Kasia Borowska, co-founder and managing director at Brainpool AI took us through an interesting analogy between vipassana and machine learning to attain equanimity, at Rising 2020. With a degree in mathematics and cognitive sciences, she is also deeply immersed in the world of vipassana, Indias most ancient meditation techniques which has been in practice for more than 2500 years. And it was during her learnings in meditation, she found that machine learning is in fact, in many ways similar to attaining vipassana.

Understanding that your brain is an extremely sophisticated machine learning system can help us acknowledge and come to peace with whatever happens to us, she said.

Highlighting some of the similarities between meditation and machine learning she said that both have become popular in a decades time and while a lot is known about them in a theoretical world, there hasnt been enough translation of that knowledge into actual practice. Corporates see machine learning as one of the key technologies to be applied in organisations, but incorporating it comes with many challenges scaling it, integrating it into systems to name a few.

Further explaining the similarity, she said, everything we experience with our senses is our input data, everything we think and do is an output. What happens inside of the black box which we call our brain is still a mystery, and vipassana can give us an insight into these complex algorithms that are trained in the brain throughout our lifetime.

Talking about how these two work, she said that any ML system works by inputting data, making and bettering the algorithms and generating outputs. It is the same way as the brain works where sensory inputs are the data which is processed by the brain to give output in the form of thoughts and actions.

Just the way the human brain has conscious and subconscious ways of thinking, the machines may process data in a similar way which are called priming and bias in systems. She pointed out a few areas where humans see bias and may creep in machines in a similar way:

These biases which are in-built in humans may be found in machines. Similarly, priming is also a way that defines how machines work. It is interesting to note that priming and bias which are the features or humans has also been picked by AI. The way humans are primed about certain content, for instance, in social media, machine learning systems work on these priming as well.

Further expanding on the comparison, she said, just the way in vipassana practice, one needs to sense and feel everything, which may sound simple but is the most challenging part of meditation. Similarly pre-processing of data in machine learning systems is the most important and challenging part.

All the concepts of machine learning, such as reinforcement learning can find a connection with how meditation works. She further highlighted that understanding the simplicity of the input-output, observing and accepting the actions can help us find the path to equanimity.

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Srishti currently works as Associate Editor at Analytics India Magazine. When not covering the analytics news, editing and writing articles, she could be found reading or capturing thoughts into pictures. Contact: srishti.deoras@analyticsindiamag.com.

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How Machine Learning Connects With Meditation or Vipassana - Analytics India Magazine

Designed to deliver improved AI performance, multiple connectivity options, and design development efficiency while enabling more affordable devices

SAN DIEGO -- Qualcomm Technologies, Inc., a subsidiary of Qualcomm Incorporated, today announced the introduction of the Qualcomm QCS610 and Qualcomm QCS410 system-on-chips (SoCs) to the Qualcomm Vision Intelligence Platform. The QCS610 and QCS410 are designed to bring premium camera technology, including powerful artificial intelligence and machine learning features formerly only available to high end devices, into mid-tier camera segments. This comes at a time when intelligence at the wireless edge and robust connectivity are increasingly becoming the bar to overcome for smart camera applications in smart cities, commercial and enterprise, homes, and vehicles.

"The rise of the IoT and the associated growth in connected devices has created a shift toward the wireless edge. As we support the combination of on-device processing and multiple connectivity options for fast decision making and critical data transfer, AI becomes an even more transformative experience for businesses," said Jeffery Torrance, vice president, business development, Qualcomm Technologies, Inc. "With the launch of QCS610 and QCS410, we are addressing increased customer demand for more integrated capabilities and improved AI-features with a variety of connectivity options - all in a cost-effective solution. This will allow our customers to put these exciting new capabilities to work in products from a wider range of ecosystem players."

