Machine Learning (ML) systems are complex, and this complexity increases the chances of failure as well. Knowing what may go wrong is critical for developing robust machine learning systems.

Machine Learning (ML) initiatives fail 85% of the time, according to Gartner. Worse yet, according to the research firm, this tendency will continue until the end of 2022.

There are a number of foreseeable reasons why machine learning initiatives fail, many of which may be avoided with the right knowledge and diligence. Here are some of the most common challenges that machine learning projects face, as well as ways to prevent them.

All AI/ML endeavors require data, which is needed for testing, training, and operating models. However, acquiring such data is a stumbling block because most organizational data is dispersed among on-premises and cloud data repositories, each with its own set of compliance and quality control standards, making data consolidation and analysis that much more complex.

Another stumbling block is data silos. When teams use multiple systems to store and handle data sets, data silos collections of data controlled by one team but not completely available to others can form. That might, however, be a result of a siloed organizational structure.

In reality, no one knows everything. It is critical to have at least one ML expert on the team, to be able to do the foundational work, for the successful adoption and implementation of ML in enterprise projects. Being overly confident, without the right skill, sets in the team will only add to the chances of failure.

Organizations are nearly drowning in large volumes of observational data. Thanks to developments in technology such as integrated smart devices and telematics as well as relatively inexpensive and available big data storage and a desire to incorporate more data science into business decisions. However, a high level of data availability might result in observational data dumpster diving.

Also Read: How Enterprises can Keep Machine Learning Models on Track with Crucial Guard Rails

When adopting a strong tool like machine learning, it pays to be more aware about what organizations are searching for. Businesses should take advantage of their large observational data resources to uncover potentially valuable insights, but evaluate those hypotheses through AB or multivariate testing to distinguish reality from fiction.

The ability to evaluate the overall performance of a trained model is crucial in machine learning. Its critical to assess how well the model performs when compared to both training and test data. This data will be used to choose the model to use, the hyper-parameters to utilize, and decide if the model is ready for production use.

It is vital to select the right assessment measures for the job at hand when evaluating model performance.

Machine learning has become more accessible in various ways. There are far more machine learning tools available today than there were even a few years ago, and data science knowledge has multiplied.

Having a data science team to work on an AI and ML project in isolation, on the other hand, might drive the organization down the most difficult path to success. They may come across unanticipated difficulties unless they have prior familiarity with them. Unfortunately, they can also get into the thick of a project before recognizing they are not adequately prepared.

Its imperative to make sure that domain specialists like process engineers and plant operators are not left out of the process because they are familiar with its complexity and the context of relevant data.

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Five Machine Learning Project Pitfalls to Avoid in 2022 - EnterpriseTalk

SAN FRANCISCO--(BUSINESS WIRE)--Mperativ, the Revenue Marketing Platform that aligns marketing with sales, customer success, and finance on the cause and effect relationships between marketing activities and revenue outcomes, today announced the appointment of Nohyun Myung as Vice President of Applied Data Science, Machine Learning and AI. In this new role, Nohyun will lead the development of new Mperativ platform capabilities to help marketers realize the value of AI predictions and seamlessly connect data across the customer journey without having to build a data science practice.

Nohyun has unique and important experience in data science, analytics and AI that will be critical to the growth of the Mperativ Data Science and AI practices, said Jim McHugh, CEO and co-founder of Mperativ. He not only brings the knowledge and skill set to help accelerate the evolution of the Mperativ platform, but his involvement in the technical side of sales organizations will give us a unique perspective on how AI and forecasting can be used to help address the challenges go-to-market teams face.

Nohyun brings over 20 years of experience as a data and analytics practitioner. Prior to Mperativ he built and scaled high-functioning, multi-disciplinary teams in his roles as Vice President of Global Solution Engineering & Customer Success at OmniSci and as Vice President of Global Solution Engineering at Kinetica. He has worked closely with industry leaders across Telco, Utilities, Automotive and Government verticals to deliver enterprise-grade AI and advanced analytics capabilities to their data practices, pioneering work across autonomous vehicle deployments to telecommunications network optimization and uncovering anomalies from object-detected features of satellite imagery. Nohyuns prior experience has led to the advancement of enterprise-class AI capabilities spanning Autonomous Vehicles, automating Object Detection from optical imagery and Global-Scale Smart Infrastructure initiatives across various industries.

Throughout my career Ive become acutely familiar with the immense challenges that go-to-market teams face when trying to get a comprehensive and accurate picture of the customer journey, said Nohyun. As the world sprints towards becoming more prescriptive and predictive, having operational tools and platforms that can augment business without having to build it in-house will become essential across B2B organizations. I look forward to working with the talented team at Mperativ to bring the true value of AI to marketing leaders so they can better execute engagement strategies that produce their desired revenue outcomes.

