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Amwell CMO: Google partnership will focus on AI, machine learning to expand into new markets – FierceHealthcare

Amwell is looking to evolve virtual care beyond just imitating in-person care.

To do that, the telehealth companyexpects to use its latestpartnership with Google Cloud toenable it to tap into artificial intelligence and machine learning technologies to create a better healthcare experience, according to Peter Antall, M.D., Amwell's chief medical officer.

"We have a shared vision to advance universal access to care thats cost-effective. We have a shared vision to expand beyond our borders to look at other markets. Ultimately, its a strategic technology collaboration that were most interested in," Antall said of the company's partnership with the tech giant during a STATvirtual event Tuesday.

"What we bring to the table is that we can help provide applications for those technologiesthat will have meaningful effects on consumers and providers," he said.

The use of AI and machine learning can improve bot-based interactions or decision support for providers, he said. The two companies also want to explore the use of natural language processing and automated translation to provide more "value to clients and consumers," he said.

Joining a rush of healthcare technology IPOs in 2020, Amwell went public in August, raising$742 million. Google Cloud and Amwell also announced amultiyear strategic partnership aimed at expanding access to virtual care, accompanied by a$100 million investmentfrom Google.

During an HLTH virtual event earlier this month, Google Cloud director of healthcare solutions Aashima Gupta said cloud and artificial intelligence will "revolutionize telemedicine as we know it."

RELATED:Amwell files to go public with $100M boost from Google

"There's a collective realization in the industry that the future will not look like the past," said Gupta during the HTLH panel.

During the STAT event, Antall said Amwellis putting a big focus onvirtual primary care, which has become an area of interest for health plans and employers.

"It seems to be the next big frontier. Weve been working on it for three years, and were very excited. So much of healthcare is ongoing chronic conditions and so much of the healthcare spend is taking care ofchronic conditionsandtaking care of those conditions in the right care setting and not in the emergency department," he said.

The companyworks with 55 health plans, which support over 36,000 employers and collectively represent more than 80million covered lives, as well as 150 of the nations largest health systems. To date, Amwell says it has powered over 5.6million telehealth visits for its clients, including more than 2.9million in the six months ended June 30, 2020.

Amwell is interested in interacting with patients beyond telehealth visits through what Antall called "nudges" and synchronous communication to encouragecompliance with healthy behaviors, he said.

RELATED:Amwell CEOs on the telehealth boom and why it will 'democratize' healthcare

It's an area where Livongo, recently acquired by Amwell competitor Teladoc,has become the category leader by using digital health tools to help with chronic condition management.

"Were moving into similar areas, but doing it in a slightly different matter interms of how we address ongoing continuity of care and how we address certain disease states and overall wellness," Antallsaid, in reference to Livongo's capabilities.

The telehealth company also wants to expand into home healthcare through the integration of telehealth and remote care devices.

Virtual care companies have been actively pursuing deals to build out their service and product lines as the use of telehealth soars. To this end, Amwell recently deepened its relationship with remote device company Tyto Care. Through the partnership, the TytoHome handheld examination device that allows patients to exam their heart, lungs, skin, ears, abdomen, and throat at home, is nowpaired withAmwells telehealth platform.

Looking forward, there is the potential for patients to getlab testing, diagnostic testing, and virtual visits with physicians all at home, Antall said.

"I think were going to see a real revolution in terms ofhow much more we can do in the home going forward," he said.

RELATED:Amwell's stock jumps on speculation of potential UnitedHealth deal: media report

Amwell also is exploring the use of televisions in the home to interact with patients, he said.

"We've done work with some partners and we're working toward a future where, if it's easier for you to click your remote and initiate a telehealth visit that way, thats one option. In some populations, particularly the elderly, a TV could serve as a remote patient device where a doctor or nurse could proactively 'ring the doorbell' on the TV and askto check on the patient," Antall said.

"Its video technology that'salready there in most homes, you just need a camera to go with it and a little bit of software.Its one part of our strategy to be available for the whole spectrum of care and be able to interact in a variety of ways," he said.

