Automation and Machine Learning Intern (Summer) job with AARP | 40983222 – Washington Post

Business Unit Description

AARP is a nonprofit, nonpartisan organization, with a membership of nearly 38 million that helps people turn their goals and dreams into 'Real Possibilities' by changing the way America defines aging. With staffed offices in all 50 states, the District of Columbia, Puerto Rico, and the U.S. Virgin Islands, AARP works to strengthen communities and promote the issues that matter most to families such as healthcare security, financial security and personal fulfillment. AARP also advocates for individuals in the marketplace by selecting products and services of high quality and value to carry the AARP name. As a trusted source for news and information, AARP produces the world's largest circulation magazine, AARP The Magazine and AARP Bulletin.

Information Technology Solutions (ITS) is AARP's technology leader in positive social change and member value, enabling a more effective workforce and globally connecting employees, members, volunteers, partners and advocates to maximize engagement. Summary

Are you a software developer who wants to create solutions, broaden your abilities, and apply new skills? Our IT team is advancing AARP's automation and machine learning efforts related to workflows in the financial planning and analysis space and identifying creative ways to automate workflows in rapid delivery, agile and low-code, no-code projects.

As an Intern, you will have the opportunity to work across technical teams to apply your creativity and technical expertise to solve challenging business problems.

On any given day, you will:

-Work on a self-organized and cross-functional team

-Harness Lean thinking to create a Kanban board

-Prioritize the highest-value work

-Manage stakeholder relationships and incorporate frequent stakeholder feedback

-Participate in frequent demonstrations of RPA prototypes and other products

-Contribute in retrospectives to continuously improve how the team performs and communicates

We'll challenge you to adopt an agile mindset to your work and consider the outcomes that you desire from your internship. We are committed to your growth and will cultivate your continued learning and development.

Soft-skills desired:

Prospective interns must be currently enrolled in a degree program at an accredited college or university; be considered rising undergraduate juniors or seniors, graduate students, or post-doctoral students; and remain academically enrolled throughout the internship.

AARP also considers non-traditional interns who are looking to re-enter the workforce or change careers. This may include those who have previously graduated from college and enrolled in a continuing education program. Benefits Offered

Internships are non-exempt positions and are not eligible for employee benefits. Equal Employment Opportunity

AARP is an equal opportunity employer committed to hiring a diverse workforce and sustaining an inclusive culture. AARP does not discriminate on the basis of race, ethnicity, religion, sex, color, national origin, age, sexual orientation, gender identity or expression, mental or physical disability, genetic information, veteran status, or on any other basis prohibited by applicable law.

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Automation and Machine Learning Intern (Summer) job with AARP | 40983222 - Washington Post

Who knows the secret of the black magic box? Boffins seek the secrets of AI learning by mapping digital neurons – The Register

Roundup OpenAI Microscope: Neural networks, often described as black boxes, are complicated; its difficult to understand how all the neurons in the different layers interact with one another. As a result, machine learning engineers have a hard time trying to interpret their models.

OpenAI Microscope, a new project launched this week, shows that it is possible to see which groups of neurons are activated in a model when it processes an image. In other words, its possible to see what features these neurons in the different layers are learning. For example, the tools show what parts of a neural network are looking at the wheels or the windows in an image of a car.

There are eight different visualisations that take you through eight popular models - you can explore them all here.

At the moment, its more of an educational resource. The Microscope tools wont help you interpret your own models because they cant be applied to custom neural networks.

Generating the millions of images and underlying data for a Microscope visualization requires running lots of distributed jobs, OpenAI explained. At present, our tooling for doing this isn't usable by anyone other than us and is entangled with other infrastructure.

The researchers hope that their visualisation tools might inspire people to study the connections between neurons. Were excited to see how the community will use Microscope, and we encourage you to reuse these assets. In particular, we think it has a lot of potential in supporting the Circuits collaborationa project to reverse engineer neural networks by analyzing individual neurons and their connectionsor similar work, it concluded.

Don't stand so close to me: Current social distancing guidelines require people to stay at least six feet away from each other to prevent the spread of the novel coronavirus.

