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Category Archives: Machine Learning
SAN FRANCISCO, Jan. 13, 2020 /PRNewswire/ -- The global machine learning marketsize is expected to reach USD 96.7 billion by 2025, according to a new report by Grand View Research, Inc. The market is anticipated to expand at a CAGR of 43.8% from 2019 to 2025. Production of massive amounts of data has increased the adoption of technologies that can provide a smart analysis of that data.
Key suggestions from the report:
Read 100 page research report with ToC on "Machine Learning Market Size, Share & Trends Analysis Report By Component, By Enterprise Size, By End Use (Healthcare, BFSI, Law, Retail, Advertising & Media), And Segment Forecasts, 2019 - 2025" at: https://www.grandviewresearch.com/industry-analysis/machine-learning-market
Technologies such as Machine Learning (ML) are being rapidly adopted across various applications in order to automatically detect meaningful patterns within a data set. Software based on ML algorithms, such as search engines, anti-spam software, and fraud detection software, are being increasingly used, thereby contributing to market growth.
The rapid emergence of ML technology has increased its adoption across various application areas. It provides cloud computing optimization along with intelligent voice assistance. In healthcare, it is used for the diagnosis of individuals. In case of businesses, the use of ML models that are open source and have a standards-based structure has increased in recent years. These models can be easily deployed in various business programs and can help companies bridge the skills gap between IT programmers and information scientists.
Developments such as fine-tuned personalization, hyper-targeting, searching engine optimization, no-code environment, self-learning bots, and others are projected to change the machine learning landscape. The development of capsule network has replaced neural networks in order to provide more accuracy in pattern detection, with fewer errors. These advanced developments are anticipated to proliferate market growth in the foreseeable future.
Grand View Research has segmented the global machine learning market based on component, enterprise size, end use, and region:
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About Grand View Research
Grand View Research, U.S.-based market research and consulting company, provides syndicated as well as customized research reports and consulting services. Registered in California and headquartered in San Francisco, the company comprises over 425 analysts and consultants, adding more than 1200 market research reports to its vast database each year. These reports offer in-depth analysis on 46 industries across 25 major countries worldwide. With the help of an interactive market intelligence platform, Grand View Research helps Fortune 500 companies and renowned academic institutes understand the global and regional business environment and gauge the opportunities that lie ahead.
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Machine Learning Market Size Worth $96.7 Billion by 2025 ...
What Is Machine Learning?
Machine learning is theconcept that a computer program can learn and adapt to new data without human interference. Machine learning is a field of artificial intelligence (AI) that keeps a computers built-in algorithms current regardless of changes in the worldwide economy.
Various sectors of the economy are dealing with huge amounts of data available in different formats from disparate sources. The enormous amount of data, known as big data, is becoming easily available and accessible due to the progressive use of technology. Companies and governments realize the huge insights that can be gained from tapping into big data but lack the resources and time required to comb through its wealth of information. As such, artificial intelligence measures are being employed by different industries to gather, process, communicate, and share useful information from data sets. One method of AI that is increasingly utilized for big data processing is machine learning.
The various data applications of machine learning are formed through a complex algorithm or source code built into the machine or computer. This programming code creates a model that identifies the data and builds predictions around the data it identifies. The model uses parameters built in the algorithm to form patterns for its decision-making process. When new or additional data becomes available, the algorithm automatically adjusts the parameters to check for a pattern change, if any. However, the model shouldnt change.
Machine learning is used in different sectors for various reasons. Trading systems can be calibrated to identify new investment opportunities. Marketing and e-commerce platforms can be tuned to provide accurate and personalized recommendations to their users based on the users internet search history or previous transactions. Lending institutions can incorporate machine learning to predict bad loans and build a credit risk model. Information hubs can use machine learning to cover huge amounts of news stories from all corners of the world. Banks can create fraud detection tools from machine learning techniques. The incorporation of machine learning in the digital-savvy era is endless as businesses and governments become more aware of the opportunities that big data presents.
How machine learning works can be better explained by an illustration in the financial world. Traditionally, investment players in the securities market like financial researchers, analysts, asset managers, individual investors scour through a lot of information from different companies around the world to make profitable investment decisions. However, some pertinent information may not be widely publicized by the media and may be privy to only a select few who have the advantage of being employees of the company or residents of the country where the information stems from. In addition, theres only so much information humans can collect and process within a given time frame. This is where machine learning comes in.