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93% of security operations centers employing AI and machine learning tools to detect advanced threats – Security Magazine

93% of security operations center employing AI and machine learning tools to detect advanced threats | 2020-10-30 | Security Magazine This website requires certain cookies to work and uses other cookies to help you have the best experience. By visiting this website, certain cookies have already been set, which you may delete and block. By closing this message or continuing to use our site, you agree to the use of cookies. Visit our updated privacy and cookie policy to learn more. This Website Uses CookiesBy closing this message or continuing to use our site, you agree to our cookie policy. Learn MoreThis website requires certain cookies to work and uses other cookies to help you have the best experience. By visiting this website, certain cookies have already been set, which you may delete and block. By closing this message or continuing to use our site, you agree to the use of cookies. Visit our updated privacy and cookie policy to learn more.

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Microsoft/MITRE group declares war on machine learning vulnerabilities with Adversarial ML Threat Matrix – Diginomica

(Pixabay)

The extraordinary advances in machine learning that drive the increasing accuracy and reliability of artificial intelligence systems have been matched by a corresponding growth in malicious attacks by bad actors seeking to exploit a new breed of vulnerabilities designed to distort the results.

Microsoft reports it has seen a notable increase in attacks on commercial ML systems over the past four years. Other reports have also brought attention to this problem.Gartner's Top 10 Strategic Technology Trends for 2020, published in October 2019, predicts that:

Through 2022, 30% of all AI cyberattacks will leverage training-data poisoning, AI model theft, or adversarial samples to attack AI-powered systems.

Training data poisoning happens when an adversary is able to introduce bad data into your model's training pool, and hence get it to learn things that are wrong. One approach is to target your ML's availability; the other targets its integrity (commonly known as "backdoor" attacks). Availability attacks aim to inject so much bad data into your system that whatever boundaries your model learns are basically worthless. Integrity attacks are more insidious because the developer isn't aware of them so attackers can sneak in and get the system to do what they want.

Model theft techniques are used to recover models or information about data used during training which is a major concern because AI models represent valuable intellectual property trained on potentially sensitive data including financial trades, medical records, or user transactions.The aim of adversaries is to recreate AI models by utilizing the public API and refining their own model using it as a guide.

Adversarial examples are inputs to machine learning models that attackers haveintentionally designed to cause the model to make a mistake.Basically, they are like optical illusions for machines.

All of these methods are dangerous and growing in both volume and sophistication. As Ann Johnson Corporate Vice President, SCI Business Development at Microsoft wrote in ablog post:

Despite the compelling reasons to secure ML systems, Microsoft's survey spanning 28 businesses found that most industry practitioners have yet to come to terms with adversarial machine learning. Twenty-five out of the 28 businesses indicated that they don't have the right tools in place to secure their ML systems. What's more, they are explicitly looking for guidance. We found that preparation is not just limited to smaller organizations. We spoke to Fortune 500 companies, governments, non-profits, and small and mid-sized organizations.

Responding to the growing threat, last week, Microsoft, the nonprofit MITRE Corporation, and 11 organizations including IBM, Nvidia, Airbus, and Bosch released theAdversarial ML Threat Matrix, an industry-focused open framework designed to help security analysts to detect, respond to, and remediate threats against machine learning systems. Microsoft says it worked with MITRE to build a schema that organizes the approaches employed by malicious actors in subverting machine learning models, bolstering monitoring strategies around organizations' mission-critical systems.Said Johnson:

Microsoft worked with MITRE to create the Adversarial ML Threat Matrix, because we believe the first step in empowering security teams to defend against attacks on ML systems, is to have a framework that systematically organizes the techniques employed by malicious adversaries in subverting ML systems. We hope that the security community can use the tabulated tactics and techniques to bolster their monitoring strategies around their organization's mission critical ML systems.

The Adversarial ML Threat, modeled after the MITRE ATT&CK Framework, aims to address the problem with a curated set of vulnerabilities and adversary behaviors that Microsoft and MITRE vetted to be effective against production systems. With input from researchers at the University of Toronto, Cardiff University, and the Software Engineering Institute at Carnegie Mellon University, Microsoft and MITRE created a list of tactics that correspond to broad categories of adversary action.

