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Machine learning: The new language of data and analytics – ITProPortal

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Machine learning is all the rage in todays analytical market. According to Kenneth Research, the value of machine learning is growing sharply and is expected to reach over $23B by 2023 an annual growth rate of 43 percent between 2018-2023. IDC enforces this point predicting that worldwide spend on cognitive & AI systems, which includes machine learning, will reach $110B by 2024. Likewise, Gartner believes the business value machine learning and AI will create will be about $3.9T in 2022. With these kinds of predictions, its no surprise organizations want to incorporate these popular (and lucrative) methods into their analytical processes.

Machine learning is not a new concept in the analytical lifecycle data scientists have been using machine learning to help facilitate analytical processes and drive insights for decades. What is new is the use of machine learning for data preparation tasks to accelerate data processes and expedite analytical efforts. Here are four ways data preparation efforts can leverage machine learning for more effective and faster data reconditioning efforts:

1. Data transformation recommendations built into solutions suggest how data needs to be standardized and converted to meet analytical needs. This feature can proactively look at the quality of the data set and identify what quality transformation should be executed to ensure the data is ready for analytics. These recommendations are based on historical preparation tasks while using AI/machine learning to present new recommendations to the user.

2. Automated analytical partitioning applies AI/machine learning to determine the best way to partition the data for analytics. It also provides transparency on which method should be used and why. This helps speed up the analytical process because the data is automatically grouped together for training, validation and test buckets.

3. Smart matching incorporates AI/machine learning to proactively group like data elements together. Using the most effective matching discipline allows the user to decide if they want to automatically build a golden record and assign unique keys to the data.

4. Intelligent data assignment provides the data and analytics community quick understanding of the classification of the data type (e.g., name, address, product, sku), which allows simple tasks like gender assignment to be performed without user intervention. Data automatically populates a data catalog and uses natural language processing to explain the data, while contributing to the lineage for quick impact analysis.

The main objective of applying machine learning techniques to the data preparation process in innovative ways is to find hidden treasures in the data. These found treasures in the data can have a positive impact across many facets of business enterprises such as competitive advantage, regulation requirements, supply chain fulfillment and optimization, manufacturing health, medical insights, etc. To be specific, here is an exploration of how machine learning can impact a critical business initiative like fraud detection and prevention.

1. Unsupervised learning added to the fraud environment enables organizations to find edge cases in the data and proactively identify abnormal behaviors not found in traditional methods. These abnormal behaviors can be moved into a supervised learning process, like regression or classification analytics, to predict if these outliers are new types of fraudulent activities that require additional investigation.

2. Text analytics provide unique insights by disambiguating certain data attributes that numerical data cant identify and therefore helping to identify unknown patterns between text and traditional data components. These insights may lead to new fraud patterns for consideration.

3. Hibernation can be used for smart alerting to apply a scoring model across all data - active and historical - to identify new fraud patterns that need attention. This process consolidates scores into one entity-level score for risk assessment and transaction monitoring, helping to identify new, out-of-threshold incidents for additional investigation.

4. Adding automated natural language processing (NLP) to the fraud mix provides human language translations to complex analytical findings, delivering the information in a way that humans can use and understand. Coupling NLP with image recognition helps identify document types using context analytics on text classifications, improving the accuracy rates of fraud detection.

5. Through dynamic ranking, more data is available for machine learning processes, resulting in more complete cluster analysis, identification of better risk predictors and elimination of false variables. Machine learning will teach itself about the normal data conditions and proactively monitor and update risk scores for more data-driven results.

6. Intelligent due diligence provides entity resolutions across product and business lines. Machine learning creates profiling for peer groupings and identifies expected behaviors using network and graph analytics. Because machine learning identifies expected behaviors, it can also point out unexpected behaviors that may indicate suspicious activities or a market shift that needs to be addressed.

7. Smart alerting takes traditional alerting data and combines it with additional data to unearth new conditions that need to be investigated. With machine learning, the tools can teach themselves what alerts can be handled automatically and what alerts need a human eye. Intelligent detection optimizes existing detection models by including more data and AI/machine learning techniques to identify new scenarios using newly combined targeted subgroups to find additional detections or alerts for consideration.

In summary, the machine learning marketspace is exploding, bringing business value to organizations across all industries. Machine learning produces new insights and allows organizations to leverage more or all the data to make better and smarter decisions. So, lets start speaking the new machine learning language of data and analytics today!

Kim Kaluba, Senior Manager for Data Management Solutions, SAS

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December 3rd, 2020 at 4:58 am

Posted in Machine Learning

Injecting Machine Learning And Bayesian Optimization Into HPC – The Next Platform

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No matter what kind of traditional HPC simulation and modeling system you have, no matter what kind of fancy new machine learning AI system you have, IBM has an appliance that it wants to sell you to help make these systems work better and work better together if you are mixing HPC and AI.

It is called the Bayesian Optimization Accelerator, and it is a homegrown statistical analytics stack that runs on one or more of Big Blues Witherspoon Power AC922 hybrid CPU-GPU supercomputer nodes the ones that are used in the Summit supercomputer at Oak Ridge National Laboratories and the Sierra supercomputer used at Lawrence Livermore National Laboratory.

