Category Archives: Machine Learning

Banking and finance rely on experts but the new expert on the scene is your AI/ML combo, able to do far more, do it fast and do it accurately.

Making the right decisions and grabbing opportunities in the fast moving world of finance can make a difference to your bottom line. This is where artificial intelligence and machine learning make a tangential difference. Engage machine learning development services in your finance segment and life will not be the same. Markets and Markets study shows that artificial intelligence in financial segment will grow to over $ 7300 million by 2022.

Data

The simple reason you need machine learning development company to help you make better decisions with the help of AI/ML is data. Data flows in torrents from diverse sources and contains precious nuggets of information. This can be the basis of understanding customer behaviors and it can help you gain predictive capabilities. Data analysis with ML can also help identify patterns that could be indicative of attempts at fraud and you save your reputation and money by tackling it in time.

The key

Normalize huge sets of data and derive information in real time according to specifiable parameters. Machine Learning algorithms can help you train the system to carry out fast analysis and deliver results based on algorithm models created for the purpose by Machine Learning Development Company for you. As it ages the system actually becomes smarter because it learns as it goes along.

To achieve the same result manually using standard IT solutions you would employ a team of IT specialists but even then it is doubtful if you could get outputs in time to help you take decisive action.

Fraud prevention

This is one case where prevention is better than cure. A typical bank may have hundreds of thousands of customers carry out any number of different transactions. All such data is under the watchful eye of the ML imbued system and it is quick to detect anomalies. In fact, ML has been known to cause misunderstanding because a customer not familiar with credit card operations repeatedly fumbled and that raised a false alarm. Still, it is better to be safe than sorry and carry out firefighting after the event.

Stock trading

Day trading went algorithmic quite a few years back and helped brokers profit by getting the system to make automatic profitable trades. Apart from day trading there are derivatives, forex, commodities and binary where specific models for ML can help you, as a trader or a broker, anticipate price movements. This is one area where price is influenced not just by demand-supply but also by political factors, climate, company results and unforeseen calamities. ML keeps track of all and integrates them into a predictive capability to keep you ahead of the game.

Investment decisions

Likewise, investments in other areas like bonds, mutual funds and real estate need to be based on smart analysis of present and future while factoring external influencers. No one, for example, foresaw the covid-19 devastation that froze economies and dried up sources of funds that have an impact on investments, especially in real estate. However, if you have machine learning based system it would keep track of developments and alert you in advance so that you can be prepared. Then there are more mundane tasks in finance sector where ML does help. Portfolio managers always walk a tight rope and rely on experts who can make false decisions and affect clients capital. Tap into the power of ML to stay on top and grow wealth of wealthy clients. Their recommendations will get you more clients making the investment in ML solutions more than worthwhile. It could be the best investment you make.

Automation

Banks, private lenders, institutions and insurance companies routinely carry out repetitive and mundane tasks like attending to inquiries, processing forms and handling transactions. This does involve extreme manpower usage leading to high costs. Your employees work under a deluge of such tasks and cannot do anything productive. Switch to ML technologies to automate such repetitive tasks. You will have two benefits:

The second one alone is worth the investment. In the normal course of things you would have to devote considerable energies to identify developing patterns whereas the ML solution presents trends based on which you can modify services, design offers or address customer pain points and ensure loyalty.

Risk mitigation

Smart operators are always gaming the system such as finding ways to improve credit score and obtain credit despite being ineligible. Such operators would pass the normal scanning technique of banks. However, if you have ML for assessment of loan application the system delves deeper and digs to find out all relevant information, collate it and analyze it to help you get a true picture. Non-performing assets cause immense losses to banks and this is one area where Machine Learning solutions put in place by expert machine learning development services can and does prove immensely valuable.

Banking and finance rely on experts but the new expert on the scene is your AI/ML combo, able to do far more, do it fast and do it accurately.

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Finance Sector Benefits from Machine Learning Development and AI - Legal Reader

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No financial marketer questions the tectonic shift digital media has wrought on marketing and advertising. Yet even the most ardent digital marketing proponent might be startled by the prediction that 100% of advertising will be online and automated by 2025. Startled, and perhaps more than a bit skeptical. Although the pandemic has changed the situation to varying degrees, many financial marketers continue to find value in TV, radio, print and outdoor and human input into what appears there.

The 100% figure is a little less startling, however, when you consider that about 55% of U.S. advertising was already online as of 2019, according to Nicolas Darveau-Garneau, Chief Evangelist at Google. The marketing executive, who is in touch regularly with the search giants biggest advertisers, also notes that the 100% consists of two components: First, about 65% of the ads in 2025 will be online ads. Second, the other 35% will also be digital, but not online.

