Category Archives: Machine Learning

CHICAGO, April 26, 2022 (GLOBE NEWSWIRE) -- VelocityEHS,the global leader in cloud-based environmental, health, safety (EHS) and environmental, social, and corporate governance (ESG) software, announced the latest additions to the Accelerate Platform, including a highly anticipated new feature,Active Causes & Controls, to its award-winning Industrial Ergonomics Solution. Rooted in ActiveEHS the proprietary VelocityEHS methodology that leverages AI & machine learning to help non-experts produce expert-level results this enhancement kicks off a new era in the prevention of musculoskeletal disorders (MSDs).

Designed, engineered, and embedded with expertise by an unmatched group of board-certified ergonomists, the ActiveEHS powered Active Causes and Controls feature helps companies reduce training time, maintain process consistency across locations, and focus on implementing changes that maximize business results. Starting with the industrys best sensorless, motion-capture technology, which performs ergonomics assessments faster, easier, and more accurately than any human could, the solution then guides users through suggested root causes and job improvement controls. Recommendations are based on AI and machine learning insights fed by data collected from hundreds of global enterprise customers and millions of MSD risk data points.

The result is an unparalleled opportunity to prevent MSD risk, reduce overall injury costs, drive productivity, and provide employees with quality-of-life changing improvements in the workplace.

These are exciting times for anyone who cares about EHS and ESG, said John Damgaard, CEO of VelocityEHS. While its true, the job of a C-suite executive or EHS professional has never been more challenging and complex; its also true that leaders have never had this kind of advanced, highly usable, and easy-to-deploy technology at their fingertips. Ergonomics is just the start; ActiveEHS will transform how we think about health, safety, and sustainability going forward. It is the key to evolving from a reactive documentation and compliance mindset to a proactive continuous improvement cycle of prediction, intervention, and outcomes.

MSDs are a major burden on workers and a huge cost to employers.According to the Bureau of Labor Statistics, for employers in the U.S. private sector alone, MDSs cause more than 300,000 days away from work and per OSHA, are responsible for $20 billioneveryyear in workers compensation claims.

Also Announced Today: New Training & Learning Content, Enhancements to Automated Utility Data Management, and Improved workflows for the Control of Work Solution.

The VelocityEHS Safety Solution, which includes robust Training & Learning capabilities, is undergoing a major expansion of its online training content library. To enable companies to meet more of their training responsibilities, the training content library is growing from approximately 100 courses to over 750. They will be available in multiple languages, including 300+ courses in Spanish. The new content will feature microlearning modules, which have gained popularity in recent years as workers prefer shorter, easily digestible training sessions. This results in less time in front of the screen for workers, while employers report better engagement and overall retention of the material.

The VelocityEHS Climate Solution continues to capitalize on the VelocityEHS partnership with Urjanet the engine behind the recently announced Automated Utility Data Management capabilities. Now, in addition to saving time and reducing costs related to the collection of utility data, users can automatically port their energy, gas and water usage data into the VelocityEHS Climate Solution to perform GHG calculations and report on Scope 1,2, and 3 emissions, without any manual effort.

The Companys Control of Work Solution boasts a new streamlined navigation and enhanced functionality that allows customers to add new, pre-approved roles for improved compliance and approval workflows.

Industrial Ergonomics, Safety, Climate, and Control of Work solutions are all part of the VelocityEHS AcceleratePlatform, which delivers best-in-class performance in the areas of health, safety, risk, ESG, and operational excellence. Backed by the largest global software community of EHS experts and thought leaders, the software drives expert processes so every team member can produce outstanding results.

For more information about VelocityEHS and its complete offering of award-winning software solutions, visit http://www.EHS.com.

AboutVelocityEHS Trusted by more than 19,000 customers worldwide, VelocityEHS is the global leader in true SaaS enterprise EHS technology. Through the VelocityEHS Accelerate Platform, the company helps global enterprises drive operational excellence by delivering best-in-class capabilities for health, safety, environmental compliance, training, operational risk, and environmental, social, and corporate governance (ESG). The VelocityEHS team includes unparalleled industry expertise, with more certified experts in health, safety, industrial hygiene, ergonomics, sustainability, the environment, AI, and machine learning than any EHS software provider. Recognized by the EHS industrys top independent analysts as a Leader in the Verdantix 2021 Green Quadrant AnalysisVelocityEHS is committed to industry thought leadership and to accelerating the pace of innovation through its software solutions and vision.