IBM has been touting the ideas behind the BOA system for more than two years now, and it is finally being commercialized after some initial testing in specific domains that illustrate the principles that can be modified and applied to all kinds of simulation and modeling workloads. Dave Turek, now retired from IBM but the longtime executive steering the companys HPC efforts, walked us through the theory behind the BOA software stack, which presumably came out of IBM Research, way back at SC18 two years ago. As far as we can tell, this is still the best English language description of what BOA does and how it does it. Turek gave us an update on BOA at our HPC Day event ahead of SC19 last year, focusing specifically on how Bayesian statistical principles can be applied to ensembles of simulations in classical HPC applications to do better work and get to results faster.

In the HPC world, we tend to try to throw more hardware at the problem and then figure out how to scale up frameworks to share memory and scale out applications across the more capacious hardware, but this is different. With BOA, the ideas can be applied to any HPC system, regardless of vendor or architecture. This is not only transformational for IBM in that it feels more like a service encapsulated in an appliance and will have an annuity-like revenue stream across many thousands of potential HPC installations. It is also important for IBM in that the next generation exascale machines in the United States, where IBM won the big deals for Summit and Sierra, are not based on the combination of IBM Power processors, Nvidia GPU accelerators, and Mellanox InfiniBand interconnects. The follow-on Frontier and El Capitan systems at these labs are rather using AMD CPU and GPU compute engines and a mix of Infinity Fabric for in-node connectivity and Cray Slingshot Ethernet (now part of Hewlett Packard Enterprise) for lashing nodes together. Even these machines might benefit from BOA, which gives Big Blue some play across the HPC spectrum, much as its Spectrum Scale (formerly GPFS) parallel file system is often used in systems where IBM is not the primary contractor. BOA is even more open in this sense, although like GPFS, the underlying software stack used in the BOA appliance is not open source anymore than GPFS is. This is very unlikely to change, even with IBM acquiring Red Hat last year and becoming the largest vendor of support contracts for tested and integrated open source software stacks in the world.

So what is this thing that IBM is selling? As the name suggests, it is based on Bayesian optimization, a field of mathematics that was created by Jonas Mockus in the 1970s and that has been applied to all kinds of algorithms including various kinds of reinforcement learning systems in the artificial intelligence field. But it is important to note that Bayesian optimization does not itself involve machine learning based on neural networks, but what IBM is in fact doing is using Bayesian optimization and machine learning together to drive ensembles of HPC simulations and models. This is the clever bit.

With Bayesian optimization, you know there is a function in the world and it is in a black box (mathematically speaking, not literally). You have a set of inputs and you see how it behaves through its outputs. The optimization part is to build a database of inputs and outputs and to statistically infer something about what is going on between the two, and then create a mathematical guess about what a better set of inputs might be to get a desired output. The trick is to use machine learning training to watch what a database of inputs yields for outputs, and you use the results of that to infer what the next set of inputs should be. In the case of HPC simulations, this means you can figure out what should be simulated instead of trying to simulate all possible scenarios or at least a very large number of them. BOA doesnt change the simulation code one bit and that is important. It just is given a sense of the desired goal of the simulation thats the tricky part that requires the domain expertise that IBM Research can supply and watches the inputs and outputs of simulations and offers suggested inputs.

The net effect of BOA is that, over time, you need less computing to run an HPC ensemble, and you also can converge to the answer is less time as well. Or, more of that computing can be dedicated to driving larger or more fine-grained simulations because the number of runs in an ensemble is a lot lower. We all know that time is fluid money and that hardware is also frozen money depreciated one little trickle at a time through use, and add them together and there is a lot of money that can potentially be saved.

Chris Porter, offering manager for HPC cloud for Power Systems at IBM, walked us through how BOA is being commercialized and some of the data from the early use cases where BOA was deployed.

One of the early use cases was at the Texas Advanced Computing Center at the University of Texas at Austin, where Mary Wheeler, a world-renowned expert in numerical methods for partial differential equations as they apply to oil and gas reservoir models, used the BOA appliance in some simulations. To be specific, Wheelers reservoir model is called the Integrated Parallel Accurate Reservoir Simulator, or IPARS, and it has gradient descent/ascent model built within it. Using their standard technique for maximizing the oil extraction from a reservoir with the model, it would take on the order of 200 evaluations of the model to get what Porter characterized as a good result. But by injecting BOA into the flow of simulations, they could get the same result with only 73 evaluations. That is a 63.5 percent reduction in the number of evaluations performed.

IBMs own Power10 design team also used BOA in its electronic design automation (EDA) workflow, specifically to check the signal integrity of the design. To do so using the raw EDA software took over 5,600 simulations, and IBM did all of that work as it normally would do. But then IBM added BOA to the stack and redid all of the work, and go to the same level of accuracy in analyzing the signal integrity of the Power10 chips traces with only 140 simulations. That is a 97.5 percent reduction in computing needed or a factor of 40X speedup if you want to look at it that way. (Porter warns that not all simulations will see this kind of huge bump.)