Techniques in the schema fall within one tactic and are illustrated by a series of case studies covering how well-known attacks such as the Microsoft Tay poisoning, the Proofpoint evasion attack, and other attacks could be analyzed using the Threat Matrix. Noted Charles Clancy, MITRE's chief futurist, senior vice president, and general manager of MITRE Labs:

Unlike traditional cybersecurity vulnerabilities that are tied to specific software and hardware systems, adversarial ML vulnerabilities are enabled by inherent limitations underlying ML algorithms. Data can be weaponized in new ways which requires an extension of how we model cyber adversary behavior, to reflect emerging threat vectors and the rapidly evolving adversarial machine learning attack lifecycle.

Mikel Rodriguez, a machine learning researcher at MITRE who also oversees MITRE's Decision Science research programs, said that AI is now at the same stage now where the internet was in the late 1980s when people were focused on getting the technology to work and not thinking that much about longer term implications for security and privacy. That, he says, was a mistake that we can learn from.

The Adversarial ML Threat Matrix will allow security analysts to work with threat models that are grounded in real-world incidents that emulate adversary behavior with machine learning and to develop a common language that allows for better communications and collaboration.

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Leveraging Machine Learning and IDP to Scale Your Automation Program – AiiA

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As document and input types get more and more complex, legacy business process automation technologies, like Robotic Process Automation (RPA), can struggle to keep up. Designed to execute precise rules and work with structured data inputs, these approaches lack the intelligence to handle the variability and ambiguity of diverse, real-world document processing workflows, making it a partial enterprise solution that needs to be supplemented with more intelligent technology.

Fortunately, theres a sea change coming to back offices, reshaping the way organizations operate. Recent breakthroughs in Artificial Intelligence - and Machine Learning, specifically - are helping businesses replace aging tech stacks and inflexible workflows with technology that supports the kind of responsiveness and innovation required to keep pace with an ever-changing market. These advances bring forth a reliable path towards automating processes that have previously only been possible by people, and they finally fulfill the promises made (and broken) by earlier technologies many times before.

Intelligent Document Processing (IDP) solutions, in particular, leverage the latest in ML and AI to capture data from documents (e.g. text, PDFs, scanned images, emails), and categorizes and extracts relevant data for further processing. Leading IDP solutions that leverage the latest in Machine Learning continue to learn on the data theyre exposed to, driving lower error rates and greater automation.

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Machine Learning in Insurance Market(COVID-19 Analysis): Indoor Applications Projected to be the Most Attractive Segment during 2020-2027 – Global…

COVID-19 can affect the global economy in three main ways: by directly affecting production and demand, by creating supply chain and market disruption, and by its financial impact on firms and financial markets. Global Machine Learning in Insurance Market size has covered and analysed the potential of Worldwide market Industry and provides statistics and information on market dynamics, growth factors, key challenges, major drivers & restraints, opportunities and forecast. This report presents a comprehensive overview, market shares, and growth opportunities of market 2020 by product type, application, key manufacturers and key regions and countries.

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Top key players:State Farm, Liberty Mutual, Allstate, Progressive, Accenture

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In this study, the years considered to estimate the size ofMachine Learning in Insuranceare as follows:

History Year: 2015-2018

Base Year: 2019

Forecast Year 2020 to 2028.

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5 machine learning skills you need in the cloud – TechTarget

Machine learning and AI continue to reach further into IT services and complement applications developed by software engineers. IT teams need to sharpen their machine learning skills if they want to keep up.

Cloud computing services support an array of functionality needed to build and deploy AI and machine learning applications. In many ways, AI systems are managed much like other software that IT pros are familiar with in the cloud. But just because someone can deploy an application, that does not necessarily mean they can successfully deploy a machine learning model.

While the commonalities may partially smooth the transition, there are significant differences. Members of your IT teams need specific machine learning and AI knowledge, in addition to software engineering skills. Beyond the technological expertise, they also need to understand the cloud tools currently available to support their team's initiatives.