VelocityEHS is headquartered in Chicago, Illinois, with locations in Ann Arbor, Michigan; Tampa, Florida; Oakville, Ontario; London, England; Perth, Western Australia; and Cork, Ireland. For more information, visit http://www.EHS.com.

Media Contact Brad Harbaugh 312.881.2855 bharbaugh@ehs.com

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VelocityEHS Industrial Ergonomics Solution Harnesses AI and Machine Learning to Drive ... - KULR-TV

Gimme, whose technology helps foodservice and grocery store delivery operators automate merchandising, announced they were named as a top machine learning company in Georgia by Data Magazine. The rankings were based upon four categories including innovation, growth, management and societal impact. The magazine showcased its top picks for the best Georgia-based machine learning companies, noting these startups and companies are taking a variety of approaches to innovating the machine learning industry, but are all exceptional companies well worth a follow.

Gimme has been dedicated to investing and developing our machine learning and AI infrastructure, so to be recognized for this innovation is exciting, said Cory Hewett, co-founder and CEO of Gimme. Our plans for 2022 include continued accelerating of our AI progress with tools like vendor receipt import from pictures, stock-out detection from visit photos, and AI schedule suggestions. These new tools along with others will expand our use of AI across our platform, increasing speed in our data handling.

Gimme's technology provides management for operators of grocery, convenience, vending machines, micro markets and office coffee. Gimmes use of artificial intelligence, computer vision and machine learning technologies impacts not only its own products and services but also how the unattended retail industry operates. The technology provides machine status data to help operators focus on cash accountability and inventory tracking to reduce stockouts, accelerate warehousing and restocking, and streamline product planning. The companys hardware product, the Gimme Key, is now the #1 wireless DEX adapter for direct store delivery, using Bluetooth Low Energy technology and replacing previous outdated legacy handhelds.

To learn more about the Gimmes management platform, visitwww.vms.aior for grocery delivery platform atwww.dsd.ai.

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Gimme named top machine learning company in Georgia - Vending Market Watch

London, Chicago, April 26, 2022 (GLOBE NEWSWIRE) -- Control Risks, the specialist risk consultancy, today announced it is expanding its technology offering with Reveal, the global provider of the leading AI-powered eDiscovery and investigations platform. Reveal uses adaptive AI, behavioral analysis, and pre-trained AI model libraries to help uncover connections and patterns buried in large volumes of unstructured data.

Corporate legal and compliance teams, and their outside counsel, are looking to technology to better understand data, reduce risks and costs, and extract key insights faster across an ever-increasing volume and variety of data. We look forward to leveraging Reveals data visualization, AI and machine learning functionality to drive innovation with our clients, said Brad Kolacinski, Partner, Control Risks.

Control Risks will leverage the platform globally to unlock intelligence that will help clients mitigate risks across a range of areas including litigation, investigations, compliance, ethics, fraud, human resources, privacy and security.

We work with clients and their counsel on large, complex, cross-border forensics and investigations engagements. It is no secret that AI, ML and analytics are now required tools in matters where we need to sift through enormous quantities of data and deliver insights to clients efficiently, says Torsten Duwenhorst, Partner, Control Risks. Offering the full range of Reveals capabilities globally will benefit our clients enormously.

As we continue to expand the depth and breadth of Reveals marketplace offerings, we are excited to partner with Control Risks, a demonstrated leader in security, compliance and organizational resilience offerings that are more critical now than ever, said Wendell Jisa, Reveals CEO. By taking full advantage of Reveals powerful platform, Control Risks now has access to the industrys leading SaaS-based, AI-powered technology stack, helping them and their clients solve their most complex problems with greater intelligence.

For more information about Reveal-Brainspace and its AI platform for legal, enterprise and government organizations, visit http://www.revealdata.com.

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About Control Risks

Control Risks is a specialist global risk consultancy that helps to create secure, compliant and resilient organizations in an age of ever-changing risk. Working across disciplines, technologies and geographies, everything we do is based on our belief that taking risks is essential to our clients success. We provide our clients with the insight to focus resources and ensure they are prepared to resolve the issues and crises that occur in any ambitious global organization. We go beyond problem-solving and provide the insights and intelligence needed to realize opportunities and grow. Control Risks will initially provide Reveal-Brainspace in the US, Europe and Asia Pacific. Visit us online at http://www.controlrisks.com.

