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If greater efficiencies are to be made at each stage of production, machines must adapt and become smarter. Interest in intelligent machine behavior is increasing, and with it, the challenge of digital technology. Sensors remain the source of information, and integrated software offers a solution for evaluating and communicating networked data. However, the Industry 4.0 trend means there is an urgent need for reformed thinking in IT on data complexity. Deep learning is essential and its the path SICK and its customers are taking for modern plant processes.

Deep learning is a machine learning technique and is often seen as a significant part of the future of artificial intelligence. SICK applies this key technology to its sensors, offering customers added value for greater productivity and flexibility.

Deep learning requires algorithms capable of detecting and processing vast, complex amounts of patterns and data. The artificial neural network mimics human thinking and learns from examples. It learns from experience and learns to adapt to new, updated information.

As a result, a range of optimizations are possible today that would have been unthinkable just a few years ago. Machines and plants, in combination with intelligent data and specialized sensors, can find solutions to the most complex tasks.

Most of SICKs current deep learning projects are in the field of optical quality inspection. In logistics automation, deep learning cameras can automatically detect, verify, classify, and localize trained objects or features by analyzing the taught-in image base.

For example, they can check whether any flats are present in the sorter trays, optimizing sorter cell assignment and increasing throughput. They detect strapping bands on parcels even white bands on white parcels. This improves quality control in the automatic packaging process and makes sure that transported objects are analyzed.

If packages are dented or damaged, or if the material properties of the parcel need to be determined, SICK sensors can intelligently capture and evaluate structures or features during live operation. They ensure that the next steps in the sorting process are initiated. This feature is unique in this form and could previously be performed only by the human eye. The ultimate aim of all SICK projects is to apply deep learning to improving processes and increasing plant effectiveness.

Once deep learning processes are put in place, it is essential to continue to maintain plant effectiveness by keeping machines, sensors, and other technology in prime condition. Services from SICK ensure success throughout the product and machine lifecycle. And now with the addition of virtual machine support available through SICK, manufacturers can access a SICK expert whenever they need one.

These industry leading experts have decades of experience in designing, specifying, commissioning, and supporting technologies such as machine safety, industry 4.0, integration, machine vision, and more. These services can be access at any time, from anywhere, day or night for a virtual consultation to ensure all processes run smoothly to maintain deep learning technology.

SICKs portfolio of services and support can start with consulting (either on-site or virtually) and help in selecting appropriate products, but thats just the start. SICK offers a full menu of pre- and post- sales support, maintenance, and lifecycle services including:

The demand is not for a universal solution. Rather, the focus is on a solution tailored to a specific case. Although modern 2D and 3D cameras are continually becoming faster and more powerful, their performance is currently restricted by traditional image processing algorithms. In order to assess different applications and conditions, SICKs deep learning experts work closely with the clients process and quality experts. Their unique process expertise forms the basis of simulation training and the heart of subsequent deep learning algorithms in the sensor.

A complex network architecture processes the enormous quantity of information. In spite of this, the time needed to train a deep learning network comes to little more than a few hours. Deep learning networks can also be retrained and adapted to new conditions. For big data pools and neuronal network training, SICK uses powerful independent, internal processing and IT systems. The deep learning algorithms generated are placed on the sensor locally via the cloud, making them fail-safe and directly available on an intelligent camera.

Theres still a long time to go before machines truly reign supreme, yet even today, deep learning is achieving impressive results and offers many benefits. The essential work, however, is still being done by humans. Only time will tell how many companies and industries will decide to fuel their growth by stepping up their investment in this digital technology.

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Deep Learning is the Future for Increased Efficiencies and Virtual Machine Support - Modern Materials Handling

Futuristic Reports, The growth and development of Global Machine Learning as a Service (MLaaS) Market Report 2020 by Players, Regions, Type, and Application, forecast to 2026 provides industry analysis and forecast from 2020-2026. Global Machine Learning as a Service (MLaaS) Market analysis delivers important insights and provides a competitive and useful advantage to the pursuers. Machine Learning as a Service (MLaaS) processes, economic growth is analyzed as well. The data chart is also backed up by using statistical tools.

