Companies are collecting increasing amounts of data, a trend that is driving the development of better analytical tools and tougher security. Analysis and security are now converging as confidential computing prepares to deliver a critical boost to artificial intelligence.

Intel has been investing heavily in confidential computing as a way to expand the amount and types of data companies will manage through cloud services. According to Intel Fellow Ron Perez, who works on security architecture with the Intel Data Center Group, the company believes the emerging security standard will allow enterprises and large organizations to explore new ways to share the data needed to fuel AI and machine learning.

We see this as a long-term effort, Perez said. But the reason why were investing is that it has the potential to be a huge shift for cloud and utility computing.

Confidential computing is a standard that moves past policy-based privacy and security to implement safeguards on a deeper technical level. By using encryption that can only be unlocked via keys the client holds, confidential computing ensures companies hosting data and applications in the cloud have no way to access underlying data, whether it is stored in a database or passes through an application.

The concept is gaining momentum because it allows data to remain encrypted even as its being processed and used in applications. Because the company hosting the data cant access it, this security standard should prevent hackers from grabbing unencrypted data when it moves to the application layer. It would also theoretically allow companies to share data, even between competitors, to perform security checks on customers and weed out fraud.

In August 2019, Intel became one of the founding members of the Confidential Computing Consortium, an open source effort managed by the Linux Foundation that aims to develop the hardware and software standards needed to further adoption. Companies like IBM,Google, and Microsoft have begun to highlight their work in this area as a way to encourage large enterprises, particularly in areas such as finance and health care, to put more of their sensitive data in the cloud.

Perez leads a group of senior technologists at Intel focused on security architecture through a program dubbed Pathfinding. Perez describes it as the pursuit of interesting challenges that our customers are facing. In Perezs case, the goal is to develop a pipeline of security technologies for Intels datacenter customers.

Intel began its work in this area before the term confidential computing came into vogue, with Perez pointing to the companys launch of software guard extensions in 2015. The SGXs are security coding built directly into Intel processors that create separate memory enclaves where data could be placed to limit access. This idea of using hardware and software to protect data while allowing it to be processed is at the heart of confidential computing.

Microsoft used these Intel processors for its Azure cloud to enable its own confidential computing service. Last month, Intel announced it was expanding these capabilities in a new generation of its Xeon Scalable platform.

Our approach has been to drive continuous innovation and deep collaboration with our technology partners to improve the confidentiality and integrity of all data, wherever it is, Perez said.

Proponents of confidential computing argue that it will lead to a new wave of cloud innovation as companies become more comfortable putting their most sensitive data online. Perez said that helps drive AI and machine learning in a couple of ways.

The first is indirect. AI and ML have advanced in recent years, thanks to the growing datasets available to refine algorithms. Confidential computing, by bringing even more and richer data online, will benefit that development.

The main connection to machine learning and artificial intelligence is the fact that were generating more and more data, Perez said. Were analyzing this data with various machine learning technologies. And that explosion of data is whats really driving the interest in confidential computing, whether its used for machine learning or not. Machine learning just happens to be one of its main uses.

No matter the type of underlying data, if it must be decrypted to be used, the security of algorithms it passes through is critical.

How do you protect these algorithms across this very broad spectrum of use cases? Perez said. We see confidential computing as a paradigm shift for cloud computing. The infrastructure providers are providing the capabilities that allow cloud companies to deliver these services as a utility, and they dont have to take responsibility for the protection of the data themselves.

Beyond that, confidential computing is enabling different types of collaboration around data to drive machine learning. Perez pointed to the example of a brain tumor project at the University of Pennsylvania.

Penns Perelman School of Medicine has teamed up with 29 other health care and research institutions around the world, including in the U.K., Germany, and India. The group is using Intels confidential computing to develop a distributed approach to machine learning that allows them to share patient data, including medical imaging. Because such data can remain encrypted while it is being used for machine learning, the group can safely share that data and collaborate in a way that otherwise might not be possible.

Thats critical because data is urgently needed to train machine learning, but no single institution has enough to achieve this on its own. Previously, Penn Medicine and Intel Labs published a study showing that federated learning (a collaborative approach) could train a machine learning model far more effectively than working alone. In this case, the group believes the combination of confidential computing and federated learning will allow them to make rapid breakthroughs in AI models that identify brain tumors.

Merchants are also tapping the ability to allow new types of collaboration for customer and partner data, as are enterprises. While analysts like Gartner believe the real impact of confidential computing may still be several years away, Perez said it is already helping some sectors accelerate their AI and machine learning capabilities in the short term.

There are multiple aspects of the computing stack that need to be protected, Perez said. Confidential computing solves problems that couldnt be solved before. The concept that I can use any computing capability that may reside in any country around the world and still have some preservation of the privacy and confidentiality of my data, thats pretty powerful.

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Why Intel believes confidential computing will boost AI and machine learning - VentureBeat

December 03, 2020 04:47 ET | Source: Fortune Business Insights

Pune, Dec. 03, 2020 (GLOBE NEWSWIRE) -- The global machine learning market size is anticipated to rise remarkably on account of the advancement in deep learning. This, coupled with the amalgamation of analytics-driven solutions with ML abilities, is expected to aid in favor of the market in the coming years. As per a recent report by Fortune Business Insights, titled, Machine Learning Market Size, Share & Covid-19 Impact Analysis, By Component (Solution, and Services), By Enterprise Size (SMEs, and Large Enterprises), By Deployment (Cloud and On-premise), By Industry (Healthcare, Retail, IT and Telecommunication, BFSI, Automotive and Transportation, Advertising and Media, Manufacturing, and Others), and Regional Forecast, 2020-2027, the value of this market was USD 8.43 billion in 2019 and is likely to exhibit a CAGR of 39.2% to reach USD 117.19 billion by the end of 2027.