In a third use case, a petroleum company that creates industrial lubricants, whom Porter could not name, was creating a lubricant that had three components. There are myriad different proportions to mix them in to get a desired viscosity and slipperiness, and the important factor is that one of these components was very expensive and the other two were not. Maximizing the performance of the lubricant while minimizing the amount of the expensive item was the task in this case, and this company ran the simulation without and then with the BOA appliance plugged in. Heres the fun bit: BOA found a totally unusual configuration that this companys scientists would have never thought of and was able to find the right mix with four orders of magnitude more certainty than prior ensemble simulations and did one-third as many simulations to get to the result.

These are dramatic speedups, and demonstrate the principle that changing algorithms and methods is as important as changing hardware to run older algorithms and methods.

IBM is being a bit secretive about what is in the BOA software stack, but it is using PyTorch and TensorFlow for machine learning frameworks in different stages and GP Pro for sparse Gaussian process analysis, all of which have been tuned to run across the IBM Power9 and Nvidia V100 GPU accelerators in a hybrid (and memory coherent) fashion. The BOA stack could, in theory, run on any system with any CPU and any GPU, but it really is tuned up for the Power AC922 hardware.

At the moment, IBM is selling two different configurations of the BOA appliance. One has two V100 GPU accelerators, each with 16 GB of HBM2 memory, and two Power9 processors with a total of 40 cores running at a base 2 GHz and a turbo boost 2.87 GHz and 256 GB of their own DDR4 memory. The second BOA hardware configuration has a pair of Power9 chips with a total of 44 cores running at a base 1.9 GHz and a turbo boost to 3.1 GHz with its own 1 TB of memory, plus four of the V100 GPU accelerators with 16 GB of HBM2 memory each.

IBM is not providing pricing for these two machines, or the BOA stack on top of it, but Porter says that it is sold under an annual subscription that runs to hundreds of thousands of dollars per server per year. That may sound like a lot, but considering the cost of an HPC cluster, which runs from millions of dollars to hundreds of millions of dollars, this is a small percentage of the overall cost and can help boost the effective performance of the machine by an order of magnitude or more.

The BOA appliance became available on November 27. Initial target customers are in molecular modeling, aerospace and auto manufacturing, drug discovery, and oil and gas reservoir modeling and a bit of seismic processing, too.

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Injecting Machine Learning And Bayesian Optimization Into HPC - The Next Platform

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December 3rd, 2020 at 4:58 am

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QA Increasingly Benefits from AI and Machine Learning – RTInsights

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By Erik Fogg | November 30, 2020

While the human element will still exist, incorporating AI/ML will improve the QA testing within an organization.

The needle in quality assurance (QA) testing is moving in the direction of increased use of artificial intelligence (AI) and machine learning (ML). However, the integration of AI/ML in the testing process is not across the board. The adoption of advanced technologies still tends to be skewed towards large companies.

Some companies have held back, waiting to see if AI met the initial hype as being a disruptor in various industries. However, the growing consensus is that the use of AI benefits the organizations that have implemented it and improves efficiencies.

Small- and mid-sized could benefit from testing software using AI/ML to meet some of the challenges faced by QA teams. While AI and ML are not substitutes for human testing, they can be a supplement to the testing methodology.

See also: Real-time Applications and Business Transformation

As development is completed and moves to the testing stage of the system development life cycle, QA teams must prove that end-users can use the application as intended and without issue. Part of end-to-end (E2E) testing includes identifying the following:

E2E testing plans should incorporate all of these to improve deployment success. Even while facing time constraints and ever-changing requirements, testing cycles are increasingly quick and short. Yet, they still demand high quality in order to meet end-user needs.

Lets look at some of the specific ways AI and ML can streamline the testing process while also making it more robust.

AI in software testing reduces the time spent on manually testing. Teams are then able to apply their efforts to more complex tasks that require human interpretation.

Developers and QA staff will need to apply less effort in designing, prioritizing, writing, and maintaining E2E tests. This will expedite timelines for delivery and free up resources to work on developing new products rather than testing a new release.

With more rapid deployment, there is an increased need for regression testing, to the point where humans cannot realistically keep up. Companies can use AI for some of the more tedious regression testing tasks, where ML can be used to generate test scripts.

In the example of a UI change, AI/ML can be used to scan for color, shape, size, or overlap. Where these would otherwise be manual tests, AI can be used for validation of the changes that a QA tester may miss.

When introducing a change, how many tests are needed to pass QA and validate that there are no issues? Leveraging ML can determine how many tests to run based on code changes and the outcomes of past changes and tests.

ML can also select the appropriate tests to run by identifying the particular subset of scenarios affected and the likelihood of failure. This creates more targeted testing.

With changes that may impact a large number of fields, AI/ML automate the validation of these fields. For example, a scenario might be Every field that is a percentage should display two decimals. Rather than manually checking each field, this can be automated.

ML can adapt to minor code changes so that the code can self-correct or self-heal over time. This is something that could otherwise take hours for a human to fix and re-test.

While QA testers are good at finding and addressing complex problems and proving out test scenarios, they are still human. Errors can occur in testing, especially from burnout syndrome of completing tedious processing. AI is not affected by the number of repeat tests and therefore yields more accurate and reliable results.

