Professor Manfred Minimair, Data Science, Seton Hall University

As part of its online M.S. in Data Science program, Seton Hall University in South Orange, New Jersey, has announced new courses in Text Mining and Machine Learning.

Seton Hall's master's program in Data Science is the first 100% online program of its kind in New Jersey and one of very few in the nation.

Quickly emerging as a critical field in a variety of industries, data science encompasses activities ranging from collecting raw data and processing and extracting knowledge from that data, to effectively communicating those findings to assist in decision making and implementing solutions. Data scientists have extensive knowledge in the overlapping realms of business needs, domain knowledge, analytics, and software and systems engineering.

"We're in the midst of a pivotal moment in history," said Professor Manfred Minimair, director of Seton Hall's Data Science program. "We've moved from being an agrarian society through to the industrial revolution and now squarely into the age of information," he noted. "The last decade has been witness to a veritable explosion in data informatics. Where once business could only look at dribs and drabs of customer and logistics dataas through a glass darklynow organizations can be easily blinded by the sheer volume of data available at any given moment. Data science gives students the tools necessary to collect and turn those oceans of data into clear and readily actionable information."

These tools will be provided by Seton Hall in new ways this spring, when Text Mining and Machine Learning make their debut.

Text MiningTaught by Professor Nathan Kahl, text mining is the process of extracting high-quality information from text, which is typically done by developing patterns and trends through means such as statistical pattern learning. Professor Nathan Kahl is an Associate Professor in the Department of Mathematics and Computer Science. He has extensive experience in teaching data analytics at Seton Hall University. Some of his recent research lies in the area of network analysis, another important topic which is also taught in the M.S. program.

Professor Kahl notes, "The need for people with these skills in business, industry and government service has never been greater, and our curriculum is specifically designed to prepare our students for these careers." According to EAB (formerly known as the Education Advisory Board), the national growth in demand for data science practitioners over the last two years alone was 252%. According to Glassdoor, the median base salary for these jobs is $108,000.

Machine LearningIn many ways, machine learning represents the next wave in data science. It is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. The course will be taught by Sophine Clachar, a data engineer with more than 10 years of experience. Her past research has focused on aviation safety and large-scale and complex aviation data repositories at the University of North Dakota. She was also a recipient of the Airport Cooperative Research Program Graduate Research Award, which fostered the development of machine learning algorithms that identify anomalies in aircraft data.

"Machine learning is profoundly changing our society," Professor Clachar remarks. "Software enhanced with artificial intelligence capabilities will benefit humans in many ways, for example, by helping design more efficient treatments for complex diseases and improve flight training to make air travel more secure."

Active Relationships with Google, Facebook, Celgene, Comcast, Chase, B&N and AmazonStudents in the Data Science program, with its strong focus on computer science, statistics and applied mathematics, learn skills in cloud computing technology and Tableau, which allows them to pursue certification in Amazon Web Services and Tableau. The material is continuously updated to deliver the latest skills in artificial intelligence/machine learning for automating data science tasks. Their education is bolstered by real world projects and internships, made possible through the program's active relationships with such leading companies as Google, Facebook, Celgene, Comcast, Chase, Barnes and Noble and Amazon. The program also fosters relationships with businesses and organizations through its advisory board, which includes members from WarnerMedia, Highstep Technologies, Snowflake Computing, Compass and Celgene. As a result, students are immersed in the knowledge and competencies required to become successful data science and analytics professionals.

"Among the members of our Advisory Board are Seton Hall graduates and leaders in the field," said Minimair. "Their expertise at the cutting edge of industry is reflected within our curriculum and coupled with the data science and academic expertise of our professors. That combination will allow our students to flourish in the world of data science and informatics."

Learn more about the M.S. in Data Science at Seton Hall

Read more here:
Seton Hall Announces New Courses in Text Mining and Machine Learning - Seton Hall University News & Events

(Left- Wilson To, Head of Worldwide Healthcare BD, Amazon Web Services (AWS) & Patrick Combes, Worldwide Technical Leader Healthcare and Life Sciences at Amazon Web Services (AWS)- Right)

Navigating the healthcare system is often a complex journey involving multiple physicians from hospitals, clinics, and general practices. At each junction, healthcare providers collect data that serve as pieces in a patients medical puzzle. When all of that data can be shared at each point, the puzzle is complete and practitioners can better diagnose, care for, and treat that patient. However, a lack of interoperability inhibits the sharing of data across providers, meaning pieces of the puzzle can go unseen and potentially impact patient health.

