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Grants totaling $4.6 million support the use of machine learning to improve outcomes of people with HIV – Brown University

December 22, 2021 | Author: admin

PROVIDENCE, R.I.[Brown University] Over the past four decades of treating HIV/AIDS, two important facts have been established: HIV-positive patients need to be put on treatment as soon as theyre diagnosed and then kept on an effective treatment plan. This response can help turn HIV into a chronic but manageable disease and can essentially help people live normal, healthy lives, said Joseph Hogan a professor of public health and of biostatistics at Brown University, who has been researching HIV/AIDS for 25 years.

Hogan is one of the primary investigators on two recently awarded grants from the National Institutes of Health, totaling nearly $4.6 million over five years, to support the creation and utilization of data-driven tools that will allow care programs in Kenya to meet these key treatment goals.

If the system works as designed, then we have confidence that well improve the health outcomes of people with HIV, Hogan said.

The first part of the project involves using data science to understand whats called the HIV care cascade, said Hogan, who is the co-director of the biostatistics program for Academic Model Providing Access to Healthcare (AMPATH), a consortium of 14 North American universities who collaborate with Moi University in Eldoret, Kenya, on HIV research, care and training.

Hogan will collaborate with longtime scientific partner Ann Mwangi, associate professor of biostatistics at Moi University, who received a Ph.D. in biostatistics from Brown in 2011. Using AMPATH-developed electronic health record database, a team co-led by Hogan and Mwangi will develop algorithm-based statistical machine learning tools to predict when and why patients might drop out of care and when their viral load levels indicate they are at risk of treatment failure.

These algorithms, Hogan said, will then be integrated into the electronic health record system to deliver the information at the point of care, through handheld tablets that the physicians can use when sitting in the exam room with the patient. In consultation with experts in user interface design, the team will assess and test the most effective ways to communicate the results of the algorithm to the care providers so that they can use them to make decisions about patient care, Hogan said.

The predictive modeling system the team is developing, Hogan said, will alert a physician to red flags in the patients treatment plan at the point of care. This way, interventions can be developed to help a patient get to their treatment appointments, for example, before the patient needs to miss or cancel them. Or if a patient is predicted to have high viral load, Hogan said, a clinician can refer them for additional monitoring to identify and treat the increase before it becomes a problem.

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Grants totaling $4.6 million support the use of machine learning to improve outcomes of people with HIV - Brown University

Chat Commerce, machine learning and a stronger privacy focus eCommerce predictions for 2022 – BetaNews

December 22, 2021 | Author: admin

One of the side effects of the pandemic over the last two years has been a boom in online shopping. But is this something that's here to stay? And what are we likely to see happening in the eCommerce field in 2022?

Here is what some of the industrys experts think will be next year's trends.

"Chat Commerce is the third wave of digital commerce, following on from eCommerce and app commerce," says Pieter de Villiers, CEO and co-founder at Clickatell. "More than 7.7 billion people use some form of chat several times a day, making chat the largest digital engagement channel in the world. With COVID-19 accelerating digital commerce adoption and businesses fast tracking their digital transformation to meet consumers where they are, we can expect an increased demand for and deployment of Chat Commerce services and experiences."

Gabriel Straub, chief data scientist at Ocado Technology believes we'll see machine learning used to improve the customer experience.

Organizations can improve customers' experience by making the full interaction feel like a continuous conversation that flows naturally from end-to-end. For Ocado Technology, from a data point of view, this means thinking about the customer's journey through the shop from the selection of a delivery slot all the way to the checkout, rather than as a series of discrete interactions. Where in the customer flow would it make sense to add a bit of friction now, that will allow us to remove more friction later in the journey? Where can we explicitly ask a user about an assumption that we might have about them to ensure that we really understand their needs and preferences? And how do we make sure that the way we think about ML in a product covers the whole journey rather than just a single interaction or click?

Nowhere is this more important than in the world of grocery retail where an average basket size is significantly larger and shopping frequency much higher than in most other retail segments. Combining algorithms to create a more consistent user experience across the user journey will be a key focus.

Nathanael Coffing, CSO and co-founder of Cloudentity thinks privacy and consumer control over data will be a key factor. "Consumers today are calling for more control over their online data and how its being used by companies. While government regulators enforcing privacy laws such as GDPR, CCPA and CPRA are a step in the right direction, more needs to be done to protect consumers privacy and this needs to start at registration and continue through API-based data sharing. Every website or app should display an icon (similar to SSL) as soon as a user opens the page that rates the certifications the company is meeting to protect their customers' data. These must be written in a way that is easy for consumers to understand as well -- no hiding behind confusing legal jargon. Then, organizations will have no choice but to be transparent with how they are harvesting, using and sharing their users' data. The icon must provide consumers the ability to control their privacy settings on an attribute level, control their sharing of that attribute and delete their data after they are done with the website/app, so the user remains in control of their personal information at all times."