Software development teams are also ultimately composed of people, and therefore personalities. Friction can occur between developers and QA analysts, particularly under time constraints or the outcomes found during testing. AI/ML can remove those human interactions that may cause holdups in the testing process by providing objective results.

Often when a failure occurs during testing, the QA tester or developer will need to determine the root cause. This can include parsing out the code to determine the exact point of failure and resolving it from there.

In place of going through thousands of lines of codes, AI will be able to sort through the log files, scan the codes, and detect errors within seconds. This saves hours of time and allows the developer to dive into the specific part of the code to fix the problem.

While the human element will still exist, introducing testing software that incorporates AI/ML will overall improve the QA testing within an organization. Equally as important as knowing when to use AI and ML is knowing when not to use it. Specific scenario testing or applying human logic in a scenario to verify the outcome are not well suited for AI and ML.

But for understanding user behavior, gathering data analytics will build the appropriate test cases. This information identifies the failures that are most likely to occur, which makes for better testing models.

AI/ML can also specify patterns over time, build test environments, and stabilize test scripts. All of these allow the organization to spend more time developing new product and less time testing.

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QA Increasingly Benefits from AI and Machine Learning - RTInsights

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December 3rd, 2020 at 4:58 am

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Everything to Know About Machine Learning as a Service (MLaaS) – Analytics Insight

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Machine learning is set to change the manner in which we work together. Machine learning joins mathematics, statistics, and artificial intelligence into another discipline of study. Big data and faster computing power are opening up new capacities for this innovation that appeared to be outlandish just 10 years back. It is being utilized to drive vehicles, recognize faces, trade stocks, and invent lifesaving medicines.

Data is the driver of artificial intelligence and machine learning. Consider it its food the more it eats up the greater, more complex and natural it becomes. A significant number of the worlds driving cloud suppliers currently offer machine learning tools, including Microsoft, Amazon, Google and IBM. The primary benefit these organizations have over their rivals is their admittance to and ability to produce their own big data, which places them in a totally extraordinary class compared to other smaller businesses or startups who cant rival the amount of information these cloud suppliers create consistently.

This has driven these big tech companies to give machine learning as a service to organizations over the globe, permitting customers to choose from a range of the microservices machine learning has made possible.

To truly benefit from AI, organizations should do one of two things: Invest a ton of resources (cash) in data scientists or developers with a foundation in machine learning, or use machine learning as a service (MLaaS) offerings.

Machine learning as a service (MLaaS) is a range of services that offer ML tools as a feature of cloud computing services, as the name proposes. MLaaS suppliers offer tools including data visualization, APIs, natural language processing, deep learning, face recognition, predictive analytics, etc. The suppliers data centers handle the actual computation.

Machine learning as a service alludes to various services cloud suppliers are providing. The fundamental attraction of these services is that users can begin immediately with machine learning without installing software or setting up their own servers, much like any other cloud service.

Aside from the various advantages MLaaS gives, organizations dont have to bear the relentless and repetitive software installation processes.

Four vital participants in the MLaaS market:

Buying a machine learning service from a cloud provider is only the initial phase of using AI. Whenever you have chosen to deploy a natural language processing (NLP) or computer vision solution, you actually need to train the service or algorithm to give appropriate yields. With an absence of data scientists in the workforce, as well as an absence of assets to enlist those that are accessible, usage and consulting partners will flourish because of their understanding of AI and MLaaS.

Machine learning as a service has various conspicuous advantages, for example, quick and low-cost compute options, independence from the weight of building in-house infrastructure from scratch, no compelling reason to put intensely in storage facilities and computing power, and no compelling reason to recruit costly ML architects and data scientists.

The MLaaS platforms can be the most ideal decision for freelance data scientists, new businesses, or organizations where machine learning isnt a fundamental part of their operations. Large organizations, particularly in the tech business and with a heavy spotlight on machine learning, will in general form in-house ML infrastructure that will fulfill their particular necessities and prerequisites.

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Everything to Know About Machine Learning as a Service (MLaaS) - Analytics Insight

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December 3rd, 2020 at 4:58 am

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How the Food and Beverage Industry is Affected by Machine Learning and AI – IoT For All

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In general, when thinking about the food industry, we are likely to think about customer service and takeaway gig-economy services. More recently, the COVID-19 pandemic and how it ties into making or breaking food businesses are at the forefront. Perhaps one of the last things to come to mind when discussing the food industry is modern technology, especially artificial intelligence, and machine learning. However, these technologies have a massive impact on the food and drink industry, and today were going to explore how.

Whether youre looking at the food or the industrys beverage side, every aspect of the process is impacted by machine learning or AI. Hygiene is a massive and important part of the food industry process, specifically when minimizing cross-contamination and maintaining high standards during a pandemic.

In the past, these tasks would be tedious, time and resource-intensive, and potentially expensive if a mistake was made or overlooked. In large manufacturing plants, complex machines would actually need to be disassembled and then put back together for them to be cleaned properly and pumping a large volume of substances through them.

However, with modern technology, this is no longer the case.