Whether youre buying a billboard or youre buying television, it will be a lot more like buying YouTube, he says. Machine learning algorithms are going to automate most advertising in the next five years. The time that bank and credit union marketers spend today optimizing media, selecting keywords and placing the right targeting on banner ads will be done by machines more and more, says Darveau-Garneau.

Machine learning is doubling in power every four to six months, he points out. Even as that rate begins to slow, there will still be a multiple thousand X improvement in machine learning power within the next ten years.

That kind of dramatic change prompts two big questions from CMOs the Google exec speaks with:

In answering the first question, Darveau-Garneau, who made spoke during a WPromote virtual presentation explores three key points:

As he says, all three need to happen for institutions to be able to compete. The effort to accomplish that, which is difficult, also takes care of the question about job security: There will plenty of marketing jobs, just different, which Darveau-Garneau expands upon below.

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Before he joined Google about nine years ago, Darveau-Garneau was steeped in performance marketing essentially the modern digital marketing milieu of data and metrics with everything measurable. Yet he believes that to compete more effectively in the rapidly automating marketing world, many CMOs need to shift their thinking about performance marketing. They should create a marketing strategy that, simply put, makes you as much money as possible, trying to squeeze every ounce of profit you can, Darveau-Garneau states. Thats the most important KPI, he emphasizes.

While that advice may seem self evident, the Google marketer says few advertisers he works with are trying to make as much money as possible. Instead, many are trying to achieve the highest ROI.

And that is not the same thing.

Maximizing cash flow is very different than maximizing ROI, Darveau-Garneau states. The best advice I can give you in your performance marketing strategy is to build a dashboard that motivates your marketing teams to maximize profitability, as opposed to efficiency.

Dont fall in love with your ROASGoogle tools today cannot automatically maximize a financial institutions profitability, but Darveau-Garneau says they can produce maximum revenues out of a certain return on ad spend (ROAS). So a bank CMO can incorporate maximum profitability as a criteria for finding the right ROAS. But the Google exec advises being careful in selecting the right ROAS whether that is five-to-one, seven-to-one or another number.

Dont fall in love with your ROAS, Darveau-Garneau states. Test various numbers up and down to see which one makes you the most money. Once you know what the right ROAS is for your business, he adds, then make sure you get enough budget to cover full demand.

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Why customer lifetime value makes so much senseManaging a company based on customer lifetime value is the future of business, Darveau-Garneau firmly believes. He ran four marketing (CX?) startups before joining Google. In hindsight, he says, he should have narrowed his customer database at these firms by building marketing based on customer lifetime value (CLV). This requires determining who a companys top customers are and then acquiring more people like that.

While financial inclusion is a major theme in banking today, banks and credit union marketers can benefit from a CLV focus in terms of outreach and messaging for loans, savings, investments and many other products.

The best advice I can give you, says Darveau-Garneau, is dont try to forecast customer lifetime value perfectly. Just do it approximately quintiles or deciles. One example of how to use CLV as part of efforts to personalize marketing is do A/B testing of landing page conversions to see which one converts better for your high CLV customers compared with average customers.

Dont worry, marketing jobs arent going awayWhile automation will increasingly handle things like selecting brand placements, Darveau-Garneau maintains that marketing work will shift to things such as building CLV models, segmenting customers in clever ways, optimizing creative and having the right data structure and the data sets to feed into the machine learning algorithms.

I actually think there will be more people doing marketing five years from now than there are now, he states, because its going to be easier in some ways, but much more complex in other ways.

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You could describe Nicolas Darveau-Garneau as a reformed performance marketer. For much of his career, he never did any brand marketing. As he describes it, performance marketers always have this tension about brand marketing because they like to measure things accurately to be sure theyre not wasting money. Hes changed his tune now to the point where Build a strong brand is the second of his three key strategies for marketers to be ready for the future.

Darveau-Garneau points to the fintech Credit Karma as a great example of a company combining performance marketing with great brand marketing. There is an extraordinary amount of value created by building a strong brand, he insists. This includes having consumers go directly to your site, or searching specifically for your brand on Google, or generating higher conversions.

These advantages are harder to measure than the clicks, leads or sales that result from pay-for-performance advertising, but they can be measured over time. Darveau-Garneau counsels patience to those skeptical of brand marketings benefits. It takes three months to a year, he says, to see the impact of a consideration or awareness campaign.

