacher said.

More time and money is needed to provide increasingly incremental improvements. Over time, only the most well funded startups can afford to stay in the game, he said.

Whenever someone says autonomy is ten years away thats almost certainly what their thought is. There arent many startups that can survive ten years without shipping, which means that almost no current autonomous team will ever ship AI decision makers if this is the case, he warned.

If Seltz-Axmacher is right, then we should start seeing smaller autonomous driving startups shutting down in the near future too. Watch this space.

Waymo to pause testing during Bay Area lockdown: Waymo, Googles self-driving car stablemate, announced it was pausing its operations in California to abide by the lockdown orders in place in Bay Area counties, including San Francisco, Santa Clara, San Mateo, Marin, Contra Costa and Alameda. Businesses deemed non-essential were advised to close and residents were told to stay at home, only popping out for things like buying groceries.

It will, however, continue to perform rides for deliveries and trucking services for its riders and partners in Phoenix, Arizona. These drives will be entirely driverless, however, to minimise the chance of spreading COVID-19.

Waymo also launched its Open Dataset Challenge. Developers can take part in a contest that looks for solutions to these problems:

Cash prizes are up for grabs too. The winner can expect to pocket $15,000, second place will get you $5,000, while third is $2,000.

You can find out more details on the rules of the competition and how to enter here. The challenge is open until 31 May.

More free resources to fight COVID-19 with AI: Tech companies are trying to chip in and do what they can to help quell the coronavirus pandemic. Nvidia and Scale AI both offered free resources to help developers using machine learning to further COVID-19 research.

Nvidia is providing a free 90-day license to Parabricks, a software package that speeds up the process of analyzing genome sequences using GPUs. The rush is on to analyze the genetic information of people that have been infected with COVID-19 to find out how the disease spreads and which communities are most at risk. Sequencing genomes requires a lot of number crunching, Parabricks slashes the time needed to complete the task.

Given the unprecedented spread of the pandemic, getting results in hours versus days could have an extraordinary impact on understanding the viruss evolution and the development of vaccines, it said this week.

Interested customers who have access to Nvidias GPUs should fill out a form requesting access to Parabricks.

Nvidia is inviting our family of partners to join us in matching this urgent effort to assist the research community. Were in discussions with cloud service providers and supercomputing centers to provide compute resources and access to Parabricks on their platforms.

Next up is Scale AI, the San Francisco based startup focused on annotating data for machine learning models. It is offering its labeling services for free to any researcher working on a potential vaccine, or on tracking, containing, or diagnosing COVID-19.

Given the scale of the pandemic, researchers should have every tool at their disposal as they try to track and counter this virus, it said in a statement.

Researchers have already shown how new machine learning techniques can help shed new light on this virus. But as with all new diseases, this work is much harder when there is so little existing data to go on.

In those situations, the role of well-annotated data to train models o diagnostic tools is even more critical. If you have a lot of data to analyse and think Scale AI could help then apply for their help here.

PyTorch users, AWS has finally integrated the framework: Amazon has finally integrated PyTorch support into Amazon Elastic Inference, its service that allows users to select the right amount of GPU resources on top of CPUs rented out in its cloud services Amazon SageMaker and Amazon EC2, in order to run inference operations on machine learning models.

Amazon Elastic Inference works like this: instead of paying for expensive GPUs, users select the right amount of GPU-powered inference acceleration on top of cheaper CPUs to zip through the inference process.

In order to use the service, however, users will have to convert their PyTorch code into TorchScript, another framework. You can run your models in any production environment by converting PyTorch models into TorchScript, Amazon said this week. That code is then processed by an API in order to use Amazon Elastic Inference.

The instructions to convert PyTorch models into the right format for the service have been described here.

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Self-driving truck boss: 'Supervised machine learning doesnt live up to the hype. It isnt C-3PO, its sophisticated pattern matching' - The Register

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What Researches says on Machine learning with COVID-19 – –

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COVID-19 will change how most of us live and work, at any rate temporarily. Its additionally making a test for tech organizations, for example, Facebook, Twitter, and Google, that usually depend on parcels and heaps of personal work to direct substance. Are AI furthermore, AI propelled enough to enable these organizations to deal with the interruption?

Its essential that, even though Facebook has initiated a general work-from-home strategy to ensure its laborers (alongside Google and a rising number of different firms), it at first required its contractual workers who moderate substance to keep on coming into the workplace. That circumstance just changed after fights, as per The Intercept.

Presently, Facebook is paying those contractual workers. At the same time, they sit at home since the idea of their work (examining people groups posts for content that damages Facebooks terms of administration) is amazingly security delicate. Heres Facebooks announcement:

For both our full-time representatives and agreement workforce, there is some work that is impossible from home because of wellbeing, security, and legitimate reasons. We have played it safe to secure our laborers by chopping down the number of individuals in some random office, executing prescribed work from home all-inclusive, truly spreading individuals out at some random office, and doing extra cleaning. Given the quickly developing general wellbeing concerns, we are finding a way to ensure our groups. We will be working with our accomplices throughout this week to send all contractors who perform content survey home, until further notification. Well guarantee the payment of all employees during this time.

Facebook, Twitter, Reddit, and different organizations are in the equivalent world-renowned pontoon: Theres an expanding need to politicize their stages, just to take out counterfeit news about COVID-19. Yet the volunteers who handle such assignments cant do as such from home, particularly on their workstations. The potential arrangement? Human-made reasoning (AI) and AI calculations intended to examine the flawed substance and settle on a choice about whether to dispense with it.

Heres Googles announcement on the issue, using its YouTube Creator Blog.