But how do you enforce this rule? Well, you cant really but you can try. Landing AI, a Silicon Valley startup led by Andrew Ng, has built what it calls an AI-enabled social distancing detection tool.

Heres how it works: Machine learning software analyses camera footage of people walking around and translates the frames into a birds eye view, where each person is represented as a green dot. A calibration tool estimates how far apart these people or dots are from one another by counting the pixels between them in the images. If theyre less than six feet apart, the dots turn red.

Landing AI said it built the tool to help the manufacturing and pharmaceutical industries. For example, at a factory that produces protective equipment, technicians could integrate this software into their security camera systems to monitor the working environment with easy calibration steps, it said.

The detector could highlight people whose distance is below the minimum acceptable distance in red, and draw a line between to emphasize this. The system will also be able to issue an alert to remind people to keep a safe distance if the protocol is violated.

Landing AI built this prototype at the request of customers whose businesses are deemed essential during this time, a spokesperson told The Register.

The productionization of this system is still early and we are exploring a few ways to notify people when the social distancing protocol is not followed. The methods being explored include issuing an audible alert if people pass too closely to each other on the factory floor, and a nightly report that can help managers get additional insights into their team so that they can make decisions like rearranging the workspace if needed.

You can read more about the prototype here.

Amazon improves Alexas reading voice: Amazon has added a new speaking style for its digital assistant Alexa.

The long-form speaking style will supposedly make Alexa sound more natural when its reading webpages or articles aloud. The feature, built from a text-to-speech AI model, introduces more natural pauses as it recites paragraphs of text or switches from one character to another in dialogues.

Unfortunately, this function is only available for customers in the US at the moment. To learn how to implement the long-form speaking style, follow the rules here.

Zoox settles with Tesla over IP use: Self-driving car startup Zoox announced it had settled its lawsuit with Tesla and agreed to pay Musks auto biz damages of an undisclosed fee.

Zoox acknowledges that certain of its new hires from Tesla were in possession of Tesla documents pertaining to shipping, receiving, and warehouse procedures when they joined Zooxs logistics team, and Zoox regrets the actions of those employees, according to a statement. As part of the settlement, Zoox will also conduct enhanced confidentiality training to ensure that all Zoox employees are aware of and respect their confidentiality obligations.

The case [PDF], initially filed by Teslas lawyers last year, accused the startup and four of its employees of stealing proprietary documents describing its warehouses and operations, and attempting to get more of its employees to join Zoox.

NeurIPS deadline extended: Heres a bit of good news for AI researchers amid all the doom and gloom of the current coronavirus pandemic: the deadline for submitting research papers to the annual NeurIPS AI conference has been extended.

Now, academics have until 27 May to submit their abstracts and 3 June to submit their finished papers. It can be hard to work during current lockdown situations as people juggle looking after children and their jobs.

Due to continued COVID-19 disruption, we have decided to extend the NeurIPS submission deadline by just over three weeks, the program chairs announced this week.

Sponsored: Practical tips for Office 365 tenant-to-tenant migration

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Who knows the secret of the black magic box? Boffins seek the secrets of AI learning by mapping digital neurons - The Register

SAP Makes Support Experience Even Smarter With ML and AI – AiThority

SAP SE announced several updates, including the Schedule a ManagerandAsk an Expert Peerservices, to its Next-Generation Support approach focused on the customer support experience and enabling customer success. Based on artificial intelligence (AI) and machine learning technologies, SAP has further developed existing functionalities with new, automated capabilities such as theIncident Solution Matching service and automatic translation.

When it comes to customer support, weve seen great success in flipping the customer engagement model by leveraging AI and machine learning technologies across our product support functionalities and solutions, saidAndreas Heckmann, head of Customer Solution Support and Innovation and executive vice president, SAP. To simplify and enhance the customer experience through our award-winning support channels, were making huge steps towards our goal of meeting customers needs by anticipating what they may need before it even occurs.