To financial marketers who still need convincing, he recommends starting small and trying out a branding campaign in one state (or possibly one part of a state) and tracking how business does there over six months. This doesnt require a big investment in a hardcore attribution model. If successful, it can then be expanded.

Brand marketing is becoming a lot more like performance marketing, the Google exec states. Brand marketing should be optimized in real time, and held accountable, he states, but give it some time to work.

Also to the point raised earlier as machine learning makes performance marketing easier, it diminishes the competitive advantage. That makes building a strong brand that much more important, Darveau-Garneau emphasizes. Ideally, financial institution marketers that can combine the skill sets of both disciplines will be in a good position, he believes.

Bank and credit union marketers can be doing great performance marketing and great brand marketing, but if youre sending these clicks to a site that doesnt perform very well, its going to be hard to compete, stresses Darveau-Garneau. A simple example is having a fast mobile site. He cites data from Chinese ecommerce giant Alibaba in which an already good conversion rate jumped 76% when they built a much faster mobile site.

Friction is the enemy of great digital experience, which in turn robs marketing of much of its power. The Google executive counsels CMOs to remove anything that creates significant friction: remove one field from a form, for example, or add Google Pay or Apple Pay to your app. Get the ball rolling so your marketing and digital banking teams start looking for things to remove to streamline the customer experience.

Dont get hung up on mega projects that are huge investments and take forever, like breaking down data silos and merging them all into one vast data lake, Darveau-Garneau advises. Such projects should be undertaken over the long term, but think about small projects in the short term.

Ive seen a lot of marketers trying to get things perfect from the beginning, as opposed to peeling the onion and just getting better every day, Darveau-Garneau observes.

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With the surge in ecommerce unleashed by the pandemics arrival, financial marketers may be wondering whether omnichannel marketing even makes sense any more versus concentrating solely on online digital.

While acknowledging the difficulty of forecasting what will happen to in-person commerce (and in-person banking), Darveau-Garneau firmly believes that whatever new normal arises, people will once again venture into retail facilities, so having an omni-channel strategy makes a lot of sense.

Financial marketers should be sure to include in-branch and other channel data beyond website and mobile data in what they share with the machine learning application they use. In the case of Google, Darveau-Garneau advises not to think of the company as driving just your online business. We can help you drive your store business as well. The company now has tools to integrate data, revenue and margin, for example, from physical locations into its smart bidding algorithms.

Importantly, Darveau-Garneau says Google has found that for many customers, including those in banking, consumers who buy both online and in-store often are much better customers than those who dont.

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Google's Vision for the Future of Bank Marketing, AI, Data and Brand - The Financial Brand

Some people have spent their quarantine downtime bakingsourdough bread. Others experiment with tie-dye. But others namely Toronto-based artist Daniel Voshart have createdpainstaking portraits of all 54 Roman emperors of the Principate period, which spanned from 27 BC to 285 AD.

The portraits help people visualize what the Roman emperors would have looked like when they were alive.

Included are Vosharts best artistic guesses of the faces of emperors Augustus, Nero, Caligula, Marcus Aurelius and Claudius, among others. They dont look particularly heroic or epic rather, they look like regular people, with craggy foreheads, receding hairlines and bags under their eyes.

To make the portraits, Voshart used a design software called Artbreeder, which relies on a kind of artificial intelligence called generative adversarial networks (GANs).

Voshart starts by feeding the GANs hundreds of images of the emperors collected from ancient sculpted busts, coins and statues. Then he gets a composite image, which he tweaks in Photoshop. To choose characteristics such as hair color and eye color, Voshart researches the emperors backgrounds and lineages.

It was a bit of a challenge, he says. About a quarter of the project was doing research, trying to figure out if theres something written about their appearance.

He also needed to find good images to feed the GANs.

Another quarter of the research was finding the bust, finding when it was carved because a lot of these busts are recarvings or carved hundreds of years later, he says.

In a statement posted on Medium, Voshartwrites: My goal was not to romanticize emperors or make them seem heroic. In choosing bust/sculptures, my approach was to favor the bust that was made when the emperor was alive. Otherwise, I favored the bust made with the greatest craftsmanship and where the emperor was stereotypically uglier my pet theory being that artists were likely trying to flatter their subjects.

Related:Battle of the bums: Museums complete over best artistic behinds

Voshart is not a Rome expert. His background is in architecture and design, and by day he works in the art department of the TV show "Star Trek: Discovery," where he designs virtual reality walkthroughs of the sets before they'rebuilt.