About Mperativ

Mperativ provides the first strategic platform to align marketing with sales, customer success, and finance on the cause and effect relationships between marketing activities and revenue outcomes. Despite pouring significant effort into custom analytics, marketers are struggling to convey the value of their initiatives. By recentering marketing metrics around revenue, Mperativ makes it possible to uncover data narratives and extract trends across the entire customer journey, with beautifully-designed interactive visualizations that demonstrate the effectiveness of marketing in a new revenue-centric language. As a serverless data warehouse, Mperativ eliminates the complexity of surfacing compelling marketing insights. Connect marketing strategy to revenue results with Mperativ. To learn more, visit us at http://www.mperativ.io or contact us at info@mperativ.io.

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Mperativ Adds New Vice President of Applied Data Science, Machine Learning and AI to Advance Vision for AI in Revenue Marketing - Business Wire

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

Opinion Machine learning's abiding weakness is verification. Is your AI telling the truth? How can you tell?

This problem isn't unique to ML. It plagues chip design, bathroom scales, and prime ministers. Still, with so many new business models depending on AI's promise to bring the holy grail of scale to real-world data analysis, this lack of testability has new economic consequences.

The basic mechanisms of machine learning are sound, or at least statistically reliable. Within the parameters of its training data, an ML process will deliver what the underlying mathematics promise. If you understand the limits, you can trust it.

But what if there's a backdoor, a fraudulent tweak of that training data set which will trigger misbehavior? What if there's a particular quirk in someone's loan request submitted at exactly 00:45 on the 5th and the amount requested checksums to 7 that triggers automatic acceptance, regardless of risk?

Like an innocent assassin unaware they'd had a kill word implanted under hypnosis, your AI would behave impeccably until the bad guys wanted it otherwise.

Intuitively, we know that's a possibility. Now it has been shown mathematically that not only can this happen, researchers say, it's not theoretically detectable. An AI backdoor exploit engineered through training is not only just as much a problem as a traditionally coded backdoor, it's not amenable to inspection or version-on-version comparison or, indeed, anything. As far as the AI's concerned, everything is working perfectly, Harry Palmer could never confess to wanting to shoot JFK, he had no idea he did.

The mitigations suggested by researchers aren't very practical. Complete transparency of training data and process between AI company and client is a nice idea, except that the training data is the company's crown jewels and if they're fraudulent, how does it help?

At this point, we run into another much more general tech industry weakness, the idea that you can always engineer a singular solution to a particular problem. Pay the man, Janet, and let's go home. That doesn't work here; computer says no is one thing, mathematics says no quite another. If we carry on assuming that there'll be a fix akin to a patch, some new function that makes future AIs resistant to this class of fraud, we will be defrauded.

Conversely, the industry does genuinely advance once fundamental flaws are admitted and accepted, and the ecosystem itself changes in recognition.

AI has an ongoing history of not working as well as we thought, and it's not just this or that project. For example, an entire sub-industry has evolved to prove you are not a robot. Using its own trained robots to silently watch you as you move around online. If these machine monitors deem you too robotic, they spring a Voight-Kampff test on you in the guise of a Completely Automated Public Turing test to tell Computers and Humans Apart more widely known, and loathed, as a Captcha. You then have to pass a quiz designed to filter out automata. How undignified.

Do they work? It's still economically viable for the bad guys to carry on producing untold millions of programmatic fraudsters intent on deceiving the advertising industry, so that's a no on the false positives. And it's still common to be bounced from a login because your eyes aren't good enough, or the question too ambiguous, or the feature you relied on has been taken away. Not being able to prove you are not a robot doesn't get you shot by Harrison Ford, at least for now, but you may not be able to get into eBay.

The answer here is not to build a "better" AI and feed it with more and "better" surveillance signals. It's to find a different model to identify humans online, without endangering their privacy. That's not going to be a single solution invented by a company, that's an industry-wide adoption of new standards, new methods.

Likewise, you will never be able to buy a third-party AI that is testably pure of heart. To tell the truth, you'll never be able to build one yourself, at least not if you've got a big enough team or a corporate culture where internal fraud can happen. That's a team of two or more, and any workable corporate culture yet invented.

That's OK, once you stop looking for that particular unicorn. We can't theoretically verify non-trivial computing systems of any kind. When we have to use computers where failure is not an option, like flying aircraft or exploring space, we use multiple independent systems and majority voting.

If it seems that building a grand scheme on the back of the "perfect" black box works as badly as designing a human society on the model of the perfectly rational human, congratulations. Handling the complexities of real world data at real world scale means accepting that any system is fallible in ways that can't be patched or programmed out of. We're not at the point where AI engineering is edging into AI psychology, but it's coming.

Meanwhile, there's no need to give up on your AI-powered financial fraud detection. Buy three AIs from three different companies. Use them to check each other. If one goes wonky, use the other two until you can replace the first.

Can't afford three AIs? You don't have a workable business model. At least AI is very good at proving that.

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Your AI can't tell you it's lying if it thinks it's telling the truth. That's a problem - The Register

The "America's AI in the Retail Market - Growth, Trends, COVID-19 Impact, and Forecasts (2022 - 2027)" report has been added to ResearchAndMarkets.com's offering.