Explore the five machine learning skills IT pros need to successfully use AI in the cloud and get to know the products Amazon, Microsoft and Google offer to support them. There is some overlap in the skill sets, but don't expect one individual to do it all. Put your organization in the best position to utilize cloud-based machine learning by developing a team of people with these skills.

IT pros need to understand data engineering if they want to pursue any type of AI strategy in the cloud. Data engineering is comprised of a broad set of skills that requires data wrangling and workflow development, as well as some knowledge of software architecture.

These different areas of IT expertise can be broken down into different tasks IT pros should be able to accomplish. For example, data wrangling typically involves data source identification, data extraction, data quality assessments, data integration and pipeline development to carry out these operations in a production environment.

Data engineers should be comfortable working with relational databases, NoSQL databases and object storage systems. Python is a popular programming language that can be used with batch and stream processing platforms, like Apache Beam, and distributed computing platforms, such as Apache Spark. Even if you are not an expert Python programmer, having some knowledge of the language will enable you to draw from a broad array of open source tools for data engineering and machine learning.

Data engineering is well supported in all the major clouds. AWS has a full range of services to support data engineering, such as AWS Glue, Amazon Managed Streaming for Apache Kafka (MSK) and various Amazon Kinesis services. AWS Glue is a data catalog and extract, transform and load (ETL) service that includes support for scheduled jobs. MSK is a useful building block for data engineering pipelines, while Kinesis services are especially useful for deploying scalable stream processing pipelines.

Google Cloud Platform offers Cloud Dataflow, a managed Apache Beam service that supports batch and steam processing. For ETL processes, Google Cloud Data Fusion provides a Hadoop-based data integration service. Microsoft Azure also provides several managed data tools, such as Azure Cosmos DB, Data Catalog and Data Lake Analytics, among others.

Machine learning is a well-developed discipline, and you can make a career out of studying and developing machine learning algorithms.

IT teams use the data delivered by engineers to build models and create software that can make recommendations, predict values and classify items. It is important to understand the basics of machine learning technologies, even though much of the model building process is automated in the cloud.

As a model builder, you need to understand the data and business objectives. It's your job to formulate the solution to the problem and understand how it will integrate with existing systems.

Some products on the market include Google's Cloud AutoML, which is a suite of services that help build custom models using structured data as well as images, video and natural language without requiring much understanding of machine learning. Azure offers ML.NET Model Builder in Visual Studio, which provides an interface to build, train and deploy models. Amazon SageMaker is another managed service for building and deploying machine learning models in the cloud.

These tools can choose algorithms, determine which features or attributes in your data are most informative and optimize models using a process known as hyperparameter tuning. These kinds of services have expanded the potential use of machine learning and AI strategies. Just as you do not have to be a mechanical engineer to drive a car, you do not need a graduate degree in machine learning to build effective models.

Algorithms make decisions that directly and significantly impact individuals. For example, financial services use AI to make decisions about credit, which could be unintentionally biased against particular groups of people. This not only has the potential to harm individuals by denying credit but it also puts the financial institution at risk of violating regulations, like the Equal Credit Opportunity Act.

These seemingly menial tasks are imperative to AI and machine learning models. Detecting bias in a model can require savvy statistical and machine learning skills but, as with model building, some of the heavy lifting can be done by machines.

FairML is an open source tool for auditing predictive models that helps developers identify biases in their work. Experience with detecting bias in models can also help inform the data engineering and model building process. Google Cloud leads the market with fairness tools that include the What-If Tool, Fairness Indicators and Explainable AI services.

Part of the model building process is to evaluate how well a machine learning model performs. Classifiers, for example, are evaluated in terms of accuracy, precision and recall. Regression models, such as those that predict the price at which a house will sell, are evaluated by measuring their average error rate.

A model that performs well today may not perform as well in the future. The problem is not that the model is somehow broken, but that the model was trained on data that no longer reflects the world in which it is used. Even without sudden, major events, data drift can occur. It is important to evaluate models and continue to monitor them as long as they are in production.

Services such as Amazon SageMaker, Azure Machine Learning Studio and Google Cloud AutoML include an array of model performance evaluation tools.

Domain knowledge is not specifically a machine learning skill, but it is one of the most important parts of a successful machine learning strategy.