Our Community Guidelines requirement today depends on a blend of individuals and innovation: Machine learning recognizes possibly destructive substance and afterward sends it to human analysts for evaluation. Because of the new estimates were taking, we will incidentally begin depending more on innovation to help with a portion of the work regularly done by commentators. This implies computerized frameworks will begin evacuating some substance without human audit, so we can keep on acting rapidly to expel violative substances and ensure our environment. At the same time, we have a working environment assurances set up.

Also, the tech business has been traveling right now sometime. Depending on the multitudes of individuals to peruse each bit of substance on the web is costly, tedious, and inclined to mistake. Be that as it may, AI, whats more, AI is as yet early, despite the promotion. Google itself, in the previously mentioned blog posting, brought up how its computerized frameworks may hail inappropriate recordings. Facebook is additionally getting analysis that its robotized against spam framework is whacking inappropriate posts, remembering those that offer essential data for the spread of COVID-19.

In the case of the COVID-19 emergency delay, more organizations will not surely turn to machine learning as a potential answer for interruptions in their work process and different procedures. That will drive a precarious expectation to absorb information; over and over, the rollout of AI stages has exhibited that, while the capability of the innovation is there, execution is regularly an unpleasant and costly proceduresimply see Google Duplex.

In any case, a forceful grasp of AI will likewise make more open doors for those technologists who have aced AI, whats more, AI aptitudes of any kind; these people may wind up entrusted with making sense of how to mechanize center procedures to keep organizations running.

Before the infection developed, Burning Glass (which breaks down a great many activity postings from over the US), evaluated that employments that include AI would grow 40.1 percent throughout the following decade. That rate could increase considerably higher if the emergency on a fundamental level changes how individuals over the world live and work. (The average compensation for these positions is $105,007; for those with a Ph.D., it floats up to $112,300.)

With regards to irresistible illnesses, counteraction, surveillance, and fast reaction endeavors can go far toward easing back or slowing down flare-ups. At the point when a pandemic, for example, the ongoing coronavirus episode occurs, it can make enormous difficulties for the administration and general wellbeing authorities to accumulate data rapidly and facilitate a reaction.

In such a circumstance, machine learning can assume an immense job in foreseeing a flare-up and limiting or slowing down its spread.

Human-made intelligence calculations can help mine through news reports and online substances from around the globe, assisting specialists in perceiving oddities even before it arrives at pestilence extents. The crown episode itself is an extraordinary model where specialists applied AI to examine flight voyager information to anticipate where the novel coronavirus could spring up straightaway. A National Geographic report shows how checking the web or online life can help identify the beginning periods.

Practical usage of prescient demonstrating could speak to a significant jump forward in the battle to free the universe of probably the most irresistible maladies. Substantial information examination can enable de-to to concentrate the procedure and empower the convenient investigation of far-reaching informational collections created through the Internet of Things (IoT) and cell phones progressively.

Artificial intelligence and colossal information examination have a significant task to carry out in current genome sequencing techniques. High.

As of late, weve all observed great pictures of medicinal services experts over the globe working vigorously to treat COVID-19 patients, frequently putting their own lives in danger. Computer-based intelligence could assume a critical job in relieving their burden while guaranteeing that the nature of care doesnt endure. For example, the Tampa General Hospital in Florida is utilizing AI to recognize fever in guests with a primary facial output. Human-made intelligence is additionally helping specialists at the Sheba Medical Center.

The job of AI and massive information in treating worldwide pandemics and other social insurance challenges is just set to develop. Hence, it does not shock anyone that interest for experts with AI aptitudes has dramatically increased in recent years. Experts working in social insurance innovations, getting taught on the uses of AI in medicinal services, and building the correct ranges of abilities will end up being critical.

As AI rapidly becomes standard, medicinal services is undoubtedly a territory where it will assume a significant job in keeping us more secure and more advantageous.

The subject of how machine learning can add to controlling the COVID-19 pandemic is being presented to specialists in human-made consciousness (AI) everywhere throughout the world.

Artificial intelligence instruments can help from multiple points of view. They are being utilized to foresee the spread of the coronavirus, map its hereditary advancement as it transmits from human to human, accelerate analysis, and in the improvement of potential medications, while additionally helping policymakers adapt to related issues, for example, the effect on transport, nourishment supplies, and travel.

In any case, in every one of these cases, AI is just potent on the off chance that it has adequate guides. As COVID-19 has brought the world into the unchartered domain, the profound learning frameworks, which PCs use to obtain new capacities, dont have the information they have to deliver helpful yields.

Machine leaning is acceptable at anticipating nonexclusive conduct, yet isnt truly adept at extrapolating that to an emergency circumstance when nearly everything that happens is new, alerts Leo Krkkinen, a teacher at the Department of Electrical Engineering and Automation in Aalto University, Helsinki and an individual with Nokias Bell Labs. On the off chance that individuals respond in new manners, at that point AI cant foresee it. Until you have seen it, you cant gain from it.

Regardless of this clause, Krkkinen says powerful AI-based numerical models are assuming a significant job in helping policymakers see how COVID-19 is spreading and when the pace of diseases is set to top. By drawing on information from the field, for example, the number of passings, AI models can assist with identifying what number of contaminations are uninformed, he includes, alluding to undetected cases that are as yet irresistible. That information would then be able to be utilized to advise the foundation regarding isolate zones and other social removing measures.

It is likewise the situation that AI-based diagnostics that are being applied in related zones can rapidly be repurposed for diagnosing COVID-19 contaminations., which has a calculation for consequently recognizing both malignant lung growth and fallen lungs from X-beams, provided details regarding Monday that the count can rapidly distinguish chest X-beams from COVID-19 patients as unusual. Right now, triage might accelerate finding and guarantee assets are dispensed appropriately.