The Challenge of Achieving Interoperability

True interoperability requires two parts: syntactic and semantic. Syntactic interoperability requires a common structure so that data can be exchanged and interpreted between health information technology (IT) systems, while semantic interoperability requires a common language so that the meaning of data is transferred along with the data itself.This combination supports data fluidity. But for this to work, organizations must look to technologies like artificial intelligence (AI) and machine learning (ML) to apply across that data to shift the industry from a fee-for-service where government agencies reimburse healthcare providers based on the number of services they provide or procedures ordered to a value-based model that puts focus back on the patient.

The industry has started to make significant strides toward reducing barriers to interoperability. For example, industry guidelines and resources like the Fast Healthcare Interoperability Resources (FHIR) have helped to set a standard, but there is still more work to be done. Among the biggest barriers in healthcare right now is the fact there are significant variations in the way data is shared, read, and understood across healthcare systems, which can result in information being siloed and overlooked or misinterpreted.

For example, a doctor may know that a diagnosis of dropsy or edema may be indicative of congestive heart failure, however, a computer alone may not be able to draw that parallel. Without syntactic and semantic interoperability, that diagnosis runs the risk of getting lost in translation when shared digitally with multiple health providers.

Employing AI, ML and Interoperability in Healthcare

Change Healthcare is one organization making strides to enable interoperability and help health organizations achieve this triple aim. Recently, Change Healthcareannounced that it is providing free interoperability services that breakdown information silos to enhance patients access to their medical records and support clinical decisions that influence patients health and wellbeing.

While companies like Change Healthcare are creating services that better allow for interoperability, others like Fred Hutchinson Cancer Research Center and Beth Israel Deaconess Medical Center (BIDMC) are using AI and ML to further break down obstacles to quality care.

For example, Fred Hutch is using ML to help identify patients for clinical trials who may benefit from specific cancer therapies. By using ML to evaluate millions of clinical notes and extract and index medical conditions, medications, and choice of cancer therapeutic options, Fred Hutch reduced the time to process each document from hours, to seconds, meaning they could connect more patients to more potentially life-saving clinical trials.

In addition, BIDMC is using AI and ML to ensure medical forms are completed when scheduling surgeries. By identifying incomplete forms or missing information, BIDMC can prevent delays in surgeries, ultimately enhancing the patient experience, improving hospital operations, and reducing costs.

An Opportunity to Transform The Industry

As technology creates more data across healthcare organizations, AI and ML will be essential to help take that data and create the shared structure and meaning necessary to achieve interoperability.

As an example, Cernera U.S. supplier of health information technology solutionsis deploying interoperability solutions that pull together anonymized patient data into longitudinal records that can be developed along with physician correlations. Coupled with other unstructured data, Cerner uses the data to power machine learning models and algorithms that help with earlier detection of congestive heart failure.

As healthcare organizations take the necessary steps toward syntactic and semantic interoperability, the industry will be able to use data to place a renewed focus on patient care. In practice, Philips HealthSuite digital platform stores and analyses 15 petabytes of patient data from 390 million imaging studies, medical records and patient inputsadding as much as one petabyte of new data each month.

With machine learning applied to this data, the company can identify at-risk patients, deliver definitive diagnoses and develop evidence-based treatment plans to drive meaningful patient results. That orchestration and execution of data is the definition of valuable patient-focused careand the future of what we see for interoperability drive by AI and ML in the United States. With access to the right information at the right time that informs the right care, health practitioners will have access to all pieces of a patients medical puzzleand that will bring meaningful improvement not only in care decisions, but in patients lives.

About Wilson To, Global Healthcare Business Development lead at AWS & Patrick Combes, Global Healthcare IT Lead at AWS

Wilson To is the Head Worldwide Healthcare Business Development at Amazon Web Services (AWS). currently leads business development efforts across the AWS worldwide healthcare practice.To has led teams across startup and corporate environments, receiving international recognition for his work in global health efforts. Wilson joined Amazon Web Services in October 2016 to lead product management and strategic initiatives.

Patrick Combes is the Worldwide Technical Leader for Healthcare Life & Sciences at Amazon (AWS) where he is responsible for AWS world-wide technical strategy in Healthcare and Life Sciences (HCLS). Patrick helps develop and implement the strategic plan to engage customers and partners in the industry and leads the community of technically focused HCLS specialists within AWS wide technical strategy in Healthcare and Life Sciences (HCLS). Patrick helps develop and implement the strategic plan to engage customers and partners in the industry and leads the community of technically focused HCLS specialists within AWS.