Using a technology known as SOCIP, or Self-Cleaning-in-Place, machines can use powerful ultrasonic sensors and fluorescence optical imaging to track food remains on machinery, as well as microbial debris of the equipment, meaning machines only need to be cleaned when they need to, and only in the parts that need cleaning. While this is a new technology and the current problem of overcleaning, it will still save the UK food industry alone around 100 million pounds a year.

Of course, the food and drink industrys waste aspect is a highly debated and criticized part of the industry. The foodservice industry in the UK alone loses around 2.4 billion in wasted food alone, so its only natural that technology is being used to save this money.

Throughout the worlds supply chains, AI is being used to track every single stage of the manufacturing and supply chain process, such as tracking prices, managing inventory stock levels, and even countries of origin.

Solutions that already exist, such as Symphony Retail AI, uses this information to track transportation costs accurately, all pricing mentioned above, and inventory levels to estimate how much food is needed and where to minimize the waste produced.

No matter where you go in the world, food safety standards are always important to follow, and regulations seem to be becoming stricter all the time. In the US, the Food Safety Modernization Act ensures this happens, especially with COVID-19, and countries become more aware of how contaminated food can be.

Fortunately, robots that use AI and machine learning can handle and process food, basically eliminating the chances that contamination can take place through touch. Robots and machinery cannot transmit diseases and such in a way that humans can, thus minimizing the risk of it becoming a problem.

Even in food testing facilities, robot solutions, such as Next Generation Sequencing, a DNA testing solution for food data capturing, and Electric Noses, a machine solution that tests and records the odors of food, are being used in place for humans for more accurate results. At the time of writing, its estimated that around 30% of the food industry currently works with AI and Machine Learning in this way, although this number is set to grow over the coming years.

Theres no doubt that food production uses a ton of water and resources, especially in the meat and livestock industries. This is extremely unsustainable for the planet and very expensive for the producers. To help curb costs and become more sustainable, AI is being used to manage the power and water consumption needed, thus making it as accurate as possible.

This creates instant benefits to the costs of production and profit margins in all areas of the food and drink sector. When you start adding the ability to manage light sources, food for plants and ingredients, and basically introducing a smart way to grow food at its core, then you really start to see better food, more sustainable production practices, and more profits and savings at each stage of the food chain.

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How the Food and Beverage Industry is Affected by Machine Learning and AI - IoT For All

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December 3rd, 2020 at 4:57 am

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Amazon announces new machine learning tools to help customers monitor machines and worker safety – www.computing.co.uk

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Amazon announces new machine learning tools to help customers monitor machines and worker safety

Amazon Web Services (AWS) on Tuesday launched five new industrial machine learning services aimed at helping industrial plants and factories to improve safety, operational efficiency, and quality control at their workplace.

The company said that companies can use these services to embed artificial intelligence (AI) in their production processes to identify productivity bottlenecks, potential equipment faults, and worker safety and compliance violations.

The five tools, named Amazon Monitron, AWS Panorama Software Development Kit (SDK), AWS Panorama Appliance, Amazon Lookout for Vision and Amazon Lookout for Equipment, combine computer vision, sensor analysis and machine learning capabilities to address technical challenges faced by industrial customers.

The launching of these new services also indicates Amazon's growing ambitions to strengthen its position as a leading player in the industrial cloud sector.

According to Amazon, its Monitron tool is comprised of a gateway, sensors, and machine learning software. The small sensor in Monitron can be attached to equipment to detect abnormal conditions, such as high or low temperatures or vibrations, and predict potential failures.

AWS says it is already using 1,000 Monitron sensors at its fulfilment centres near Mnchengladbach in Germany to monitor conveyor belts handling packages.

AWS Panorama Appliance, meanwhile, enables industrial facilities to use their existing cameras to improve safety and quality control. The tool uses computer vision to analyse video footage and detect safety and compliance issues.

According to the Financial Times, AWS Panorama can be used to detect vehicles bring driven in places where they are not supposed to be. Some big companies, including Deloitte and Siemens, are already testing the system, it said. AWS Panorama SDK allows industrial camera makers to embed computer vision capabilities in their new cameras.

Amazon Lookout for Vision is designed to find flaws and anomalies in processes or products by utilising AWS-trained computer vision models on videos and images.

Amazon Lookout for Equipment gives customers with existing equipment sensors the ability to use machine learning models to detect unusual equipment behaviour to predict future faults.

While AWS claims that industrial plants can use these new tools to improve productivity and safety at their workplaces, privacy campaigners have also raised concerns about these tools.

Earlier this week, the Trades Union Congress (TUC) in the UK released its report into the impact of AI-powered tools on well-being of workers. The report warned that some intrusive technologies being used in companies can have potentially negative effects on "workers' well-being, right to privacy, data protection rights and the right not be discriminated against".

Silkie Carlo, director of privacy group Big Brother Watch, told the BBC that automated workplace monitoring "rarely results in benefits for employees".

"It's a great shame that social distancing has been leapt on by Amazon as yet another excuse for data collection and surveillance," she added.