But when the coronavirus pandemic hit, Voshart was furloughed. He used the extra time on his hands to learn how to use the Artbreeder software.The idea for the Roman emperor project came from a Reddit threadwhere people were posting realistic-looking images theyd created on Artbreeder using photos of Roman busts. Voshart gave it a try and went into exacting detail with his research and design process, doing multiple iterations of the images.

Voshart says he made some mistakes along the way. For example, Voshart initially based his portrait of Caligula, a notoriously sadistic emperor, on a beautifully preserved bust in the Metropolitan Museum of Art. But the bust was too perfect-looking, Voshart says.

Multiple people told me he was disfigured, and another bust was more accurate, he says.

So, for the second iteration of the portrait, Voshart favored a different bust where one eye was lower than the other.

People have been telling me my first depiction of Caligula was hot, he says. Now, no ones telling me that.

Voshart says people who see his portraits on Twitter and Reddit often approach them like theyd approachTinder profiles.

I get maybe a few too many comments, like such-and-such is hot. But a lot of these emperors are such awful people!

I get maybe a few too many comments, like such-and-such is hot. But a lot of these emperors are such awful people! Voshart says.

Voshart keeps a list on his computer of all the funny comparisons people have made to present-day celebrities and public figures.

Ive heard Nero looks like a football player. Augustus looks like Daniel Craigmy early depiction of Marcus Aurelius looks like the Dude from 'The Big Lebowski.'

But the No. 1 comment? Augustus looks like Putin.

Related:UNESCO says scammers are using its logo to defraudartcollectors

No one knows for sure whether Augustus actually looked like Vladimir Putin in real life.Voshart says his portraits are speculative.

Its definitely an artistic interpretation, he says. Im sure if you time-traveled, youd be very angry at me."

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This artist used machine learning to create realistic portraits of Roman emperors - The World

Machine Learning Chips Market 2018: Global Industry Insights by Global Players, Regional Segmentation, Growth, Applications, Major Drivers, Value and Foreseen till 2024

The recent published research report sheds light on critical aspects of the global Machine Learning Chips market such as vendor landscape, competitive strategies, market drivers and challenges along with the regional analysis. The report helps the readers to draw a suitable conclusion and clearly understand the current and future scenario and trends of global Machine Learning Chips market. The research study comes out as a compilation of useful guidelines for players to understand and define their strategies more efficiently in order to keep themselves ahead of their competitors. The report profiles leading companies of the global Machine Learning Chips market along with the emerging new ventures who are creating an impact on the global market with their latest innovations and technologies.

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The recent published study includes information on key segmentation of the global Machine Learning Chips market on the basis of type/product, application and geography (country/region). Each of the segments included in the report is studies in relations to different factors such as market size, market share, value, growth rate and other quantitate information.

The competitive analysis included in the global Machine Learning Chips market study allows their readers to understand the difference between players and how they are operating amounts themselves on global scale. The research study gives a deep insight on the current and future trends of the market along with the opportunities for the new players who are in process of entering global Machine Learning Chips market. Market dynamic analysis such as market drivers, market restraints are explained thoroughly in the most detailed and easiest possible manner. The companies can also find several recommendations improve their business on the global scale.

The readers of the Machine Learning Chips Market report can also extract several key insights such as market size of varies products and application along with their market share and growth rate. The report also includes information for next five years as forested data and past five years as historical data and the market share of the several key information.

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Global Machine Learning Chips Market by Companies:

The company profile section of the report offers great insights such as market revenue and market share of global Machine Learning Chips market. Key companies listed in the report are:

Market Segment AnalysisThe research report includes specific segments by Type and by Application. Each type provides information about the production during the forecast period of 2015 to 2026. Application segment also provides consumption during the forecast period of 2015 to 2026. Understanding the segments helps in identifying the importance of different factors that aid the market growth.Segment by TypeNeuromorphic ChipGraphics Processing Unit (GPU) ChipFlash Based ChipField Programmable Gate Array (FPGA) ChipOther

Segment by ApplicationRobotics IndustryConsumer ElectronicsAutomotiveHealthcareOther