Recommended AI News: Kofax Presents Partner of the Year Awards

AI and machine learning technologies are key to improving and simplifying the customer support experience. They continue to play an important role in expanding Next-Generation Support to help SAP deliver maximum business outcomes for customers. SAP has expanded its offerings by adding new features to existing services, for example:

Recommended AI News: Kyocera Selects Skyhook to Power Precision Location Services for Rugged DuraXV Extreme

Customers expect their issues to be resolved quickly, and SAP strives toward a consistent line of communication across all support channels, including real-time options. SAP continues to improve, innovate and extend live support for technical issues by connecting directly with customers to provide a personal customer experience. Building on top of live support services, such asExpert ChatandSchedule an Expert, SAP has made significant strides in upgrading its real-time support channels. For example, it now offers the Schedule a Manager service and a peer-to-peer collaboration channel through the Ask an Expert Peer service.

By continuing to invest in AI and machine learningbased technologies, SAP enables more efficient support processes for customers while providing the foundation for predictive support functionalities and superior customer support experiences.

Customers can learn more about the Next-Generation Support approach through theProduct Support Accreditation program, available to SAP customers and partners at no additional cost. Customers can be empowered to get the best out of SAPs product support tools and the Next-Generation Support approach.

Recommended AI News: O.C. Tanner Recognized as a Leader in Everest Group PEAK Matrix Rewards & Recognition Solutions Assessment 2020

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SAP Makes Support Experience Even Smarter With ML and AI - AiThority

May 31 | Predictive Analytics World for Business Virtual Edition 2020 | Moorpark – Patch.com

Predictive Analytics World for Business is the leading cross-vendor data science conference series covering the commercial deployment of machine learning and predictive analytics. Hear from the horse's mouth precisely how Fortune 500 analytics competitors deploy machine learning and the kind of business results they achieve.

The conference schedule features a 2-Day conference program filled with insightful keynote presentations, industry-leading networking opportunities - plus optional training workshop days.

Predictive Analytics World for Business' main two-day program is packed with sessions that are divided among three tracks:

Business: Analytics operationalization and management

Tech: Machine learning methods and advanced topics

Case Studies: Cross-industry business applications of machine learning

Have a look at some highlight sessions:

The Foundation of Analytics-Driven Organizations: Jennifer Redmon, Chief Data Evangelist - Cisco Systems, Inc

Machine Learning at Facebook Scale: Mohamed Fawzy, Senior Software Engineering Lead - AI Infra - Facebook

How We Learned to Stop Worrying and Gained Company-Wide Adoption of Machine Learning: Clayton Clouse, Senior Data Scientist - FedEx

Efficiently Exploring (Possibly Infinite) Parameter Space with ML-Guided Adaptive Experiments: Clinton Brownley, Data Scientist - WhatsApp

URLs:Website: https://go.evvnt.com/563581-0?...Tickets: https://go.evvnt.com/563581-2?...

Date and Time: On Sunday May 31, 2020 at 9:00 am ends Thursday June 04, 2020 at 6:00 pm

Category: Conferences | Business and Economics

Prices:Early Bird Price (Ends Feb 21): USD 1895.00,Regular Price (Ends April 17): USD 1995.00,Late/Pre-event Price (Ends May 30): USD 2195.00,Onsite Price (From May 31): USD 2395.00

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May 31 | Predictive Analytics World for Business Virtual Edition 2020 | Moorpark - Patch.com

Teslas acquisition of DeepScale starts to pay off with new IP in machine learning – Electrek

Teslas acquisition of machine-learning startup DeepScale is starting to pay off, with the team hired through the acquisition starting to deliver new IP for the automaker.

Late last year, it was revealed that Tesla acquired DeepScale, a Bay Area-based startup that focuses on Deep Neural Network (DNN) for self-driving vehicles, for an undisclosed amount.

They specialized in computing power-efficient deep learning systems, which is also an area of focus for Tesla, who decided to design its own computer chip to power its self-driving software.

There was speculation that Tesla acquired the small startup team in order to accelerate its machine learning development.

Now we are seeing some of that teams work, thanks to a new patent application.

Just days after Tesla acquired the startup in October 2019, the automaker applied for a new patent with three members of DeepScale listed as inventors: Matthew Cooper, Paras Jain, and Harsimran Singh Sidhu.