View original post here:
Leveraging AI and Machine Learning to Advance Interoperability in Healthcare - - HIT Consultant

Emotional intelligence and in-car voice biometrics will create opportunities for OEMs and start-ups seeking new business models, finds Frost & Sullivan

High Investment in AI and Machine Learning will Enhance Automotive Digital Assistants by 2025

SANTA CLARA, Calif. , Jan. 16, 2020 /CNW/ --Digital assistants are rapidly emerging the primary input medium in human-machine interface (HMI), creating new opportunities for in-vehicle engagement services. Digital assistants offer a smart and intuitive way to operate features in the vehicle and assure minimum driver distraction. Presently, their capabilities are targeted at systems that deliver navigation and entertainment services in cars to enhance users' multimedia experience; however, future use-cases will focus on the safety and security of the vehicle and the driver.

"With the rising popularity of connected services such as traffic information and local search, digital assistants have become a key differentiator for original equipment manufacturers (OEMs). OEM-branded digital assistants will help automakers strengthen their brand and convert one-time sales into continual service-centric relationships," said Anubhav Grover , Research Analyst, Mobility. "OEMs are aiming to create their own branded digital assistants that will co-exist and integrate with third-party and tech-branded digital assistants. BMW has already launched its own Intelligent Personal Assistant (IPA), which uses Alexa to access Amazon's e-commerce and Cortana for Microsoft Office."

Frost & Sullivan's recent analysis, Strategic Analysis of Automotive Digital Assistants, Forecast to 2025, studies the competitive landscape, business models, and future focus areas of OEMs, digital assistant suppliers, and technology companies. It examines the trends in artificial intelligence integration and voice biometrics. Furthermore, it analyzes the different strategies adopted by OEMs, tier-I suppliers, and technology startups in North America , Europe , and China .

For further information on this analysis, please visit: http://frost.ly/3yk.

" North America is expected to continue leading the adoption of digital assistant solutions. Meanwhile, with higher penetration of long-term evolution (LTE) and greater production capacity in China , Asia-Pacific is expected to be a growth hub for OEMs," noted Grover. "Digital assistant developers are increasingly building strategic partnerships with telecom providers and communication module makers to enhance on-road safety and in-vehicle data-rich services. Flexible business models such as 'choice of network' for consumers will further improve customer retention and revenue generation."

For greater growth opportunities, digital assistant companies are likely to:

Strategic Analysis of Automotive Digital Assistants, Forecast to 2025,is part of Frost & Sullivan's global Automotive & Transportation Growth Partnership Service program.

About Frost & Sullivan

For over five decades, Frost & Sullivan has become world-renowned for its role in helping investors, corporate leaders and governments navigate economic changes and identify disruptive technologies, Mega Trends, new business models and companies to action, resulting in a continuous flow of growth opportunities to drive future success. Contact us: Start the discussion.

Strategic Analysis of Automotive Digital Assistants, Forecast to 2025K329-18

Story continues

See the original post:
High Investment in AI and Machine Learning will Enhance Automotive Digital Assistants by 2025 - Yahoo Finance

Google and Machine Learning can be regarded as two things that are always on the run to make the world a better place to live. While the later is governed by Google itself, the company, on Monday, showedtheir research, which is based on machine learning method and takes help from Radar images. By doing so, Google hopes to accurately forecast rainstorms and other weather events that can arise suddenly.

Prior to this, predicting the short-term weather events was more of a tough challenge. The numerical methods that record atmospheric dynamics, ocean effects, thermal radiation, and other processes become limited because of the computational resources. For example, even giants like the National Oceanic and Atmospheric Administration (NOAA) gets to collect a data of around 100 terabytes per day.

The numerical method is also slow as it takes a number of hours to compute one round of forecast. In more common scenarios, experts get to commute the forecast after 6 hours, which further leads them to only 3-4 runs a day and 6+ hour old data of forecasts.

By tackling the similar issues, the search giant hopes to help people when it comes to making the important immediate decisions like traffic routing, logistics, or evacuation planning as well.

With their method, Google is planning to use radar data and deal with weather prediction as a computer vision problem. The team working at it has set up a neural network that will learn about atmospheric physics with real examples and with no inclusion of previous knowledge regarding how the atmosphere operates.