The dire need to comprehend what sorts of approach intercessions are powerful against COVID-19 has driven different governments to grant awards to outfit AI rapidly. One beneficiary is David Buckeridge, a teacher in the Department of Epidemiology, Biostatistics and Occupational Health at McGill University in Montreal. Equipped with an award of C$500,000 (323,000), his group is joining ordinary language preparing innovation with AI devices, for example, neural systems (a lot of calculations intended to perceive designs), to break down more than 2,000,000 customary media and internet-based life reports regarding the spread of the coronavirus from everywhere throughout the world. This is unstructured free content traditional techniques cant manage it, Buckeridge said. We need to remove a timetable from online media, that shows whats working where, accurately.

The group at McGill is utilizing a blend of managed and solo AI techniques to distill the key snippets of data from the online media reports. Directed learning includes taking care of a neural system with information that has been commented on, though solo adapting just utilizes crude information. We need a structure for predisposition various media sources have an alternate point of view, and there are distinctive government controls, says Buckeridge. People are acceptable at recognizing that, yet it should be incorporated with the AI models.

The data obtained from the news reports will be joined with other information, for example, COVID-19 case answers, to give policymakers and wellbeing specialists a significantly more complete image of how and why the infection is spreading distinctively in various nations. This is applied research in which we will hope to find significant solutions quick, Buckeridge noted. We ought to have a few consequences of significance to general wellbeing in April.

Simulated intelligence can likewise be utilized to help recognize people who may be accidentally tainted with COVID-19. Chinese tech organization Baidu says its new AI-empowered infrared sensor framework can screen the temperature of individuals in the nearness and rapidly decide if they may have a fever, one of the indications of the coronavirus. In an 11 March article in the MIT Technology Review, Baidu said the innovation is being utilized in Beijings Qinghe Railway Station to recognize travelers who are conceivably contaminated, where it can look at up to 200 individuals in a single moment without upsetting traveler stream. A report given out from the World Health Organization on how China has reacted to the coronavirus says the nation has additionally utilized essential information and AI to reinforce contact following and the administration of need populaces.

Human-made intelligence apparatuses are additionally being sent to all the more likely comprehend the science and science of the coronavirus and prepare for the advancement of viable medicines and an immunization. For instance, fire up Benevolent AI says its man-made intelligence determined information diagram of organized clinical data has empowered the recognizable proof of a potential restorative. In a letter to The Lancet, the organization depicted how its calculations questioned this chart to recognize a gathering of affirmed sedates that could restrain the viral disease of cells. Generous AI inferred that the medication baricitinib, which is endorsed for the treatment of rheumatoid joint inflammation, could be useful in countering COVID-19 diseases, subject to fitting clinical testing.

So also, US biotech Insilico Medicine is utilizing AI calculations to structure new particles that could restrict COVID-19s capacity to duplicate in cells. In a paper distributed in February, the organization says it has exploited late advances in profound figuring out how to expel the need to physically configuration includes and learn nonlinear mappings between sub-atomic structures and their natural and pharmacological properties. An aggregate of 28 AI models created atomic structures and upgraded them with fortification getting the hang of utilizing a scoring framework that mirrored the ideal attributes, the analysts said.

A portion of the worlds best-resourced programming organizations is likewise thinking about this test. DeepMind, the London-based AI pro possessed by Googles parent organization Alphabet, accepts its neural systems that can accelerate the regularly painful procedure of settling the structures of viral proteins. It has created two strategies for preparing neural networks to foresee the properties of a protein from its hereditary arrangement. We would like to add to the logical exertion by discharging structure forecasts of a few under-contemplated proteins related to SARS-CoV-2, the infection that causes COVID-19, the organization said. These can assist scientists with building comprehension of how the infection capacities and be utilized in medicate revelation.

The pandemic has driven endeavor programming organization Salesforce to differentiate into life sciences, in an investigation showing that AI models can gain proficiency with the language of science, similarly as they can do discourse and picture acknowledgment. The thought is that the AI framework will, at that point, have the option to plan proteins, or recognize complex proteins, that have specific properties, which could be utilized to treat COVID-19.

Salesforce took care of the corrosive amino arrangements of proteins and their related metadata into its ProGen AI framework. The framework takes each preparation test and details a game where it attempts to foresee the following amino corrosive in succession.

Before the finish of preparing, ProGen has gotten a specialist at foreseeing the following amino corrosive by playing this game roughly one trillion times, said Ali Madani, an analyst at Salesforce. ProGen would then be able to be utilized practically speaking for protein age by iteratively anticipating the following doubtlessly amino corrosive and producing new proteins it has never observed. Salesforce is presently looking to collaborate with scholars to apply the innovation.

As governments and wellbeing associations scramble to contain the spread of coronavirus, they need all the assistance they with canning get, including from machine learning. Even though present AI innovations are a long way from recreating human knowledge, they are ending up being useful in following the episode, diagnosing patients, sanitizing regions, and accelerating the way toward finding a remedy for COVID-19.

Information science and AI maybe two of the best weapons we have in the battle against the coronavirus episode.

Not long before the turn of the year, BlueDot, a human-made consciousness stage that tracks irresistible illnesses around the globe, hailed a group of bizarre pneumonia cases occurring around a market in Wuhan, China. After nine days, the World Health Organization (WHO) discharged an announcement proclaiming the disclosure of a novel coronavirus in a hospitalized individual with pneumonia in Wuhan.