Every industry has a body of knowledge that must be studied in some capacity, especially when building algorithmic decision-makers. Machine learning models are constrained to reflect the data used to train them. Humans with domain knowledge are essential to knowing where to apply AI and to assess its effectiveness.

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Machine learning approach could detect drivers of atrial fibrillation – Cardiac Rhythm News

Mapping of the explanted human heart

Researchers have designed a new machine learning-based approach for detecting atrial fibrillation (AF) drivers, small patches of the heart muscle that are hypothesised to cause this most common type of cardiac arrhythmia. This approach may lead to more efficient targeted medical interventions to treat the condition, according to the authors of the paper published in the journal Circulation: Arrhythmia and Electrophysiology.

The mechanism behind AF is yet unclear, although research suggests it may be caused and maintained by re-entrant AF drivers, localised sources of repetitive rotational activity that lead to irregular heart rhythm. These drivers can be burnt via a surgical procedure, which can mitigate the condition or even restore the normal functioning of the heart.

To locate these re-entrant AF drivers for subsequent destruction, doctors use multi-electrode mapping, a technique that allows them to record multiple electrograms inside the using a catheter and build a map of electrical activity within the atria. However, clinical applications of this technique often produce a lot of false negatives, when an existing AF driver is not found, and false positives, when a driver is detected where there really is none.

Recently, researchers have tapped machine learning algorithms for the task of interpreting ECGs to look for AF; however, these algorithms require labelled data with the true location of the driver, and the accuracy of multi-electrode mapping is insufficient. The authors of the new study, co-led by Dmitry Dylov from the Skoltech Center of Computational and Data-Intensive Science and Engineering (CDISE, Moscow, Russia) and Vadim Fedorov from the Ohio State University (Columbus, USA) used high-resolution near-infrared optical mapping (NIOM) to locate AF drivers and stuck with it as a reference for training.

NIOM is based on well-penetrating infrared optical signals and therefore can record the electrical activity from within the heart muscle, whereas conventional clinical electrodes can only measure the signals on the surface. Add to this trait the excellent optical resolution, and the optical mapping becomes a no-brainer modality if you want to visualize and understand the electrical signal propagation through the heart tissue, said Dylov.

The team tested their approach on 11 explanted human hearts, all donated posthumously for research purposes. Researchers performed the simultaneous optical and multi-electrode mapping of AF episodes induced in the hearts. ML model can indeed efficiently interpret electrograms from multielectrode mapping to locate AF drivers, with an accuracy of up to 81%. They believe that larger training datasets, validated by NIOM, can improve machine learning-based algorithms enough for them to become complementary tools in clinical practice.

The dataset of recording from 11 human hearts is both extremely priceless and too small. We realiaed that clinical translation would require a much larger sample size for representative sampling, yet we had to make sure we extracted every piece of available information from the still-beating explanted human hearts. Dedication and scrutiny of two of our PhD students must be acknowledged here: Sasha Zolotarev spent several months on the academic mobility trip to Fedorovs lab understanding the specifics of the imaging workflow and present the pilot study at the HRS conference the biggest arrhythmology meeting in the world, and Katya Ivanova partook in the frequency and visualization analysis from within the walls of Skoltech. These two young researchers have squeezed out everything one possibly could, to train the machine learning model using optical measurements, Dylov notes.

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Vanderbilt trans-institutional team shows how next-gen wearable sensor algorithms powered by machine learning could be key to preventing injuries that…

A trans-institutional team of Vanderbilt engineering, data science and clinical researchers has developed a novel approach for monitoring bone stress in recreational and professional athletes, with the goal of anticipating and preventing injury. Using machine learning and biomechanical modeling techniques, the researchers built multisensory algorithms that combine data from lightweight, low-profile wearable sensors in shoes to estimate forces on the tibia, or shin bonea common place for runners stress fractures.