With concerns about workplace surveillance rising, this week Microsoft apologised for a new productivity score featured introduced in Microsoft 365, which could be used to track individuals' detailed usage of the cloud based productivity suite by administrators. Microsoft says it will remove individual usernames from the productivity score feature.

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December 3rd, 2020 at 4:57 am

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COVID-19 Impact on Global Machine Learning and Location Data ApplicationsMarket Professional Survey Research Report 2020-2027

The global Machine Learning and Location Data Applicationsmarket report examines the market position and viewpoint of the market worldwide, from various angles, such as from the key players point, geological regions, types of product and application. This Machine Learning and Location Data Applicationsreport highlights the key driving factors, constraint, opportunities, challenges in the competitive market. It also offers thorough Machine Learning and Location Data Applicationsanalysis on the market stake, classification, and revenue projection. The Machine Learning and Location Data Applicationsmarket report delivers market status from the readers point of view, providing certain market stats and business intuitions. The global Machine Learning and Location Data Applicationsindustry includes historical and futuristic data related to the industry. It also includes company information of each market player, capacity, profit, Machine Learning and Location Data Applicationsproduct information, price, and so on.

The latestMachine Learning and Location Data Applicationsmarket report published by Reports and Markets offers a competency-based analysis and global market estimate, developed using evaluable methods, to provide a clear view of current and expected growth patterns. The report also contains market analysis by geographic location across the globe as well as major markets.

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Key Players

This report provides information on the key players in theMachine Learning and Location Data Applicationsmarket, the report covers various vendors in the market along with the strategies used by them to grow in the market. The report discusses the strategies used by key players to have an edge over their counterparts, build a unique business portfolio, and expand their market size in the global market. This analysis would help the companies entering the Machine Learning and Location Data Applicationsmarket to find out the growth opportunities in the market.

The key manufacturers covered in this report are @Lockheed Martin, Raytheon, Northrop Grumman, Thales Group, Boeing, Unisys, IBM, FLIR Systems, BAE Systems, General Dynamics, Honeywell International, Elbit Systems, SAIC, Booz Allen Hamilton, Harris, Leidos, and MotoRoLA Solutio

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The report also inspects the financial standing of the leading companies, which includes gross profit, revenue generation, sales volume, sales revenue, manufacturing cost, individual growth rate, and other financial ratios.

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customized specific regional and country-wise analysis of the key geographical regions as follows:

North America

Europe

Asia Pacific Counter

Middle East & Africa

Latin America

America Country (United States, Canada)

South America

Asia Country (China, Japan, India, Korea)

Europe Country (Germany, UK, France, Italy)

Other Country (Middle East, Africa, GCC)

Crucial points encompassed in the report:

In the end, Machine Learning and Location Data ApplicationsMarket Report delivers a conclusion that includes Breakdown and Data Triangulation, Consumer Needs/Customer Preference Change, Research Findings, Market Size Estimation, Data Source. These factors will increase the business overall. Major queries related Global Machine Learning and Location Data ApplicationsMarket with covid-19 effect resolves in the report: 1. How market players are performing in this covid-19 event? 2. How the pricing of essential raw material and related market affects Machine Learning and Location Data Applicationsmarket. 3. Is covid-19 pandemic already affected on projected region or what will be the maximum impact of covid-19 in region? 4. What will be the CAGR growth of the Machine Learning and Location Data Applicationsmarket during the forecast period? 5. In 2026 what will be the estimated value of Machine Learning and Location Data Applicationsmarket?

TABLE OF CONTENT

1 Report Overview

2 Global Growth Trends

3 Market Share by Key Players

4 Breakdown Data by Type and Application

5 United States

6 Europe

7 China

8 Japan

9 Southeast Asia

10 India

11 Central & South America

12 International Players Profiles

13 Market Forecast 2020-2027

14 Analysts Viewpoints/Conclusions

15 Appendix

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Commentary: Chain of Demand applies AI, machine learning to retail supply chain profitability – FreightWaves

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The views expressed here are solely those of the author and do not necessarily represent the views of FreightWaves or its affiliates.

In this installment of the AI in Supply Chain series (#AIinSupplyChain), we explore how Chain of Demand, an early-stage startup based in Hong Kong, is helping companies in the retail industry apply AI and machine learning to increase their profitability and sustainability.

I spoke with AJ Mak, founder and CEO of Chain of Demand. As is customary with these #AIinSupplyChain articles, my first question for him was, What is the problem that Chain of Demand solves for its customers? Who is the typical customer?

He said: Our goal is to improve profitability and sustainability for the retail and supply chain industries. By using our AI analytics, we help retailers to optimize their inventory, which improves margins by minimizing their inventory risk, markdowns and excess inventory. Reducing excess inventory is a huge factor in reducing carbon emissions and water wastage, and this is now more important than ever.

He added, Our typical customers would be omnichannel retailers and brands in the apparel, footwear and beauty and cosmetics categories.

Next I asked, What is the secret sauce that makes Chain of Demand successful? What is unique about your approach? Deep learning seems to be all the rage these days. Does Pathmind use a form of deep learning? Reinforcement learning? Supervised learning? Unsupervised learning? Federated learning?

