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 ageneral work-from-home strategy to ensure its laborers (alongside Google and arising number of different firms), it at first required its contractual workerswho moderate substance to keep on coming into the workplace. That circumstancejust changed after fights, as per The Intercept.

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

For both our full-time representatives and agreementworkforce, there is some work that is impossible from home because ofwellbeing, security, and legitimate reasons. We have played it safe to secureour laborers by chopping down the number of individuals in some random office,executing prescribed work from home all-inclusive, truly spreading individualsout at some random office, and doing extra cleaning. Given the quicklydeveloping general wellbeing concerns, we are finding a way to ensure ourgroups. We will be working with our accomplices throughout this week to sendall 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 inthe equivalent world-renowned pontoon: Theres an expanding need to politicizetheir stages, just to take out counterfeit news about COVID-19. Yetthe volunteers who handle such assignments cant do as such from home,particularly on their workstations. The potential arrangement? Human-madereasoning (AI) and AI calculations intended to examine the flawed substance andsettle 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 ablend of individuals and innovation: Machine learning recognizes possiblydestructive substance and afterward sends it to human analysts for evaluation.Because of the new estimates were taking, we will incidentally begin dependingmore on innovation to help with a portion of the work regularly done bycommentators. This implies computerized frameworks will begin evacuating somesubstance without human audit, so we can keep on acting rapidly to expelviolative substances and ensure our environment. At the same time, we have aworking 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 onthe 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 thepreviously mentioned blog posting, brought up how its computerized frameworksmay hail inappropriate recordings. Facebook is additionally getting analysisthat 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 organizationswill not surely turn to machine learning as a potential answer forinterruptions in their work process and different procedures. That will drive aprecarious expectation to absorb information; over and over, the rollout of AIstages has exhibited that, while the capability of the innovation is there,execution is regularly an unpleasant and costly proceduresimply see GoogleDuplex.

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

Before the infection developed, Burning Glass (which breaks downa great many activity postings from over the US), evaluated that employmentsthat include AI would grow 40.1 percent throughout the following decade. Thatrate could increase considerably higher if the emergency on a fundamental levelchanges how individuals over the world live and work. (The average compensationfor 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 downflare-ups. At the point when a pandemic, for example, the ongoing coronavirusepisode occurs, it can make enormous difficulties for the administration andgeneral wellbeing authorities to accumulate data rapidly and facilitate areaction.

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

Human-made intelligence calculations can help mine through newsreports and online substances from around the globe, assisting specialists inperceiving oddities even before it arrives at pestilence extents. The crownepisode itself is an extraordinary model where specialists applied AI toexamine flight voyager information to anticipate where the novel coronaviruscould spring up straightaway. A National Geographic report shows how checkingthe web or online life can help identify the beginning periods.

Practical usage of prescient demonstrating could speak to asignificant jump forward in the battle to free the universe of probably themost irresistible maladies. Substantial information examination can enablede-to to concentrate the procedure and empower the convenient investigation offar-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 medicinalservices experts over the globe working vigorously to treat COVID-19 patients,frequently putting their own lives in danger. Computer-based intelligence couldassume a critical job in relieving their burden while guaranteeing that thenature of care doesnt endure. For example, the Tampa General Hospital inFlorida is utilizing AI to recognize fever in guests with a primary facialoutput. Human-made intelligence is additionally helping specialists at theSheba Medical Center.

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

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

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

Artificial intelligence instruments can help from multiplepoints of view. They are being utilized to foresee the spread of thecoronavirus, map its hereditary advancement as it transmits from human tohuman, 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 onthe off chance that it has adequate guides. As COVID-19 has brought the worldinto the unchartered domain, the profound learning frameworks,which PCs use to obtain new capacities, dont have the information they have todeliver helpful yields.