The patent application called Systems and Methods for Training Machine Models with Augmented Data was published yesterday.

Tesla writes about it in the application:

Systems and methods for training machine models with augmented data. An example method includes identifying a set of images captured by a set of cameras while affixed to one or more image collection systems. For each image in the set of images, a training output for the image is identified. For one or more images in the set of images, an augmented image for a set of augmented images is generated. Generating an augmented image includes modifying the image with an image manipulation function that maintains camera properties of the image. The augmented training image is associated with the training output of the image. A set of parameters of the predictive computer model are trained to predict the training output based on an image training set including the images and the set of augmented images.

The system that the DeepScale team, now working under Tesla, is trying to patent here is related to training a neural net using data from several different sensors observing scenes, like the eight cameras in Teslas Autopilot sensor array.

They write about the difficulties of such a situation in the patent application:

In typical machine learning applications, data may be augmented in various ways to avoid overfitting the model to the characteristics of the capture equipment used to obtain the training data. For example, in typical sets of images used for training computer models, the images may represent objects captured with many different capture environments having varying sensor characteristics with respect to the objects being captured. For example, such images may be captured by various sensor characteristics, such as various scales (e.g., significantly different distances within the image), with various focal lengths, by various lens types, with various pre- or post-processing, different software environments, sensor array hardware, and so forth. These sensors may also differ with respect to different extrinsic parameters, such as the position and orientation of the imaging sensors with respect to the environment as the image is captured. All of these different types of sensor characteristics can cause the captured images to present differently and variously throughout the different images in the image set and make it more difficult to properly train a computer model.

Here they summarize their solution to the problem:

One embodiment is a method for training a set of parameters of a predictive computer model. This embodiment may include: identifying a set of images captured by a set of cameras while affixed to one or more image collection systems; for each image in the set of images, identifying a training output for the image; for one or more images in the set of images, generating an augmented image for a set of augmented images by: generating an augmented image for a set of augmented images by modifying the image with an image manipulation function that maintains camera properties of the image, and associating the augmented training image with the training output of the image; training the set of parameters of the predictive computer model to predict the training output based on an image training set including the images and the set of augmented images.

An additional embodiment may include a system having one or more processors and non-transitory computer storage media storing instructions that when executed by the one or more processors, cause the processors to perform operations comprising: identifying a set of images captured by a set of cameras while affixed to one or more image collection systems; for each image in the set of images, identifying a training output for the image; for one or more images in the set of images, generating an augmented image for a set of augmented images by: generating an augmented image for a set of augmented images by modifying the image with an image manipulation function that maintains camera properties of the image, and associating the augmented training image with the training output of the image; training the set of parameters of the predictive computer model to predict the training output based on an image training set including the images and the set of augmented images.

Another embodiment may include a non-transitory computer-readable medium having instructions for execution by a processor, the instructions when executed by the processor causing the processor to: identify a set of images captured by a set of cameras while affixed to one or more image collection systems; for each image in the set of images, identify a training output for the image; for one or more images in the set of images, generate an augmented image for a set of augmented images by: generate an augmented image for a set of augmented images by modifying the image with an image manipulation function that maintains camera properties of the image, and associate the augmented training image with the training output of the image; train the computer model to learn to predict the training output based on an image training set including the images and the set of augmented images.

As we previously reported, Tesla is going through a significant foundational rewrite in the Tesla Autopilot. As part of the rewrite, CEO Elon Musk says that the neural net is absorbing more and more of the problem.

It will also include a more in-depth labeling system.

Musk described 3D labeling as a game-changer:

Its where the car goes into a scene with eight cameras, and kind of paint a path, and then you can label that path in 3D.

This new way to train machine learning systems with multiple cameras, like Teslas Autopilot, with augmented data could be part of this new Autopilot update.

Here are some drawings from the patent application:

Heres Teslas patent application in full:

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Teslas acquisition of DeepScale starts to pay off with new IP in machine learning - Electrek

Windows 10 news recap: Halo 2 Anniversary beta invites being sent out, machine learning utilised to identify security bugs, and more – OnMSFT

Welcome back to our Windows 10 news recap, where we go over the top stories of the past week in the world of Microsofts flagship operating system.