Our secret sauce includes our veteran experience and domain expertise in retail, and predictive models tailored for the industry, Mak said. We use deep learning for our image recognition and modeling, which includes supervised learning, unsupervised learning and reinforcement learning.

Data is consistently an issue. I asked, How do you handle the lack of high-quality data for AI and machine learning applied to legacy industries?

Part of our AI is used to extract, transform and load dirty data from legacy systems, Mak said. We have done a lot of data cleaning from many different legacy systems, and we have been able to streamline the ETL (extract, transform and load) process for the retail industry.

In a case study published on its website, Chain of Demand describes how it helps its customers.

Bluebell Group helps luxury brands establish a presence in Asia through a platform consisting of 600 online and brick-and-mortar stores spread over more than 10 countries in the region.

Due to changes in the behavior of shoppers, Bluebell needed to help Jimmy Choo Taiwan reconcile how much revenue would be generated by in-store sales in comparison to online purchases. Using Chain of Demand to test and incorporate AI during the merchandise planning process, Bluebell achieved a 90% improvement in the accuracy of its predictions of best- and worst-selling items. Bluebell also increased its accuracy predicting the number of units sold by 81%.

In my conversation with Mak, he pointed out that one reason he believes Chain of Demand fares well against the alternatives is that his family has operated in the apparel and fashion retail supply chain management business since 1981. He spent nearly a decade in the business, gaining an understanding of the problems in global apparel and fashion retail supply chains. That experience and those insights inform how Chain of Demand goes about building its product.

When I asked him about competitors, he mentioned Blue Yonder and Celect.

Coincidentally, Jos P. Chan, who was then the vice president of business development for Celect, was a speaker at #TNYSCM04 Artificial Intelligence & Supply Chains, organized by The New York Supply Chain Meetup in March 2018.

Celect was purchased by Nike in August 2019 for a reported price of $110 million.

Companies like Chain of Demand want to get large companies away from using spreadsheets for sales forecasting and demand planning. As it becomes necessary to take an increasing number of sources and types of data into account, the case for shifting away from simple spreadsheets and onto more robust and sophisticated platforms will only gain strength.

That must sound like music to Maks ears.

Conclusion

If you are a team working on innovations that you believe have the potential to significantly refashion global supply chains, wed love to tell your story in FreightWaves. I am easy to reach on LinkedIn and Twitter. Alternatively, you can reach out to any member of the editorial team at FreightWaves at media@freightwaves.com.

Dig deeper into the #AIinSupplyChain Series with FreightWaves.

Commentary: Optimal Dynamics the decision layer of logistics? (July 7)

Commentary: Combine optimization, machine learning and simulation to move freight (July 17)

Commentary: SmartHop brings AI to owner-operators and brokers (July 22)

Commentary: Optimizing a truck fleet using artificial intelligence (July 28)

Commentary: FleetOps tries to solve data fragmentation issues in trucking (Aug. 5)

Commentary: Bulgarias Transmetrics uses augmented intelligence to help customers (Aug. 11)

Commentary: Applying AI to decision-making in shipping and commodities markets (Aug. 27)

Commentary: The enabling technologies for the factories of the future (Sept. 3)

Commentary: The enabling technologies for the networks of the future (Sept. 10)

Commentary: Understanding the data issues that slow adoption of industrial AI (Sept. 16)

Commentary: How AI and machine learning improve supply chain visibility, shipping insurance (Sept. 24)

Commentary: How AI, machine learning are streamlining workflows in freight forwarding, customs brokerage (Oct. 1)

Commentary: Can AI and machine learning improve the economy? (Oct. 8)

Commentary: Savitude and StyleSage leverage AI, machine learning in fashion retail (Oct. 15)

Commentary: How Japans ABEJA helps large companies operationalize AI, machine learning (Oct. 26)

Commentary: Pathmind applies AI, machine learning to industrial operations (Nov. 20)

Authors disclosure: I am not an investor in any early stage startups mentioned in this article, either personally or through REFASHIOND Ventures. I have no other financial relationship with any entities mentioned in this article.

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Machine learning – it’s all about the data – KHL Group

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When it comes to the construction industry machine learning means many things. However, at its core, it all comes back to one thing: data.

The more data that is produced through telematics, the more advanced artificial intelligence (AI) becomes, due to it having more data to learn from. The more complex the data the better for AI, and as AI becomes more advanced its decision-making improves. This means that construction is becoming more efficient thanks to a loop where data and AI are feeding into each other.

Machine learning is an application of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. As Jim Coleman, director of global IP at Trimble says succinctly, Data is the fuel for AI.

Artificial intelligence

Coleman expands on that statement and the notion that AI and data are in a loop, helping each other to develop.

The more data we can get, the more problems we can solve and the more processing we can throw on top of that, the broader set of problems well be able to solve, he comments.

Theres a lot of work out there to be done at AI and it all centres around this notion of collecting data, organising the data and then mining and evaluating that data.

Karthik Venkatasubramanian, vice president of data and analytics at Oracle Construction and Engineering agrees that data is key, saying: Data is the lifeblood for any AI and machine learning strategy to work. Many construction businesses already have data available to them without realising it.