Stanley Huang, CTO and co-founder of Moxtra believes we'll see a blurring of boundaries between consumer-facing and backend systems. "Backend systems are designed to manage internal data, processes and operations in an organized fashion. They help determine which ROI measurements are about productivity, management efficiency and cost savings. On the other side, customer communication channels are designed as unstructured, data agnostic utilities that often focus on omni-channel and bots to understand customer intent. In 2022, businesses will need to blend the backend and customer communication channels together to ensure data and communication are deeply coupled to offer the best customer experience possible. When businesses think about the customer experience holistically with a customer-centric backend leading to a more analytical frontend channel, the customer experience will be improved across the entire lifecycle."

Photo Credit: Nonnakrit/Shutterstock

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Chat Commerce, machine learning and a stronger privacy focus eCommerce predictions for 2022 - BetaNews

New platform uses machine-learning and mass spectrometer to rapidly process COVID-19 tests – UC Davis Health

December 22, 2021 | Author: admin

(SACRAMENTO)

UC Davis Health, in partnership with SpectraPass, is evaluating a new type of rapid COVID-19 test. The research will involve about 2,000 people in Sacramento and Las Vegas.

The idea behind the new platform is a scalable system that can quickly and accurately perform on-site tests for hundreds or potentially thousands of people.

Nam Tran is a professor of clinical pathology in the UC Davis School of Medicine and a co-developer of the novel testing platform with SpectraPass, a Las Vegas-based startup.

Tran explained that the system doesnt look for the SARS-CoV-2 virus like a PCR test does. Instead, it detects an infection by analyzing the bodys response to it. When ill, the body produces differing protein profiles in response to infection. These profiles may indicate different types of infection, which can be detected by machine learning.

The goal of this study is to have enough COVID-19 positive and negative individuals to train our machine learning algorithm to identify patients infected by SARS-CoV-2, said Tran.

A study published by Tran and his colleagues earlier this year in Nature Scientific Reports found the novel method to be 98.3% accurate for positive COVID-19 tests and 96% for negative tests.

In addition to identifying positive cases of COVID-19, the platform also uses next-generation sequencing to confirm multiple respiratory pathogens like the flu and the common cold.

The sequencing panel at UC Davis Health can detect over 280 respiratory pathogens, including SARS-CoV-2 and related variants allowing the study to train the machine-learning algorithms to differentiate COVID-19 from other respiratory diseases.

So far, the study has not seen any participants with the new omicron variant.

Our team has tested the system with samples from patients infected with delta and other variants of the SARS-CoV-2 virus. We are fairly certain that omicron will be detected as well, but we wont know for sure until we encounter a study participant with the variant, Tran said.

The Emergency Department (ED) at the UC Davis Medical Center is conducting the testing in Sacramento. Collection for testing in Las Vegas is conducted at multiple businesses and locations.

The team expects the study will continue until the end of winter. The results from the new study will be used to seek emergency use authorization (EUA) from the Food and Drug Administration.

The novel testing system uses an analytical instrument known as a mass spectrometer. Its paired with machine learning algorithms produced by software called the Machine Intelligence Learning Optimizer or MILO. MILO was developed by Tran, Hooman Rashidi, a professor in the Department of Pathology and Laboratory Medicine, and Samer Albahra, assistant professor and medical director of pathology artificial intelligence in the Department of Pathology and Laboratory Medicine.

As with many other COVID-19 tests, a nasal swab is used to collect a sample. Proteins from the nasal sample are ionized with the mass spectrometers laser, then measured and analyzed by the MILO machine learning algorithms to generate a positive or negative result.

In addition to conducting the mass spectrometry testing, UC Davis serves as a reference site for the study, performing droplet digital PCR (ddPCR) tests, the gold standard for COVID-19 testing, to assess the accuracy of the mass spectrometry tests.

The project originated with Maurice J. Gallagher, Jr., chairman and CEO of Allegiant Travel Company and founder of SpectraPass. Gallagher is also a UC Davis alumnus and a longtime supporter of innovation and entrepreneurship at UC Davis.