Machine leaning is acceptable at anticipating nonexclusiveconduct, yet isnt truly adept at extrapolating that to an emergencycircumstance when nearly everything that happens is new, alerts LeoKrkkinen, a teacher at the Department of Electrical Engineering andAutomation in Aalto University, Helsinki and an individual with Nokias BellLabs. On the off chance that individuals respond in new manners, at thatpoint AI cant foresee it. Until you have seen it, you cant gain fromit.

Regardless of this clause, Krkkinen says powerful AI-basednumerical models are assuming a significant job in helping policymakers see howCOVID-19 is spreading and when the pace of diseases is set to top. Bydrawing on information from the field, for example, the number of passings, AImodels can assist with identifying what number of contaminations areuninformed, he includes, alluding to undetected cases that are as yetirresistible. That information would then be able to be utilized to advise thefoundation regarding isolate zones and other social removing measures.

It is likewise the situation that AI-based diagnostics that arebeing applied in related zones can rapidly be repurposed for diagnosingCOVID-19 contaminations. Behold.ai, which has a calculation for consequentlyrecognizing both malignant lung growth and fallen lungs from X-beams, provideddetails regarding Monday that the count can rapidly distinguish chest X-beamsfrom COVID-19 patients as unusual. Right now, triage might accelerate findingand guarantee assets are dispensed appropriately.

The dire need to comprehend what sorts of approach intercessionsare powerful against COVID-19 has driven different governments to grant awardsto outfit AI rapidly. One beneficiary is David Buckeridge, a teacher in theDepartment of Epidemiology, Biostatistics and Occupational Health at McGillUniversity in Montreal. Equipped with an award of C$500,000 (323,000), hisgroup is joining ordinary language preparing innovation with AI devices, forexample, neural systems (a lot of calculations intended to perceive designs),to break down more than 2,000,000 customary media and internet-based lifereports regarding the spread of the coronavirus from everywhere throughout theworld. This is unstructured free content traditional techniques cantmanage it, Buckeridge said. We need to remove a timetable fromonline 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 withother information, for example, COVID-19 case answers, to give policymakers andwellbeing specialists a significantly more complete image of how and why theinfection is spreading distinctively in various nations. This is appliedresearch in which we will hope to find significant solutions quick,Buckeridge noted. We ought to have a few consequences of significance togeneral wellbeing in April.

Simulated intelligence can likewise be utilized to helprecognize people who may be accidentally tainted with COVID-19. Chinese techorganization Baidu says its new AI-empowered infrared sensor framework canscreen the temperature of individuals in the nearness and rapidly decide ifthey may have a fever, one of the indications of the coronavirus. In an 11March article in the MIT Technology Review, Baidu said the innovation is beingutilized in Beijings Qinghe Railway Station to recognize travelers who areconceivably contaminated, where it can look at up to 200 individuals in asingle moment without upsetting traveler stream. A report given out fromthe World Health Organization on how China has reacted to the coronavirus saysthe nation has additionally utilized essential information and AI to reinforcecontact 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 programmingorganizations is likewise thinking about this test. DeepMind, the London-basedAI pro possessed by Googles parent organization Alphabet, accepts its neuralsystems that can accelerate the regularly painful procedure of settling thestructures of viral proteins. It has created two strategies for preparingneural networks to foresee the properties of a protein from its hereditaryarrangement. We would like to add to the logical exertion bydischarging structure forecasts of a few under-contemplated proteins related toSARS-CoV-2, the infection that causes COVID-19, the organization said.These can assist scientists with building comprehension of how the infectioncapacities and be utilized in medicate revelation.

The pandemic has driven endeavor programming organizationSalesforce to differentiate into life sciences, in an investigation showingthat AI models can gain proficiency with the language of science, similarly asthey can do discourse and picture acknowledgment. The thought is that the AIframework will, at that point, have the option to plan proteins, or recognizecomplex proteins, that have specific properties, which could be utilized totreat COVID-19.

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

Before the finish of preparing, ProGen has gotten aspecialist at foreseeing the following amino corrosive by playing this gameroughly one trillion times, said Ali Madani, an analyst at Salesforce.ProGen would then be able to be utilized practically speaking for proteinage by iteratively anticipating the following doubtlessly amino corrosive andproducing new proteins it has never observed. Salesforce is presentlylooking to collaborate with scholars to apply the innovation.

