Category Archives: Machine Learning
The impact of machine learning on the legal industry – 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
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The impact of machine learning on the legal industry - ITProPortal
WekaIO Recognized as One of CRN’s Top 100 Storage Vendors for 2020 – AiThority
WekaIO, the innovation leader in high-performance and scalable file storage for data-intensive applications, announced that it is being recognized by CRN, a brand ofThe Channel Company, in its first-ever 2020 Storage 100 list. This new list, carefully chosen by a panel of respected CRN editors, acknowledges leading storage vendors that offer transformative, cutting-edge solutions.
Todays data-intensive applications, stemming from artificial intelligence (AI), machine learning (ML), analytics, and genomics workloads, have placed extraordinary pressure on IT infrastructure demanding highly scalable storage that delivers extreme performance
According to CRN, not only do these Storage 100 companies push the boundaries of innovation, but the list itself is also a valuable tool for solution providers looking to find vendors who can guide them through the intricate storage technology market. The Storage 100 list will become an annual reference for solution providers who are seeking out vendors offering superior storage solutions in areas such as software-defined storage, data protection, data management, and storage components.
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CRNs Storage 100 list is our newest recognition of the best of the best in storage innovation, said Bob Skelley, CEO of The Channel Company. These companies are at the forefront of storage technology advancements, delivering state-of-the-art solutions built for the future. We acknowledge and congratulate them for their investment in R&D, engineering, and innovation. Their efforts enable solution providers to offer the best technology for their customers.
Our flagship solution, the Weka File System, is revolutionizing the storage world by breaking through the limitations of previous generation products, said Liran Zvibel, CEO and co-founder at WekaIO. WekaFS was uniquely built for organizations that solve big problems in their industry and demand datacenter agility. We deliver that by running on-premises, in the cloud, or with a hybrid approach; and our customers get unprecedented throughput and low latency performance with any Infiniband or Ethernet-enabled CPU or GPU-based cluster. Furthermore, we provide high security with state-of-the-art encryption, enterprise features, and the ease of use of shared NAS, including multiprotocol support for NFS and SMB.
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Todays data-intensive applications, stemming from artificial intelligence (AI), machine learning (ML), analytics, and genomics workloads, have placed extraordinary pressure on IT infrastructure demanding highly scalable storage that delivers extreme performance, added Barbara Murphy, vice president of marketing at WekaIO. Weka delivers the industrys best performance at any scale, with 10x the performance of legacy network-attached storage (NAS) systems and 3x the performance of local server storage. The current release introduces additional security and management features: encryption that ensures that data is kept safe both in-flight and at-rest, and snapshot-to-object that facilitates workload migration, disaster recovery, and archiving.
WekaFS was purpose-built for high-performance technical computing and data-intensive applications. Our clients across industries see immediate business value in how WekaFS can get a them to the next level in gleaning value from their data, said Frederic Van Haren, CTO of HighFens, a Weka Innovation Network (WIN) Leader partner.
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WekaIO Recognized as One of CRN's Top 100 Storage Vendors for 2020 - AiThority
Global Machine Learning Market expected to grow USD XX.X million by 2025 , at a CAGR of XX% during forecast period: Microsoft, IBM, SAP, SAS, Google,…
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Global Machine Learning Market expected to grow USD XX.X million by 2025 , at a CAGR of XX% during forecast period: Microsoft, IBM, SAP, SAS, Google,...
Artificial Intelligence Is Going to Revolutionize the Executive Search World – BRINK
Todays machine learning and predictive analytics technologies are about to bring revolutionary changes to the executive search industry.
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From the 1950s to the mid-1990s, executive recruiters sourced candidates by leveraging their Rolodexes; they made a lot of phone calls, starting with people they knew and requesting possible candidates and referrals. Their success as recruiters was largely governed by their personal network.
Internet job boards and resume databases began to change this paradigm.
For the first time, information about the workforce became freely available to diligent researchers. LinkedIn, for example, with its hundreds of millions of active profiles, allows recruiters to consider sources and candidates outside their phone networks. But combing through LinkedIn is an eye-wateringly laborious process; for every person of interest there are tens of thousands who sound similar but are not, and the information is not always accurate or up to date.