An asset management firm may employ machine learning in its investment analysis and research area. Say the asset manager only invests in mining stocks. The model built into the system scans the web and collects all types of news events from businesses, industries, cities, and countries, and this information gathered makes up the data set. The asset managers and researchers of the firm would not have been able to get the information in the data set using their human powers and intellects. The parameters built alongside the model extracts only data about mining companies, regulatory policies on the exploration sector, and political events in select countries from the data set. Saya mining company XYZ just discovered a diamond mine in a small town in South Africa, the machine learning app would highlight this as relevant data. The model could then use an analytics tool called predictive analytics to make predictions on whether the mining industry will be profitable for a time period, or which mining stocks are likely to increase in value at a certain time. This information is relayed to the asset manager to analyze and make a decision for his portfolio. The asset manager may make a decision to invest millions of dollars into XYZ stock.
In the wake of an unfavorable event, such as South African miners going on strike, the computer algorithm adjusts its parameters automatically to create a new pattern. This way, the computational model built into the machine stays current even with changes in world events and without needing a human to tweak its code to reflect the changes. Because the asset manager received this new data on time, they are able to limit his losses by exiting the stock.
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Machine Learning Definition
Faced with mounting compliance costs and regulatory pressures, financial institutions are rapidly adopting Artificial Intelligence (AI) solutions, including machine learning and robotic process automation (RPA) to combat sophisticated and evolving financial crimes.
Over one third of financial institutions have deployed machine learning solutions, recognizing that AI has the potential to improve the financial services industry by aiding with fraud identification, AML transaction monitoring, sanctions screening and know your customer (KYC) checks (Financier Worldwide Magazine).
When deployed in financial crime management solutions, analytical agents that leverage machine learning can help to reduce false positives, without compromising regulatory or compliance needs.
It is well known that conventional, rules-based fraud detection and AML programs generate large volumes of false positive alerts. In 2018, Forbes reported With false positive rates sometimes exceeding 90%, something is awry with most banks legacy compliance processes to fight financial crimes such as money laundering.
Such high false positive rates force investigators to waste valuable time and resources working through large alert queues, performing needless investigations, and reconciling disparate data sources to piece together evidence.
The highly regulated environment makes AML a complex, persistent and expensive challenge for FIs but increasingly, AI can help FIs control not only the complexity of their AML provisions, but also the cost (Financier Worldwide Magazine).
In an effort to reduce the costs of fraud prevention and BSA/AML compliance efforts, financial institutions should consider AI solutions, including machine learning analytical agents, for their financial crime management programs.
Machine learning agents use mathematical and statistical models to learn from data without being explicitly programmed. Financial institutions can deploy dynamic machine learning solutions to:
To effectively identify patterns, machine learning agents must process and train with a large amount of quality data. Institutions should augment data from core banking systems with:
When fighting financial crime, a single financial institution may not have enough data to effectively train high-performance analytical agents. By gathering large volumes of properly labeled data in a cloud-based environment, machine learning agents can continuously improve and evolve to accurately detect fraud and money laundering activities, and significantly improve compliance efforts for institutions.
Importing and analyzing over a billion transactions every week in our Cloud environment, Verafins big data intelligence approach allows us to build, train, and refine a proven library of machine learning agents. Leveraging this immense data set, Verafins analytical agents outperform conventional detection analytics, reducing false positives and allowing investigators to focus their efforts on truly suspicious activity. For example:
With proven behavior-based fraud detection capabilities, Verafins Deposit Fraud analytics consistently deliver 1-in-7 true positive alerts.
By deploying machine learning, Verafin was able to further improve upon these high-performing analytics resulting in an additional 66% reduction in false positives. Training our machine learning agents on check returns mapped as true fraud in the Cloud, the Deposit Fraud detection rate improved to 1-in-3 true positive alerts, while maintaining true fraud detection.
These results clearly outline the benefits of applying machine learning analytics to a large data set in a Cloud environment. In todays complex and costly financial crime landscape, financial institutions should deploy financial crime management solutions with machine learning to significantly reduce false positives, while maintaining regulatory compliance.