BlueDot utilizes everyday language preparation and AI calculations to scrutinize data from many hotspots for early indications of irresistible pestilences. The AI takes a gander at articulations from wellbeing associations, business flights, animal wellbeing reports, atmosphere information from satellites, and news reports. With so much information being created on coronavirus consistently, the AI calculations can help home in on the bits that can give appropriate data on the spread of the infection. It can likewise discover significant connections betweens information focuses, for example, the development examples of the individuals who are living in the zones generally influenced by the infection.

The organization additionally utilizes many specialists who have some expertise in the scope of orders, including geographic data frameworks, spatial examination, information perception, PC sciences, just as clinical specialists in irresistible clinical ailments, travel and tropical medication, and general wellbeing. The specialists audit the data that has been hailed by the AI and convey writes about their discoveries.

Joined with the help of human specialists, BlueDots AI can anticipate the beginning of a pandemic, yet additionally, conjecture how it will spread. On account of COVID-19, the AI effectively recognized the urban communities where the infection would be moved to after it surfaced in Wuhan. AI calculations considering make a trip design had the option to foresee where the individuals who had contracted coronavirus were probably going to travel.

Presently, AI calculations can play out the equivalent everywhere scale. An AI framework created by Chinese tech monster Baidu utilizes cameras furnished with PC vision and infrared sensors to foresee individuals temperatures in open territories. The frame can screen up to 200 individuals for every moment and distinguish their temperature inside the scope of 0.5 degrees Celsius. The AI banners any individual who has a temperature above 37.3 degrees. The innovation is currently being used in Beijings Qinghe Railway Station.

Alibaba, another Chinese tech monster, has built up an AI framework that can recognize coronavirus in chest CT filters. As indicated by the analysts who built up the structure, the AI has a 96-percent exactness. The AI was prepared on information from 5,000 coronavirus cases and can play out the test in 20 seconds instead of the 15 minutes it takes a human master to analyze patients. It can likewise differentiate among coronavirus and common viral pneumonia. The calculation can give a lift to the clinical focuses that are as of now under a ton of strain to screen patients for COVID-19 disease. The framework is supposedly being embraced in 100 clinics in China.

A different AI created by specialists from Renmin Hospital of Wuhan University, Wuhan EndoAngel Medical Technology Company, and the China University of Geosciences purportedly shows 95-percent precision on distinguishing COVID-19 in chest CT checks. The framework is a profound learning calculation prepared on 45,000 anonymized CT checks. As per a preprint paper distributed on medRxiv, the AIs exhibition is practically identical to master radiologists.

One of the fundamental approaches to forestall the spread of the novel coronavirus is to decrease contact between tainted patients and individuals who have not gotten the infection. To this end, a few organizations and associations have occupied with endeavors to robotize a portion of the methods that recently required wellbeing laborers and clinical staff to cooperate with patients.

Chinese firms are utilizing automatons and robots to perform contactless conveyance and to splash disinfectants in open zones to limit the danger of cross-contamination. Different robots are checking individuals for fever and other COVID-19 manifestations and administering free hand sanitizer foam and gel.

Inside emergency clinics, robots are conveying nourishment and medication to patients and purifying their rooms to hinder the requirement for the nearness of attendants. Different robots are caught up with cooking rice without human supervision, decreasing the quantity of staff required to run the office.

In Seattle, specialists utilized a robot to speak with and treat patients remotely to limit the introduction of clinical staff to contaminated individuals.

By the days end, the war on the novel coronavirus isnt over until we build up an immunization that can vaccinate everybody against the infection. Be that as it may, growing new medications and medication is an exceptionally protracted and expensive procedure. It can cost more than a billion dollars and take as long as 12 years. That is the sort of period we dont have as the infection keeps on spreading at a quickening pace.

Luckily, AI can assist speed with increasing the procedure. DeepMind, the AI investigate lab procured by Google in 2014, as of late announced that it has utilized profound figuring out how to discover new data about the structure of proteins related to COVID-19. This is a procedure that could have taken a lot more months.

Understanding protein structures can give significant insights into the coronavirus immunization recipe. DeepMind is one of a few associations that are occupied with the race to open the coronavirus immunization. It has utilized the consequence of many years of AI progress, just as research on protein collapsing.

Its imperative to take note of that our structure forecast framework is still being developed, and we cant be sure of the precision of the structures we are giving, even though we are sure that the framework is more exact than our prior CASP13 framework, DeepMinds scientists composed on the AI labs site. We affirmed that our framework gave an exact forecast to the tentatively decided SARS-CoV-2 spike protein structure partook in the Protein Data Bank, and this gave us the certainty that our model expectations on different proteins might be valuable.

Even though it might be too soon to tell whether were going the correct way, the endeavors are excellent. Consistently spared in finding the coronavirus antibody can save hundredsor thousandsof lives.

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Google is using AI to design chips that will accelerate AI – MIT Technology Review

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A new reinforcement-learning algorithm has learned to optimize the placement of components on a computer chip to make it more efficient and less power-hungry.

3D Tetris: Chip placement, also known as chip floor planning, is a complex three-dimensional design problem. It requires the careful configuration of hundreds, sometimes thousands, of components across multiple layers in a constrained area. Traditionally, engineers will manually design configurations that minimize the amount of wire used between components as a proxy for efficiency. They then use electronic design automation software to simulate and verify their performance, which can take up to 30 hours for a single floor plan.

Time lag: Because of the time investment put into each chip design, chips are traditionally supposed to last between two and five years. But as machine-learning algorithms have rapidly advanced, the need for new chip architectures has also accelerated. In recent years, several algorithms for optimizing chip floor planning have sought to speed up the design process, but theyve been limited in their ability to optimize across multiple goals, including the chips power draw, computational performance, and area.