Simultaneously, we classify different Machine Learning as a Service (MLaaS) markets based on their definitions. Downstream consumers and upstream materials scrutiny are also carried out. Each segment includes an in-depth explanation of the factors that are useful to drive and restrain it.

Key Players Mentioned in the study are Broadcom, EMC, GEMALTO, SYMANTEC, VASCO DATA SECURITY INTERNATIONAL, AUTHENTIFY, ENTRUST DATACARD, SECUREAUTH, SECURENVOY, TELESIGN

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Key Issues Addressed by Machine Learning as a Service (MLaaS) Market: It is very significant to have Machine Learning as a Service (MLaaS) segmentation analysis to figure out the essential factors of growth and development of the market in a particular sector. The Machine Learning as a Service (MLaaS) report offers well summarized and reliable information about every segment of growth, development, production, demand, types, application of the specific product which will be useful for players to focus and highlight on.

Businesses Segmentation of Machine Learning as a Service (MLaaS) Market:

On the basis on the applications, this report focuses on the status and Machine Learning as a Service (MLaaS) outlook for major applications/end users, sales volume, and growth rate for each application, including-

BFSI MarketMedical MarketThe IT MarketThe Retail MarketEntertainment MarketLogistics MarketOther

On the basis of types/products, this Machine Learning as a Service (MLaaS) report displays the revenue (Million USD), product price, market share, and growth rate of each type, split into-

Small And Medium-Sized EnterprisesBig Companies

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NOTE : Our team is studying Covid-19 impact analysis on various industry verticals and Country Level impact for a better analysis of markets and industries. The 2020 latest edition of this report is entitled to provide additional commentary on latest scenario, economic slowdown and COVID-19 impact on overall industry. Further it will also provide qualitative information about when industry could come back on track and what possible measures industry players are taking to deal with current situation.

or

You just drop an Email to: [emailprotected] us if you are looking for any Economical analysis to shift towards the New Normal on any Country or Industry Verticals.

Machine Learning as a Service (MLaaS) Market Regional Analysis Includes:

Asia-Pacific (Vietnam, China, Malaysia, Japan, Philippines, Korea, Thailand, India, Indonesia, and Australia) Europe (Turkey, Germany, Russia UK, Italy, France, etc.) North America (the United States, Mexico, and Canada.) South America (Brazil etc.) The Middle East and Africa (GCC Countries and Egypt.)

Machine Learning as a Service (MLaaS) Insights that Study is going to provide:

Gain perceptive study of this current Machine Learning as a Service (MLaaS) sector and also possess a comprehension of the industry; Describe the Machine Learning as a Service (MLaaS) advancements, key issues, and methods to moderate the advancement threats; Competitors In this chapter, leading players are studied with respect to their company profile, product portfolio, capacity, price, cost, and revenue. A separate chapter on Machine Learning as a Service (MLaaS) market structure to gain insights on Leaders confrontational towards market [Merger and Acquisition / Recent Investment and Key Developments] Patent Analysis** Number of patents filed in recent years.

Table of Content:

Global Machine Learning as a Service (MLaaS) Market Size, Status and Forecast 20261. Market Introduction and Market Overview2. Industry Chain Analysis3. Machine Learning as a Service (MLaaS) Market, by Type4. Machine Learning as a Service (MLaaS) Market, by Application5. Production, Value ($) by Regions6. Production, Consumption, Export, Import by Regions (2016-2020)7. Market Status and SWOT Analysis by Regions (Sales Point)8. Competitive Landscape9. Analysis and Forecast by Type and Application10. Channel Analysis11. New Project Feasibility Analysis12. Market Forecast 2020-202613. Conclusion

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Machine Learning as a Service (MLaaS) Market Significant Growth with Increasing Production to 2026 | Broadcom, EMC, GEMALTO Cole Reports - Cole of...