This is one of the reasons why recruiters are generally supported by large research teams and why the average search still takes three to five months from inception to completion.
Todays machine learning and predictive analytics technologies, however, with their ability to sift through huge volumes of data with previously unimaginable speed and precision, are about to bring revolutionary changes to the search world.
For the executive search industry, AIs most imminent and revolutionary application will be its ability to compile large, constantly evolving data sets and draw inferential deductions from that data.
Though it would have seemed impossible just a few years ago, AI algorithms can now aggregate personal and organizational profiles from billions of social, public and enterprise sources and use them to build a continuously updated portrait of the labor mark.
Odgers Berndtsons proprietary database, for example, updates every 30 to 45 days, adding 600,000 new executive profiles a month.
This data portrait, valuable in its own right, is then subjected to a highly nuanced machine learning engine, which can contextualize company and candidate profiles across a wide variety of key metrics.
Whereas a keyword-matching system measures a candidate against a few pre-programmed words deemed necessary for a role, proper machine learning tools can understand candidates and companies in the context of their ecosystem and make inferential deductions about their qualities, relationships and likely behavior.
In practice, this means two things.
First: AI-enabled search consultants have on-demand access to millions of corporate and candidate profiles.
Second: They have on-demand access to nuanced and customizable evaluations of those profiles and the relationships between them.
AI algorithms are capable of completing millions of pattern-matching comparisons per second and in some cases have seen and compared as many as two billion career progressions. They make complex and qualitative inferences about individual and corporate profiles and can do so on an incredible scale.
What this means, in practice, is that AI can evaluate candidates and companies with incredible precision.
Rather than simply filtering candidates by static traditional metrics job experience, education, diversity and leaving humans to make qualitative inferences, AI can identify candidates whove demonstrated patterns of excellence over the course of their careers.
It can sort relevant candidates by their likelihood to be interested in a new position.
And it can provide a quantitative and contextually comprehensive understanding of the moves of successful candidates going from one company to another over the last fifteen years, for example.
AI will be for executive search firms what the first tractors were to farmers: It wont change the substance of what search firms do, but it will allow them to do a better job faster.
Rather than spending weeks building a comprehensive, three-dimensional, long list of candidates, todays AI-enabled recruiters can compile nuanced long lists of candidates simply by feeding AI with a perfect profile and having it sweep through the database, identifying profiles that have similar skills, career trajectories and job titles.
This added efficiency will noticeably shorten the time and resources firms put into the front end of each search, freeing recruiters to focus on value-adding aspects of the job like candidate development, contract negotiation and onboarding.
In the long term, as AI becomes more ubiquitous, these efficiencies may shift industry expectations about search durations, decreasing the average project length from months to weeks.
These efficiency gains have structural implications for the recruiting landscape, particularly at the middle and lower ends of the hiring pyramid where commonalities across searches lend themselves to comprehensive automation.
Because machine learning algorithms learn from the tasks they accomplish, by the time an algorithm has finished 100 comptroller searches for 100 industrial companies, it will be pretty good at distinguishing between long-list and finalist-quality candidates.
At the executive level, however, each search is unique and even the minor differences between finalist candidates will have major implications for a clients future. AI will play a major role in the early phase of these searches, but its influence will fade in later stages.
AI has the ability to hugely reduce human bias in all levels of the talent acquisition landscape.
A search firm can now, for example, conduct the whole research phase of a project without knowing the candidates names, ethnicities, genders, sexual orientations, or places of origin. Candidate masking of this sort helps to reduce unconscious human biases and makes it far easier to embed diversity into the search process, allowing for real and numbers-based accountability in diversity efforts.
AIs far-reaching intelligence and numerical rationality can also help to combat other human biases, like those that favor some collegiate institutions over others.
An AI algorithm can be taught to draw its own conclusions about performance and quality; it makes judgments without relying on limited polls, human opinions or historic reputations. It can, for example, weigh an Ivy League university against a small, little-known college on an unbiased scale.