Microsoft to introduce PowerToys launcher for Windows 10 in May

A new report suggests that a new update for PowerToys is being prepared that includes a Mac OS style Spotlight launcher, making it easier find apps and files on a Windows 10 PC.

concept design for PowerToys Launcher UX

Microsoft starts sending invites for first Halo 2 Anniversary beta on PC

Invites for the Halo 2 Anniversary beta on PC have started to be sent out this week. Members of the Halo Insider program who have opted into PC flighting will receive an email with the invite.

Microsoft is using machine learning to identify security bugs during software development

In order to help Microsoft identify security bugs and resolve them before public release of software, the company is employing machine learning to find security bugs.

Thats it for this week. Well be back next week with more Windows 10 news.

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Windows 10 news recap: Halo 2 Anniversary beta invites being sent out, machine learning utilised to identify security bugs, and more - OnMSFT

SAP Added to its Next-Generation Support – ARC Advisory Group

SAP SE announced several updates, including the Schedule a Manager and Ask an Expert Peer services, to its Next-Generation Support approach focused on the customer support experience and enabling customer success.

Based on artificial intelligence (AI) and machine learning technologies, SAP has further developed existing functionalities with new, automated capabilities, such as the Incident Solution Matching service and automatic translation.

AI and machine learning technologies are key to improving and simplifying the customer support experience. They continue to play an important role in expanding Next-Generation Support to help SAP deliver maximum business outcomes for customers. SAP has expanded its offerings by adding new features to existing services, for example:

Customers expect their issues to be resolved quickly, and SAP strives toward a consistent line of communication across all support channels, including real-time options. SAP continues to improve, innovate and extend live support for technical issues by connecting directly with customers to provide a personal customer experience. Building on top of live support services, such as Expert Chat and Schedule an Expert, SAP has made significant strides in upgrading its real-time support channels. For example, it now offers the Schedule a Manager service and a peer-to-peer collaboration channel through the Ask an Expert Peer service.

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SAP Added to its Next-Generation Support - ARC Advisory Group

Podcast of the Week: TWIML AI Podcast – 9to5Mac

During the COVID19 pandemic, I decided that I wanted to use the time at home to invest in myself. One of the things I was challenged by in a recent episode of Business Casual was when Mark Cuban discussed the role of Artificial Intelligence in the future and recommended some tools to learn more. He mentioned some Coursera courses, so I am currently working my way through some of their AI training, but he also mentioned an AI-focused podcast called theTWIMLAI Podcast that I added to my podcast subscription list.

9to5Macs Podcast of the Week is a weekly recommendation of a podcast you should add to your subscription list.

TWIML (This Week in Machine Learning and AI) is a perfect way to hear from industry experts about how Machine Learning and AI will change our world. I plan to work through the back catalog soon, but the newest episodes have been informative. I particularly enjoyed this episode with Cathy Wu, Gilbert W. Winslow Career Development Assistant Professor in the Department of Civil and Environmental Engineering at MIT where they discussed simulating the future of traffic.

Machine learning and artificial intelligence are dramatically changing the way businesses operate and people live. By sharing and amplifying the voices of a broad and diverse spectrum of machine learning and AI researchers, practitioners, and innovators, our programs help make ML and AI more accessible, and enhance the lives of our audience and their communities.

TWIML has its origins in This Week in Machine Learning & AI, a podcast Sam launched in mid2016 to a small but enthusiastic reception. Fast forward three years, and the TWIML AI Podcast is now a leading voice in the field, with over five million downloads and a large and engaged community following. Our offerings now include online meetups and study groups, conferences, and a variety of educational content.

Subscribe to the TWIML AI Podcast on Apple Podcasts, Spotify, Castro, Overcast, Pocket Casts, and RSS.

Dont forget about the great lineup of podcasts on the 9to5 Network.