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

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

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

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

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

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

Presently, AI calculations can play out the equivalenteverywhere scale. An AI framework created by Chinese tech monster Baiduutilizes cameras furnished with PC vision and infrared sensors to foreseeindividuals temperatures in open territories. The frame can screen up to 200individuals for every moment and distinguish their temperature inside the scopeof 0.5 degrees Celsius. The AI banners any individual who has a temperatureabove 37.3 degrees. The innovation is currently being used in Beijings QingheRailway 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 ofWuhan University, Wuhan EndoAngel Medical Technology Company, and the ChinaUniversity of Geosciences purportedly shows 95-percent precision ondistinguishing COVID-19 in chest CT checks. The framework is a profoundlearning calculation prepared on 45,000 anonymized CT checks. As per a preprintpaper distributed on medRxiv, the AIs exhibition is practically identical tomaster radiologists.

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

Chinese firms are utilizing automatons and robots to performcontactless conveyance and to splash disinfectants in open zones to limit thedanger of cross-contamination. Different robots are checking individuals forfever and other COVID-19 manifestations and administering free hand sanitizerfoam and gel.

Inside emergency clinics, robots are conveying nourishment andmedication to patients and purifying their rooms to hinder the requirement forthe nearness of attendants. Different robots are caught up with cooking ricewithout human supervision, decreasing the quantity of staff required to run theoffice.

In Seattle, specialists utilized a robot to speak with and treatpatients remotely to limit the introduction of clinical staff to contaminatedindividuals.

By the days end, the war on the novel coronavirus isnt overuntil we build up an immunization that can vaccinate everybody against theinfection. Be that as it may, growing new medications and medication is anexceptionally protracted and expensive procedure. It can cost more than abillion dollars and take as long as 12 years. That is the sort of period wedont 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 lateannounced that it has utilized profound figuring out how to discover new dataabout the structure of proteins related to COVID-19. This is a procedure thatcould have taken a lot more months.

Understanding protein structures can give significant insightsinto the coronavirus immunization recipe. DeepMind is one of a few associationsthat are occupied with the race to open the coronavirus immunization. It hasutilized the consequence of many years of AI progress, just as research onprotein collapsing.

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

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

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What Researches says on Machine learning with COVID-19 - Techiexpert.com - TechiExpert.com

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

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

A nanodegree in autonomous vehicles is just one of 40 programs that Udacity is offering for free to workers laid off in the wake of the COVID-19 pandemic.

Udacity

Online learning platform Udacity is responding to the COVID-19 pandemic by offering free tech training to workers laid off as a result of the crisis.

On Thursday the Mountain View, California-based company revealed that in the wake of layoffs and furloughs by major U.S. corporations, including Marriott International, Hilton Hotels and GE Aviation, it will offer its courses known as nanodegrees for free to individuals in the U.S. who have been let go because of the coronavirus. The average price for an individual signing up for a nanodegree is about $400 a month, and the degrees take anywhere from four to six months to complete, according to the company.

The hope is that while individuals wait to go back to work, or in the event that the layoff is permanent, they can get training in fields that are driving so much of today's digital transformation. Udacity's courses include artificial intelligence, machine learning, digital marketing, product management, data analysis, cloud computing, autonomous vehicles, among others.

Gabe Dalporto, CEO of Udacity, said that over the past few weeks, as he and his senior leadership team heard projections of skyrocketing unemployment numbers as a result of COVID-19, he felt the need to act. "I think those reports were a giant wake-up call for everybody," he says. "This [virus] will create disruption across the board and in many industries, and we wanted to do our part to help."

A nanodegree in autonomous vehicles is just one of 40 programs that Udacity is offering for free to workers laid off in the wake of the COVID-19 pandemic.