The QCS610 and QCS410 bring a highly integrated solution designed with multiple features to deliver a one-stop shop for our customers building camera-based devices. The new platform is built with our upgraded Qualcomm KryoTM CPU, Qualcomm AdrenoTM GPU and Qualcomm HexagonTM DSP and includes our Qualcomm Artificial Intelligence (AI) Engine designed to deliver up to 50% improved AI performance than the previous generation.This latest generation was rearchitected to deliver improved efficiencies and faster inferencing in the DSP resulting in more computing power and AI inferencing at the device level. Keeping the key workloads on the device can provide privacy and significantly reduce latency for the best user experience.

The Qualcomm Vision Intelligence Platform supports Linux and Android OS for a variety of IoT segments, including camera, Edge AI box, retail and robotics. Further enhanced capabilities include support for Microsoft Azure Machine Learning and Azure services. Dual ISPs support Video capture, Integrated audio, GNSS, hardware-based security and Qualcomm Technologies' multiple connectivity options including 5G/4G, Wi-Fi, Bluetooth and Ethernet make this one of the most robust and advanced set of features available for non-premium smart cameras in a single SoC.

Some of the key features supported:

- Optimized Heterogenous Computing Architecture: Custom CPU, GPU and DSP design provides powerful compute capability that is engineered specifically for camera applications designed to utilize intensive processing while consuming less power.

- Superior Image Processing Support for dual 14-bit Qualcomm Spectra(TM) 230 ISP.

- Optimized AI Software Deployment: The Qualcomm Neural Processing SDK completes the AI portfolio by provisioning a virtually seamless path for NN deployment. The modular tool supports varied frameworks like Caffe/Caffe2, TensorFlow/TensorFlow Lite and ONNX and performs optimized execution by utilizing the heterogeneous architecture to achieve desired performance.

- Modular Software and Support for Open Source Frameworks: Modular Linux software that can enable customizations including flexibility to build an optimal SW footprint and also extensive support for open source multimedia frameworks (GStreamer, Pulse-Audio and Wayland/Weston etc.,) and AI/ML framework (such as TF-Lite).

- Application Specific AI at the Edge Solutions: Work with an ecosystem of specialized companies to develop DNN solutions for specific AI use cases including face detection, face recognition, object tracking and people counting. Developers can take advantage of the Qualcomm Neural Processing SDK for their custom network deployment.

Ecosystem and Availability

The QCS610 and QCS410 are sampling now. To accelerate development and further differentiate their products, manufacturers can rely on a strong ecosystem of technology providers that compliment or extend the value of the Qualcomm Vision Intelligence Platform series. Qualcomm Technologies is collaborating with original device manufacturers (ODMs), independent software vendors (ISVs), authorized design centers and distributors for the reference platforms and Development Kits.

Altek

"We look forward to continuing our long-standing strategic collaboration with Qualcomm Technologies. The new QCS610 and QCS410 SoC brings our Edge Vision AI series including AI camera and AI box to the next level by offering new experiences to our customers with extremely efficient AI inferencing across different edge vision AI applications," said Alex Hsia, Founder & CEO, Altek.

A Qualcomm QCS610-based camera reference design hardware development kit from Altek Corporation, a leading original design manufacturer for edge AI cameras, is expected to be available in Q3 2020.

Arrow Electronics

"With the addition of Qualcomm Technologies' Compute and Vision Intelligent IoT Chipsets, including the latest QCS610/410, Arrow Electronics can now offer Qualcomm Technologies' scalable highly integrated embedded computing platforms to the mass market," said Aiden Mitchell, vice president, supplier & engineering services. "The Qualcomm Technologies' offering combined with our engineering services allow customers to innovate, transform and scale through powerful edge intelligence."

eInfochips

"Over the last 25-plus years, eInfochips has developed over 500 products with embedded MPUs. Our solutions, SOMs, SBCs and chipdown designs based, on the Qualcomm SnapdragonTM mobile platform, have given customers across the world an opportunity to accelerate their product development. We are confident that the launch of QCS610/410, our ability to address manufacturing and certification requirements, and global support leveraging the Arrow sales and FAE team, will enhance the already competitive portfolio of offerings," said Parag Mehta, chief business development officer at eInfochips. "To strengthen our offerings on connected IoT solutions, digital transformation, and Arrow connected IoT solutions, eInfochips will be launching modules and development kits based on the QCS410 and QCS610 processors. With Arrow being a global distributor of IoT chipsets, eInfochips will support custom product design, development and manufacturing on these latest chipsets."