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Podcast of the Week: TWIML AI Podcast - 9to5Mac

2020 AI Zest Automated Machine Learning Market Opportunities, Analysis, Future and Forecast by Key Players, Products, Types and Applications – Germany…

AI Zest Automated Machine Learning:

This comprehensive AI Zest Automated Machine Learning Market research report includes a brief on these trends that can help the businesses operating in the industry to understand the market and strategize for their business expansion accordingly. The research report analyzes the market size, industry share, growth, key segments, CAGR and key drivers.

New vendors in the market are facing tough competition from established international vendors as they struggle with technological innovations, reliability and quality issues. The report will answer questions about the current market developments and the scope of competition, opportunity cost and more.

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The AI Zest Automated Machine Learning market is a comprehensive report which offers a meticulous overview of the market share, size, trends, demand, product analysis, application analysis, regional outlook, competitive strategies, forecasts, and strategies impacting the AI Zest Automated Machine Learning Industry. The report includes a detailed analysis of the market competitive landscape, with the help of detailed business profiles, SWOT analysis, project feasibility analysis, and several other details about the key companies operating in the market.

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Get a Free Sample Copy @ https://www.reportsandmarkets.com/sample-request/global-ai-zest-automated-machine-learning-market-report-2019

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Our analysts are experts in covering all types of geographical markets from developing to mature ones. You can expect a comprehensive research analysis of key regional and country-level markets such as Europe, North America, South America, Asia-Pacific, and the Middle East & Africa. With accurate statistical patterns and regional classification, our domain experts provide you one of the most detailed and easily understandable regional analyses of the AI Zest Automated Machine Learning market.

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Moreover, the research report assessed market key features, consisting of revenue, capacity utilization rate, price, gross, growth rate, consumption, production, export, supply, cost, market size & share, industry demand, export & import analysis, and CAGR.

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2020 AI Zest Automated Machine Learning Market Opportunities, Analysis, Future and Forecast by Key Players, Products, Types and Applications - Germany...

Machine Learning as a Service (MLaaS) Market | Outlook and Opportunities in Grooming Regions with Forecast to 2029 – Jewish Life News

Documenting the Industry Development of Machine Learning as a Service (MLaaS) Market concentrating on the industry that holds a massive market share 2020 both concerning volume and value With top countries data, Manufacturers, Suppliers, In-depth research on market dynamics, export research report and forecast to 2029

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Get a Sample Report for More Insightful Information(Use official eMail ID to Get Higher Priority):https://market.us/report/machine-learning-as-a-service-mlaas-market/request-sample/

***[Note: Our Complimentary Sample Report Accommodate a Brief Introduction To The Synopsis, TOC, List of Tables and Figures, Competitive Landscape and Geographic Segmentation, Innovation and Future Developments Based on Research Methodology are also Included]

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The report is a detailed competitive outlook including the Machine Learning as a Service (MLaaS) Market updates, future growth, business prospects, forthcoming developments and future investments by forecast to 2029. The region-wise analysis of machine learning as a service (mlaas) market is done in the report that covers revenue, volume, size, value, and such valuable data. The report mentions a brief overview of the manufacturer base of this industry, which is comprised of companies such as- Google, IBM Corporation, Microsoft Corporation, Amazon Web Services, BigML, FICO, Yottamine Analytics, Ersatz Labs, Predictron Labs, H2O.ai, AT and T, Sift Science.

Segmentation Overview:

Product Type Segmentation :

Software Tools, Cloud and Web-based Application Programming Interface (APIs), Other

Application Segmentation :

Manufacturing, Retail, Healthcare and Life Sciences, Telecom, BFSI, Other (Energy and Utilities, Education, Government)

To know more about how the report uncovers exhaustive insights |Enquire Here: https://market.us/report/machine-learning-as-a-service-mlaas-market/#inquiry

Key Highlights of the Machine Learning as a Service (MLaaS) Market:

The fundamental details related to Machine Learning as a Service (MLaaS) industry like the product definition, product segmentation, price, a variety of statements, demand and supply statistics are covered in this article.

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Machine Learning as a Service (MLaaS) Market | Outlook and Opportunities in Grooming Regions with Forecast to 2029 - Jewish Life News