Udacity

Dalporto says Udacity is funding the scholarships completely and that displaced workers can apply for them at udacity.com/pledge-to-americas-workers beginning March 26. Udacity will take the first 50 eligible applicants from each company that applies, and within 48 hours individuals should be able to begin the coursework. Dalporto says the offer will be good for the first 20 companies that apply and that "after that we'll evaluate and figure out how many more scholarships we are going to fund."

The company also announced this week that any individual, regardless of whether they've been laid off, can enroll for free in any one of Udacity's 40 different nanodegree programs. Users will get the first month free when they enroll in a monthly subscription, but Dalporto pointed out that many students can complete a course in a month if they dedicate enough time to it.

Udacity's offerings at this time underscore the growing disconnect between the skills workers have and the talent that organizations need today and in the years ahead. The company recently signed a deal with Royal Dutch Shell, for instance, to provide training in artificial intelligence. Shell says about 2,000 of its 82,000 employees have either expressed interest in the AI offerings or have been approached by their managers about taking the courses on everything from Python programming to training neural networks. Shell says the training is completely voluntary.

We have to be asking how are we going to help them get the skills they need to be successful in their careers moving forward when this is all behind us.

Gabe Dalporto

CEO of Udacity

And as more workers lose their jobs in the wake of the COVID-19 pandemic, it will be even more crucial that they're able to reenter the job market armed with the skills companies are looking for. According to the World Economic Forum's Future of Jobs report, at least 54% of all employees will need reskilling and upskilling by 2022. Yet only 30% of employees at risk of job displacement because of technological change received any training over the past year.

"America is facing a massive shortage of workers with the right technical skills, and as employers, retraining your existing workforce to address that shortage is the most efficient, cost-effective way to fill those gaps in an organization," Dalporto says. "The great irony in the world right now is that at the same time that a lot of people are going to lose their jobs, there are areas in corporations where managers just can't hire enough people for jobs in data analytics, cloud computing and AI."

Dalporto, who grew up in West Virginia, says he sees this point vividly every time he revisits his hometown. "When I go back, I see so many businesses and companies boarded up and people laid off because they didn't keep pace with automation and people didn't upskill," he says. As a result, many of these workers wind up in minimum wage jobs and that "just creates a lot of pain for them and their families," he adds. What's happening now is only fueling that cycleone that Dalporto says can be minimized with the right action.

"Laying people off is never an easy decision, but companies have to move the conversation beyond how many weeks of severance they're going to offer," he says. "We have to be asking how are we going to help them get the skills they need to be successful in their careers moving forward when this is all behind us."

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Udacity offers free tech training to laid-off workers due to the coronavirus pandemic - CNBC

Noble.AI, whose artificial intelligence (AI) software is purpose-built for engineers, scientists, and researchers and enables them to innovate and make discoveries faster, announced that it had completed contributions to TensorFlow, the worlds most popular open-source framework for deep learning created by Google.

Part of Nobles mission is building AI thats accessible to engineers, scientists and researchers, anytime and anywhere, without needing to learn or re-skill into computer science or AI theory, said Dr.Matthew C. Levy, Founder and CEO of Noble.AI. He continued, The reason why were making this symbolic contribution open-source is so people have greater access to tools amenable to R&D problems.

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TensorFlow is an end-to-end open source platform for machine learning originally developed by the Google Brain team. Today it is used by more than 60,000 GitHub developers and has achieved more than 140,000 stars and 80,000 forks of the codebase.

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Noble.AIs specific contribution helps to augment the sparse matrix capabilities of TensorFlow. Often, matrices represent mathematical operations that need to be performed on input data, such as in calculating the temporal derivative of time-series data. In many common physics and R&D scenarios these matrices can be sparsely populated such that a tiny fraction, often less than one percent, of all elements in the matrix are non-zero. In this setting, storing the entire matrix in a computers memory is cumbersome and often impossible all together at R&D industrial scale. In these cases, it often becomes advantageous to use sparse matrix operations.

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Noble.AI Contributes to TensorFlow, Google's Open-Source AI Library and the Most Popular - AiThority