Lantronix

"Lantronix's Open-Q(TM) 610 ?SOM is an ultra-compact (50mm x 25mm) production-ready SOM based on the powerful QCS610 SoC. This advanced SOM is aimed at connected visual intelligence applications with high resolution camera capabilities, on-device AI processing, and native Ethernet interface," said Jonathan Shipman, VP of Strategy at Lantronix Inc. "Our long and successful relationship with Qualcomm Technologies, Inc. enables us to deliver powerful SOM solutions that can accelerate IoT design and implementation, empowering innovators to create IoT applications that go beyond their wildest dreams."

Pilot AI

"Pilot AI, using Qualcomm Technologies' solutions, delivers superior AI performance directly on-device. The QCS610 SoC is an exciting step forwards in compute, and Pilot AI has been able to leverage the Hexagon DSP to improve overall inference speed while also maximizing power efficiency when performing AI/ML workloads. We look forward to the new use cases this will enable at the edge" said Jon Su, CEO of Pilot AI.

For more information about Pilot AI's products, please visit http://www.pilot.ai.

Security & Safety Things

"Having Qualcomm Technologies as a key player in the Security & Safety Things ecosystem together with the other Open Security and Safety Alliance members is a great benefit," said Justin Frints, VP Operating Systems at Security & Safety Things. "With launch of the new QCS610, now we can combine the flexibility that is offered by our camera application store with powerful neural network computing on security cameras. This can allow end-users to leverage AI enablement with machine learning to drive multi sensor use cases and true IoT deployments where cameras are able to communicate and cooperate with one another. Thus, superior technology proved by Qualcomm Technologies and Security & Safety Things will continue to open doors to new advanced use cases with their newest chipsets for security cameras that will likely change entire industries."

Sercomm

"As a leading supplier of IP cameras in the market, Sercomm has invested in IP camera product development for more than two decades and has accumulated great experience in this domain," said Ben Lin, CTO of Sercomm. "QCS610 offers superior data and image processing power and also comes with strong video analytic capability that is perfect for industrial and enterprise market segments that needs to stream 4K grade of high-resolution video and make real-time intelligent decision at the edge. Sercomm is delighted to work with Qualcomm Technologies to offer more high-end IP cameras in the market."

Thundercomm

"Thundercomm and Qualcomm Technologies have a long history of collaboration to deliver new and cutting-edge technologies and IoT products," said Hiro Cai, CEO of Thundercomm. "Based on the newly launched QCS610/410 SoC, Thundercomm has introduced the commercial-ready TurboX C610/C410 SOM and reference design enabling developers and manufacturers to quickly develop smart camera and intelligent IoT devices across consumer and enterprise applications, including video conference, surveillance cameras, enterprise security, 360 cameras, dash cams and wearable cameras. In addition, C610/ C410 SOM has been integrated with Thundercomm TurboX Edge Framework, which enables an easy, secure and high-performance camera connection and AI analysis."

TurboX C610 SoM is available now. Visit Thundercomm product page for more information on TurboX C610/410.

Tymphany

"Tymphany, a worldwide leader in audio ODM services, is working with Qualcomm Technologies to bring to the market an integrated audio/video conferencing platform leveraging the QCS610 SoC. The QCS610 allows us to architect a cost optimized, single SoC solution for all audio, video, and optical processing. Based on the QCS610 AI capabilities, Tymphany is also running face and body detection algorithms to enable advanced auto-framing for a superior video collaboration experience for the user. The QCS610 can also enable a low-latency architecture that is critical to enable certifications for today's leading video collaboration software applications like MS Teams," said Phil McPhee, Vice President Professional Audio Division, Tymphany. "With the launch of the QCS610, we will bring unique value to the video collaboration product categories."