Intelligent design: In response to these challenges, Google researchers Anna Goldie and Azalia Mirhoseini took a new approach: reinforcement learning. Reinforcement-learning algorithms use positive and negative feedback to learn complicated tasks. So the researchers designed whats known as a reward function to punish and reward the algorithm according to the performance of its designs. The algorithm then produced tens to hundreds of thousands of new designs, each within a fraction of a second, and evaluated them using the reward function. Over time, it converged on a final strategy for placing chip components in an optimal way.

Validation: After checking the designs with the electronic design automation software, the researchers found that many of the algorithms floor plans performed better than those designed by human engineers. It also taught its human counterparts some new tricks, the researchers said.

Production line: Throughout the field's history, progress in AI has been tightly interlinked with progress in chip design. The hope is this algorithm will speed up the chip design process and lead to a new generation of improved architectures, in turn accelerating AI advancement.

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Google is using AI to design chips that will accelerate AI - MIT Technology Review

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PSD2: How machine learning reduces friction and satisfies SCA – The Paypers

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Andy Renshaw, Feedzai: It crosses borders but doesnt have a passport. Its meant to protect people but can make them angry. Its competitive by nature but doesnt want you to fail. What is it?

If the PSD2 regulations and Strong Customer Authentication (SCA) feel like a riddle to you, youre not alone. SCA places strict two-factor authentication requirements upon financial institutions (FIs) at a time when FIs are facing stiff competition for customers. On top of that, the variety of payment types, along with the sheer number of transactions, continue to increase.

According to UK Finance, the number of debit card transactions surpassed cash transactions since 2017, while mobile banking surged over the past year, particularly for contactless payments. The number of contactless payment transactions per customer is growing; this increase in transactions also raises the potential for customer friction.

The number of transactions isnt the only thing thats shown an exponential increase; the speed at which FIs must process them is too. Customers expect to send, receive, and access money with the swipe of a screen. Driven by customer expectations, instant payments are gaining traction across the globe with no sign of slowing down.

Considering the sheer number of transactions combined with the need to authenticate payments in real-time, the demands placed on FIs can create a real dilemma. In this competitive environment, how can organisations reduce fraud and satisfy regulations without increasing customer friction?

For countries that fall under PSD2s regulation, the answer lies in the one known way to avoid customer friction while meeting the regulatory requirement: keep fraud rates at or below SCA exemption thresholds.

How machine learning keeps fraud rates below the exemption threshold to bypass SCA requirements

Demonstrating significantly low fraud rates allows financial institutions to bypass the SCA requirement. The logic behind this is simple: if the FIs systems can prevent fraud at such high rates, they've demonstrated their systems are secure without authentication.

SCA exemption thresholds are:

Exemption Threshold Value

Remote electronic card-based payment

Remote electronic credit transfers

EUR 500

below 0.01% fraud rate

below 0.01% fraud rate

EUR 250

below 0.06% fraud rate

below 0.01% fraud rate

EUR 100

below 0.13% fraud rate

below 0.015% fraud rate

Looking at these numbers, you might think that achieving SCA exemption thresholds is impossible. After all, bank transfer scams rose 40% in the first six months of 2019. But state-of-the-art technology rises to the challenge of increased fraud. Artificial intelligence, and more specifically machine learning, makes achieving SCA exemption thresholds possible.

How machine learning achieves SCA exemption threshold values

Every transaction has hundreds of data points, called entities. Entities include time, date, location, device, card, cardless, sender, receiver, merchant, customer age the possibilities are almost endless. When data is cleaned and connected, meaning it doesnt live in siloed systems, the power of machine learning to provide actionable insights on that data is historically unprecedented.

Robust machine learning technology uses both rules and models and learns from both historical and real-time profiles of virtually every data point or entity in a transaction. The more data we feed the machine, the better it gets at learning fraud patterns. Over time, the machine learns to accurately score transactions in less than a second without the need for customer authentication.

Machine learning creates streamlined and flexible workflows

Of course, sometimes, authentication is inevitable. For example, if a customer who generally initiates a transaction in Brighton, suddenly initiates a transaction from Mumbai without a travel note on the account, authentication should be required. But if machine learning platforms have flexible data science environments that embed authentication steps seamlessly into the transaction workflow, the experience can be as customer-centric as possible.

Streamlined workflows must extend to the fraud analysts job

Flexible workflows arent just important to instant payments theyre important to all payments. And they cant just be a back-end experience in the data science environment. Fraud analysts need flexibility in their workflows too. They're under pressure to make decisions quickly and accurately, which means they need a full view of the customer not just the transaction.

Information provided at a transactional level doesnt allow analysts to connect all the dots. In this scenario, analysts are left opening up several case managers in an attempt to piece together a complete and accurate fraud picture. Its time-consuming and ultimately costly, not to mention the wear and tear on employee satisfaction. But some machine learning risk platforms can show both authentication and fraud decisions at the customer level, ensuring analysts have a 360-degree view of the customer.

Machine learning prevents instant payments from becoming instant losses

Instant payments can provide immediate customer satisfaction, but also instant fraud losses. Scoring transactions in real-time means institutions can increase the security around the payments going through their system before its too late.

Real-time transaction scoring requires a colossal amount of processing power because it cant use batch processing, an efficient method when dealing with high volumes of data. Thats because the lag time between when a customer transacts and when a batch is processed makes this method incongruent with instant payments. Therefore, scoring transactions in real-time requires supercomputers with super processing powers. The costs associated with this make hosting systems on the cloud more practical than hosting at the FIs premises, often referred to as on prem. Of course, FIs need to consider other factors, including cybersecurity concerns before determining where they should host their machine learning platform.