Because AI is working with real data, however, and because that data is generated by and reflective of a society in which bias has played a structurally organizing role, AI can accidentally perpetuate, rather than surpass, human prejudice.
To circumvent this and ensure that AI is not perpetuating the prejudices implicit in human society, AI algorithms can be trained to develop strategies to identify, quantify and work around the biases it finds.
Rather than simply evaluate individual performance in a diversity-blind way, for example, AI can measure the overall historic relationship between employees of diverse backgrounds and the companies theyve worked for, analyzing (a) how bias interacts with their career progressions, and (b) how each candidate ranks relative to each other in that same context.
In other words, it can look at whether a company seems to exhibit bias against certain employees, then judge those employees in ways that take these biases against them into account. This gives promising candidates of diverse backgrounds a way of being found by the algorithm, even when systemic bias would otherwise negatively impact their visibility.
The fact that these algorithms can be used to produce shortlists, pipelines and talent market maps of distinct subsets of the labor market is revolutionary.
For example, it will soon be feasible to identify roughly how many Native Americans have worked in New Yorks investment banks over the last decade what roles theyve had, how they performed and who the top performers were. That is valuable data. And as search firms get better at building and maintaining their AI databases, they will begin selling market insights like this as a commodity.
Though AI will streamline the search business and though it may eventually be technically capable of removing humans from the equation it is unlikely to fully obviate the need for human interaction.
Executive recruiters are valued not simply for their ability to find candidates, but for their ability to negotiate the details of recruiting packages for candidates and clients. They are, in a sense, allies to both sides.
To the candidate, a recruiter serves as a coach, career adviser and advocate; to the client, they are a market expert, deal negotiator and strategy consultant. Most importantly, executive search professionals are good at finding the best candidate for the client, then persuading this candidate that the role is important, that they are uniquely able to fill it and that this is an opportunity that they should consider and they do this by contextualizing data with narrative.
AI does not compare to humans in this sphere; it cannot take information about a candidate, a client or a strategy and turn it into the kind of compelling, fact-supported story with which humans make important decisions. But this is exactly what executive search consultants have done for clients and candidates since the industrys inception: They tell stories.
They tell stories about the candidates career and how this job is its logical next chapter; they tell stories about the role itself, how it interacts with the companys goals and how the candidate is acutely qualified for it; and they tell stories about the company, what it stands for, where its going and how being a member of that team will inform the candidates own career.
What AI can do is enrich the details in the storytelling.
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Artificial Intelligence Is Going to Revolutionize the Executive Search World - BRINK
What Is The Difference Between Artificial Intelligence And …
Artificial Intelligence (AI) and Machine Learning (ML) are two very hot buzzwords right now, and often seem to be used interchangeably.
They are not quite the same thing, but the perception that they are can sometimes lead to some confusion. So I thought it would be worth writing a piece to explain the difference.
Both terms crop up very frequently when the topic is Big Data, analytics, and the broader waves of technological change which are sweeping through our world.
In short, the best answer is that:
Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider smart.
And,
Machine Learning is a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves.
Early Days
Artificial Intelligence has been around for a long time the Greek myths contain stories of mechanical men designed to mimic our own behavior. Very early European computers were conceived as logical machines and by reproducing capabilities such as basic arithmetic and memory, engineers saw their job, fundamentally, as attempting to create mechanical brains.
As technology, and, importantly, our understanding of how our minds work, has progressed, our concept of what constitutes AI has changed. Rather than increasingly complex calculations, work in the field of AI concentrated on mimicking human decision making processes and carrying out tasks in ever more human ways.
Artificial Intelligences devices designed to act intelligently are often classified into one of two fundamental groups applied or general. Applied AI is far more common systems designed to intelligently trade stocks and shares, or maneuver an autonomous vehicle would fall into this category.
Neural Networks - Artificial Intelligence And Machine Learning (Source: Shutterstock)
Generalized AIs systems or devices which can in theory handle any task are less common, but this is where some of the most exciting advancements are happening today. It is also the area that has led to the development of Machine Learning. Often referred to as a subset of AI, its really more accurate to think of it as the current state-of-the-art.