For more information on Tymphany's video collaboration platforms visit http://www.tymphany.com/contact.

WNC

"WNC is excited to move several QCS610-based designs into full production in 2020," said Johnson Hsu, GM of WNC's Connected Home Business Group. "WNC looks forward to our customers rolling out intelligent and resource-efficient video telematics solutions at an ever-increasing pace. Our relationship with Qualcomm Technologies is gaining some very positive momentum."

Qualcomm Vision Intelligence Platform

The Qualcomm Vision Intelligence Platform is a platform for the internet of things that fully utilizes the emerging power of AI/machine learning, edge intelligence, and the cloud. Drawing upon Qualcomm Technologies' proven mobile heritage, the Qualcomm Vision Intelligence Platform is purpose-built for the unique requirements of the IoT and optimized in application-specific configurations for IP cameras, dash cams, 360/VR cameras, video conferencing, and more.

About Qualcomm

Qualcomm is the world's leading wireless technology innovator and the driving force behind the development, launch, and expansion of 5G. When we connected the phone to the internet, the mobile revolution was born. Today, our foundational technologies enable the mobile ecosystem and are found in every 3G, 4G and 5G smartphone. We bring the benefits of mobile to new industries, including automotive, the internet of things, and computing, and are leading the way to a world where everything and everyone can communicate and interact seamlessly.

Qualcomm Incorporated includes our licensing business, QTL, and the vast majority of our patent portfolio. Qualcomm Technologies, Inc., a subsidiary of Qualcomm Incorporated, operates, along with its subsidiaries, substantially all of our engineering, research and development functions, and substantially all of our products and services businesses, including our QCT semiconductor business. Qualcomm contacts: Pete Lancia Mauricio Lopez-Hodoyan

.

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Altek : Qualcomm Brings Advanced Artificial Intelligence and Machine Learning Capabilities to Address Multiple Tiers of Smart Cameras with New...

Youve undoubtedly read about self-supervised learning or unsupervised AI cybersecurity. As their descriptions imply, these security platforms offer a degree of autonomous AI oversight.

Still, what does this mean, exactly? Is there a meaningful difference between supervised and unsupervised AI?

The answer is a resounding, yes. Lets dig in.

AI technology experts have made advances in the field over the past few decades that have dramatically increased its potential as a network security solution. The U.S. Defense Advanced Research Projects Agency (DARPA) outlines three eras of AI:

Today, many security platforms utilize second-wave AI, even while making claims that frame their capabilities as third-wave solutions. The good news is that its relatively simple to evaluate these claims as you seek a more robust cybersecurity solution.

Machine learning AI relies on classifying massive datasets with informative tags or labels. Network security engineers create a training algorithm to define data regions and create text-based descriptions of these regions.

The algorithm builds the central source of truth or network baseline, which is used to analyze future network behavior. Network activity that differs from the baseline triggers the machine learning security platform to flag it for review.

Importantly, this baseline information is only as current as the last manual update and is only as accurate as the labels applied to network data.

Another critical limitation of supervised machine learning is its inability to analyze network behavior with context. For the security platform to understand why a new network behavior is acceptable, the underlying baseline source of truth must be current.

Situations like the recent shift to telecommuting in response to COVID-19 restrictions can effectively break machine-learning security platforms. These systems churn out hundreds of anomalous behavior alerts because the baseline expectation is that the network is usually accessed onsite. A sudden influx of remote connections wont match baseline expectations unless the baseline is adjusted.

Modern, third-wave, self-supervised AI technology is every bit as sophisticated and futuristic as it might sound, but at a high level, the concept is straightforward.

