As we reached the digital era, where computers became an integral part of the everyday lifestyle, people cannot help but be amazed at how far we have come since the time immemorial. The creation of the computers, as well as the internet, had led us into a more complex thinking, making information available to us with just a click. You just type in the words and information will be readily available for you.

However, as we approached this era, a lot of inventions and terms became confusing. Have you heard about Artificial intelligence? How about Deep Learning? Moreover, Machine Learning? These three words are familiar to us and can be used interchangeably, however, the exact meaning of this becomes uncertain. The more people used it, the more confusing it gets.

Also Read:Top 5 Data Science and Machine Learning Courses

Deep Learning and Machine Learning are words that followed after Artificial Intelligence was created. It is like breaking down the function of AI and naming them Deep Learning and Machine Learning. But before this gets more confusing, let us differentiate the three starting off with Artificial Intelligence.

AI is the like creating intelligence artificially. Artificial Intelligence is the broad umbrella term for attempting to make computers think the way humans think, be able to simulate the kinds of things that humans do and ultimately to solve problems in a better and faster way than we do. The AI itself is a rather generic term for solving tasks that are easy for humans, but hard for computers. It includes all kinds of tasks, such as doing creative work, planning, moving around, speaking, recognizing objects and sounds, performing social or business transactions and a lot more.

Digital era, brought an explosion of data in all forms and from every region of the world. This data, known simply as Big Data, is drawn from sources like social media, internet search engines, e-commerce platforms, online cinemas, etc. This enormous amount of data is readily accessible and can be shared through various applications like cloud computing. However, the data, which normally is unstructured, is so vast that it could take decades for humans to comprehend it and extract relevant information. Companies realize the incredible potential that can result from unraveling this wealth of information and are increasingly adapting to Artificial Intelligence (AI) systems for automated support.

More and more plans to try different approaches to use AI leads to the most promising and relevant area which is the Machine Learning. The most common way to process the Big Data is called Machine Learning. It is a self-adaptive algorithm that gets better and better analysis and patterns with experience or with newly added data.

For example, if a digital payments company wanted to detect the occurrence of or potential for fraud in its system, it could employ machine learning tools for this purpose. The computational algorithm built into a computer model will process all transactions happening on the digital platform, find patterns in the data set, and point out any anomaly detected by the pattern.

Deep learning, on the other hand, is a subset of machine learning, utilizes a hierarchical level of artificial neural networks to carry out the process of machine learning. The artificial neural networks are built like the human brain, with neuron nodes connected together like a web. While traditional programs build analysis with data in a linear way, the hierarchical function of deep learning systems enables machines to process data with a non-linear approach.

A traditional approach to detecting fraud or money laundering might rely on the amount of transaction that ensues, while a deep learning non-linear technique to weeding out a fraudulent transaction would include time, geographic location, IP address, type of retailer, and any other feature that is likely to make up a fraudulent activity.

Thus, these three are like a triangle where the AI to be the top that leads to the creation of Machine Learning with a subset of Deep Learning. These three had made our life easier as time goes by and helped make a faster and better way of gathering information that cannot be done by humans because of the enormous amount of information available.

Humans will take forever just to get a single information while these AI will only take minutes. As we become more and more comfortable using technology, the better humans can develop them into a better version of itself. You should also check our latest article:5 Best Programming Languages for Artificial Intelligence Systems

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Difference between AI, Machine Learning and Deep 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.

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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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Sherry JamesCorporate Sales Specialist, USA Grand View Research, Inc. Phone: +1-415-349-0058 Toll Free: 1-888-202-9519 Email: sales@grandviewresearch.comWeb: https://www.grandviewresearch.comFollow Us: LinkedIn| Twitter

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Machine Learning Market Size Worth $96.7 Billion by 2025 ...

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.

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Machine Learning: Higher Performance Analytics for Lower ...