Providing exceptional customer experiences by keeping fraud at or below PSD2s SCA threshold can seem like a magic trick, but its not. Its the combined intelligence of humans and machines to provide the most effective method we have today to curb and prevent fraud losses. Its how we solve the friction-security puzzle and deliver customer satisfaction while satisfying SCA.

About Andy Renshaw

Andy Renshaw, Vice President of Banking Solutions at Feedzai, has over 20 years of experience in banking and the financial services industry, leading large programs and teams in fraud management and AML. Prior to joining Feedzai, Andy held roles in global financial institutions such as Lloyds Banking Group, Citibank, and Capital One, where he helped fight against the ever-evolving financial crime landscape as a technical expert, fraud prevention expert, and a lead product owner for fraud transformation.

About Feedzai

Feedzai is the market leader in fighting fraud with AI. Were coding the future of commerce with todays most advanced risk management platform powered by big data and machine learning. Founded and developed by data scientists and aerospace engineers, Feedzai has one mission: to make banking and commerce safe. The worlds largest banks, processors, and retailers use Feedzais fraud prevention and anti-money laundering products to manage risk while improving customer experience.

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PSD2: How machine learning reduces friction and satisfies SCA - The Paypers

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Neural networks facilitate optimization in the search for new materials – MIT News

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When searching through theoretical lists of possible new materials for particular applications, such as batteries or other energy-related devices, there are often millions of potential materials that could be considered, and multiple criteria that need to be met and optimized at once. Now, researchers at MIT have found a way to dramatically streamline the discovery process, using a machine learning system.

As a demonstration, the team arrived at a set of the eight most promising materials, out of nearly 3 million candidates, for an energy storage system called a flow battery. This culling process would have taken 50 years by conventional analytical methods, they say, but they accomplished it in five weeks.

The findings are reported in the journal ACS Central Science, in a paper by MIT professor of chemical engineering Heather Kulik, Jon Paul Janet PhD 19, Sahasrajit Ramesh, and graduate student Chenru Duan.

The study looked at a set of materials called transition metal complexes. These can exist in a vast number of different forms, and Kulik says they are really fascinating, functional materials that are unlike a lot of other material phases. The only way to understand why they work the way they do is to study them using quantum mechanics.

To predict the properties of any one of millions of these materials would require either time-consuming and resource-intensive spectroscopy and other lab work, or time-consuming, highly complex physics-based computer modeling for each possible candidate material or combination of materials. Each such study could consume hours to days of work.

Instead, Kulik and her team took a small number of different possible materials and used them to teach an advanced machine-learning neural network about the relationship between the materials chemical compositions and their physical properties. That knowledge was then applied to generate suggestions for the next generation of possible materials to be used for the next round of training of the neural network. Through four successive iterations of this process, the neural network improved significantly each time, until reaching a point where it was clear that further iterations would not yield any further improvements.

This iterative optimization system greatly streamlined the process of arriving at potential solutions that satisfied the two conflicting criteria being sought. This kind of process of finding the best solutions in situations, where improving one factor tends to worsen the other, is known as a Pareto front, representing a graph of the points such that any further improvement of one factor would make the other worse. In other words, the graph represents the best possible compromise points, depending on the relative importance assigned to each factor.

Training typical neural networks requires very large data sets, ranging from thousands to millions of examples, but Kulik and her team were able to use this iterative process, based on the Pareto front model, to streamline the process and provide reliable results using only the few hundred samples.

In the case of screening for the flow battery materials, the desired characteristics were in conflict, as is often the case: The optimum material would have high solubility and a high energy density (the ability to store energy for a given weight). But increasing solubility tends to decrease the energy density, and vice versa.

Not only was the neural network able to rapidly come up with promising candidates, it also was able to assign levels of confidence to its different predictions through each iteration, which helped to allow the refinement of the sample selection at each step. We developed a better than best-in-class uncertainty quantification technique for really knowing when these models were going to fail, Kulik says.

The challenge they chose for the proof-of-concept trial was materials for use in redox flow batteries, a type of battery that holds promise for large, grid-scale batteries that could play a significant role in enabling clean, renewable energy. Transition metal complexes are the preferred category of materials for such batteries, Kulik says, but there are too many possibilities to evaluate by conventional means. They started out with a list of 3 million such complexes before ultimately whittling that down to the eight good candidates, along with a set of design rules that should enable experimentalists to explore the potential of these candidates and their variations.

Through that process, the neural net both gets increasingly smarter about the [design] space, but also increasingly pessimistic that anything beyond what weve already characterized can further improve on what we already know, she says.

Apart from the specific transition metal complexes suggested for further investigation using this system, she says, the method itself could have much broader applications. We do view it as the framework that can be applied to any materials design challenge where you're really trying to address multiple objectives at once. You know, all of the most interesting materials design challenges are ones where you have one thing you're trying to improve, but improving that worsens another. And for us, the redox flow battery redox couple was just a good demonstration of where we think we can go with this machine learning and accelerated materials discovery.

For example, optimizing catalysts for various chemical and industrial processes is another kind of such complex materials search, Kulik says. Presently used catalysts often involve rare and expensive elements, so finding similarly effective compounds based on abundant and inexpensive materials could be a significant advantage.

This paper represents, I believe, the first application of multidimensional directed improvement in the chemical sciences, she says. But the long-term significance of the work is in the methodology itself, because of things that might not be possible at all otherwise. You start to realize that even with parallel computations, these are cases where we wouldn't have come up with a design principle in any other way. And these leads that are coming out of our work, these are not necessarily at all ideas that were already known from the literature or that an expert would have been able to point you to.