Self-supervised AI learns organically in the same way people learn. During early development, humans dont need to be taught every step involved with walking, talking, or eating. The same can be said of unsupervised AI. We can equip this technology with the seeds it needs to become a living, ever-evolving part of a given organizations network.

Real unsupervised AI spots security issues sooner and predicts future behavior more accurately than older first- and second-wave solutions. Self-supervised AI technology draws on an understanding of the fundamental nature of the network where it lives, an understanding that isnt possible with supervised-AI.

Self-supervised learning takes into account an evolving network baseline. Essentially, it is smart enough to quickly adapt baseline expectations to changing situations like the shift to telecommuting due to COVID-19. This is a crucial distinction for two reasons:

1. SecOps teams can respond to genuine threats quicker because they dont need to wade through a massive list of false-positive alarms.

2. The system doesnt need constant babysitting.

The result is a security solution that is more accurate, more responsive, and less demanding on security resources, including skilled labor.

The network security market is flooded with companies promoting ultra-modern, AI-enhanced platforms. At first glance, it might appear that hands-off security solutions are the norm at this point. Take a closer look, and it will become clear that AI has multiple meanings throughout the security market.

The general perception many people have about AI is that the primary feature of this technology is its ability to handle tasks free from human intervention. In reality, network security products integrated with so-called AI often require a great deal of oversight and input. Most security firms have not evolved to the point where they can offer authentic self-supervised learning AI.

MixMode can empower your organization to apply modern security solutions to modern security threats. Learn more and set up a demo today.

Guide: The Next Generation SOC Tool Stack The Convergence of SIEM, NDR and NTA

Redefining the Definition of Baseline in Cybersecurity

MixMode CTO Responds to Self-Supervised AI Hopes

Why Training Matters And How Adversarial AI Takes Advantage of It

Encryption = Privacy Security

Self-Supervised Learning The Third-Wave in Cybersecurity AI

How the Role of the Modern Security Analyst is Changing

One Thing All Cybersecurity teams Should Have During COVID-19

Read this article:
The (Recent) History of Self-Supervised Learning - Security Boulevard

Google Cloud and Nvidia said Tuesday that Google would be the first cloud provider to offer Nvidias latest GPU for machine learning as a service.

The Nvidia A100 Tensor Core GPU, based on the chipmakers new Ampere architecture, represents the largest intergenerational leap in performance in Nvidias history. The company said the part performed 20 times better than its previous-gen product.

Related: How Google Cloud Plans to Win Enterprises from AWS and Azure

Another way Ampere is different from its predecessors is that its designed for both training and inference machine learning workloads. Nvidia designed a different GPU for each of the two types of workload in prior generations.

And clients can now kick the tires on Ampere in Google Cloud, as part of a new type of cloud instance the provider also announced Tuesday: Accelerator-Optimized VM, or A2.

Related: MLPerf Is Changing the AI Hardware Performance Conversation. Heres how

The beefiest configuration of Google'sA2 cloud instance comes with 16 Ampere GPUs, all interconnected by NVSwitch, Nvidias technology for interconnecting many GPUs to form a single computing fabric. Thats 640GB of GPU memory, 1.3TB of system memory, and 9.6TB/s of aggregate bandwidth.

Smaller A2 configurations are available as well.

For now, A100 instances are only available in alpha. Google Cloud was also first to launch Nvidias older T4 GPUs, in November 2018, also in alpha. T4 beta came about three months later, and general availability was announced after four more months.

Google Cloud may be first to offer Ampere GPUs as a service, but Nvidia had delivered the chips to all the major cloud providers as of mid-May, when it announced the chip publicly, the chipmakers CEO, Jensen Huang, told Bloomberg at the time. The others (AWS, Azure, Alibaba, Oracle, IBM) will likely roll out their own Ampere cloud infrastructure soon.