The Rise of Machine Learning
Two important breakthroughs led to the emergence of Machine Learning as the vehicle which is driving AI development forward with the speed it currently has.
One of these was the realization credited to Arthur Samuel in 1959 that rather than teaching computers everything they need to know about the world and how to carry out tasks, it might be possible to teach them to learn for themselves.
The second, more recently, was the emergence of the internet, and the huge increase in the amount of digital information being generated, stored, and made available for analysis.
Once these innovations were in place, engineers realized that rather than teaching computers and machines how to do everything, it would be far more efficient to code them to think like human beings, and then plug them into the internet to give them access to all of the information in the world.
Neural Networks
The development of neural networks has been key to teaching computers to think and understand the world in the way we do, while retaining the innate advantages they hold over us such as speed, accuracy and lack of bias.
A Neural Network is a computer system designed to work by classifying information in the same way a human brain does. It can be taught to recognize, for example, images, and classify them according to elements they contain.
Essentially it works on a system of probability based on data fed to it, it is able to make statements, decisions or predictions with a degree of certainty. The addition of a feedback loop enables learning by sensing or being told whether its decisions are right or wrong, it modifies the approach it takes in the future.
Machine Learning applications can read text and work out whether the person who wrote it is making a complaint or offering congratulations. They can also listen to a piece of music, decide whether it is likely to make someone happy or sad, and find other pieces of music to match the mood. In some cases, they can even compose their own music expressing the same themes, or which they know is likely to be appreciated by the admirers of the original piece.
These are all possibilities offered by systems based around ML and neural networks. Thanks in no small part to science fiction, the idea has also emerged that we should be able to communicate and interact with electronic devices and digital information, as naturally as we would with another human being. To this end, another field of AI Natural Language Processing (NLP) has become a source of hugely exciting innovation in recent years, and one which is heavily reliant on ML.
NLP applications attempt to understand natural human communication, either written or spoken, and communicate in return with us using similar, natural language. ML is used here to help machines understand the vast nuances in human language, and to learn to respond in a way that a particular audience is likely to comprehend.
A Case Of Branding?
Artificial Intelligence and in particular today ML certainly has a lot to offer. With its promise of automating mundane tasks as well as offering creative insight, industries in every sector from banking to healthcare and manufacturing are reaping the benefits. So, its important to bear in mind that AI and ML are something else they are products which are being sold consistently, and lucratively.
Machine Learning has certainly been seized as an opportunity by marketers. After AI has been around for so long, its possible that it started to be seen as something thats in some way old hat even before its potential has ever truly been achieved. There have been a few false starts along the road to the AI revolution, and the term Machine Learning certainly gives marketers something new, shiny and, importantly, firmly grounded in the here-and-now, to offer.
The fact that we will eventually develop human-like AI has often been treated as something of an inevitability by technologists. Certainly, today we are closer than ever and we are moving towards that goal with increasing speed. Much of the exciting progress that we have seen in recent years is thanks to the fundamental changes in how we envisage AI working, which have been brought about by ML. I hope this piece has helped a few people understand the distinction between AI and ML. In another piece on this subject I go deeper literally as I explain the theories behind another trending buzzword Deep Learning.
Check out these links for more information on artificial intelligence and many practical AI case examples.
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What Is The Difference Between Artificial Intelligence And ...
Machine Learning: Making Sense of Unstructured Data and Automation in Alt Investments – Traders Magazine
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 Improves Weather and Climate Models – Eos
Both weather and climate models have improved drastically in recent years, as advances in one field have tended to benefit the other. But there is still significant uncertainty in model outputs that are not quantified accurately. Thats because the processes that drive climate and weather are chaotic, complex, and interconnected in ways that researchers have yet to describe in the complex equations that power numerical models.
Historically, researchers have used approximations called parameterizations to model the relationships underlying small-scale atmospheric processes and their interactions with large-scale atmospheric processes. Stochastic parameterizations have become increasingly common for representing the uncertainty in subgrid-scale processes, and they are capable of producing fairly accurate weather forecasts and climate projections. But its still a mathematically challenging method. Now researchers are turning to machine learning to provide more efficiency to mathematical models.