America's AI in the retail market is expected to register a CAGR of 30% during the forecast period, 2021 - 2026.

Companies Mentioned

Key Market Trends

Machine Learning Technology is Expected to Grow Significantly

Food and Grocery to Augment Significant Growth

Key Topics Covered:

1 INTRODUCTION

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET DYNAMICS

4.1 Market Overview

4.2 Market Drivers

4.2.1 Hardware Advancement Acting as a Key Enabler for AI in Retail

4.2.2 Disruptive Developments in Retail, including AR, VR, IOT, and New Metrics

4.2.3 Rise of AI First Organizations

4.2.4 Need for Efficiency in Supply Chain Optimization

4.3 Market Restraints

4.3.1 Lack of Professionals, as well as In-house Knowledge for Cultural Readiness

4.4 Industry Value Chain Analysis

4.5 Porter's Five Forces Analysis

4.6 Industry Policies

4.7 Assessment of Impact of COVID-19 on the Industry

5 AI Adoption in the Retail Industry

5.1 AI Penetration with Retailers (Historical, Current, and Forecast)

5.2 AI penetration by Retailer Size (Large and Medium)

5.3 AI Use Cases in Operations

5.3.1 Logistics and Distribution

5.3.2 Planning and Procurement

5.3.3 Production

5.3.4 In-store Operations

5.3.5 Sales and Marketing

5.4 AI Retail Startups (Equity Funding vs Equity Deals)

5.5 Road Ahead for AI in Retail

6 MARKET SEGMENTATION

6.1 Channel

6.2 Solution

6.3 Application

6.4 Technology

7 COMPETITIVE LANDSCAPE

7.1 Company Profiles

8 INVESTMENT ANALYSIS

9 MARKET TRENDS AND FUTURE OPPORTUNITIES

For more information about this report visit https://www.researchandmarkets.com/r/kddpm3

View source version on businesswire.com: https://www.businesswire.com/news/home/20220427005894/en/

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America's AI in Retail Industry Report to 2026 - Machine Learning Technology is Expected to Grow Signific - Benzinga

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.

Communication Systems for Maritime Control Centres

Integrated Electric Propulsion Systems for Ships

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

Together Striveworks and Figure Eight Federal Enhance the AI Capabilities for the Department of Defense and Federal Law Enforcement

AUSTIN, Texas and ARLINGTON, Va., April 27, 2022 /PRNewswire/ -- Striveworks and Figure Eight Federal are excited to announce their strategic alliance to jointly support the government's emerging capabilities in AI technologies.

David Poirier, President of Figure Eight Federal, said "Our efforts to assist federal customers parallels that of Striveworks and therefore we are excited to work with Striveworks to achieve our common goals."

Figure Eight Federal has more than 15 years of experience assisting its federal customers with their advanced annotation needs. Data annotation is the process of labeling data to enable a model to make decisions and take action. To take action, a model must be trained to understand specific information. With Figure Eight Federal this is done with training data that is annotated and properly categorized giving you confidence for each specific use case.

Data annotated by Figure Eight can be directly integrated with Striveworks' Chariot MLOps platform for model development, training, and deployment within operational timelines. Striveworks has an extensive record of positive performance in delivering software and data science products and services within DoD operational environments. Earlier this year, Striveworks was awarded a basic ordering agreement for the The Data Readiness for Artificial Intelligence Development (DRAID) by U.S. Contracting Command on behalf of the Joint Artificial Intelligence Center (JAIC).

The strategic alliance of these companies will help customers in Defense and Federal law enforcement to step into using artificial intelligence solutions across their wide data landscapes.

Striveworks Executive Vice President Quay Barnett said, "The Striveworks and Figure Eight partnership brings our customers a scalable impact for accurate and rapid decision advantage from their data. Figure Eight's low code annotation platform integrates with our low code Chariot MLOps platform to accelerate AI solutions for our joint customers."

Story continues

About Striveworks

Striveworks is a pioneer in operational data science for national security and other highly regulated spaces. Striveworks' flagship MLOps platform is Chariot, purpose-built to enable engineers and business professionals to transform their data into actionable insights. Founded in 2018, Striveworks was highlighted as an exemplar in the National Security Commission for AI 2020 Final Report.

About Figure Eight Federal

Figure Eight Federal's AI & data enrichment platform includes multiple toolsets and algorithms that have been used by some of the world's largest tech companies and Government Agencies. Our data scientists and AI/ML experts have deep knowledge and understanding of many types of data and their use cases including Natural Language Process and Computer Vision. We have the skills and technology required to make AI/ML testing and evaluation more systematic and scalable allowing analysts to easily make comparisons, determine accuracy, bias and vulnerability.

Contact: info@F8Federal.com Media Contact: Janet Waring

Website: F8Federal.com

Address: 1735 N Lynn St, #730Arlington, VA 22209

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SOURCE Figure Eight Federal

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Striveworks and Figure Eight Federal Enter into Strategic Partnership for Enhanced Annotation Capabilities within Machine Learning Operations Platform...

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