Google Cloud was also first to roll out cloud instances powered by AMDs Epyc 2 chips, which beat comparable Intel parts on both performance and price. The instances, according to Google, would be the most powerful VMs available to Google Cloud users.

Epyc 2 is also the CPU in Nvidias own Ampere-based supercomputer for machine learning, the DGX A100, which it announced along with A100 GPUs in May.

Read the rest here:
Google and Nvidia Take Cloud AI Performance to the Next Level - Data Center Knowledge

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Global Machine Learning Market 2020: Comprehensive Insights and Growth Potential In The Future - Jewish Life News

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It assists in making informed business decisions by performing a pin-point analysis of market segments and by having complete insights of the Cloud Machine Learning market.

This report helps the readers understand key product segments and their future.

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In the end, the Cloud Machine Learning market is analyzed for revenue, sales, price, and gross margin. These points are examined for companies, types, applications, and regions.

To summarize, the global Cloud Machine Learning market report studies the contemporary market to forecast the growth prospects, challenges, opportunities, risks, threats, and the trends observed in the market that can either propel or curtail the growth rate of the industry. The market factors impacting the global sector also include provincial trade policies, international trade disputes, entry barriers, and other regulatory restrictions.

About Us:

Market Research Intellect provides syndicated and customized research reports to clients from various industries and organizations with the aim of delivering functional expertise. We provide reports for all industries including Energy, Technology, Manufacturing and Construction, Chemicals and Materials, Food and Beverage, and more. These reports deliver an in-depth study of the market with industry analysis, the market value for regions and countries, and trends that are pertinent to the industry.

Contact Us:

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Market Research Intellect

New Jersey ( USA )

Tel: +1-650-781-4080

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Cloud Machine Learning Market Growth By Manufacturers, Type And Application, Forecast To 2026 - 3rd Watch News

New Jersey, United States,- Market Research Intellect sheds light on the market scope, potential, and performance perspective of the Global Machine Learning as a Service Market by carrying out an extensive market analysis. Pivotal market aspects like market trends, the shift in customer preferences, fluctuating consumption, cost volatility, the product range available in the market, growth rate, drivers and constraints, financial standing, and challenges existing in the market are comprehensively evaluated to deduce their impact on the growth of the market in the coming years. The report also gives an industry-wide competitive analysis, highlighting the different market segments, individual market share of leading players, and the contemporary market scenario and the most vital elements to study while assessing the global Machine Learning as a Service market.

The research study includes the latest updates about the COVID-19 impact on the Machine Learning as a Service sector. The outbreak has broadly influenced the global economic landscape. The report contains a complete breakdown of the current situation in the ever-evolving business sector and estimates the aftereffects of the outbreak on the overall economy.

Leading Machine Learning as a Service manufacturers/companies operating at both regional and global levels:

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The Machine Learning as a Service market report provides successfully marked contemplated policy changes, favorable circumstances, industry news, developments, and trends. This information can help readers fortify their market position. It packs various parts of information gathered from secondary sources, including press releases, web, magazines, and journals as numbers, tables, pie-charts, and graphs. The information is verified and validated through primary interviews and questionnaires. The data on growth and trends focuses on new technologies, market capacities, raw materials, CAPEX cycle, and the dynamic structure of the Machine Learning as a Service market.

This study analyzes the growth of Machine Learning as a Service based on the present, past and futuristic data and will render complete information about the Machine Learning as a Service industry to the market-leading industry players that will guide the direction of the Machine Learning as a Service market through the forecast period. All of these players are analyzed in detail so as to get details concerning their recent announcements and partnerships, product/services, and investment strategies, among others.

Sales Forecast:

The report contains historical revenue and volume that backing information about the market capacity, and it helps to evaluate conjecture numbers for key areas in the Machine Learning as a Service market. Additionally, it includes a share of each segment of the Machine Learning as a Service market, giving methodical information about types and applications of the market.