Here Gagne et al. evaluate the use of a class of machine learning networks known as generative adversarial networks (GANs) with a toy model of the extratropical atmospherea model first presented by Edward Lorenz in 1996 and thus known as the L96 system that has been frequently used as a test bed for stochastic parameterization schemes. The researchers trained 20 GANs, with varied noise magnitudes, and identified a set that outperformed a hand-tuned parameterization in L96. The authors found that the success of the GANs in providing accurate weather forecasts was predictive of their performance in climate simulations: The GANs that provided the most accurate weather forecasts also performed best for climate simulations, but they did not perform as well in offline evaluations.
The study provides one of the first practically relevant evaluations for machine learning for uncertain parameterizations. The authors conclude that GANs are a promising approach for the parameterization of small-scale but uncertain processes in weather and climate models. (Journal of Advances in Modeling Earth Systems (JAMES), https://doi.org/10.1029/2019MS001896, 2020)
Kate Wheeling, Science Writer
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Machine Learning Improves Weather and Climate Models - Eos
What Will Be the Future Prospects Of the Machine Learning Software Market? Trends, Factors, Opportunities and Restraints – Science In Me
Regal Intelligence has added latest report on Machine Learning Software Market in its offering. The global market for Machine Learning Software is expected to grow impressive CAGR during the forecast period. Furthermore, this report provides a complete overview of the Machine Learning Software Market offering a comprehensive insight into historical market trends, performance and 2020 outlook.
The report sheds light on the highly lucrative Global Machine Learning Software Market and its dynamic nature. The report provides a detailed analysis of the market to define, describe, and forecast the global Machine Learning Software market, based on components (solutions and services), deployment types, applications, and regions with respect to individual growth trends and contributions toward the overall market.
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Market Segment as follows:
The global Machine Learning Software Market report highly focuses on key industry players to identify the potential growth opportunities, along with the increased marketing activities is projected to accelerate market growth throughout the forecast period. Additionally, the market is expected to grow immensely throughout the forecast period owing to some primary factors fuelling the growth of this global market. Finally, the report provides detailed profile and data information analysis of leading Machine Learning Software company.
Key Companies included in this report: Microsoft, Google, TensorFlow, Kount, Warwick Analytics, Valohai, Torch, Apache SINGA, AWS, BigML, Figure Eight, Floyd Labs
Market by Application: Application A, Application B, Application C
Market by Types: On-Premises, Cloud Based
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The Machine Learning Software Market research presents a study by combining primary as well as secondary research. The report gives insights on the key factors concerned with generating and limiting Machine Learning Software market growth. Additionally, the report also studies competitive developments, such as mergers and acquisitions, new partnerships, new contracts, and new product developments in the global Machine Learning Software market. The past trends and future prospects included in this report makes it highly comprehensible for the analysis of the market. Moreover, The latest trends, product portfolio, demographics, geographical segmentation, and regulatory framework of the Machine Learning Software market have also been included in the study.
Global Machine Learning Software Market Research Report 2020
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To conclude, the report presents SWOT analysis to sum up the information covered in the global Machine Learning Software market report, making it easier for the customers to plan their activities accordingly and make informed decisions. To know more about the report, get in touch with Regal Intelligence.
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What Will Be the Future Prospects Of the Machine Learning Software Market? Trends, Factors, Opportunities and Restraints - Science In Me
How Microsoft Teams will use AI to filter out typing, barking, and other noise from video calls – VentureBeat
Last month, Microsoft announced that Teams, its competitor to Slack, Facebooks Workplace, and Googles Hangouts Chat, had passed 44 million daily active users. The milestone overshadowed its unveiling of a few new features coming later this year. Most were straightforward: a hand-raising feature to indicate you have something to say, offline and low-bandwidth support to read chat messages and write responses even if you have poor or no internet connection, and an option to pop chats out into a separate window. But one feature, real-time noise suppression, stood out Microsoft demoed how the AI minimized distracting background noise during a call.