About Reveal

Reveal, with Brainspace technology, is a global provider of the leading AI-powered eDiscovery platform. Fueled by powerful AI technology and backed by the most experienced team of data scientists in the industry, Reveals cloud-based software offers a full suite of eDiscovery solutions all on one seamless platform. Users of Reveal include law firms, Fortune 500 corporations, legal service providers, government agencies and financial institutions in more than 40 countries across five continents. Featuring deployment options in the cloud or on-premise, an intuitive user design and multilingual user interfaces, Reveal is modernizing the practice of law, saving users time and money and offering them a competitive advantage. For more information, visit http://www.revealdata.com.

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Control Risks Taps Reveal-Brainspace to Bolster its Suite of Analytics, AI and Machine Learning Capabilities - GlobeNewswire

The proportion of ship equipment supply, product and services companies hiring for machine learning related positions rose in March 2022 compared with the equivalent month last year, with 20.6% of the companies included in our analysis recruiting for at least one such position.

This latest figure was higher than the 16.2% of companies who were hiring for machine learning related jobs a year ago but a decrease compared to the figure of 22.6% in February 2022.

When it came to the rate of all job openings that were linked to machine learning, related job postings dropped in March 2022, with 0.4% of newly posted job advertisements being linked to the topic.

This latest figure was a decrease compared to the 0.5% of newly advertised jobs that were linked to machine learning in the equivalent month a year ago.

Machine learning is one of the topics that GlobalData, from whom our data for this article is taken, have identified as being a key disruptive force facing companies in the coming years. Companies that excel and invest in these areas now are thought to be better prepared for the future business landscape and better equipped to survive unforeseen challenges.

Our analysis of the data shows that ship equipment supply, product and services companies are currently hiring for machine learning jobs at a rate lower than the average for all companies within GlobalData's job analytics database. The average among all companies stood at 1.3% in March 2022.

GlobalData's job analytics database tracks the daily hiring patterns of thousands of companies across the world, drawing in jobs as they're posted and tagging them with additional layers of data on everything from the seniority of each position to whether a job is linked to wider industry trends.

Ship Windows, Glass and Frame Constructions

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Machine learning hiring levels in the ship industry rose in March 2022 - Ship Technology

Research in the field of machine learning and AI, now a key technology in practically every industry and company, is far too voluminous for anyone to read it all. This column aims to collect some of the most relevant recent discoveries and papers particularly in, but not limited to, artificial intelligence and explain why they matter.

This week in AI, scientists conducted a fascinating experiment to predict how market-driven platforms like food delivery and ride-hailing businesses affect the overall economy when theyre optimized for different objectives, like maximizing revenue. Elsewhere, demonstrating the versatility of AI, a team hailing from ETH Zurich developed a system that can read tree heights from satellite images, while a separate group of researchers tested a system to predict a startups success from public web data.

The market-driven platform work builds on Salesforces AI Economist, an open source research environment for understanding how AI could improve economic policy. In fact, some of the researchers behind the AI Economist were involved in the new work, which was detailed in a study originally published in March.

As the co-authors explained to TechCrunch via email, the goal was to investigate two-sided marketplaces like Amazon, DoorDash, Uber and TaskRabbit that enjoy larger market power due to surging demand and supply. Using reinforcement learning a type of AI system that learns to solve a multi-level problem by trial and error the researchers trained a system to understand the impact of interactions between platforms (e.g. Lyft) and consumers (e.g. riders).

Image Credit: Xintong Wang et al.

We use reinforcement learning to reason about how a platform would operate under different design objectives [Our] simulator enables evaluating reinforcement learning policies in diverse settings under different objectives and model assumptions, the co-authors told TechCrunch via email. We explored a total of 15 different market settings i.e. a combination of market structure, buyer knowledge about sellers, [economic] shock intensity and design objective.

Using their AI system, the researchers arrived at the conclusion that a platform designed to maximize revenue tends to raise fees and extract more profits from buyers and sellers during economic shocks at the expense of social welfare. When platform fees are fixed (e.g. due to regulation), they found a platforms revenue-maximizing incentive generally aligns with the welfare considerations of the overall economy.

The findings might not be Earth-shattering, but the coauthors believe the system which they plan to open source could provide a foundation for either a business or policymaker to analyze a platform economy under different conditions, designs and regulatory considerations. We adopt reinforcement learning as a methodology to describe strategic operations of platform businesses that optimize their pricing and matching in response to changes in the environment, either the economic shock or some regulation, they added. This may give new insights about platform economies that go beyond this work or those that can be generated analytically.