Reasons for Buying Machine Learning as a Service Market Report

This report gives a forward-looking prospect of various factors driving or restraining market growth.

It renders an in-depth analysis for changing competitive dynamics.

It presents a detailed analysis of changing competition dynamics and puts you ahead of competitors.

It gives a six-year forecast evaluated on the basis of how the market is predicted to grow.

It assists in making informed business decisions by performing a pin-point analysis of market segments and by having complete insights of the Machine Learning as a Service market.

This report helps the readers understand key product segments and their future.

Have Any Query? Ask Our Expert @ https://www.marketresearchintellect.com/need-customization/?rid=195381&utm_source=3WN&utm_medium=888

In the end, the Machine Learning as a Service market is analyzed for revenue, sales, price, and gross margin. These points are examined for companies, types, applications, and regions.

To summarize, the global Machine Learning as a Service market report studies the contemporary market to forecast the growth prospects, challenges, opportunities, risks, threats, and the trends observed in the market that can either propel or curtail the growth rate of the industry. The market factors impacting the global sector also include provincial trade policies, international trade disputes, entry barriers, and other regulatory restrictions.

About Us:

Market Research Intellect provides syndicated and customized research reports to clients from various industries and organizations with the aim of delivering functional expertise. We provide reports for all industries including Energy, Technology, Manufacturing and Construction, Chemicals and Materials, Food and Beverage, and more. These reports deliver an in-depth study of the market with industry analysis, the market value for regions and countries, and trends that are pertinent to the industry.

Contact Us:

Mr. Steven Fernandes

Market Research Intellect

New Jersey ( USA )

Tel: +1-650-781-4080

Our Trending Reports

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Machine Learning as a Service Market Growth By Manufacturers, Type And Application, Forecast To 2026 - 3rd Watch News

SEATTLE--(BUSINESS WIRE)--ExtraHop, the leader in cloud-native network detection and response, today announced that it has been named to the 2020 Forbes AI 50 list for its advances and innovation in the use of machine learning and artificial intelligence for cybersecurity.

Forbes evaluated hundreds of applications to recognize 50 private, U.S.-based companies for their innovative use of artificial intelligence to drive outcomes for customers and within their own organizations. ExtraHop was selected on the basis of company growth and revenue, as well as for its leadership in the use of machine learning and AI for cybersecurity threat detection, investigation, and response.

Increasingly advanced cybersecurity attacks, including those sponsored by nation-states, require a sophisticated approach to threat detection and response, said Arif Kareem, CEO of ExtraHop. This is not possible without machine learning and AI. At ExtraHop, weve applied these techniques to enable enterprises to detect and respond to cyber threats across complex enterprise and cloud environments at scale. Our inclusion on this list recognizes the strength of our approach.

ExtraHop Reveal(x) applies the scalable computing resources of the cloud for ML and AI. This cloud-scale machine learning is unique in the network detection and response (NDR) market, allowing Reveal(x) to apply millions of models to over 5,000 features of data derived from 4-plus petabytes of anonymized threat telemetry collected from more than 15 million devices and workloads worldwide every day. The sheer volume of data on which ExtraHops AI learns makes its detections focused, precise, and uniquely reliable as well as up to 95 percent faster.

To view the 2020 Forbes AI 50 List check out the entire list on Forbes.com.

ExtraHop was also listed as a 20 Best Cybersecurity Startups To Watch In 2020 on Forbes.com.

To learn more about ExtraHops approach to machine learning, visit https://www.extrahop.com/products/machine-learning/

About ExtraHop

ExtraHop delivers cloud-native network detection and response to secure the hybrid enterprise. Our breakthrough approach applies advanced machine learning to all cloud and network traffic to provide complete visibility, real-time threat detection, and intelligent response. With this approach, we give the world's leading enterprises including The Home Depot, Credit Suisse, Liberty Global and Caesars Entertainment the perspective they need to rise above the noise to detect threats, ensure the availability of critical applications, and secure their investment in cloud. To experience the power of ExtraHop, explore our interactive online demo or connect with us on LinkedIn and Twitter.