The research builds off the researchers2019 study,which found that commercially available wearables do not accurately monitor stress fracture risks.Karl Zelik, assistant professor of mechanical engineering, biomedical engineering and physical medicine and rehabilitation, sought to develop a better technique to solve this problem.Todays wearablesmeasure ground reaction forceshow hard the foot impacts or pushes against the groundto assess injury risks like stress fractures to the leg, Zelik said. While it may seem intuitive to runners and clinicians that the force under your foot causes loading on your leg bones, most of your bone loading is actually from muscle contractions. Its this repetitive loading on the bone that causes wear and tear and increases injury risk to bones, including the tibia.

The article, Combining wearable sensor signals, machine learning and biomechanics to estimate tibial bone force and damage during running was publishedonlinein the journalHuman Movement Scienceon Oct. 22.

The algorithms have resulted in bone force data that is up to four times more accurate than available wearables, and the study found that traditional wearable metrics based on how hard the foot hits the ground may be no more accurate for monitoring tibial bone load than counting steps with a pedometer.

Bones naturally heal themselves, but if the rate of microdamage from repeated bone loading outpaces the rate of tissue healing, there is an increased risk of a stress fracture that can put a runner out of commission for two to three months. Small changes in bone load equate to exponential differences in bone microdamage, said Emily Matijevich, a graduate student and the director of theCenter for Rehabilitation Engineering and Assistive TechnologyMotion Analysis Lab. We have found that 10 percent errors in force estimates cause 100 percent errors in damage estimates. Largely over- or under-estimating the bone damage that results from running has severe consequences for athletes trying to understand their injury risk over time. This highlights why it is so important for us to develop more accurate techniques to monitor bone load and design next-generation wearables. The ultimate goal of this tech is to better understand overuse injury risk factors and then prompt runners to take rest days or modify training before an injury occurs.

The machine learning algorithm leverages the Least Absolute Shrinkage and Selection Operator regression, using a small group of sensors to generate highly accurate bone load estimates, with average errors of less than three percent, while simultaneously identifying the most valuable sensor inputs, saidPeter Volgyesi, a research scientist at the Vanderbilt Institute for Software Integrated Systems. I enjoyed being part of the team.This is a highly practical application of machine learning, markedly demonstrating the power of interdisciplinary collaboration with real-life broader impact.

This research represents a major leap forward in health monitoring capabilities. This innovation is one of the first examples of a wearable technology that is both practical to wear in daily life and can accuratelymonitor forces on and microdamage to musculoskeletal tissues.The team has begun applying similar techniques to monitor low back loading and injury risks, designed for people in occupations that require repetitive lifting and bending. These wearables could track the efficacy of post-injury rehab or inform return-to-play or return-to-work decisions.

We are excited about the potential for this kind of wearable technology to improve assessment, treatment and prevention of other injuries like Achilles tendonitis, heel stress fractures or low back strains, saidMatijevich, the papers corresponding author.The group has filed multiple patents on their invention and is in discussions with wearable tech companies to commercialize these innovations.

This research was funded by National Institutes of Health grant R01EB028105 and the Vanderbilt University Discovery Grant program.

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Machine Learning & Big Data Analytics Education Market Size And Forecast (2020-2026)| With Post Impact Of Covid-19 By Top Leading Players-…

This report studies the Machine Learning & Big Data Analytics Education Market with many aspects of the industry like the market size, market status, market trends and forecast, the report also provides brief information of the competitors and the specific growth opportunities with key market drivers. Find the complete Machine Learning & Big Data Analytics Education Market analysis segmented by companies, region, type and applications in the report.

The report offers valuable insight into the Machine Learning & Big Data Analytics Education market progress and approaches related to the Machine Learning & Big Data Analytics Education market with an analysis of each region. The report goes on to talk about the dominant aspects of the market and examine each segment.

Key Players:DreamBox Learning,Jenzabar, Inc.,,com, Inc.,,Cognizant,IBM Corporation,Metacog, Inc.,Querium Corporation.,Pearson,Blackboard, Inc.,Fishtree,Quantum Adaptive Learning, LLC,Third Space Learning,Bridge-U,Century-Tech Ltd,Microsoft Corporation,Knewton, Inc.,Google,Jellynote.