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

When deciding on a machine learning conference, why go to Machine Learning Week 2020? This five-conference event May 31 June 4, 2020 at Caesars Palace, Las Vegas delivers brand-name, cross-industry, vendor-neutral case studies purely on machine learnings commercial deployment, and the hottest topics and techniques. In this video, Predictive Analytics World Founder Eric Siegel spills on the details and lists five reasons this is the most valuable machine learning event to attend this year.

Note: This article is based on the transcript of a special episode of The Dr. Data Show click here to view.

In this article, I give five reasons that Machine Learning Week May 31 June 4, 2020 at Caesars Palace, Las Vegas is the most valuable machine learning event to attend this year. MLW is the largest annual five-conference blow-out part of the Predictive Analytics World conference series, of which I am the founder.

First, some background info. Your business needs machine learning to thrive and even just survive. You need it to compete, grow, improve, and optimize. Your team needs it, your boss demands it, and your career loves machine learning.

And so we bring you Predictive Analytics World, the leading cross-vendor conference series covering the commercial deployment of machine learning. By design, PAW is where to meet the whos who and keep up on the latest techniques.

This June in Vegas, Machine Learning Week brings together five different industry-focused events: PAW Business, PAW Financial, PAW Industry 4.0, PAW Healthcare, and Deep Learning World. This is five simultaneous two-day conferences all happening alongside one another at Caesars Palace in Vegas. Plus, a diverse range of full-day training workshops, which take place in the days just before and after.

Machine Learning Week delivers brand-name, cross-industry, vendor-neutral case studies purely on machine learning deployment, and the hottest topics and techniques.

This mega event covers all the bases for both senior-level expert practitioners as well as newcomers, project leaders, and executives. Depending on the topic, sessions and workshops are either demarcated as the Expert/practitioner level, or for All audiences. So, you can bring your team, your supervisor, and even the line-of-business managers you work with on model deployment. About 60-70% of attendees are on the hands-on practitioner side, but, as you know, successful machine learning deployment requires deep collaboration between both sides of the equation.

PAW and Deep Learning World also takes place in Germany, and Data Driven Government takes place in Washington DC but this article is about Machine Learning Week, so see predictiveanalyticsworld.com for details about the others.

Here are the five reasons to go.

Five Reasons to Go to Machine Learning Week June 2020 in Vegas

1) Brand-name case studies

Number one, youll access brand-name case studies. At PAW, youll hear directly from the horses mouth precisely how Fortune 500 analytics competitors and other companies of interest deploy machine learning and the kind of business results they achieve. More than most events, we pack the agenda as densely as possible with named case studies. Each day features a ton of leading in-house expert practitioners who get things done in the trenches at these enterprises and come to PAW to spill on the inside scoop. In addition, a smaller portion of the program features rock star consultants, who often present on work theyve done for one of their notable clients.

2) Cross-industry coverage

Number two, youll benefit from cross-industry coverage. As I mentioned, Machine Learning Week features these five industry-focused events. This amounts to a total of eight parallel tracks of sessions.

Bringing these all together at once fosters unique cross-industry sharing, and achieves a certain critical mass in expertise about methods that apply across industries. If your work spans industries, Machine Learning Week is one-stop shopping. Not to mention that convening the key industry figures across sectors greatly expands the networking potential.

The first of these, PAW Business, itself covers a great expanse of business application areas across many industries. Marketing and sales applications, of course. And many other applications in retail, telecommunications, e-commerce, non-profits, etc., etc.

The track topics of PAW Business 2020

PAW Business is a three-track event with track topics that include: analytics operationalization & management i.e., the business side core machine learning methods and advanced algorithms i.e., the technical side innovative business applications covered as case studies, and a lot more.

PAW Financial covers machine learning applications in banking including credit scoring insurance applications, fraud detection, algorithmic trading, innovative approaches to risk management, and more.

PAW Industry 4.0 and PAW Healthcare are also entire universes unto themselves. You can check out the details about all four of these PAWs at predictiveanalyticsworld.com.