Turning our attention from platform businesses to the venture capital that fuels them, researchers hailing from Skopai, a startup that uses AI to characterize companies based on criteria like technology, market and finances, claims to be able to predict the ability of a startup to attract investments using publicly available data. Relying on data from startup websites, social media, and company registries, the co-authors say that they can obtain prediction results comparable to the ones making also use of structured data available in private databases.

Image Credits: Mariia Garkavenko et al.

Applying AI to due diligence is nothing new. Correlation Ventures, EQT Ventures and SignalFire are among the firms currently using algorithms to inform their investments. Gartner predicts that 75% of VCs will use AI to make investment decisions by 2025, up from less than 5% today. But while some see the value in the technology, dangers lurk beneath the surface. In 2020, Harvard Business Review (HBR) found that an investment algorithm outperformed novice investors but exhibited biases, for example frequently selecting white and male entrepreneurs. HBR noted that this reflects the real world, highlighting AIs tendency to amplify existing prejudices.

In more encouraging news, scientists at MIT, alongside researchers at Cornell and Microsoft, claim to have developed a computer vision algorithm STEGO that can identify images down to the individual pixel. While this might not sound significant, its a vast improvement over the conventional method of teaching an algorithm to spot and classify objects in pictures and videos.

Traditionally, computer vision algorithms learn to recognize objects (e.g. trees, cars, tumors, etc.) by being shown many examples of the objects that have been labeled by humans. STEGO does away with this time-consuming, labor-intensive workflow by instead applying a class label to each pixel in the image. The system isnt perfect it sometimes confuses grits with pasta, for example but STEGO can successfully segment out things like roads, people and street signs, the researchers say.

On the topic of object recognition, it appears were approaching the day when academic work like DALL-E 2, OpenAIs image-generating system, becomes productized. New research out of Columbia University shows a system called Opal thats designed to create featured images for news stories from text descriptions, guiding users through the process with visual prompts.

Image Credits: Vivian Liu et al.

When they tested it with a group of users, the researchers said that those who tried Opal were more efficient at creating featured images for articles, creating over two times more usable results than users without. Its not difficult to imagine a tool like Opal eventually making its way into content management systems like WordPress, perhaps as a plugin or extension.

Given an article text, Opal guides users through a structured search for visual concepts and provides pipelines allowing users to illustrate based on an articles tone, subjects and intended illustration style, the co-authors wrote. [Opal] generates diverse sets of editorial illustrations, graphic assets and concept ideas.

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Deep Science: AI simulates economies and predicts which startups receive funding - TechCrunch

United State-Machine learning is a process of data analysis that comprises of statistical data analysis performed to derive desired predictive output without the implementation of explicit programming. It is designed to incorporate the functionalities of artificial intelligence (AI) and cognitive computing involving a series of algorithms and is used to understand the relationship between datasets to obtain a desired output. Machine learning as a service (MLaaS) incorporates range of services that offer machine learning tools through cloud computing services.

The global machine learning as a service market was valued at $571 million in 2016, and is projected to reach $5,537 million by 2023, growing at a CAGR of 39.0% from 2017 to 2023.

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Market Statistics:

The file offers market sizing and forecast throughout 5 primary currencies USD, EUR GBP, JPY, and AUD. It helps corporation leaders make higher choices when foreign money change records are available with ease. In this report, the years 2020 and 2021 are regarded as historic years, 2020 as the base year, 2021 as the estimated year, and years from 2022 to 2030 are viewed as the forecast period.

Increased penetration of cloud-based solutions, growth associated with artificial intelligence and cognitive computing market, and increase in market for prediction solutions drive the market growth. In addition, growth in IT expenditure in emerging nations and technological advancements for workflow optimization fuel the demand for advanced analytical systems driving the machine learning as a service market growth. However, dearth of trained professionals is expected to impede the machine learning as a service market share. Furthermore, increased application areas and growth of IoT is expected to create lucrative opportunities for machine learning as a service market growth.

The global machine learning as a service market is segmented based on component, organization size, end-use industry, application, and geography. The component segment is bifurcated into software and services. Based on organization size, it is divided into large enterprises and small & medium enterprises. The application segment is categorized into marketing & advertising, fraud detection & risk management, predictive analytics, augmented & virtual reality, natural language processing, computer vision, security & surveillance, and others. On the basis of end-use industry, it is classified into aerospace & defense, IT & telecom, energy & utilities, public sector, manufacturing, BFSI, healthcare, retail, and others. By geography, the machine learning as a service market is analyzed across North America, Europe, Asia-Pacific, and LAMEA.