2020 ExtraHop Networks, Inc., Reveal(x), Reveal(x) 360, Reveal(x) Enterprise, and ExtraHop are registered trademarks or marks of ExtraHop Networks, Inc.

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ExtraHop Named to the Forbes AI 50 List for 2020 - Business Wire

This article was written for our sponsor, Consumer Education Services Inc.

According to a study by Northwestern Mutual, the average American has around $38,000 in personal debt exclusive of home mortgages.

As a growing debt load that stems from student loans, credit card bills and medical expenses begins to affect a greater number of Americans each year, some companies have started leveraging technology in an attempt to alleviate the pressure of the repayment process for consumers.

Typically, debt collection efforts include scheduled letters, emails and phone calls from the agency where the debt is owed. If an individual becomes delinquent on their debts, then the original creditor may engage a collection agency for more aggressive attempts to collect the balance. This can understandably be stressful for debtors, as theyre constantly reminded their outstanding payments are accumulating interest, affecting their credit scores, and impacting their financial future.

In many cases, machine learning technology can help alleviate the pressure of consumer debt repayment. By analyzing an individuals past repayment behavior, AI programs can improve the repayment process for the consumer by developing more effective repayment schedules and recommended payment amounts.

With machine learning technology, financial counselors can aggregate budget, income, debt load and savings data from a number of consumers and build models that can tell us, for example, whether or not an individual may be more successful using a bi-monthly payment program, explained Mike Croxson, CEO of non-profit credit counseling agency Consumer Education Services Inc. These are the kinds of things that machine learning can do to help consumers be more successful.

Although this specific type of technology is still in the preliminary stages, its already changing the way companies and consumers think about the debt repayment landscape. And its been in use for longer than you might think.

Ive been in this industry for more than 20 years and financial institutions such as banks and investment firms have already been using machine learning, said Diane Chen, executive director of the Institute of Consumer Money Management. Using that model, the traditional credit counseling industry can refine their process, which typically has a one-size-fits-all approach to payments. Machine learning can really help us offer a more individualized approach to client communication and repayment schedules.

Chens team at ICMM uses aggregated data from non-profit credit counseling agencies to analyze repayment trends by a variety of demographic categories. This helps build a scoring model to determine a more personalized approach to the debt repayment process.

The model for ICMMs machine learning initiative took 18 months to build. Once it was finished, the company teamed up with CESI to beta test the technology and refine the model using real-time analysis of client data.

Were using this technology in the financial counseling process to help us integrate communication and interventions to help clients be as successful as possible, said Croxon.

CESI has tested its machine learning initiative multiple times, which has yielded a model capable of generating substantial benefits, as well as more accurate predictive capabilities. The ability to test the accuracy of consumer behavior based on modeled assumptions has been a significant advantage of the collaboration between CESI and ICMM.

Information gleaned from the testing also revealed key information about the target audience for AI-based debt repayment technology.

We made the presumption that millennials would want to be communicated with by text message, whereas older generations like me would be more interested in traditional methods of communication, Croxson said. Ultimately, the data told us we were wrong everyone wanted more contemporary methods of communication instead of traditional means.

Currently, the principal data the model collects revolves around payment history, but Chen hopes in the future theyll be able to analyze things like monthly spending categories, incomes and more to improve the technologys predictive capabilities.

For the time being, CESI and ICMM hope theyve positioned themselves to be uniquely ahead of the machine learning debt repayment curve, and they believe this trend will likely become the norm.

The credit counseling industry is important for consumers financial well-being, and that means we need to have an eye toward the technology of the future, Croxson said. Somebody in our industry needs to lead the effort. If not us, then who?

This article was written for our sponsor, Consumer Education Services Inc.

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Can technology predict your debt? How AI is changing the repayment landscape - WRAL Tech Wire