In 2020, when the COVID-19 pandemic brought the travel and hospitality industries almost to a standstill, Gallagher began conceptualizing approaches to allow people to gather again safely. He teamed with researchers at UC Davis Health to develop the new platform and launched SpectraPass.

In addition to the novel testing solution, SpectraPass is also developing digital systems to accompany the testing technology. Those include tools to authenticate and track verified test results from the system so an individual can access and use them. The goal is to facilitate accurate, large-scale rapid testing that will help keep businesses and the economy open through the current and any future pandemics.

The official start of our multi-center study across multiple locations marks an important milestone in our journey at SpectraPass. We are excited to test and generate data on a broader scale. Our goal is to move the platform from a promising new technology to a proven solution that can ultimately benefit the broader population, said Greg Ourednik, president of SpectraPass.

New rapid COVID-19 test the result of university-industry partnership

Meet MILO, a powerful machine learning AI tool from UC Davis Health

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New platform uses machine-learning and mass spectrometer to rapidly process COVID-19 tests - UC Davis Health

Top Computer Vision Jobs to Apply in December 2021 – Analytics Insight

December 22, 2021 | Author: admin

You can apply for these computer vision jobs this December

Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos, and other visual inputs and take actions or make recommendations based on that information. If AI enables computers to think, computer vision enables them to see, observe and understand. Computer vision works much the same as human vision, except humans have a head start. Human sight has the advantage of lifetimes of context to train how to tell objects apart, how far away they are, whether they are moving, and whether there is something wrong in an image.

New Delhi

Endovision is a Hong Kong-based med-tech company, which is helping endoscopists to reduce cancer miss rates with the aid of real-time video analysis using AI. We have generated interest worldwide, and this is the hottest area of research in the field of endoscopy. Our partners are located in Hong Kong, Japan, and India, with the primary focus on Hong Kong at the moment. They are looking to hire a new Research Engineer in our team. Youd help create AI-first products implementing state-of-the-art research papers, contributing to the company IP, and technology stack deployed in Nvidia Jetson ecosystem. Youd work on cutting-edge deep learning, computer vision, and graphics problems with an emphasis on endoscopy, with an opportunity to collaborate with research scientists and engineers at the Endovision and its partnering institutions. Candidates should have: Experience (academic or industry) with computer vision and deep learning in at least two Neural networks CNNs, RNNs, autoencoders, etc., and transfer learning generative deep learning method, esp. for image generation from images and videos numerical optimization.

Apply here.

Gurugram, Haryana

Profile Description:

A passionate developer with a drive to work in a hot startup. You will be working in a team in the area of computer vision, machine learning, algorithm design, security, and hardware in a fast-paced and dynamic environment. You will get the opportunity to showcase your talents and capabilities in development, research, and technical operations. The ability to multi-task, solve challenging problems, learn new technology areas quickly, persistence, hard work, and humility are necessary prerequisites to fulfill your role.

Apply here.

Chennai, Tamil Nadu

The company is creating a home design solution (AI) A platform to explore new Architectural designs for consumers.

It is an upcoming organization in need of staff.

Apply here.

Bengaluru

Experience: 2-5 years;

Computer Vision and Image Processing: In this position, you will be involved in the given Roles and Responsibilities Roles and Responsibilities Working on computer vision/image processing applications like object classification, segmentation, etc. Working on deep learning algorithms for machine vision-based inspection use cases. Working on Machine Vision Cameras involving a variety of Lens, Filters, and other Optical Elements to collect data. Having full-stack development experience will be a plus. Educational Qualification Bachelors degree in marketing, business or related field. Skill(s) required OpenCV, Python, Linux and C programming, Python, PHP, Django, cloud, TensorFlow, machine vision camera experience will be a bonus.

Image processing, algorithms, Python, c programming, machine vision, Django, Linux, PHP, OpenCV

Apply here.

Education Requirements:

Apply here.

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Top Computer Vision Jobs to Apply in December 2021 - Analytics Insight

Machine Learning as a Service (MLaaS) Market will witness a CAGR of 49% 2021: Global Industry Insights by Global Players, Regional Segmentation,…

December 22, 2021 | Author: admin

Machine Learning as a Service (MLaaS) market report contains detailed information on factors influencing demand, growth, opportunities, challenges, and restraints. It provides detailed information about the structure and prospects for global and regional industries. In addition, the report includes data on research & development, new product launches, product responses from the global and local markets by leading players. The structured analysis offers a graphical representation and a diagrammatic breakdown of the Machine Learning as a Service (MLaaS) market by region.