There is no doubt in the fact that Googles machine learning powered rain forecasting method outclasses all of the three popular forecasting models that have been used till date. The predictions coming out are instantaneous and hence the forecasts turn out to be based on fresh data for short term.

Google is also looking forward to join its system together with High Resolution Rapid Refresh (HRRR) with an aim to make long-term forecasts better as well since HRRR would go with 3D physical model.

Photo: 400tmax via Getty Images

Read next: Google Maps Introduces Hyperspace Animation When Switching Between Planets

Follow this link:
Google Is Now Able To Do More Accurate Rain "Nowcasting" With Machine Learning - Digital Information World

Geotab CEO Neil Cawse stresses the importance of the company's open platform that gives fleet managers the ability to customize the software options they need to successfully run their businesses.

So it is no surprise that during theGeotab Connect 2020conference in San Diego, the company announced numerous new integrations and offerings for theGeotab Marketplace.

On Jan. 15, Geotab andEleosjointly rolled out Unify, an integrated solution that offers a fleet management system, driver workflow platform and electronic logging device (ELD).

Unify is the latest example of Geotabs mission to provide fleet owners in the heavy-duty truck market with open customizable software and industry-leading hardware that will enable them to maximize productivity, safety and profitability, said Scott Sutarik, vice president of commercial vehicle solutions at Geotab.

Eleos offers mobile workforce management solutions to help drivers maximize productivity throughout the day. The company is based in Greenville, S.C.

Unify allows fleet managers to leverage pre-built components to craft a custom mobile app for fleet drivers, giving them the control they want and the flexibility they need, said Kevin Survance, CEO at Eleos.

Also during the conference, Geotab said that theDrivewyzePreClear weigh station bypass service is now available on the Geotab Marketplace. Drivewyze helps fleets and drivers save time by providing bypass at more than 800 sites in 47 states and Canadian provinces.

We are confident that this partnership will help make it easier for safety-focused commercial vehicle operators to access the largest electronic pre-screening and clearance services network in Canada and the United States without the need to install additional equipment in their trucks, said Brian Heath, president of Drivewyze.

Trimbleannounced its video intelligence solution is now part of the Geotab Marketplace.This offering includes a two-channel DVR and forward-facing camera, with the option to add secondary camera.

Separately, Trimblesaid last week it signed a definitive agreement to acquire Kuebix, a leading transportation management system provider.

Bendix Commercial Vehicle Systemsannounced the addition of Geotab to the list of telematics platforms that can support SafetyDirect.

Safety Direct is aweb portal that combines a video-based driver safety solution with an active safety system.All fleets will have availability to this offering by the middle of this year.

Geotab also announced the availability of the Geotab Integrated Solution forGeneral Motors. The solution allows fleet managers to access their compatible vehicle data within the MyGeotab platform via a factory-fit, GM-engineered embedded OnStar module.

No installation or additional hardware is required to leverage the new offering. With the Geotab Integrated Solution for GM, compatible fleets will be equipped with advanced telematics tools that provide a deep dive into vehicle information such as fuel usage, vehicle health and driver behavior, said Sherry Calkins, Geotabs vice president of strategic partners.

In October, Geotab announced a similar agreement withFord Motors.

As the event opened on Jan. 14, Geotab unveiled anintegration withLytx, which creates a seamless experience within a single interface, allowing fleet managers to browse video and data from DriveCam event recorders through the Geotab platform.

See the rest here:
BlackBerry combines AI and machine learning to create connected fleet security solution - Fleet Owner

Originally published in EthicalSystems.org, December 1, 2019

In 2004, when a webcam was relatively unheard-of tech, Mark Newman knew that it would be the future of hiring. One of the first things the 20-year old did, after getting his degree in international business, was to co-found HireVue, a company offering a digital interviewing platform. Business trickled in. While Newman lived at his parents house, in Salt Lake City, the company, in its first five years, made just $100,000 in revenue. HireVue later received some outside capital, expanded and, in 2012, boasted some 200 clientsincluding Nike, Starbucks, and Walmartwhich would pay HireVue, depending on project volume, between $5,000 and $1 million. Recently, HireVue, which was bought earlier this year by the Carlyle Group, has become the source of some alarm, or at least trepidation, for its foray into the application of artificial intelligence in the hiring process. No longer does the company merely offer clients an asynchronous interviewing service, a way for hiring managers to screen thousands of applicants quickly by reviewing their video interview HireVue can now give companies the option of letting machine-learning algorithms choose the best candidates for them, based on, among other things, applicants tone, facial expressions, and sentence construction.