The Respond Analyst Simplifies Endpoint Analysis, Delivers Real-Time, Expert Diagnosis of Security Incidents at a Fraction of the Cost of Manual Monitoring and Investigation

Respond Software today announced analysis support of Endpoint Detection and Response (EDR) data from Carbon Black, CrowdStrike and SentinelOneby the Respond Analyst the virtual cybersecurity analyst for security operations. The Respond Analyst provides customers with expert EDR analysis right out of the box, creating immediate business value in security operations for organizations across industries.

The Respond Analyst provides a highly cost-effective and thorough way to analyze security-related alerts and data to free up people and budget from initial monitoring and investigative tasks. The software uses integrated reasoning decision-making that leverages multiple alerting telemetries, contextual sources and threat intelligence to actively monitor and triage security events in near real-time. Respond Software is now applying this unique approach to EDR data to reduce the number of false positives from noisy EDR feeds and turn transactional sensor data into actionable security insights.

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Mike Armistead, CEO and co-founder, Respond Software, said: As security teams increase investment in EDR capabilities, they not only must find and retain endpoint analysis capabilities but also sift through massive amounts of data to separate false positives from real security incidents. The Respond Analyst augments security personnel with our unique Robotic Decision Automation software that delivers thorough, consistent and 24x7x365 analysis of security data from network to endpoint saving budget and time for the security team. It derivesmaximum value from EDR at a level of speed and efficiency unmatched by any other solution today.

Jim Routh,head of enterprise information risk management,MassMutual, said:Data science is the foundation for MassMutuals cybersecurity program.Applying mathematics and machine learning models to security operations functions to improve productivity and analytic capability is an important part of this foundation.

Jon Davis, CEO of SecureNation, said:SecureNation has made a commitment to its customers to deliver the right technology that enables the right security automation at lower operating costs. The EDR skills enabled by the Respond Analyst will make it possible for SecureNation to continue to provide the most comprehensive, responsive managed detection and response service available to support the escalating needs of enterprises today and into the future.

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EDR solutions capture and evaluate a broad spectrum of attacks spanning the MITRE ATT&CK Framework. These products often produce alerts with a high degree of uncertainty, requiring costly triage by skilled security analysts that can take five to 15 minutes on average to complete. A security analyst must pivot to piece together information from various security product consoles, generating multiple manual queries per system, process and account. The analyst must also conduct context and scoping queries. All this analysis requires deep expert system knowledge in order to isolate specific threats.

The Respond Analyst removes the need for multiple console interactions by automating the investigation, scoping and prioritization of alerts into real, actionable incidents. With the addition of EDR analysis, Respond Software broadens the integrated reasoning capabilities of the Respond Analyst to include endpoint system details identifying incidents related to suspect activity from binaries, client apps, PowerShell and other suspicious entities.

Combining EDR analysis with insights from network intrusion detection, web filtering and other network telemetries, the Respond Analyst extends its already comprehensive coverage. This allows security operations centers to increase visibility, efficiency and effectiveness, thereby reducing false positives and increasing the probability of identifying true malicious and actionable activity early in the attack cycle.

Recommended: AiThority Interview with Josh Poduska, Chief Data Scientist at Domino Data Lab

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Respond Software Unlocks the Value in EDR Data with Robotic Decision - AiThority

The following was written byHarald Collet, CEO at Alkymi andHugues Chabanis, Product Portfolio Manager,Alternative Investments at SimCorp

Institutional investors are buckling under the operational constraint of processing hundreds of data streams from unstructured data sources such as email, PDF documents, and spreadsheets. These data formats bury employees in low-value copy-paste workflows andblockfirms from capturing valuable data. Here, we explore how Machine Learning(ML)paired with a better operational workflow, can enable firms to more quickly extract insights for informed decision-making, and help governthe value of data.

According to McKinsey, the average professional spends 28% of the workday reading and answering an average of 120 emails on top ofthe19% spent on searching and processing data.The issue is even more pronouncedininformation-intensive industries such as financial services,asvaluable employees are also required to spendneedlesshoursevery dayprocessing and synthesizing unstructured data. Transformational change, however,is finally on the horizon. Gartner research estimates thatby 2022, one in five workers engaged in mostly non-routine tasks will rely on artificial intelligence (AI) to do their jobs. And embracing ML will be a necessity for digital transformation demanded both by the market and the changing expectations of the workforce.