Click here to get the short-term and long-term impacts of COVID-19 on this Market.

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Coronavirus has not only brought about health issues and created social distance among people but it has also hampered the industrial and commercial sectors drastically. The whole world is following home quarantine, and we are unsure when we can freely roam the streets again. The governments of various nations are also making considerable efforts to bring the COVID-19 situation under control, and hopefully, we will overcome this obstacle soon.

Fortune Business Insights is offering special reports on various markets impacted by the COVID-19 pandemic. These reports provide a thorough analysis of the market and will be helpful for the players and investors to accordingly study and chalk out the growth strategies for better revenue generation.

What Are the Objectives of the Report?

The report is based on a 360-degree overview of the market that discusses major factors driving, repelling, challenging, and creating opportunities for the market. It also talks about the current trends prevalent in the market, recent industry developments, and other interesting insights that will help investors accordingly chalk out growth strategies for the future. The report also highlights the names of major segments and significant players operating in the market. For more information on the report, log on to the company website.

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Drivers & Restraints-

Huge Investment in Artificial Intelligence to Aid in Favor of Market

The e-commerce sector has showcased significant growth in the past few years, with the advent of retail analytics. Companies such as Alibaba, eBay, Amazon, and others are utilizing advanced data analytics solutions for boosting their sales graph. Thus, the advent of analytical solutions into the e-commerce sector, offering enhanced consumer experience and rise in sales graph is one of the major factors promoting the machine learning market growth. In addition to this, the use of machine intelligence solutions for encrypting and protecting data is adding boost to the market. Furthermore, massive investments in artificial intelligence (AI) and efforts to introduce innovations in this field are further expected to add impetus to the market in the coming years.

On the flipside, national security threat issues such as deep fakes and other fraudulent cases, coupled with the misuse of robots, may hamper the overall market growth. Nevertheless, the introduction and increasing popularity of self-driving cars from the automotive industry is projected to create new growth opportunities for the market in the coming years.

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Segment:

IT and Telecommunication Segment Bagged Major Share Soon to be Overpowered by Healthcare Sector

Based on segmentation by industry, the IT and telecommunication segment earned 22.0% machine learning market share and emerged dominant. But the current COVID-19 pandemic increased the popularity of wearable medical devices to keep track of personal health and diet. This is expected to help the healthcare sector emerge dominant in the coming years.

Regional Analysis-

Asia Pacific to Exhibit Fastest Growth Rate Owing to Rising Adoption by Developing Economies

Region-wise, North America emerged dominant in the market, with a revenue of USD 3.07 billion in 2019. This is attributable to the presence of significant players such as IBM Corporation, Oracle Corporation, Amazon.com, and others and their investments in research and development of better software solutions for this technology. On the other side, the market in Asia Pacific is expected to exhibit a rapid CAGR in the forecast period on account of the increasing adoption of artificial intelligence, machine learning, and other latest advancements in the rising economies such as India, China, and others.

Competitive Landscape-

Players Focusing on Development of Responsible Machine Learning to Strengthen their position

The global market generates significant revenues from companies such as Microsoft Corporation, IBM Corporation, SAS Institute Inc., Amazon.com, and others. The principal objective of these players is to develop responsible machine learning that will help prevent unauthorized use of such solutions for fraudulent or data theft crimes. Other players are engaging in collaborative efforts to strengthen their position in the market.

Major Industry Developments of this Market Include:

March 2019 The latest and most advanced ML capability was added to the 365 platforms by Microsoft. This new feature will help strengthen the internet-facing virtual machines by increasing security when merged with the integration of machine learning by Azures security center.

Some of the Key Players of the Machine Learning Market Include:

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Commerce Cloud Market Size, Share & Industry Analysis, By Component (Platform, and Services), By Enterprise Size (SMEs, and Large Enterprises), By Application (Grocery and Pharmaceuticals, Fashion and Apparel, Travel and Hospitality, Electronics, Furniture and Bookstore, and Others), By End-use (B2B, and B2C), and Regional Forecast, 2020-2027

Big Data Technology Market Size, Share & Industry Analysis, By Offering (Solution, Services), By Deployment (On-Premise, Cloud, Hybrid), By Application (Customer Analytics, Operational Analytics, Fraud Detection and Compliance, Enterprise Data Warehouse Optimization, Others), By End Use Industry (BFSI, Retail, Manufacturing, IT and Telecom, Government, Healthcare, Utility, Others) and Regional Forecast, 2019-2026

Artificial Intelligence (AI) Market Size, Share and Industry Analysis By Component (Hardware, Software, Services), By Technology (Computer Vision, Machine Learning, Natural Language Processing, Others), By Industry Vertical (BFSI, Healthcare, Manufacturing, Retail, IT & Telecom, Government, Others) and Regional Forecast, 2019-2026

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Machine Learning Market to Grow Notably Attributed to Increasing Adoption of Analytics-driven Solutions by Developing Economies, says Fortune Business...

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

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

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

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

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

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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Amazon announces new machine learning tools to help customers monitor machines and worker safety - http://www.computing.co.uk

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

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