Machine Learning as a Service (MLaaS) Market will witness a CAGR of 49% during the forecast period 2017-2023.

Machine Learning as a Service in Manufacturing Market Global Drivers Restraints Opportunities Trends and Forecasts up to 2023

Request To Download Sample of This Strategic Report:-https://reportocean.com/industry-verticals/sample-request?report_id=IR100

Market OverviewMachine learning has become a disruptive trend in the technology industry with computers learning to accomplish tasks without being explicitly programmed. The manufacturing industry is relatively new to the concept of machine learning. Machine learning is well aligned to deal with the complexities of the manufacturing industry. Manufacturers can improve their product quality ensure supply chain efficiency reduce time to market fulfil reliability standards and thus enhance their customer base through the application of machine learning.

Machine learning algorithms offer predictive insights at every stage of the production which can ensure efficiency and accuracy. Problems that earlier took months to be addressed are now being resolved quickly. The predictive failure of equipment is the biggest use case of machine learning in manufacturing. The predictions can be utilized to create predictive maintenance to be done by the service technicians. Certain algorithms can even predict the type of failure that may occur so that correct replacement parts and tools can be brought by the technician for the job.

Market AnalysisAccording to Reportocean Research Machine Learning as a Service (MLaaS) Market will witness a CAGR of 49% during the forecast period 2017-2023. The market is propelled by certain growth drivers such as the increased application of advanced analytics in manufacturing high volume of structured and unstructured data the integration of machine learning with big data and other technologies the rising importance of predictive and preventive maintenance and so on. The market growth is curbed to a certain extent by restraining factors such as implementation challenges the dearth of skilled data scientists and data inaccessibility and security concerns to name a few.

Request To Download Sample of This Strategic Report:-https://reportocean.com/industry-verticals/sample-request?report_id=IR100

Segmentation by ComponentsThe market has been analyzed and segmented by the following components Software Tools Cloud and Web-based Application Programming Interface (APIs) and Others.

Segmentation by End-usersThe market has been analyzed and segmented by the following end-users namely process industries and discrete industries. The application of machine learning is much higher in discrete than in process industries.?

Segmentation by Deployment ModeThe market has been analyzed and segmented by the following deployment mode namely public and private.

Regional AnalysisThe market has been analyzed by the following regions as Americas Europe APAC and MEA. The Americas holds the largest market share followed by Europe and APAC. The Americas is experiencing a high adoption rate of machine learning in manufacturing processes. The demand for enterprise mobility and cloud-based solutions is high in the Americas. The manufacturing sector is a major contributor to the GDP of the European countries and is witnessing AI driven transformation. Chinas dominant manufacturing industry is extensively applying machine learning techniques. China India Japan and South Korea are investing significantly on AI and machine learning. MEA is also following a high growth trajectory.

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Vendor AnalysisSome of the key players in the market are Microsoft Amazon Web Services Google Inc. and IBM Corporation. The report also includes watchlist companies such as BigML Inc. Sight Machine Eigen Innovations Inc. Seldon Technologies Ltd. and Citrine Informatics Inc.

BenefitsThe study covers and analyzes the Global MLaaS Market in the manufacturing context. Bringing out the complete key insights of the industry the report aims to provide an opportunity for players to understand the latest trends current market scenario government initiatives and technologies related to the market. In addition it helps the venture capitalists in understanding the companies better and take informed decisions.> The report covers drivers restraints and opportunities (DRO) affecting the market growth during the forecast period (2017-2023).> It also contains an analysis of vendor profiles which include financial health business units key business priorities SWOT strategy and views.> The report covers competitive landscape which includes M&A joint ventures and collaborations and competitor comparison analysis.> In the vendor profile section for the companies that are privately held financial information and revenue of segments will be limited.

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Region/Country Cover in the Report

Regions -Americas Europe APAC and MEA

Key Players Covered in the Report

Microsoft Amazon Web Services Google Inc. and IBM Corporation

This report covers aspects of the regional analysis market.The report includes data about North America, Europe, Asia Pacific, Latin America, the Middle East, and Africa.This report analyzes current and future market trends by region, providing information on product usage and consumption.Reports on the market include the growth rate of every region, based on their countries over the forecast period.

What factors are taken into consideration when assessing the key market players?

The report analyzes companies across the globe in detail.The report provides an overview of major vendors in the market, including key players.Reports include information about each manufacturer, such as profiles, revenue, product pricing, and other pertinent information about the manufactured products.This report includes a comparison of market competitors and a discussion of the standpoints of the major players.Market reports provide information regarding recent developments, mergers, and acquisitions involving key players.