And the newer sister event Deep Learning World has its own website, deeplearningworld.com. Deep learning is the hottest advanced form of machine learning with astonishing, proven value for large-signal input problems, such as image classification for self-driving cars, medical image processing, and speech recognition. These are fairly distinct domains, so Deep Learning World does well to complement the four Predictive Analytics World events.

3) Pure-play machine learning content

Number three, youll get pure-play machine learning content. PAWs agenda is not watered down with much coverage of other kinds of big data work. Instead, its ruthlessly focused specifically on the commercial application of machine learning also known as predictive analytics. The conference doesnt cover data science as a whole, which is a much broader and less well-defined area, that, for example, can include standard business intelligence reporting and such. And we dont cover AI per se. Artificial intelligence is at best a synonym for machine learning that tends to over-hype, or at worst an outright lie that promises mythological capabilities.

4) Hot new machine learning practices

Number four, youll learn the latest and greatest, the hottest new machine learning practices. Now, we launched PAW over a decade ago, so far delivering value to over 14,000 attendees across more than 60 events. To this day, PAW remains the leading commercial event because we keep up with the most valuable trends.

For example, Deep Learning World, which launched more recently in 2018 covers deep learnings commercial deployment across industry sectors. This relatively new form of neural networks has blossomed, both in buzz and in actual value. As I mentioned, it scales machine learning to process, for example, complex image data.

And what had been PAW Manufacturing for some years has now changed its name to PAW Industry 4.0. As such, the event now covers a broader area of inter-related work applying machine learning for smart manufacturing, the Internet of Things (IoT), predictive maintenance, logistics, fault prediction, and more.

In general, machine learning continues to widen its adoption and apply in new, innovative ways across sectors in marketing, financial risk, fraud detection, workforce optimization, and healthcare. PAW keeps up with these trends and covers todays best practices and the latest advanced modeling methods.

5) Vendor-neutral content

And finally, number five, youll access vendor-neutral content. PAW isnt run by an analytics vendor and the speakers arent trying to sell you on anything but good ideas. PAW speakers understand that vendor-neutral means those in attendance must be able to implement the practices covered and benefit from the insights delivered without buying any particular analytics product.

During the event, some vendors are permitted to deliver short presentations during a limited minority of demarcated sponsored sessions. These sessions often are also substantive and of great interest. In fact, you can access all the sponsors and tap into their expertise at will in the exhibit hall, where theyre set up for just that purpose.

By the way, if youre an analytics vendor yourself, check out PAWs various sponsorship opportunities. Our events bring together a great crowd of practitioners and decision makers.

Summary Five Reasons to Go

1) Brand-name case studies

2) Cross-industry coverage

3) Pure-play machine learning content

4) Hot new machine learning practices

5) Vendor-neutral content

and those are the reasons to come to Machine Learning Week: brand-name, cross-industry, vendor-neutral case studies purely on machine learnings commercial deployment, and the hottest topics and techniques.

Machine Learning Week not only delivers unique knowledge-gaining opportunities, its also a universal meeting place the industrys premier networking event. It brings together the whos who of machine learning and predictive analytics, the greatest diversity of expert speakers, perspectives, experience, viewpoints, and case studies.

This all turns the normal conference stuff into a much richer experience, including the keynotes, expert panels, and workshop days, as well as opportunities to network and talk shop during the lunches, coffee breaks, and reception.

I encourage you to check out the detailed agenda see all the speakers, case studies, and advanced methods covered. Each of the five conferences has its own agenda webpage, or you can also view the entire five-conference, eight-track mega-agenda at once. This view pertains if youre considering registering for the full Machine Learning Week pass, or if youll be attending along with other team members in order to divide and conquer.

Visit our website to see all these details, register, and sign up for informative event updates by email.

Or to learn more about the field in general, check out our Predictive Analytics Guide, our publication The Machine Learning Times, which includes revealing PAW speaker interviews, and, episodes of this show, The Dr. Data Show which, by the way, is generally about the field of machine learning in general, rather than about our PAW events.