If that gives you the creeps, youre not alone. A 2017 Pew Research Center report found few Americans to be enthused, and many worried, by the prospect of companies using hiring algorithms. More recently, around a dozen interviewees assessed by HireVues AI told the Washington Post that it felt alienating and dehumanizing to have to wow a computer before being deemed worthy of a companys time. They also wondered how their recording might be used without their knowledge. Several applicants mentioned passing on the opportunity because thinking about the AI interview, as one of them told the paper, made my skin crawl. Had these applicants sat for a standard 30-minute interview, comprised of a half-dozen questions, the AI could have analyzed up to 500,000 data points. Nathan Mondragon, HireVues chief industrial-organizational psychologist, told the Washington Post that each one of those points become ingredients in the persons calculated score, between 1 and 100, on which hiring decisions candepend. New scores are ranked against a store of traitsmostly having to do with language use and verbal skillsfrom previous candidates for a similar position, who went on to thrive on the job.

HireVue wants you to believe that this is a good thing. After all, their pitch goes, humans are biased. If something like hunger can affect a hiring managers decisionlet alone classism, sexism, lookism, and other ismsthen why not rely on the less capricious, more objective decisions of machine-learning algorithms? No doubt some job seekers agree with the sentiment Loren Larsen, HireVues Chief Technology Officer, shared recently with theTelegraph: I would much prefer having my first screening with an algorithm that treats me fairly rather than one that depends on how tired the recruiter is that day. Of course, the appeal of AI hiring isnt just about doing right by the applicants. As a 2019 white paper, from the Society for Industrial and Organizational Psychology, notes, AI applied to assessing and selecting talent offers some exciting promises for making hiring decisions less costly and more accurate for organizations while also being less burdensome and (potentially) fairer for job seekers.

Do HireVues algorithms treat potential employees fairly? Some researchers in machine learning and human-computer interaction doubt it. Luke Stark, a postdoc at Microsoft Research Montreal who studies how AI, ethics, and emotion interact, told the Washington Post that HireVues claimsthat its automated software can glean a workers personality and predict their performance from such things as toneshould make us skeptical:

Systems like HireVue, he said, have become quite skilled at spitting out data points that seem convincing, even when theyre not backed by science. And he finds this charisma of numbers really troubling because of the overconfidence employers might lend them while seeking to decide the path of applicants careers.

The best AI systems today, he said, are notoriously prone to misunderstanding meaning and intent. But he worried that even their perceived success at divining a persons true worth could help perpetuate a homogenous corporate monoculture of automatons, each new hire modeled after the last.

Eric Siegel, an expert in machine learning and author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, echoed Starks remarks. In an email, Siegel told me, Companies that buy into HireVue are inevitably, to a great degree, falling for that feeling of wonderment and speculation that a kid has when playing with a Magic Eight Ball. That, in itself, doesnt mean HireVues algorithms are completely unhelpful. Driving decisions with data has the potential to overcome human bias in some situations, but also, if not managed correctly, could easily instill, perpetuate, magnify, and automate human biases, he said.

To continue reading this article click here.

Follow this link:
The Problem with Hiring Algorithms - Machine Learning Times - machine learning & data science news - The Predictive Analytics Times

Shelf Analytics SoftServe AI Tool and Mixed Reality Shopping Assistant Leverage the Power of Mixed Reality, Machine Learning, and Artificial Intelligence for Real-Time Inventory and Marketing Insights

SoftServe, a leading digital authority and consulting company, is pioneering retail innovations that heighten customer engagement and enable vendors to make informed, data-driven business decisions. The company has released two new prototype accelerators,PlanogramandSmart Shopping Assistant, that employ mixed reality (XR), machine learning (ML), and artificial intelligence (AI) technology to facilitate a new level of understanding of inventory levels, buying trends, demand prediction, and shifting consumer preferences. With these, retailers can see forecasted outcomes of AI recommended business decisions before implementation and ensure they get the right product in front of the right customer at the right time.

Read More: Technology Watch: Dont Miss These CES 2020 Themes And Sessions

Planogram and Smart Shopping Assistant are retail accelerators that bring real-time insights and data to businesses so they can create compelling and lasting shopping experiences for customers, while significantly optimizing operational efficiency, saidValentyn Kropov, VP of client success, retail at SoftServe. These solutions allow vendors to predict and analyze demand while understanding the ever-changing preferences of their customers to ensure optimal product placements and begin shopper engagement before they even enter a store.