Access Full Report, here:- https://reportocean.com/industry-verticals/sample-request?report_id=IR100

What are the key findings of the report?This report provides comprehensive information on factors expected to influence the market growth and market share in the future.The report offers the current state of the market and future prospects for various geographical regions.This report provides both qualitative and quantitative information about the competitive landscape of the market.Combined with Porters Five Forces analysis, it serves as SWOT analysis and competitive landscape analysis.It provides an in-depth analysis of the market, highlighting its growth rates and opportunities for growth.

About Report Ocean:We are the best market research reports provider in the industry. Report Ocean believes in providing quality reports to clients to meet the top line and bottom line goals which will boost your market share in todays competitive environment. Report Ocean is a one-stop solution for individuals, organizations, and industries that are looking for innovative market research reports.

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LiveFreely Announces Apple Watch Version of ‘BUDDY,’ the Predictive AI-Driven Digital Health Assistant for Seniors and Their Loved Ones – Yahoo…

December 22, 2021 | Author: admin

Already compatible with Fitbit smartwatches, the personal health management and remote monitoring technology is now also available for Apple smartwatches

BUDDY, Your Personal Health Assistant

SAN JOSE, Calif., Dec. 22, 2021 (GLOBE NEWSWIRE) -- LiveFreely, Inc., a Silicon Valley digital health company that develops innovative technology to improve the health and well-being of seniors and their loved ones, today launched BUDDY for Apple Watches. The BUDDY app uses AI and machine learning to predict, prevent, and detect health challenges while providing support and data for seniors and their caregivers.

"BUDDY works with Fitbit, and we've been testing it on the Apple platform," says Dr. Arthur Jue, co-founder and CEO of LiveFreely. "We are pleased to now make BUDDY commercially available on both platforms a milestone in our efforts to help empower seniors to age more proactively."

BUDDY bundles a full spectrum of functions that assist users with health issues, from predicting falls to monitoring irregular health patterns to detecting wandering. Its suite of solutions addresses critical health issues faced by seniors and caregivers, including:

Predicting and preventing falls, the leading cause of death among seniors, through AI and machine learning that triggers alerts when changes in gait are detected

Automatic fall detection and alerts

Irregular health pattern detection and alerts

Location alerts that help dementia and Alzheimer's wanderers

"Code blue" alerts for cardiac events

Medication adherence and schedule reminders

User-friendly and easy to set up, the app was conceived by Jue and his brother Daniel after caring for their aging father. "While our team has worked tirelessly on BUDDY for Apple Watch, we've kept foremost in mind the many mothers, fathers, grandmothers, grandfathers, aunts, uncles, and loved ones who urgently need this technology," says Daniel Jue, LiveFreely co-founder and CTO.

Story continues

As caregiving becomes increasingly complex and expensive, the LiveFreely team designed BUDDY to be an affordable, effective way to enhance independence while empowering caregivers to respond quickly when seconds count. BUDDY is the first app to send real-time data to emergency services personnel en route in an ambulance.

LiveFreely is offering a special limited-time promotion of only $4.99 per month subscription for BUDDY. Go to http://www.buddylife.com/launch to get the special promo code. LiveFreely will also donate a portion of the proceeds to Project WeHOPE initiatives for the unhoused this holiday season.

Kerri Kasem, radio host and founder of Kasem Cares, says, "I'm thrilled that BUDDY is now available for the Apple Watch. I'm a huge fan. Had BUDDY been available when my dad Casey Kasem (American Top 40) developed dementia, things would have turned out a lot differently. I believe BUDDY is a lifesaver."

Ninety-two-year-old BUDDY user Calvin Wong agrees. "BUDDY has been a great help to my family and me because they know I'm safe. I'm at an age where I have a fear of falling. I've fallen a few times already because I only have one eye, mono-vision, and can't tell distance. I might be stepping off a curb and not know it. So, BUDDY helps a lot."

About BUDDY by LiveFreely

Through machine learning and artificial intelligence, BUDDY monitors and manages factors such as fall prediction, prevention, and detection, medication schedules and reminders, GPS location, and emergency notifications. The platform alerts smartwatch wearers, family members, caregivers, first responders, and emergency services providers of irregularities, enhancing the security, connectedness, and independence of loved ones. To learn more about BUDDY by LiveFreely, visit http://www.buddylife.com.