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The global Machine Learning & Big Data Analytics Education market is segmented by company, region (country), by Type, and by Application. Players, stakeholders, and other participants in the global Machine Learning & Big Data Analytics Education market will be able to gain the upper hand as they use the report as a powerful resource. The segmental analysis focuses on revenue and forecast by region (country), by Type, and by Application for the period 2020-2026.

Market Segment by Regions, regional analysis covers

North America (United States, Canada and Mexico)

Europe (Germany, France, UK, Russia and Italy)

Asia-Pacific (China, Japan, Korea, India and Southeast Asia)

South America (Brazil, Argentina, Colombia etc.)

Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)

Research objectives:

To study and analyze the global Machine Learning & Big Data Analytics Education market size by key regions/countries, product type and application, history data from 2013 to 2017, and forecast to 2026.

To understand the structure of Machine Learning & Big Data Analytics Education market by identifying its various sub segments.

Focuses on the key global Machine Learning & Big Data Analytics Education players, to define, describe and analyze the value, market share, market competition landscape, SWOT analysis and development plans in next few years.

To analyze the Machine Learning & Big Data Analytics Education with respect to individual growth trends, future prospects, and their contribution to the total market.

To share detailed information about the key factors influencing the growth of the market (growth potential, opportunities, drivers, industry-specific challenges and risks).

To project the size of Machine Learning & Big Data Analytics Education submarkets, with respect to key regions (along with their respective key countries).

To analyze competitive developments such as expansions, agreements, new product launches and acquisitions in the market.

To strategically profile the key players and comprehensively analyze their growth strategies.

The report lists the major players in the regions and their respective market share on the basis of global revenue. It also explains their strategic moves in the past few years, investments in product innovation, and changes in leadership to stay ahead in the competition. This will give the reader an edge over others as a well-informed decision can be made looking at the holistic picture of the market.

Table of Contents: Machine Learning & Big Data Analytics Education Market

Key questions answered in this report

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The security threat of adversarial machine learning is real – TechTalks

The Adversarial ML Threat Matrix provides guidelines that help detect and prevent attacks on machine learning systems.

This article is part ofDemystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI.

With machine learning becoming increasingly popular, one thing that has been worrying experts is the security threats the technology will entail. We are still exploring the possibilities: The breakdown of autonomous driving systems? Inconspicuous theft of sensitive data from deep neural networks? Failure of deep learningbased biometric authentication? Subtle bypass of content moderation algorithms?

Meanwhile, machine learning algorithms have already found their way into critical fields such as finance, health care, and transportation, where security failures can have severe repercussion.

Parallel to the increased adoption of machine learning algorithms in different domains, there has been growing interest in adversarial machine learning, the field of research that explores ways learning algorithms can be compromised.

And now, we finally have a framework to detect and respond to adversarial attacks against machine learning systems. Called the Adversarial ML Threat Matrix, the framework is the result of a joint effort between AI researchers at 13 organizations, including Microsoft, IBM, Nvidia, and MITRE.

While still in early stages, the ML Threat Matrix provides a consolidated view of how malicious actors can take advantage of weaknesses in machine learning algorithms to target organizations that use them. And its key message is that the threat of adversarial machine learning is real and organizations should act now to secure their AI systems.

The Adversarial ML Threat Matrix is presented in the style of ATT&CK, a tried-and-tested framework developed by MITRE to deal with cyber-threats in enterprise networks. ATT&CK provides a table that summarizes different adversarial tactics and the types of techniques that threat actors perform in each area.

Since its inception, ATT&CK has become a popular guide for cybersecurity experts and threat analysts to find weaknesses and speculate on possible attacks. The ATT&CK format of the Adversarial ML Threat Matrix makes it easier for security analysts to understand the threats of machine learning systems. It is also an accessible document for machine learning engineers who might not be deeply acquainted with cybersecurity operations.

Many industries are undergoing digital transformation and will likely adopt machine learning technology as part of service/product offerings, including making high-stakes decisions, Pin-Yu Chen, AI researcher at IBM, told TechTalks in written comments. The notion of system has evolved and become more complicated with the adoption of machine learning and deep learning.

For instance, Chen says, an automated financial loan application recommendation can change from a transparent rule-based system to a black-box neural network-oriented system, which could have considerable implications on how the system can be attacked and secured.