This article is based on a transcript from The Dr. Data Show.

CLICK HERE TO VIEW THE FULL EPISODE

About the Dr. Data Show. This new web series breaks the mold for data science infotainment, captivating the planet with short webisodes that cover the very best of machine learning and predictive analytics. Click here to view more episodes and to sign up for future episodes of The Dr. Data Show.

About the Author

Eric Siegel, Ph.D., founder of the Predictive Analytics Worldand Deep Learning World conference series and executive editor ofThe Machine Learning Times, makes the how and why of predictive analytics (aka machine learning) understandable and captivating. He is the author of the award-winning bookPredictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, the host of The Dr. Data Show web series, a former Columbia University professor, and a renowned speaker, educator, and leader in the field. Follow him at @predictanalytic.

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Five Reasons to Go to Machine Learning Week 2020 - Machine Learning Times - machine learning & data science news - The Predictive Analytics Times

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.

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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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Optimising Utilisation Forecasting with AI and Machine Learning - Gigabit Magazine - Technology News, Magazine and Website

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.

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Adventures With Artificial Intelligence and Machine Learning - Toolbox

One of the biggest machine learning events is taking place in Las Vegas just before summer, Machine Learning Week 2020

This five-day event will have 5 conferences, 8 tracks, 10 workshops, 160 speakers, more than 150 sessions, and 800 attendees.

If there is anything you want to know about machine learning for your small business, this is the event. Keynote speakers from Google, Facebook, Lyft, GM, Comcast, WhatsApp, FedEx, and LinkedIn to name just some of the companies that will be at the event.

The conferences will include predictive analytics for business, financial services, healthcare, industry and Deep Learning World.

Training workshops will include topics in big data and how it is changing business, hands-on introduction to machine learning, hands-on deep learning and much more.

Machine Learning Week will take place from May 31 to June 4, 2020, at Ceasars Palace in Las Vegas.

Click the red button and register.

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You can see a full list of events, contest and award listings or post your own events by visiting the Small Business Events Calendar.

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"With the rising popularity of connected services such as traffic information and local search, digital assistants have become a key differentiator for original equipment manufacturers (OEMs). OEM-branded digital assistants will help automakers strengthen their brand and convert one-time sales into continual service-centric relationships," said Anubhav Grover, Research Analyst, Mobility. "OEMs are aiming to create their own branded digital assistants that will co-exist and integrate with third-party and tech-branded digital assistants. BMW has already launched its own Intelligent Personal Assistant (IPA), which uses Alexa to access Amazon's e-commerce and Cortana for Microsoft Office."

Frost & Sullivan's recent analysis, Strategic Analysis of Automotive Digital Assistants, Forecast to 2025, studies the competitive landscape, business models, and future focus areas of OEMs, digital assistant suppliers, and technology companies. It examines the trends in artificial intelligence integration and voice biometrics. Furthermore, it analyzes the different strategies adopted by OEMs, tier-I suppliers, and technology startups in North America, Europe, and China.

For further information on this analysis, please visit: http://frost.ly/3yk.

"North America is expected to continue leading the adoption of digital assistant solutions. Meanwhile, with higher penetration of long-term evolution (LTE) and greater production capacity in China, Asia-Pacific is expected to be a growth hub for OEMs," noted Grover. "Digital assistant developers are increasingly building strategic partnerships with telecom providers and communication module makers to enhance on-road safety and in-vehicle data-rich services. Flexible business models such as 'choice of network' for consumers will further improve customer retention and revenue generation."

For greater growth opportunities, digital assistant companies are likely to:

Strategic Analysis of Automotive Digital Assistants, Forecast to 2025,is part of Frost & Sullivan's global Automotive & Transportation Growth Partnership Service program.

About Frost & Sullivan

For over five decades, Frost & Sullivan has become world-renowned for its role in helping investors, corporate leaders and governments navigate economic changes and identify disruptive technologies, Mega Trends, new business models and companies to action, resulting in a continuous flow of growth opportunities to drive future success. Contact us: Start the discussion.