Press Inquiries:Janet ChongInfo@livefreely.today

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BUDDY, Your Personal Health Assistant

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These are the top priorities for tech executives in 2022, survey reveals – CNBC

December 22, 2021 | Author: admin

Big software IPOs, cyberattacks and the push into the metaverse were just some of the themes coming out of the technology sector in 2021.

As technology executives look towards the year ahead, they say things like artificial intelligence, cloud computing and machine learning will be critically important to their companies in 2022, according to a recent CNBCTechnology Executive Council survey of 44 executives.

Here's a breakdown from the CNBC TEC survey of the technologies expected to receive the most time and money.

A vast majority (81%) of executives said that artificial intelligence would either be critically important or very important to their companies in 2022.

Twenty percent of respondents also said that AI is the technology that they expect to invest the most resources in over the next 12 months.

The emphasis on cloud computing shows no signs of lessening in the year ahead, as 82% of respondents said that the technology would be critically important to their company in 2022. It is also the technology where the most executives (34%) said their companies would be investing the most money.

Ninety-one percent of executives said that machine learning would be critically or very important to their companies in 2022, while 20% said this would be the area they will invest the most money in.

It is also the technology that the most executives (18%) said they would be the most excited to see grow and develop in the year ahead.

No code and low code software was the technology that saw the second-highest amount of executives (11%) say they were most excited to see it grow and develop in 2022.

Other technologies that were highlighted by multiple executives include explainable AI, robotics and software-defined security.

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These are the top priorities for tech executives in 2022, survey reveals - CNBC

Machine Learning as a Service (MLaaS) Market 2021: Big Things are Happening in Development and Future Assessment by 2031 – Digital Journal

December 22, 2021 | Author: admin

Pune, Maharashtra, India, December 17 2021 (Wiredrelease) Prudour Pvt. Ltd :High Use Of Machine Learning as a Service (MLaaS) Market|Better Business Growth, A One-Stop Guide For Growing Business In 2021

The Machine Learning as a Service (MLaaS) Market economy has improved over the last few years. There have been more entrants and technological advancement, as well as a growing rate of expansion due to the measures taken against short-term economic downturns. This report has been based on a few different types of research. The findings have been obtained from both primary and secondary tools for gathering data. The study is a perfect blend of qualitative and quantifiable information, highlighting key market developments as well industry challenges in gap analysis with new opportunities that could be trending. A variety of graphical presentation techniques are used to demonstrate the facts.

The report provides a comprehensive description of Machine Learning as a Service (MLaaS) market that presents an overview of the global market. The information in this document includes a forecast (2021-2031), trends drivers both current and future as good opinions from industry professionals on these topics with technological advancements and new entry explorations, many people are looking for economic countermeasures to increase their growth rates. The competitive nature of the industry is forcing key players to focus on new merger and acquisition methods in order to maintain their power over market share.

Looking for customized insights to raise your business for the future, ask for a sample report here:https://market.us/report/machine-learning-as-a-service-mlaas-market/request-sample/

The influential players covered in this report are:

GoogleIBM CorporationMicrosoft CorporationAmazon Web ServicesBigMLFICOYottamine AnalyticsErsatz LabsPredictron LabsH2O.aiAT and TSift Science

Figure:

Topographical segmentation of Machine Learning as a Service (MLaaS) market by top product type, best application, and key region:

Segmentation by Type:

Software ToolsCloud and Web-based Application Programming Interface (APIs)Other

Segmentation by Application:

ManufacturingRetailHealthcare and Life SciencesTelecomBFSIOther (Energy and Utilities, Education, Government)

Machine Learning as a Service (MLaaS) Market: Regional Segment Analysis

North America (USA, Canada, and Mexico)

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

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

South America (Brazil, Columbia, Argentina, etc)

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

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The main features on the report of 2021 Global Machine Learning as a Service (MLaaS) Market:

The latest mechanical enhancements and Machine Learning as a Service (MLaaS) new releases to engage our consumers to produce, settle on instructed business decisions, and build their future expected achievements.

Machine Learning as a Service (MLaaS) market focuses more on future methodology changes, current business and progressions and open entryways for the global market.

The investment return analysis, SWOT analysis, and feasibility study are also used for Machine Learning as a Service (MLaaS) market data analysis.

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

1. The report summarizes the machine learning as a service (mlaas) Market by stating the basic product definition, the number of product applications, product scope, product cost and price, supply and demand ratio, market overview.

2. Competitive landscape of all leading key players along with their business strategies, approaches, and latest machine learning as a service (mlaas) market movements.