In an upcoming article, we will explore how and when robotic process automation can benefit financial crime management solutions.
Machine Learning: Higher Performance Analytics for Lower ...
Optimising Utilisation Forecasting with AI and Machine Learning – Gigabit Magazine – Technology News, Magazine and Website
What IT team wouldnt like to have a crystal ball that could predict the IT future, letting them fix application and infrastructure performance problems before they arise? Well, the current shortage ofcrystal balls makes the union of artificial intelligence (AI), machine learning (ML), and utilisation forecasting the next best thing for anticipating and avoiding issues that threaten the overall health and performance of all IT infrastructure components. The significance of AI has not been lost to organisations in the United Kingdom, with 43 per cent of them believing that AI will play a big role in their operations.
Utilisation forecasting is a technique that applies machine learning algorithms to produce daily usage forecasts for all utilisation volumes across CPUs, physical and virtual servers, disks, storage, bandwidth, and other network elements, enabling networking teams to manage resources proactively. This technique helps IT engineers and network admins prevent downtime caused by over-utilisation.
The AI/ML driving forecasting solution produces intelligent and reliable reports by taking advantage of the current availability of ample historic records and high-performance computing algorithms. Without AI/ML, utilisation forecasting relies on reactive monitoring. You set predefined thresholds for given metrics such as uptime, resource utilisation, network bandwidth, and hardware metrics like fan speed and device temperature. When a threshold is exceeded, an alert is issued. However, that reactive approach will not detect the anomalies that happen below that threshold and create other, indirect issues. Moreover, it will not tell you when you will need to upgrade your infrastructure based on current trends.
To forecast utilisation proactively, you need accurate algorithms that can analyze usage patterns and to detect anomalieswithout false positivesin daily usage trends. Thats how you predict usage in the future. Let us take a look at a simple use case.
With proactive, AI/ML-driven utilisation forecasting, you can find a minor increase in your officebandwidth usage during the World Series, the FIFA World Cup, and other sporting events. Thatanomalous usage can be detected even if you have a huge amount of unused internet bandwidth. Similarly, proactive utilisation forecasting lets you know when to upgrade your infrastructure based on new recruitment and attrition rates.
A closer look at the predictive technologies reveals the fundamental difference between proactive and reactive forecasting. Without AI and ML, utilisation forecasting uses linear regression models to extrapolate and provide prediction based on existing data. This method involves no consideration of newly allocated memory or anomalies in utilisation patterns. Also, pattern recognition is a foreign concept. Although useful, linear regression models do not give IT admins complete visibility.
AI/ML-driven utilisation forecasting, on the other hand, uses the Seasonal and Trend decomposition using Loess (STL) method. STL lets you study the propagation and degradation of memory as well as analyze pattern matching whereby periodic changes in the metric configuration will be automatically adjusted. Bottom line, STL dramatically improves accuracy thanks to those dynamic, automated adjustments. And if any new memory is allocated, or if memory size is increased or decreased for the device, the prediction will change accordingly. This option was not possible with linear regression.
Beyond forecasting, ML can be used to improve anomaly detection. Here, adaptive thresholds for different metrics are established using ML and analysis of historic data will reveal any anomalies and trigger appropriate alerts. Other application and infrastructure monitoring functions will also be improved when enhanced with AI and ML technologies. Sometime in the not-too-distant future, AI/ML-driven forecasting and monitoring will rival the predictive powers of the fabled crystal ball.
by Rebecca D'Souza, Product Consultant, ManageEngine
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Since October of last year I have had the opportunity to work with an startup working on automated machine learning and I thought that I would share some thoughts on the experience and the details of what one might want to consider around the start of a journey with a data scientist in a box.
Ill start by saying that machine learning and artificial intelligence has almost forced itself into my work several times in the past eighteen months, all in slightly different ways.
The first brush was back in June 2018 when one of the developers I was working with wanted to demonstrate to me a scoring model for loan applications based on the analysis of some other transactional data that indicated loans that had been previously granted. The model had no explanation and no details other than the fact that it allowed you to stitch together a transactional dataset which it assessed using a nave Bayes algorithm. We had a run at showing this to a wider audience but the palate for examination seemed low and I suspect that in the end the real reason was we didnt have real data and only had a conceptual problem to be solved.