Weve all been there. How many times have you asked someone to mute themselves or to relocate from a noisy area? Real-time noise suppression will filter out someone typing on their keyboard while in a meeting, the rustling of a bag of chips (as you can see in the video above), and a vacuum cleaner running in the background. AI will remove the background noise in real time so you can hear only speech on the call. But how exactly does it work? We talked to Robert Aichner, Microsoft Teams group program manager, to find out.
The use of collaboration and video conferencing tools is exploding as the coronavirus crisis forces millions to learn and work from home. Microsoft is pushing Teams as the solution for businesses and consumers as part of its Microsoft 365 subscription suite. The company is leaning on its machine learning expertise to ensure AI features are one of its big differentiators. When it finally arrives, real-time background noise suppression will be a boon for businesses and households full of distracting noises. Additionally, how Microsoft built the feature is also instructive to other companies tapping machine learning.
Of course, noise suppression has existed in the Microsoft Teams, Skype, and Skype for Business apps for years. Other communication tools and video conferencing apps have some form of noise suppression as well. But that noise suppression covers stationary noise, such as a computer fan or air conditioner running in the background. The traditional noise suppression method is to look for speech pauses, estimate the baseline of noise, assume that the continuous background noise doesnt change over time, and filter it out.
Going forward, Microsoft Teams will suppress non-stationary noises like a dog barking or somebody shutting a door. That is not stationary, Aichner explained. You cannot estimate that in speech pauses. What machine learning now allows you to do is to create this big training set, with a lot of representative noises.
In fact, Microsoft open-sourced its training set earlier this year on GitHub to advance the research community in that field. While the first version is publicly available, Microsoft is actively working on extending the data sets. A company spokesperson confirmed that as part of the real-time noise suppression feature, certain categories of noises in the data sets will not be filtered out on calls, including musical instruments, laughter, and singing.
Microsoft cant simply isolate the sound of human voices because other noises also happen at the same frequencies. On a spectrogram of speech signal, unwanted noise appears in the gaps between speech and overlapping with the speech. Its thus next to impossible to filter out the noise if your speech and noise overlap, you cant distinguish the two. Instead, you need to train a neural network beforehand on what noise looks like and speech looks like.
To get his points across, Aichner compared machine learning models for noise suppression to machine learning models for speech recognition. For speech recognition, you need to record a large corpus of users talking into the microphone and then have humans label that speech data by writing down what was said. Instead of mapping microphone input to written words, in noise suppression youre trying to get from noisy speech to clean speech.
We train a model to understand the difference between noise and speech, and then the model is trying to just keep the speech, Aichner said. We have training data sets. We took thousands of diverse speakers and more than 100 noise types. And then what we do is we mix the clean speech without noise with the noise. So we simulate a microphone signal. And then you also give the model the clean speech as the ground truth. So youre asking the model, From this noisy data, please extract this clean signal, and this is how it should look like. Thats how you train neural networks [in] supervised learning, where you basically have some ground truth.
For speech recognition, the ground truth is what was said into the microphone. For real-time noise suppression, the ground truth is the speech without noise. By feeding a large enough data set in this case hundreds of hours of data Microsoft can effectively train its model. Its able to generalize and reduce the noise with my voice even though my voice wasnt part of the training data, Aichner said. In real time, when I speak, there is noise that the model would be able to extract the clean speech [from] and just send that to the remote person.
Comparing the functionality to speech recognition makes noise suppression sound much more achievable, even though its happening in real time. So why has it not been done before? Can Microsofts competitors quickly recreate it? Aichner listed challenges for building real-time noise suppression, including finding representative data sets, building and shrinking the model, and leveraging machine learning expertise.
We already touched on the first challenge: representative data sets. The team spent a lot of time figuring out how to produce sound files that exemplify what happens on a typical call.
They used audio books for representing male and female voices, since speech characteristics do differ between male and female voices. They used YouTube data sets with labeled data that specify that a recording includes, say, typing and music. Aichners team then combined the speech data and noises data using a synthesizer script at different signal to noise ratios. By amplifying the noise, they could imitate different realistic situations that can happen on a call.
But audiobooks are drastically different than conference calls. Would that not affect the model, and thus the noise suppression?