This is a beautiful combination of concepts in statistics, applied math, and physical science that is going to be extremely useful in engineering applications, says George Schatz, a professor of chemistry and of chemical and biological engineering at Northwestern University, who was not associated with this work. He says this research addresses how to do machine learning when there are multiple objectives. Kuliks approach uses leading edge methods to train an artificial neural network that is used to predict which combination of transition metal ions and organic ligands will be best for redox flow battery electrolytes.

Schatz says this method can be used in many different contexts, so it has the potential to transform machine learning, which is a major activity around the world.

The work was supported by the Office of Naval Research, the Defense Advanced Research Projects Agency (DARPA), the U.S. Department of Energy, the Burroughs Wellcome Fund, and the AAAS Mar ion Milligan Mason Award.

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Deep Learning: What You Need To Know – Forbes

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AI (artificial Intelligence) concept.

During the past decade, deep learning has seen groundbreaking developments in the field of AI (Artificial Intelligence). But what is this technology? And why is it so important?

Well, lets first get a definition of deep learning.Heres how Kalyan Kumar, who is the Corporate Vice President & Chief Technology Officer of IT Services at HCL Technologies, describes it:Have you ever wondered how our brain can recognize the face of a friend whom you had met years ago or can recognize the voice of your mother among so many other voices in a crowded marketplace or how our brain can learn, plan and execute complex day-to-day activities? The human brain has around 100 billion cells called neurons. These build massively parallel and distributed networks, through which we learn and carry out complex activities. Inspired from these biological neural networks, scientists started building artificial neural networks so that computers could eventually learn and exhibit intelligence like humans.

Think of it this way:You first will start with a huge amount of unstructured data, say videos.Then you will use a sophisticated model that will process this information and try to determine underlying patterns, which are often not detectable by people.

During training, you define the number of neurons and layers your neural network will be comprised of and expose it to labeled training data, said Brian Cha, who is a Product Manager and Deep Learning evangelist at FLIR Systems.With this data, the neural network learns on its own what is good or bad. For example, if you want the neural network to grade fruits, you would show it images of fruits labeled Grade A, Grade B, Grade C, and so on. The neural network uses this training data to extract and assign weights to features that are unique to fruits labelled good, such as ideal size, shape, color, consistency of color and so on. You dont need to manually define these characteristics or even program what is too big or too small, the neural network trains itself using the training data. The process of evaluating new images using a neural network to make decisions on is called inference. When you present the trained neural network with a new image, it will provide an inference, such as Grade A with 95% confidence.

What about the algorithms?According to Bob Friday, who is the CTO of Mist Systems, a Juniper Networks company, There are two kinds of popular neural network models for different use cases: the Convolutional Neural Network (CNN) model is used in image related applications, such as autonomous driving, robots and image search. Meanwhile, the Recurrent Neural Network (RNN) model is used in most of the Natural Language Processing-based (NLP) text or voice applications, such as chatbots, virtual home and office assistants and simultaneous interpreters and in networking for anomaly detection.

Of course, deep learning requires lots of sophisticated tools.But the good news is that there are many available and some are even free like TensorFlow, PyTorch and Keras.

There are also cloud-based server computer services, said Ali Osman rs, who is the Director of AI Strategy and Strategic Partnerships for ADAS at NXP Semiconductors.These are referred to as Machine Learning as a Service (MLaaS) solutions. The main providers include Amazon AWS, Microsoft Azure, and Google Cloud.

Because of the enormous data loads and complex algorithms, there is usually a need for sophisticated hardware infrastructure.Keep in mind that it can sometimes take days to train a model

The unpredictable process of training neural networks requires rapid on-demand scaling of virtual machine pools, said Brent Schroeder, who is the Chief Technology Officer at SUSE. Container based deep learning workloads managed by Kubernetes can easily be deployed to different infrastructure depending upon the specific needs. An initial model can be developed on a small local cluster, or even an individual workstation with a Jupyter Notebook. But then as training needs to scale, the workload can be deployed to large, scalable cloud resources for the duration of the training. This makes Kubernetes clusters a flexible, cost-effective option for training different types of deep learning workloads.

Deep learning has been shown to be quite efficient and accurate with models.Probably the biggest advantage of deep learning over most other machine learning approaches is that the user does not need to worry about trimming down the number of features used, said Noah Giansiracusa, who is an Assistant Professor of Mathematical Sciences at Bentley University.With deep learning, since the neurons are being trained to perform conceptual taskssuch as finding edges in a photo, or facial features within a facethe neural network is in essence figuring out on its own which features in the data itself should be used.

Yet there are some notable drawbacks to deep learning.One is cost.Deep learning networks may require hundreds of thousands or millions of hand-labeled examples, said Evan Tann, who is the CTO and co-founder of Thankful.It is extremely expensive to train in fast timeframes, as serious players will need commercial-grade GPUs from Nvidia that easily exceed $10k each.

Deep learning is also essentially a black box.This means it can be nearly impossible to understand how the model really works!

This can be particularly problematic in applications that require such documentation like FDA approval of drugs and medical devices, said Dr. Ingo Mierswa, who is the Founder of RapidMiner.

And yes, there are some ongoing complexities with deep learning models, which can create bad outcomes.Say a neural network is used to identify cats from images, said Yuheng Chen, who is the COO of rct studio.It works perfectly, but when we want it to identify cats and dogs at the same time, its performance collapses.

But then again, there continues to be rapid progress, as companies continue to invest substantial amounts into deep learning.For the most part, things are still very much in the nascent stages.