Planogram is a shelf analytics tool for retail execution strategy that tracks sales and predicts shelf profit changes, instantly monitors and receives feedback, supports more accurate inventory measurements than manual store checks, and gathers shelf data to identify trends and make strategic business recommendations. Through AI data-driven algorithms retailers can see the forecasted inventory and revenue outcomes of a recommended course of action prior to implementation.

Read More: The Future of Works Most Crucial Component: Artificial Intelligence

Smart Shopping Assistant is a solution that creates an omnichannel customer experience and allows vendors to understand the needs of the customer and provide new experiences to them through emerging technologies including XR and ML to increase revenue, grow customer loyalty, and improve marketing effectiveness.

With Smart Shopping Assistant, retailers can:

Read More: CES 2020: SecuX Launches the Worlds First All-in-One Cryptocurrency Retail Payment Solution Built with Blockchain Hardware Wallet

Read this article:
SoftServe Mixed Reality and AI Accelerators of Digital Retail Innovation - AiThority

The research report on the Machine learning market provides the brief introduction of the Machine learning industry until 2027. This report provides information on the Machine learning market for business growth, the marketing strategy to be implemented and a chronicle of costs and revenues over the next few years and a discussion of the main effective players in this market. Evolution of trends and market dynamics, mapping of opportunities in terms of technological breakthroughs with contributions from trade specialists. To calculate the market size, the report considers the revenues generated by the analysis of Machine learning distributors worldwide.

This study also explores the Machine learning market status, market share, growth rate, future trends, market drivers, opportunities and challenges, risks and barriers to entry, sales channels, distributors and analysis of Porters five strengths.

Download the free PDF brochure

Scope of the Machine learning market:

The global Machine learning market is expected to grow at a CAGR of around xx% over the next five years, reaching $ xx million in 2027, up from $ xx million in 2019, according to a new study.

This report focuses on Machine learning in the global market, particularly in North America, Europe and Asia-Pacific, South America, the Middle East and Africa. This report ranks the market based on manufacturers, regions, type and application.

The report provides information on the following pointers:

Market penetration: complete information on the product portfolios of the main players in the Machine learning market.

Product development: a complete overview of upcoming technologies, RandD activities and market launches

Competitive assessment: detailed assessment of market strategies, geographic and industrial segments of the main market players

Market development: comprehensive information on developing markets. This report analyzes the market of several segments across geographic areas

Market expansion: detailed information on new products, untapped geographic areas, modern developments and investments in the Machine learning market.

Outside of this Machine learning market, development plans and policies, marketing terminologies, manufacturing protocols, current trends, market dynamics and classification have been briefly explained in this report. The team of researchers and analysts presents the precise statistics and analytical data of the reader in the report in a simple way by means of graphs, diagrams, pie charts and other pictorial illustrations.

Machine learning market: research methodology

The research methodology is a combination of secondary research, primary research and expert reviews. Secondary research includes sources such as press releases from annual company reports and industry-related research papers. Other sources include industry magazines, trade journals, government websites and associations which can also be consulted to gather accurate data on the possibilities for business expansion in the global Machine learning market.

Inquire here Get customization and check discount for report:https://www.worldwidemarketreports.com/quiry/251159

Here is the original post:
Machine learning Market 2020 Global Analysis, Research, Review, Applications and Forecast to 2027 Dagoretti News - Dagoretti News

Were only a few weeks into the new year, but already were seeing signs that automated machine learning modeling, sometimes known asautoML, is rising to a new plateau of sophistication.

Specifically, it appears that a promising autoML approach known as neural architecture search will soon become part of data scientists core toolkits. This refers to tools and methodologies for automating creation of optimized architectures for convolutional, recurrent, and other neural network architectures at the heart of AIs machine learning models.

Neural architecture search tools optimize the structure, weights, and hyperparameters of a machine learning models algorithmic neurons in order to make them more accurate, speedy, and efficient in performing data-driven inferences. This technology has only recently begun to emerge from labs devoted to basic research in AI tools and techniques. Theresearch literatureshows that neural architecture search tools have already outperformed manually designed neural nets in many AI R&D projects.

Go here to read the rest:
Democratizing the optimization of AI's arcane neural networks - InfoWorld