3. It elements market feasibility investment, opportunities, the growth factors, restraints, market risks, and machine learning as a service (mlaas) business driving forces.

4. It performs a comprehensive study of emerging players of machine learning as a service (mlaas) business along with the existing ones.

5. It accomplishes primary and secondary research and resources to estimate top products, market size, and industrial partnerships of machine learning as a service (mlaas) business.

6. Global Machine Learning as a Service (MLaaS) market report ends by articulating research findings, data sources, results, list of dealers, sales channels, businesses and distributors along with an appendix.

Need More Information aboutMachine Learning as a Service (MLaaS) market:https://market.us/report/machine-learning-as-a-service-mlaas-market/

Key questions include:

1. What can we estimate about the anticipated growth rates and also the global machine learning as a service (mlaas) industry size by 2031?

2. Who investors will use the specifics of our research, as well as some key parameters and forecast periods to guide their investment decisions?

3. What will happen in the coming existing and emerging markets?

4. All those vendors who make a profit; some do not.

5. What would be the upcoming machine learning as a service (mlaas) market behavior forecast with trends, challenges, and drivers challenges for development?

6. What industry opportunities and dangers are faced by vendors in the market?

7. Which would be machine learning as a service (mlaas) industry opportunities and challenges faced with most vendors in the market?

8. What are the variables affecting the machine learning as a service (mlaas) market share?

9. What will be the outcomes of this market SWOT five forces analysis?

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What is Quantum Computing? | IBM

December 20, 2021 | Author: admin

Let's look at example that shows how quantum computers can succeed where classical computers fail:

A supercomputer might be great at difficult tasks like sorting through a big database of protein sequences. But it will struggle to see the subtle patterns in that data that determine how those proteins behave.

Proteins are long strings of amino acids that become useful biological machines when they fold into complex shapes. Figuring out how proteins will fold is a problem with important implications for biology and medicine.

A classical supercomputer might try to fold a protein with brute force, leveraging its many processors to check every possible way of bending the chemical chain before arriving at an answer. But as the protein sequences get longer and more complex, the supercomputer stalls. A chain of 100 amino acids could theoretically fold in any one of many trillions of ways. No computer has the working memory to handle all the possible combinations of individual folds.

Quantum algorithms take a new approach to these sorts of complex problems -- creating multidimensional spaces where the patterns linking individual data points emerge. In the case of a protein folding problem, that pattern might be the combination of folds requiring the least energy to produce. That combination of folds is the solution to the problem.

Classical computers can not create these computational spaces, so they can not find these patterns. In the case of proteins, there are already early quantum algorithms that can find folding patterns in entirely new, more efficient ways, without the laborious checking procedures of classical computers. As quantum hardware scales and these algorithms advance, they could tackle protein folding problems too complex for any supercomputer.

How complexity stumps supercomputers

Quantum computers are built for complexityQuantum algorithms take a new approach to these sorts of complex problems -- creating multidimensional spaces where the patterns linking individual data points emerge. Classical computers can not create these computational spaces, so they can not find these patterns. In the case of proteins, there are already early quantum algorithms that can find folding patterns in entirely new, more efficient ways, without the laborious checking procedures of classical computers. As quantum hardware scales and these algorithms advance, they could tackle protein folding problems too complex for any supercomputer.

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What is Quantum Computing? | IBM

What Is Quantum Computing? | NVIDIA Blog

December 20, 2021 | Author: admin

Twenty-seven years before Steve Jobs unveiled a computer you could put in your pocket, physicist Paul Benioff published a paper showing it was theoretically possible to build a much more powerful system you could hide in a thimble a quantum computer.

Named for the subatomic physics it aimed to harness, the concept Benioff described in 1980 still fuels research today, including efforts to build the next big thing in computing: a system that could make a PC look in some ways quaint as an abacus.

Richard Feynman a Nobel Prize winner whose wit-laced lectures brought physics to a broad audience helped establish the field, sketching out how such systems could simulate quirky quantum phenomena more efficiently than traditional computers. So,

Quantum computing is a sophisticated approach to making parallel calculations, using the physics that governs subatomic particles to replace the more simplistic transistors in todays computers.

Quantum computers calculate using qubits, computing units that can be on, off or any value between, instead of the bits in traditional computers that are either on or off, one or zero. The qubits ability to live in the in-between state called superposition adds a powerful capability to the computing equation, making quantum computers superior for some kinds of math.

Using qubits, quantum computers could buzz through calculations that would take classical computers a loooong time if they could even finish them.