That is a good point, Aichner conceded. Our team did make some recordings as well to make sure that we are not just training on synthetic data we generate ourselves, but that it also works on actual data. But its definitely harder to get those real recordings.
Aichners team is not allowed to look at any customer data. Additionally, Microsoft has strict privacy guidelines internally. I cant just simply say, Now I record every meeting.'
So the team couldnt use Microsoft Teams calls. Even if they could say, if some Microsoft employees opted-in to have their meetings recorded someone would still have to mark down when exactly distracting noises occurred.
And so thats why we right now have some smaller-scale effort of making sure that we collect some of these real recordings with a variety of devices and speakers and so on, said Aichner. What we then do is we make that part of the test set. So we have a test set which we believe is even more representative of real meetings. And then, we see if we use a certain training set, how well does that do on the test set? So ideally yes, I would love to have a training set, which is all Teams recordings and have all types of noises people are listening to. Its just that I cant easily get the same number of the same volume of data that I can by grabbing some other open source data set.
I pushed the point once more: How would an opt-in program to record Microsoft employees using Teams impact the feature?
You could argue that it gets better, Aichner said. If you have more representative data, it could get even better. So I think thats a good idea to potentially in the future see if we can improve even further. But I think what we are seeing so far is even with just taking public data, it works really well.
The next challenge is to figure out how to build the neural network, what the model architecture should be, and iterate. The machine learning model went through a lot of tuning. That required a lot of compute. Aichners team was of course relying on Azure, using many GPUs. Even with all that compute, however, training a large model with a large data set could take multiple days.
A lot of the machine learning happens in the cloud, Aichner said. So, for speech recognition for example, you speak into the microphone, thats sent to the cloud. The cloud has huge compute, and then you run these large models to recognize your speech. For us, since its real-time communication, I need to process every frame. Lets say its 10 or 20 millisecond frames. I need to now process that within that time, so that I can send that immediately to you. I cant send it to the cloud, wait for some noise suppression, and send it back.
For speech recognition, leveraging the cloud may make sense. For real-time noise suppression, its a nonstarter. Once you have the machine learning model, you then have to shrink it to fit on the client. You need to be able to run it on a typical phone or computer. A machine learning model only for people with high-end machines is useless.
Theres another reason why the machine learning model should live on the edge rather than the cloud. Microsoft wants to limit server use. Sometimes, there isnt even a server in the equation to begin with. For one-to-one calls in Microsoft Teams, the call setup goes through a server, but the actual audio and video signal packets are sent directly between the two participants. For group calls or scheduled meetings, there is a server in the picture, but Microsoft minimizes the load on that server. Doing a lot of server processing for each call increases costs, and every additional network hop adds latency. Its more efficient from a cost and latency perspective to do the processing on the edge.
You want to make sure that you push as much of the compute to the endpoint of the user because there isnt really any cost involved in that. You already have your laptop or your PC or your mobile phone, so now lets do some additional processing. As long as youre not overloading the CPU, that should be fine, Aichner said.
I pointed out there is a cost, especially on devices that arent plugged in: battery life. Yeah, battery life, we are obviously paying attention to that too, he said. We dont want you now to have much lower battery life just because we added some noise suppression. Thats definitely another requirement we have when we are shipping. We need to make sure that we are not regressing there.
Its not just regression that the team has to consider, but progression in the future as well. Because were talking about a machine learning model, the work never ends.
We are trying to build something which is flexible in the future because we are not going to stop investing in noise suppression after we release the first feature, Aichner said. We want to make it better and better. Maybe for some noise tests we are not doing as good as we should. We definitely want to have the ability to improve that. The Teams client will be able to download new models and improve the quality over time whenever we think we have something better.
The model itself will clock in at a few megabytes, but it wont affect the size of the client itself. He said, Thats also another requirement we have. When users download the app on the phone or on the desktop or laptop, you want to minimize the download size. You want to help the people get going as fast as possible.
Adding megabytes to that download just for some model isnt going to fly, Aichner said. After you install Microsoft Teams, later in the background it will download that model. Thats what also allows us to be flexible in the future that we could do even more, have different models.
