Mediaboss Marketing

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

For Better Understanding, Download FREE Sample Copy of Machine Learning as a Service (MLaaS) Market Report @ https://www.futuristicreports.com/request-sample/67627

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

Grab Best Discount on Machine Learning as a Service (MLaaS) Market Research Report [Single User | Multi User | Corporate Users] @ https://www.futuristicreports.com/check-discount/67627

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

Enquire More Before Buying @ https://www.futuristicreports.com/send-an-enquiry/67627

For More Information Kindly Contact:

Futuristic ReportsTel: +1-408-520-9037Media Release: https://www.futuristicreports.com/press-releases

Follow us on Blogger @ https://futuristicreports.blogspot.com/

Original post:
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.

Recommended AI News: 10 Tech Companies Donates Over $1.4bn to Fight Coronavirus

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.

Recommended AI News: Tech Taking Over Our Lives: Smart Phones and the Internet of Things (IoT)

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

Read more:
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.

Follow this link:
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

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

The legal profession, the technology industry and the relationship between the two are in a state of transition. Computer processing power has doubled every year for decades, leading to an explosion in corporate data and increasing pressure on lawyers entrusted with reviewing all of this information.

Now, the legal industry is undergoing significant change, with the advent of machine learning technology fundamentally reshaping the way lawyers conduct their day-to-day practice. Indeed, whilst technological gains might once have had lawyers sighing at the ever-increasing stack of documents in the review pile, technology is now helping where it once hindered. For the first time ever, advanced algorithms allow lawyers to review entire document sets at a glance, releasing them from wading through documents and other repetitive tasks. This means legal professionals can conduct their legal review with more insight and speed than ever before, allowing them to return to the higher-value, more enjoyable aspect of their job: providing counsel to their clients.

In this article, we take a look at how this has been made possible.

Practicing law has always been a document and paper-heavy task, but manually reading huge volumes of documentation is no longer feasible, or even sustainable, for advisors. Even conservatively, it is estimated that we create 2.5 quintillion bytes of data every day, propelled by the usage of computers, the growth of the Internet of Things (IoT) and the digitalisation of documents. Many lawyers have had no choice but resort to sampling only 10 per cent of documents, or, alternatively, rely on third-party outsourcing to meet tight deadlines and resource constraints. Whilst this was the most practical response to tackle these pressures, these methods risked jeopardising the quality of legal advice lawyers could give to their clients.

Legal technology was first developed in the early 1970s to take some of the pressure off lawyers. Most commonly, these platforms were grounded on Boolean search technology, requiring months and even years building the complex sets of rules. As well as being expensive and time-intensive, these systems were also unable to cope with the unpredictable, complex and ever-changing nature of the profession, requiring significant time investment and bespoke configuration for every new challenge that arose. Not only did this mean lawyers were investing a lot of valuable time and resources training a machine, but the rigidity of these systems limited the advice they could give to their clients. For instance, trying to configure these systems to recognise bespoke clauses or subtle discrepancies in language was a near impossibility.

Today, machine learning has become advanced enough that it has many practical applications, a key one being legal document review.

Machine learning can be broadly categorised into two types: supervised and unsupervised machine learning. Supervised machine learning occurs when a human interacts with the system in the case of the legal profession, this might be tagging a document, or categorising certain types of documents, for example. The machine then builds its understanding to generate insights to the user based on this human interaction.

Unsupervised machine learning is where the technology forms an understanding of a certain subject without any input from a human. For legal document review, the unsupervised machine learning will cluster similar documents and clauses, along with clear outliers from those standards. Because the machine requires no a priori knowledge of what the user is looking for, the system may indicate anomalies or unknown unknowns- data which no one had set out to identify because they didnt know what to look for. This allows lawyers to uncover critical hidden risks in real time.

It is the interplay between supervised and unsupervised machine learning that makes technology like Luminance so powerful. Whilst the unsupervised part can provide lawyers with an immediate insight into huge document sets, these insights only increase with every further interaction, with the technology becoming increasingly bespoke to the nuances and specialities of a firm.

This goes far beyond more simplistic contract review platforms. Machine learning algorithms, such as those developed by Luminance, are able to identify patterns and anomalies in a matter of minutes and can form an understanding of documents both on a singular level and in their relationship to each another. Gone are the days of implicit bias being built into search criteria, since the machine surfaces all relevant information, it remains the responsibility of the lawyer to draw the all-important conclusions. But crucially, by using machine learning technology, lawyers are able to make decisions fully appraised of what is contained within their document sets; they no longer need to rely on methods such as sampling, where critical risk can lay undetected. Indeed, this technology is designed to complement the lawyers natural patterns of working, for example, providing results to a clause search within the document set rather than simply extracting lists of clauses out of context. This allows lawyers to deliver faster and more informed results to their clients, but crucially, the lawyer is still the one driving the review.

With the right technology, lawyers can cut out the lower-value, repetitive work and focus on complex, higher-value analysis to solve their clients legal and business problems, resulting in time-savings of at least 50 per cent from day one of the technology being deployed. This redefines the scope of what lawyers and firms can achieve, allowing them to take on cases which would have been too time-consuming or too expensive for the client if they were conducted manually.

Machine learning is offering lawyers more insight, control and speed in their day-to-day legal work than ever before, surfacing key patterns and outliers in huge volumes of data which would normally be impossible for a single lawyer to review. Whether it be for a due diligence review, a regulatory compliance review, a contract negotiation or an eDiscovery exercise, machine learning can relieve lawyers from the burdens of time-consuming, lower value tasks and instead frees them to spend more time solving the problems they have been extensively trained to do.

In the years to come, we predict a real shift in these processes, with the latest machine learning technology advancing and growing exponentially, and lawyers spending more time providing valuable advice and building client relationships. Machine learning is bringing lawyers back to the purpose of their jobs, the reason they came into the profession and the reason their clients value their advice.

James Loxam, CTO, Luminance

Read more:
The impact of machine learning on the legal industry - ITProPortal