The adversarial threat matrix analysis (i.e., the study) bridges the gap by offering a holistic view of security in emerging ML-based systems, as well as illustrating their causes from traditional means and new risks induce by ML, Chen says.

The Adversarial ML Threat Matrix combines known and documented tactics and techniques used in attacking digital infrastructure with methods that are unique to machine learning systems. Like the original ATT&CK table, each column represents one tactic (or area of activity) such as reconnaissance or model evasion, and each cell represents a specific technique.

For instance, to attack a machine learning system, a malicious actor must first gather information about the underlying model (reconnaissance column). This can be done through the gathering of open-source information (arXiv papers, GitHub repositories, press releases, etc.) or through experimentation with the application programming interface that exposes the model.

Each new type of technology comes with its unique security and privacy implications. For instance, the advent of web applications with database backends introduced the concept SQL injection. Browser scripting languages such as JavaScript ushered in cross-site scripting attacks. The internet of things (IoT) introduced new ways to create botnets and conduct distributed denial of service (DDoS) attacks. Smartphones and mobile apps create new attack vectors for malicious actors and spying agencies.

The security landscape has evolved and continues to develop to address each of these threats. We have anti-malware software, web application firewalls, intrusion detection and prevention systems, DDoS protection solutions, and many more tools to fend off these threats.

For instance, security tools can scan binary executables for the digital fingerprints of malicious payloads, and static analysis can find vulnerabilities in software code. Many platforms such as GitHub and Google App Store already have integrated many of these tools and do a good job at finding security holes in the software they house.

But in adversarial attacks, malicious behavior and vulnerabilities are deeply embedded in the thousands and millions of parameters of deep neural networks, which is both hard to find and beyond the capabilities of current security tools.

Traditional software security usually does not involve the machine learning component because itsa new piece in the growing system, Chen says, adding thatadopting machine learning into the security landscape gives new insights and risk assessment.

The Adversarial ML Threat Matrix comes with a set of case studies of attacks that involve traditional security vulnerabilities, adversarial machine learning, and combinations of both. Whats important is that contrary to the popular belief that adversarial attacks are limited to lab environments, the case studies show that production machine learning system can and have been compromised with adversarial attacks.

For instance, in one case study, the security team at Microsoft Azure used open-source data to gather information about a target machine learning model. They then used a valid account in the server to obtain the machine learning model and its training data. They used this information to find adversarial vulnerabilities in the model and develop attacks against the API that exposed its functionality to the public.

Other case studies show how attackers can compromise various aspect of the machine learning pipeline and the software stack to conduct data poisoning attacks, bypass spam detectors, or force AI systems to reveal confidential information.

The matrix and these case studies can guide analysts in finding weak spots in their software and can guide security tool vendors in creating new tools to protect machine learning systems.

Inspecting a single dimension (machine learning vs traditional software security) only provides an incomplete security analysis of the system as a whole, Chen says. Like the old saying goes: security is only asstrong as its weakest link.

Unfortunately, developers and adopters of machine learning algorithms are not taking the necessary measures to make their models robust against adversarial attacks.

The current development pipeline is merely ensuring a model trained on a training set can generalize well to a test set, while neglecting the fact that the model isoften overconfident about the unseen (out-of-distribution) data or maliciously embbed Trojan patteninthe training set, which offers unintended avenues to evasion attacks and backdoor attacks that an adversary can leverage to control or misguide the deployed model, Chen says. In my view, similar to car model development and manufacturing, a comprehensive in-house collision test for different adversarial treats on an AI model should be the new norm to practice to better understand and mitigate potential security risks.

In his work at IBM Research, Chen has helped develop various methods to detect and patch adversarial vulnerabilities in machine learning models. With the advent Adversarial ML Threat Matrix, the efforts of Chen and other AI and security researchers will put developers in a better position to create secure and robust machine learning systems.

My hope is that with this study, the model developers and machine learning researchers can pay more attention to the security (robustness) aspect of the modeland looking beyond a single performance metric such as accuracy, Chen says.

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The security threat of adversarial machine learning is real - TechTalks

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