The power of deep learning is what allows seamless speech recognition, image recognition, and automation and personalization across every possible industry today, so it's safe to say that you are already experiencing the benefits of deep learning, said Sajid Sadi, who is the VP of Research at Samsung and the Head of Think Tank Team.

Tom (@ttaulli) is the author of Artificial Intelligence Basics: A Non-Technical Introduction and The Robotic Process Automation Handbook: A Guide to Implementing RPA Systems.

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Deep Learning: What You Need To Know - Forbes

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Data to the Rescue! Predicting and Preventing Accidents at Sea – JAXenter

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Watch Dr. Yonit Hoffman's Machine Learning Conference session

Accidents at sea happen all the time. Their costs in terms of lives, money and environmental destruction are huge. Wouldnt it be great if they could be predicted and perhaps prevented? Dr. Yonit Hoffmans Machine Learning Conference session discusses new ways of preventing sea accidents with the power of data science.

Does machine learning hold the key to preventing accidents at sea?

With more than 350 years of history, the marine insurance industry is the first data science profession to try to predict accidents and estimate future risk. Yet the old ways no longer work, new waves of data and algorithms can offer significant improvements and are going to revolutionise the industry.

In her Machine Learning Conference session, Dr. Yonit Hoffman will show that it is now possible to predict accidents, and how data on a ships behaviour such as location, speed, maps and weather can help. She will show how fragments of information on ship movements can be gathered and taken all the way to machine learning models. In this session, she discusses the challenges, including introducing machine learning to an industry that still uses paper and quills (yes, really!) and explaining the models using SHAP.

Dr. Yonit Hoffman is a Senior Data Scientist at Windward, a world leader in maritime risk analytics. Before investigating supertanker accidents, she researched human cells and cancer at the Weizmann Institute, where she received her PhD and MSc. in Bioinformatics. Yonit also holds a BSc. in computer science and biology from Tel Aviv University.


Data to the Rescue! Predicting and Preventing Accidents at Sea - JAXenter

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What are the top AI platforms? – Gigabit Magazine – Technology News, Magazine and Website

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Business Overview

Microsoft AI is a platform used to develop AI solutions in conversational AI, machine learning, data sciences, robotics, IoT, and more.

Microsoft AI prides itself on driving innovation through; protecting wildlife, better brewing, feeding the world and preserving history.

Its Cognitive Services is described as a comprehensive family of AI services and cognitive APIs to help you build intelligent apps.


Tom Bernard Krake is the Azure Cloud Executive at Microsoft, responsible for leveraging and evaluating the Azure platform. Tom is joined by a team of experienced executives to optimise the Azure platform and oversee the many cognitive services that it provides.

Notable customers

Uber uses Cognitive Services to boost its security through facial recognition to ensure that the driver using the app matches the user that is on file.

KPMG helps financial institutions save millions in compliance costs through the use of Microsofts Cognitive Services. They do this through transcribing and logging thousands of hours of calls, reducing compliance costs by as much as 80 per cent. uses Cognitive Services to provide answers to its customers by infusing its customer chatbot with the intelligence to communicate using natural language.

The services:

Decision - Make smarter decisions faster through anomaly detectors, content moderators and personalizers.

Language - Extract meaning from unstructured text through the immersive reader, language understanding, Q&A maker, text analytics and translator text.

Speech - Integrate speech processing into apps and services through Speech-to-text, Text to speech, Speech translation and Speaker recognition.

Vision - Identify and analyse content within images, videos and digital ink through computer vision, custom vision, face, form recogniser, ink recogniser and video indexer.

Web Search -Find what you are looking for through the world-wide-web through autosuggest, custom search, entity search, image search, news search, spell check, video search, visual search and web search.

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What are the top AI platforms? - Gigabit Magazine - Technology News, Magazine and Website

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With Launch of COVID-19 Data Hub, The White House Issues A ‘Call To Action’ For AI Researchers – Machine Learning Times – machine learning & data…

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Originally published in TechCrunch, March 16, 2020

In a briefing on Monday, research leaders across tech, academia and the government joined the White House to announce an open data set full of scientific literature on the novel coronavirus. The COVID-19 Open Research Dataset, known as CORD-19, will also add relevant new research moving forward, compiling it into one centralized hub. The new data set is machine readable, making it easily parsed for machine learning purposes a key advantage according to researchers involved in the ambitious project.

In a press conference, U.S. CTO Michael Kratsios called the new data set the most extensive collection of machine readable coronavirus literature to date. Kratsios characterized the project as a call to action for the AI community, which can employ machine learning techniques to surface unique insights in the body of data. To come up with guidance for researchers combing through the data, the National Academies of Sciences, Engineering, and Medicine collaborated with the World Health Organization to come up with high priority questions about the coronavirus related to genetics, incubation, treatment, symptoms and prevention.

The partnership, announced today by the White House Office of Science and Technology Policy, brings together the Chan Zuckerberg Initiative, Microsoft Research, the Allen Institute for Artificial Intelligence, the National Institutes of Healths National Library of Medicine, Georgetown Universitys Center for Security and Emerging Technology, Cold Spring Harbor Laboratory and the Kaggle AI platform, owned by Google.

The database brings together nearly 30,000 scientific articles about the virus known as SARS-CoV-2. as well as related viruses in the broader coronavirus group. Around half of those articles make the full text available. Critically, the database will include pre-publication research from resources like medRxiv and bioRxiv, open access archives for pre-print health sciences and biology research.

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With Launch of COVID-19 Data Hub, The White House Issues A 'Call To Action' For AI Researchers - Machine Learning Times - machine learning & data...

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