For institutional investors that are operating in an environment of ongoing volatility, tighter competition, and economic uncertainty, using ML to transform operations and back-office processes offers a unique opportunity. In fact, institutional investors can capture up to 15-30% efficiency gains by applying ML and intelligent process automation (Boston Consulting Group, 2019)inoperations,which in turn creates operational alpha withimproved customer service and redesigning agile processes front-to-back.

Operationalizingmachine learningworkflows

ML has finally reached the point of maturity where it can deliver on these promises. In fact, AI has flourished for decades, but the deep learning breakthroughs of the last decade has played a major role in the current AI boom. When it comes to understanding and processing unstructured data, deep learning solutions provide much higher levels of potential automation than traditional machine learning or rule-based solutions. Rapid advances in open source ML frameworks and tools including natural language processing (NLP) and computer vision have made ML solutions more widely available for data extraction.

Asset class deep-dive: Machine learning applied toAlternative investments

In a 2019 industry survey conducted byInvestOps, data collection (46%) and efficient processing of unstructured data (41%) were cited as the top two challenges European investment firms faced when supportingAlternatives.

This is no surprise as Alternatives assets present an acute data management challenge and are costly, difficult, and complex to manage, largely due to the unstructured nature ofAlternatives data. This data is typically received by investment managers in the form of email with a variety of PDF documents or Excel templates that require significant operational effort and human understanding to interpret, capture,and utilize. For example, transaction data istypicallyreceived by investment managers as a PDF document via email oran online portal. In order to make use of this mission critical data, the investment firm has to manually retrieve, interpret, and process documents in a multi-level workflow involving 3-5 employees on average.

The exceptionally low straight-through-processing (STP) rates already suffered by investment managers working with alternative investments is a problem that will further deteriorate asAlternatives investments become an increasingly important asset class,predictedbyPrequinto rise to $14 trillion AUM by 2023 from $10 trillion today.

Specific challenges faced by investment managers dealing with manual Alternatives workflows are:

WithintheAlternatives industry, variousattempts have been madeto use templatesorstandardize the exchange ofdata. However,these attempts have so far failed,or are progressing very slowly.

Applying ML to process the unstructured data will enable workflow automation and real-time insights for institutional investment managers today, without needing to wait for a wholesale industry adoption of a standardized document type like the ILPA template.

To date, the lack of straight-through-processing (STP) in Alternatives has either resulted in investment firms putting in significant operational effort to build out an internal data processing function,or reluctantly going down the path of adopting an outsourcing workaround.

However, applyinga digital approach,more specificallyML, to workflows in the front, middle and back office can drive a number of improved outcomes for investment managers, including:

Trust and control are critical when automating critical data processingworkflows.This is achieved witha human-in-the-loopdesign that puts the employee squarely in the drivers seat with features such as confidence scoring thresholds, randomized sampling of the output, and second-line verification of all STP data extractions. Validation rules on every data element can ensure that high quality output data is generated and normalized to a specific data taxonomy, making data immediately available for action. In addition, processing documents with computer vision can allow all extracted data to be traced to the exact source location in the document (such as a footnote in a long quarterly report).

Reverse outsourcing to govern the value of your data

Big data is often considered the new oil or super power, and there are, of course, many third-party service providers standing at the ready, offering to help institutional investors extract and organize the ever-increasing amount of unstructured, big data which is not easily accessible, either because of the format (emails, PDFs, etc.) or location (web traffic, satellite images, etc.). To overcome this, some turn to outsourcing, but while this removes the heavy manual burden of data processing for investment firms, it generates other challenges, including governance and lack of control.