All the above requires one final component: talent.
You also need to have the machine learning expertise to know what you want to do with that data, Aichner said. Thats why we created this machine learning team in this intelligent communications group. You need experts to know what they should do with that data. What are the right models? Deep learning has a very broad meaning. There are many different types of models you can create. We have several centers around the world in Microsoft Research, and we have a lot of audio experts there too. We are working very closely with them because they have a lot of expertise in this deep learning space.
The data is open source and can be improved upon. A lot of compute is required, but any company can simply leverage a public cloud, including the leaders Amazon Web Services, Microsoft Azure, and Google Cloud. So if another company with a video chat tool had the right machine learners, could they pull this off?
The answer is probably yes, similar to how several companies are getting speech recognition, Aichner said. They have a speech recognizer where theres also lots of data involved. Theres also lots of expertise needed to build a model. So the large companies are doing that.
Aichner believes Microsoft still has a heavy advantage because of its scale. I think that the value is the data, he said. What we want to do in the future is like what you said, have a program where Microsoft employees can give us more than enough real Teams Calls so that we have an even better analysis of what our customers are really doing, what problems they are facing, and customize it more towards that.
Infragistics Adds Predictive Analytics, Machine Learning and More to Reveal Embedded Business Intelligence Tool – GlobeNewswire
Reveal adds major new features.
Cranbury, NJ, April 03, 2020 (GLOBE NEWSWIRE) -- Infragistics is excited to announce a major upgrade to its embedded data analytics software, Reveal. In addition to its fast, easy integration into any platform or deployment option, Reveals newest features address the latest trends in data analytics: predictive and advanced analytics, machine learning, R and Python scripting, big data connectors, and much more. These enhancements allow businesses to quickly analyze and gain insights from internal and external data to sharpen decision-making.
Some of these advanced functions include:
Our new enhancements touch on the hottest topics and market trends, helping business users take actions based on predictive data, says Casey McGuigan, Reveal Product Manager. And because Reveal is easy to use, everyday users get very sophisticated capabilities in a powerfully simple platform.
Machine Learning and Predictive Analytics
Reveal's new machine learning feature identifies and visually displays predictions from user data to enable more educated business-decision making. Reveal reads data from Microsoft Azure and Google BigQuery ML Platforms to render outputs in beautiful visualizations.
R and Python Scripting
R and Python are the leading programming languages focused on data analytics. With Reveal support, users such as citizen data scientists can leverage their knowledge around R and Python directly in Reveal to create more powerful visualizations and data stories. They only need to paste a URL to their R or Python scripts in Reveal or paste their code into the Reveal script editor.
Big Data Access
With support for Azure SQL, Azure Synapse, Goggle Big Query, Salesforce, and AWS data connectors, Reveal pulls in millions of records. And it creates visualizations fastReveals been tested with 100 million records in Azure Synapse and it loads in a snap.
Additional connectors include those for Google Analytics and Microsoft SQL Server Reporting Services (SSRS). While Google Analytics offers reports and graphics, Reveal combines data from many sources, letting users build mashup-type dashboards with beautiful visualizations that tell a compelling story.
New Themes Match Apps Look and Feel
The latest Reveal version includes two new themes that work in light and dark mode. They are fully customizable to match an apps look and feel when embedding Reveal into an application and provide control over colors, fonts, shapes and more.
More Information
For in-depth information about Reveals newest features, visit the Reveal blog, Newest Reveal FeaturesPredictive Analytics, Big Data and More.
About InfragisticsOver the past 30 years,Infragisticshas becomethe world leader in providing user interface development tools andmulti-platform enterprise software products and services toaccelerate application design and development, including building business solutions for BI and dashboarding. More than two million developers use Infragistics enterprise-ready UX and UI toolkits to rapidly prototype and build high-performing applications for the cloud, web, Windows, iOS and Android devices.The company offers expert UX services and award-winning support from its locations in the U.S., U.K., Japan, India, Bulgaria and Uruguay.
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Infragistics Adds Predictive Analytics, Machine Learning and More to Reveal Embedded Business Intelligence Tool - GlobeNewswire