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High Investment in AI and Machine Learning will Enhance Automotive Digital Assistants by 2025 - PRNewswire

After a while, everything is overhyped and underwhelming. Even Artificial Intelligence has not been able to escape the inevitable reduction that follows such excessive hype. AI is everything and everywhere now and most of us wont even blink if we are toldAI is poweringsomeonestoothbrush. (It probably is).

The phrase is undoubtedly being misused but is the technology too? One thing is certain, whether we like it or not, whether we understand it or not, for good or bad, AI is playing a huge part in our everyday life today huger than we imagine. AI is being employed in health, wellness and warfare; it is scrutinizing you, helping you take better photos, making music, books and even love. (No, really. The first fully robotic sex doll is being created even as you are reading this.)

However, there is a sore lack of understanding of what AI really is, how it is shaping our future and why it is likely to alter our very psyche sooner or later. There is misinformation galore, of course. Either media coverage of AI is exaggerated (as if androids will take over the world tomorrow) or too specific and technical, creating further confusion and fuelling sci-fi-inspired imaginations of computers smarter than human beings.

So what is AI? No, we are not talking dictionary definitions here those you can Google yourself. Neither are we promising to explain everything that will need a book. We are onlyhoping to give you aglimpse into theextraordinary promise and peril of this single transformative technology as Prof Stuart Russell, one of the worlds pre-eminent AI experts, puts it.

Prof Russell has spent decades on AI research and is the author of Artificial Intelligence: A Modern Approach, which is used as a textbook on AI in over 1,400 universities around the world.

Machine learning first

Otherexperts believe our understanding of artificial intelligence should begin with comprehending machine learning, the so-called sub-field of AI butone that actually encompasses pretty much everything that is happening in AI at present.

In its very simplest definition, machine learning is enabling machines to learn on their own. The advantages of thisare easy to see. After a while, you need not tell it what to do it is your workhorse. All you need is to provide it data and it will keep coming up with smarter ways of digesting that data, spotting patterns, creating opportunities in short doing your work better than you perhaps ever could. This is the point where you need to scratch the surface. Scratch and you will stare into a dissolving ethical conundrum about what machines might end up learning. Because, remember they do not (cannot) explain their thinking process. Not yet, at least. Precisely why, the professor has a cautionary take.

The concept of intelligence is central to who we are. After more than 2,000 years of self-examination, we have arrived at a characterization of intelligence that can be boiled down to this: Humans are intelligent to the extent that our actions can be expected to achieve our objectives. Intelligence in machines has been defined in the same way: Machines are intelligent to the extent that their actions can be expected to achieve their objectives.

Whose objectives?

The problem,writes the professor, is in this very definition of machine intelligence. We say that machines are intelligent to the extent that their actions can be expected to achieve their objectives, but we have no reliable way to make sure that their objectives are the same as our objectives. He believes what we should have done all along is to tweak this definition to: Machines are beneficial to the extent that their actions can be expected to achieve our objectives.

The difficulty here is of course that our objectives are in us all eight billion of us and not in the machines. Machines will be uncertain about our objectives; after all we are uncertain about them ourselves but this is a good thing; this is a feature, not a bug. Uncertainty about objectives implies that machines will necessarily defer to humans they will ask permission, they will accept correction and they will allow themselves to be switched off.

Spilling out of the lab

This might mean a complete rethinking and rebuilding of the AI superstructure. Perhaps something that indeed is inevitable if we do not want this big event in human history to be the last, says the prof wryly. As Kai-Fu Lee, another AI researcher, said in an interview a while ago, we are at a moment where the technology is spilling out of the lab and into the world. Time to strap up then!

(With inputs from Human Compatible: AI and the Problem of Control by Stuart Russell, published by Penguin, UK. Extracted with permission.)

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Being human in the age of Artificial Intelligence - Deccan Herald