Embracing ML and unleashing its potential

Investment managers should think of ML as an in-house co-pilot that can help its employees in various ways: First, it is fast, documents are processed instantly and when confidence levels are high, processed data only requires minimum review. Second, ML is used as an initial set of eyes, to initiate proper workflows based on documents that have been received. Third, instead of just collecting the minimum data required, ML can collect everything, providing users with options to further gather and reconcile data, that may have been ignored and lost due to a lack of resources. Finally, ML will not forget the format of any historical document from yesterday or 10 years ago safeguarding institutional knowledge that is commonly lost during cyclical employee turnover.

ML has reached the maturity where it can be applied to automate narrow and well-defined cognitive tasks and can help transform how employees workin financial services. However many early adopters have paid a price for focusing too much on the ML technology and not enough on the end-to-end business process and workflow.

The critical gap has been in planning for how to operationalize ML for specific workflows. ML solutions should be designed collaboratively with business owners and target narrow and well-defined use cases that can successfully be put into production.

Alternatives assets are costly, difficult, and complex to manage, largely due to the unstructured nature of Alternatives data. Processing unstructured data with ML is a use case that generates high levels of STP through the automation of manual data extraction and data processing tasks in operations.

Using ML to automatically process unstructured data for institutional investors will generate operational alpha; a level of automation necessary to make data-driven decisions, reduce costs, and become more agile.

The views represented in this commentary are those of its author and do not reflect the opinion of Traders Magazine, Markets Media Group or its staff. Traders Magazine welcomes reader feedback on this column and on all issues relevant to the institutional trading community.

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Machine Learning: Making Sense of Unstructured Data and Automation in Alt Investments - Traders Magazine

Machine learning is by no means a new phenomenon. It has been used in various forms for decades, but it is very much a technology of the present due to the massive increase in the data upon which it thrives. It has been widely adopted by businesses, reducing the time and improving the value of the insight they can distil from large volumes of customer data.

However, in the public sector there is a different story. Despite being championed by some in government, machine learning has often faced a reaction of concern and confusion. This is not intended as general criticism and in many cases it reflects the greater value that civil servants place on being ethical and fair, than do some commercial sectors.

One fear is that, if the technology is used in place of humans, unfair judgements might not be noticed or costly mistakes in the process might occur. Furthermore, as many decisions being made by government can dramatically affect peoples lives and livelihood then often decisions become highly subjective and discretionary judgment is required. There are also those still scarred by films such as iRobot, but thats a discussion for another time.

Fear of the unknown is human nature, so fear of unfamiliar technology is thus common. But fears are often unfounded and providing an understanding of what the technology does is an essential first step in overcoming this wariness. So for successful digital transformation not only do the civil servants who are considering such technologies need to become comfortable with its use but the general public need to be reassured that the technology is there to assist, not replace, human decisions affecting their future health and well-being.

Theres a strong case to be made for greater adoption of machine learning across a diverse range of activities. The basic premise of machine learning is that a computer can derive a formula from looking at lots of historical data that enables the prediction of certain things the data describes. This formula is often termed an algorithm or a model. We use this algorithm with new data to make decisions for a specific task, or we use the additional insight that the algorithm provides to enrich our understanding and drive better decisions.

For example, machine learning can analyse patients interactions in the healthcare system and highlight which combinations of therapies in what sequence offer the highest success rates for patients; and maybe how this regime is different for different age ranges. When combined with some decisioning logic that incorporates resources (availability, effectiveness, budget, etc.) its possible to use the computers to model how scarce resources could be deployed with maximum efficiency to get the best tailored regime for patients.

When we then automate some of this, machine learning can even identify areas for improvement in real time and far faster than humans and it can do so without bias, ulterior motives or fatigue-driven error. So, rather than being a threat, it should perhaps be viewed as a reinforcement for human effort in creating fairer and more consistent service delivery.

Machine learning is an iterative process; as the machine is exposed to new data and information, it adapts through a continuous feedback loop, which in turn provides continuous improvement. As a result, it produces more reliable results over time and evermore finely tuned and improved decision-making. Ultimately, its a tool for driving better outcomes.