Key players that operate in the machine learning as a service market are Google Inc., SAS Institute Inc., FICO, Hewlett Packard Enterprise, Yottamine Analytics, Amazon Web Services, BigML, Inc., Microsoft Corporation, Predictron Labs Ltd., and IBM Corporation.

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KEY BENEFITS FOR STAKEHOLDERS

This report provides an overview of the trends, structure, drivers, challenges, and opportunities in the global machine learning as a service market.Porters Five Forces analysis highlights the potential of buyers & suppliers, and provides insights on the competitive structure of the market to determine the investment pockets.Current and future trends adopted by the key market players are highlighted to determine overall competitiveness.The quantitative analysis of the machine learning as a service market growth from 2017 to 2023 is provided to elaborate the market potential.

According to Statista, as of 2021 data, the United States held over ~36% of the global market share for information and communication technology (ICT). With a market share of 16%, the EU ranked second, followed by 12%, China ranked third. In addition, according to forecasts, the ICT market will reach more than US$ 6 trillion in 2021 and almost US$ 7 trillion by 2027. In todays society, continuous growth is another reminder of how ubiquitous and crucial technology has become. Over the next few years, traditional tech spending will be driven mainly by big data and analytics, mobile, social, and cloud computing.

This report analyses the global primary production, consumption, and fastest-growing countries in the Information and Communications Technology (ICT) market. Also included in the report are prominent and prominent players in the global Information and Communications Technology Market (ICT).

A release on June 8th, 2021, by the Bureau and Economic Analysis and U.S. The Census Bureau reports the recovery of the U.S. market. The report also described the recovery of U.S. International Trade in July 2021.In April 2021, exports in the country reached $300 billion, an increase of $13.4 billion. In April 2021, imports amounted to $294.5 billion, increasing by $17.4 billion. COVID19 is still a significant issue for economies around the globe, as evidenced by the year-over-year decline in exports in the U.S. between April 2020 and April 2021 and the increase in imports over that same period of time. The market is clearly trying to recover. Despite this, it means there will be a direct impact on the Healthcare/ICT/Chemical industries.

Key Market Segments

By Component

SoftwareServicesBy Organization Size

Large EnterprisesSmall & Medium Enterprises

By End-Use Industry

Aerospace & DefenceIT & TelecomEnergy & UtilitiesPublic sectorManufacturingBFSIHealthcareRetailOthers

By Application

Marketing & AdvertisingFraud Detection & Risk ManagementPredictive analyticsAugmented & Virtual realityNatural Language processingComputer visionSecurity & surveillanceOthers

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By Geography

North AmericaU.S.CanadaMexicoEuropeUKFranceGermanyRest of EuropeAsia-PacificChinaJapanIndiaRest of Asia-PacificLAMEALatin AmericaMiddle EastArica

Key players profiled in the report

Google Inc.SAS Institute Inc.FICOHewlett Packard EnterpriseYottamine AnalyticsAmazon Web ServicesBigML, Inc.Microsoft CorporationPredictron Labs Ltd.IBM Corporation

Table of Content:

What is the goal of the report?

Key Questions Answered in the Market Report

How did the COVID-19 pandemic impact the adoption of by various pharmaceutical and life sciences companies? What is the outlook for the impact market during the forecast period 2021-2030? What are the key trends influencing the impact market? How will they influence the market in short-, mid-, and long-term duration? What is the end user perception toward? How is the patent landscape for pharmaceutical quality? Which country/cluster witnessed the highest patent filing from January 2014-June 2021? What are the key factors impacting the impact market? What will be their impact in short-, mid-, and long-term duration? What are the key opportunities areas in the impact market? What is their potential in short-, mid-, and long-term duration? What are the key strategies adopted by companies in the impact market? What are the key application areas of the impact market? Which application is expected to hold the highest growth potential during the forecast period 2021-2030? What is the preferred deployment model for the impact? What is the growth potential of various deployment models present in the market? Who are the key end users of pharmaceutical quality? What is their respective share in the impact market? Which regional market is expected to hold the highest growth potential in the impact market during the forecast period 2021-2030? Which are the key players in the impact market?

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Machine Learning as a Service Market-Industry Analysis with Growth Prospects, Trends, Size, Supply, Share, Pipeline Projects and Survey till 2030 ...