For example, todays computers use eight bits to represent any number between 0 and 255. Thanks to features like superposition, a quantum computer can use eight qubits to represent every number between 0 and 255, simultaneously.

Its a feature like parallelism in computing: All possibilities are computed at once rather than sequentially, providing tremendous speedups.

So, while a classical computer steps through long division calculations one at a time to factor a humongous number, a quantum computer can get the answer in a single step. Boom!

That means quantum computers could reshape whole fields, like cryptography, that are based on factoring what are today impossibly large numbers.

That could be just the start. Some experts believe quantum computers will bust through limits that now hinder simulations in chemistry, materials science and anything involving worlds built on the nano-sized bricks of quantum mechanics.

Quantum computers could even extend the life of semiconductors by helping engineers create more refined simulations of the quantum effects theyre starting to find in todays smallest transistors.

Indeed, experts say quantum computers ultimately wont replace classical computers, theyll complement them. And some predict quantum computers will be used as accelerators much as GPUs accelerate todays computers.

Dont expect to build your own quantum computer like a DIY PC with parts scavenged from discount bins at the local electronics shop.

The handful of systems operating today typically require refrigeration that creates working environments just north of absolute zero. They need that computing arctic to handle the fragile quantum states that power these systems.

In a sign of how hard constructing a quantum computer can be, one prototype suspends an atom between two lasers to create a qubit. Try that in your home workshop!

Quantum computing takes nano-Herculean muscles to create something called entanglement. Thats when two or more qubits exist in a single quantum state, a condition sometimes measured by electromagnetic waves just a millimeter wide.

Crank up that wave with a hair too much energy and you lose entanglement or superposition, or both. The result is a noisy state called decoherence, the equivalent in quantum computing of the blue screen of death.

A handful of companies such as Alibaba, Google, Honeywell, IBM, IonQ and Xanadu operate early versions of quantum computers today.

Today they provide tens of qubits. But qubits can be noisy, making them sometimes unreliable. To tackle real-world problems reliably, systems need tens or hundreds of thousands of qubits.

Experts believe it could be a couple decades before we get to a high-fidelity era when quantum computers are truly useful.

Predictions of when we reach so-called quantum computing supremacy the time when quantum computers execute tasks classical ones cant is a matter of lively debate in the industry.

The good news is the world of AI and machine learning put a spotlight on accelerators like GPUs, which can perform many of the types of operations quantum computers would calculate with qubits.

So, classical computers are already finding ways to host quantum simulations with GPUs today. For example, NVIDIA ran a leading-edge quantum simulation on Selene, our in-house AI supercomputer.

NVIDIA announced in the GTC keynote the cuQuantum SDK to speed quantum circuit simulations running on GPUs. Early work suggests cuQuantum will be able to deliver orders of magnitude speedups.

The SDK takes an agnostic approach, providing a choice of tools users can pick to best fit their approach. For example, the state vector method provides high-fidelity results, but its memory requirements grow exponentially with the number of qubits.

That creates a practical limit of roughly 50 qubits on todays largest classical supercomputers. Nevertheless weve seen great results (below) using cuQuantum to accelerate quantum circuit simulations that use this method.

Researchers from the Jlich Supercomputing Centre will provide a deep dive on their work with the state vector method in session E31941 at GTC (free with registration).

A newer approach, tensor network simulations, use less memory and more computation to perform similar work.

Using this method, NVIDIA and Caltech accelerated a state-of-the-art quantum circuit simulator with cuQuantum running on NVIDIA A100 Tensor Core GPUs. It generated a sample from a full-circuit simulation of the Google Sycamore circuit in 9.3 minutes on Selene, a task that 18 months ago experts thought would take days using millions of CPU cores.

Using the Cotengra/Quimb packages, NVIDIAs newly announced cuQuantum SDK, and the Selene supercomputer, weve generated a sample of the Sycamore quantum circuit at depth m=20 in record time less than 10 minutes, said Johnnie Gray, a research scientist at Caltech.

This sets the benchmark for quantum circuit simulation performance and will help advance the field of quantum computing by improving our ability to verify the behavior of quantum circuits, said Garnet Chan, a chemistry professor at Caltech whose lab hosted the work.

NVIDIA expects the performance gains and ease of use of cuQuantum will make it a foundational element in every quantum computing framework and simulator at the cutting edge of this research.

More:
What Is Quantum Computing? | NVIDIA Blog

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What is Quantum Computing? | IBM

What Is Quantum Computing? | NVIDIA Blog

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