The second go was about six months later when another colleague in the same team came up with a way to classify data sets and in fact developed a flexible training engine and data tagging approach to determining whether certain columns in data sets were likely to be names, addresses, phone numbers and email addresses. On face value you would think this to be something simple but in reality, it is of course only as good as the training data and in this instance we could easily confuse the system and the data tagging with things like social security numbers that looked like phone numbers, postcodes that were simply numbers and ultimately could be anything and so on. Names were only as good as the locality from which the names training data was sourced and cities, towns. Streets and provinces all proved to most work ok but almost always needed region-specific training data. At any rate, this method of classifying contact data for the most part met the rough objectives of the task at hand and so we soldiered on.
A few months later I was called over to a developers desk and asked for my opinion on a side project that one of the senior developers and architects had been working on. The objective was ambitious but impressive. The solution had been built in response to three problems in the field. The first problem to be solved was decoding why certain records were deemed to be related to one another when with the naked eye they seemed to not be, or vice versa. While this piece didnt involve any ML per se, the second part of the solution did, in that it self-configured thousands of combinations of alternative fuzzy matching criteria to determine an optimal set of duplicate record matching rules.
This was understandably more impressive and practically understandable almost self-explanatory. This would serve as a great utility for a consultant, a data analyst or a relative layperson to find explainability in how potential duplicate records were determined to have a relationship. This was specifically important because it immediately could provide value to field services personnel and clients. In addition, the developer had cunningly introduced a manual matching option that allowed a user to evaluate two records and make a decision through visual assessment as to whether two records could potentially be considered related to one another.
In some respects what was produced was exactly the way that I like to see products produced. The field describes the problem; the product management organization translates that into more elaborate stories and looks for parallels in other markets, across other business areas and for ubiquity. Once those initial requirements have been gathered it is then to engineering and development to come up with a prototype that works toward solving the issue.
The more experienced the developer of course the more comprehensive the result may be and even the more mature the initial iteration may be. Product is then in a position to pitch the concept back at the field, to clients and a selective audience to get their perspective on the solution and how well it matches the for solving the previously articulated problem.
The challenge comes when you have a less tightly honed intent, a less specific message and a more general problem to solve and this comes now to the latest aspect of machine learning and artificial intelligence that I picked up.
One of the elements with dealing with data validation and data preparation is the last mile of action that you have in mind for that data. If your intent is as simple as one of, lets evaluate our data sources, clean them up and makes them suitable for online transaction processing then thats a very specific mission. You need to know what you want to evaluate, what benchmark you wish to evaluate them against and then have some sort of remediation plan for them so that they support the use case for which theyre intended say, supporting customer calls into a call centre. The only areas where you might consider artificial intelligence and machine learning for applicability in this instance might be for determining matches against the baseline but then the question is whether you simply have a Boolean decision or whether in fact, some sort of stack ranking is relevant at all. It could be argued either way, depending on the application.
When youre preparing data for something like a decision beyond data quality though, the mission is perhaps a little different. Effectively your goal may be to cut the cream of opportunities off the top of a pile of contacts, leads, opportunities or accounts. As such, you want to use some combination of traits within the data set to determine influencing factors that would determine a better (or worse) outcome. Here, linear regression analysis for scoring may be sufficient. The devil, of course, lies in the details and unless youre intimately familiar with the data and the proposition that youre trying to resolve for you have to do a lot of trial and error experimentation and validation. For statisticians and data scientists this is all very obvious and you could say, is a natural part of the work that they do. Effectively the challenge here is feature selection. A way of reducing complexity in the model that you will ultimately apply to the scoring.
The journey I am on right now with a technology partner, focuses on ways to actually optimise the features in a way that only the most necessary and optimised features will need to be considered. This, in turn, makes the model potentially simpler and faster to execute, particularly at scale. So while the regression analysis still needs to be done, determining what matters, what has significance and what should be retained vs discarded in terms of the model design, is being all factored into the model building in an automated way. This doesnt necessarily apply to all kinds of AI and ML work but for this specific objective it is perhaps more than adequate and one that doesnt require a data scientist to start delivering a rapid yield.