The opportunities for AI to enhance service delivery are many. Another example in healthcare is Computer Vision (another branch of AI), which is being used in cancer screening and diagnosis. Were already at the stage where AI, trained from huge libraries of images of cancerous growths, is better at detecting cancer than human radiologists. This application of AI has numerous examples, such as work being done at Amsterdam UMC to increase the speed and accuracy of tumour evaluations.

But lets not get this picture wrong. Here, the true value is in giving the clinician more accurate insight or a second opinion that informs their diagnosis and, ultimately, the patients final decision regarding treatment. A machine is there to do the legwork, but the human decision to start a programme for cancer treatment, remains with the humans.

Acting with this enhanced insight enables doctors to become more efficient as well as effective. Combining the results of CT scans with advanced genomics using analytics, the technology can assess how patients will respond to certain treatments. This means clinicians avoid the stress, side effects and cost of putting patients through procedures with limited efficacy, while reducing waiting times for those patients whose condition would respond well. Yet, full-scale automation could run the risk of creating a lot more VOMIT.

Victims Of Modern Imaging Technology (VOMIT) is a new phenomenon where a condition such as a malignant tumour is detected by imaging and thus at first glance it would seem wise to remove it. However, medical procedures to remove it carry a morbidity risk which may be greater than the risk the tumour presents during the patients likely lifespan. Here, ignorance could be bliss for the patient and doctors would examine the patient holistically, including mental health, emotional state, family support and many other factors that remain well beyond the grasp of AI to assimilate into an ethical decision.

All decisions like these have a direct impact on peoples health and wellbeing. With cancer, the faster and more accurate these decisions are, the better. However, whenever cost and effectiveness are combined there is an imperative for ethical judgement rather than financial arithmetic.

Healthcare is a rich seam for AI but its application is far wider. For instance, machine learning could also support policymakers in planning housebuilding and social housing allocation initiatives, where they could both reduce the time for the decision but also make it more robust. Using AI in infrastructural departments could allow road surface inspections to be continuously updated via cheap sensors or cameras in all council vehicles (or cloud-sourced in some way). The AI could not only optimise repair work (human or robot) but also potentially identify causes and then determine where strengthened roadways would cost less in whole-life costs versus regular repairs or perhaps a different road layout would reduce wear.

In the US, government researchers are already using machine learning to help officials make quick and informed policy decisions on housing. Using analytics, they analyse the impact of housing programmes on millions of lower-income citizens, drilling down into factors such as quality of life, education, health and employment. This instantly generates insightful, accessible reports for the government officials making the decisions. Now they can enact policy decisions as soon as possible for the benefit of residents.

While some of the fears about AI are fanciful, there is a genuine cause for concern about the ethical deployment of such technology. In our healthcare example, allocation of resources based on gender, sexuality, race or income wouldnt be appropriate unless these specifically had an impact on the prescribed treatment or its potential side-effects. This is self-evident to a human, but a machine would need this to be explicitly defined. Logically, a machine would likely display bias to those groups whose historical data gave better resultant outcomes, thus perpetuating any human equality gap present in the training data.

The recent review by the Committee on Standards in Public Life into AI and its ethical use by government and other public bodies concluded that there are serious deficiencies in regulation relating to the issue, although it stopped short of recommending the establishment of a new regulator.

The review was chaired by crossbench peer Lord Jonathan Evans, who commented:

Explaining AI decisions will be the key to accountability but many have warned of the prevalence of Black Box AI. However our review found that explainable AI is a realistic and attainable goal for the public sector, so long as government and private companies prioritise public standards when designing and building AI systems.

Fears of machine learning replacing all human decision-making need to be debunked as myth: this is not the purpose of the technology. Instead, it must be used to augment human decision-making, unburdening them from the time-consuming job of managing and analysing huge volumes of data. Once its role can be made clear to all those with responsibility for implementing it, machine learning can be applied across the public sector, contributing to life-changing decisions in the process.

Find out more on the use of AI and machine learning in government.

Simon Dennis, Director of AI & Analytics Innovation, SAS UK

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Machine learning: the not-so-secret way of boosting the public sector - ITProPortal