This data, arising from previous projects and activities, and collected over a number of years, can become the source of data that machine learning models require for training. Models can use this existing data repository to train on and then compare against a validation test before it is used for real world prediction scenarios.

There are countless examples of machine learning at work in construction with a large number of OEMs having their own programmes in place, not to mention whats being worked on by specialist technology companies.

One of these OEMs is USA-based John Deere. Andrew Kahler, a product marketing manager for the company says that machine learning has expanded rapidly over the past few years and has multiple applications.

Machine learning will allow key decision makers within the construction industry to manage all aspects of their jobs more easily, whether in a quarry, on a site development job, building a road, or in an underground application. Bigger picture, it will allow construction companies to function more efficiently and optimise resources, says Kahler.

He also makes the point that a key step in this process is the ability for smart construction machines to connect to a centralised, cloud-based system John Deere has its JDLink Dashboard, and most of the major OEMs have their own equivalent system.

The potential for machine learning to unlock new levels of intelligence and automation in the construction industry is somewhat limitless. However, it all depends on the quality and quantity of data were able to capture, and how well were able to put it to use though smart machines.

USA-based Built Robotics was founded in 2016 to address what they saw as gap in the market the lack of technology being used across construction sites, especially compared to other industries. The company upgrade construction equipment with AI guidance systems, enabling them to operate fully autonomously.

The company typically works with equipment comprising excavators, bulldozers, and skid steer loaders. The equipment can only work autonomously on certain repetitive tasks; for more complex tasks an operator is required.

Erol Ahmed, director of communications at Built Robotics says that founder and CEO Noah Ready-Campbell wanted to apply robotics to where it would be really helpful and have a lot of change and impact, and thus settled on the construction industry.

Ahmed says that the company are the only commercial autonomous heavy equipment and construction company available. He adds that the business which operates in the US and has recently launched operations in Australia is focused on automating specific workflows.

We want to automate specific tasks on the job site, get them working really well. Its not about developing some sort of all-encompassing robot that thinks and acts like a human and can do anything you tell it to. It is focusing on specific things, doing them well, helping them work in existing workflows. Construction sites are very complicated, so just automating one piece is very helpful and provides a lot of productivity savings.

Hydraulic system

Ahmed confirms that as long as the equipment has an electronically controlled hydraulic system converting a, for example, Caterpillar, Komatsu or a Volvo excavator isnt too different. There is obviously interest in the company as in September 2019 the company announced it had received US$33 million in investment, bringing its total funding up to US$48 million.

Of course, a large excavator or a mining truck at work without an operator is always going to catch the eye, and our attention and imagination. They are perhaps the most visual aspect of machine learning on a construction site, but there are a host of other examples that are working away in the background.

As Trimbles Coleman notes, I think one of the interesting things about good AI is you might not know whats even there, right? You just appreciate the fact that, all of a sudden, theres an increase in productivity.

AI is used in construction for specific tasks, such as informing an operator when a machine might fail or isnt being used productively to a broader and more macro sense. For instance, for contractors planning on how best to construct a project there is software with AI that can map out the most efficient processes.

The AI can make predictions about schedule delays and cost overruns. As there is often existing data on schedule and budget performance this can used to make predictions and these predictions will get better over time. As we said before; the more data that AI has, the smarter it becomes.

Venkatasubramanian from Oracle adds that smartification is happening in construction, saying that: Schedules and budgets are becoming smart by incorporating machine learning-driven recommendations.

Supply chain selection is becoming smart by using data across disparate systems and comparing performance. Risk planning is also getting smart by using machine learning to identify and quantify risks from the past that might have a bearing on the present.

There is no doubt that construction has been slower than other industries to adopt new technology, but this isnt just because of some deep-seated reluctance to new ideas.

For example, agriculture has a greater application of machine learning but it is easier for that sector to implement it every year the task for getting in the crops on a farm will be broadly similar.

New challenges

As John Downey, director of sales EMEA, Topcon Positioning Group, explains: With construction theres a slower adoption process because no two projects or indeed construction sites are the same, so the technology is always confronted with new challenges.

Downey adds that as machine learning develops it will work best with repetitive tasks like excavation, paving or milling but thinks that the potential goes beyond this.

As we move forward and AI continues to advance, well begin to apply it across all aspects of construction projects.

The potential applications are countless, and the enhanced efficiency, improved workflows and accelerated rate of industry it will bring are all within reach.

Automated construction equipment needs operators to oversee them as this sector develops it could be one person for every three or five machines, or more, it is currently unclear. With construction facing a skills shortage this is an exciting avenue. There is also AI which helps contractors to better plan, execute and monitor projects you dont need to have machine learning type intelligence to see the potential transformational benefits of this when multi-billion dollar projects are being planned and implemented

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Product Portfolio Analysis and Technological Development of Machine Learning in Medical Imaging Market during the forecasted period – Murphy’s Hockey…

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The Machine Learning in Medical Imaging Market research report recently presentedby Prophecy Market Insights which provides reliable and sincere insights related to the various segments and sub-segments of the market. The market study throws light on the various factors that are projected to impact the overall dynamics of the Machine Learning in Medical Imaging market over the forecast period (2019-2029).

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