Global Machine Learning Chips Market: Regional AnalysisThe report offers in-depth assessment of the growth and other aspects of the Machine Learning Chips market in important regions, including the U.S., Canada, Germany, France, U.K., Italy, Russia, China, Japan, South Korea, Taiwan, Southeast Asia, Mexico, and Brazil, etc. Key regions covered in the report are North America, Europe, Asia-Pacific and Latin America.The report has been curated after observing and studying various factors that determine regional growth such as economic, environmental, social, technological, and political status of the particular region. Analysts have studied the data of revenue, production, and manufacturers of each region. This section analyses region-wise revenue and volume for the forecast period of 2015 to 2026. These analyses will help the reader to understand the potential worth of investment in a particular region.Global Machine Learning Chips Market: Competitive LandscapeThis section of the report identifies various key manufacturers of the market. It helps the reader understand the strategies and collaborations that players are focusing on combat competition in the market. The comprehensive report provides a significant microscopic look at the market. The reader can identify the footprints of the manufacturers by knowing about the global revenue of manufacturers, the global price of manufacturers, and production by manufacturers during the forecast period of 2015 to 2019.The major players in the market include Wave Computing, Graphcore, Google Inc, Intel Corporation, IBM Corporation, Nvidia Corporation, Qualcomm, Taiwan Semiconductor Manufacturing, etc.

Global Machine Learning Chips Market by Geography:

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Some of the Major Highlights of TOC covers in Machine Learning Chips Market Report:

Chapter 1: Methodology & Scope of Machine Learning Chips Market

Chapter 2: Executive Summary of Machine Learning Chips Market

Chapter 3: Machine Learning Chips Industry Insights

Chapter 4: Machine Learning Chips Market, By Region

Chapter 5: Company Profile

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Machine Learning Chips Market Dynamics Analysis to Grow at Cagr with Major Companies and Forecast 2026 - The Scarlet

Machine learning era has reached the stage of interpretability where developing models and making predictions is simply not enough any more. To make a powerful impact and get good results on the data it is important to investigate and probe the dataset and the models. A good model investigation involves digging deep into the understanding of the model to find insights and inconsistencies in the developed model. This task usually involves writing a lot of custom functions. But, with tools like What-If, it makes the probing task very easy and saves time and efforts for programmers.

In this article we will learn about:

What-If tool is a visualization tool that is designed to interactively probe the machine learning models. WIT allows users to understand machine learning models like classification, regression and deep neural networks by providing methods to evaluate, analyse and compare the model. It is user friendly and can be used not only by developers but also by researchers and non-programmers very easily.

WIT was developed by Google under the People+AI research (PAIR) program. It is open-source and brings together researchers across Google to study and redesign the ways people interact with AI systems.

This tool provides multiple features and advantages for users to investigate the model.

Some of the features of using this are:

WIT can be used with a Google Colab notebook or Jupyter notebook. It can also be used with Tensorflow Board.

Let us take a sample dataset to understand the different features of WIT. I will choose the forest fire dataset available for download on Kaggle. You can click here for downloading the dataset. The goal here is to predict the areas affected by forest fires given the temperature, month, amount of rain etc.

I will implement this tool on google collaboratory. Before we load the dataset and perform the processing, we will first install the WIT. To install this tool use,

!pip install witwidget

Once we have split the data, we can convert the columns month and day to categorical values using label encoder.

Now we can build our model. I will use sklearn ensemble model and implement the gradient boosting regression model.

Now that we have the model trained, we will write a function to predict the data since we need to use this for the widget.

Next, we will write the code to call the widget.

This opens an interactive widget with two panels.

To the left, there is a panel for selecting multiple techniques to perform on the data and to the right is the data points.

As you can see on the right panel we have options to select features in the dataset along X-axis and Y-axis. I will set these values and check the graphs.

Here I have set FFMC along the X-axis and area as the target. Keep in mind that these points are displayed after the regression is performed.

Let us now explore each of the options provided to us.

You can select a random data point and highlight the point selected. You can also change the value of the datapoint and observe how the predictions change dynamically and immediately.

As you can see, changing the values changes the predicted outcomes. You can change multiple values and experiment with the model behaviour.

Another way to understand the behaviour of a model is to use counterfactuals. Counterfactuals are slight changes made that can cause a model to flip its decision.

By clicking on the slide button shown below we can identify the counterfactual which gets highlighted in green.

This plot shows the effects that the features have on the trained machine learning model.

As shown below, we can see the inference of all the features with the target value.

This tab allows us to look at the overall model performance. You can evaluate the model performance with respect to one feature or more than the one feature. There are multiple options available for analysis of the performance.

I have selected two features FFMC and temp against the area to understand performance using mean error.

If multiple training models are used their performance can be evaluated here.

The features tab is used to get the statistics of each feature in the dataset. It displays the data in the form of histograms or quantile charts.

The tab also enables us to look into the distribution of values for each feature in the dataset.

It also highlights the features that are most non-uniform in comparison to the other features in the dataset.

Identifying non-uniformity is a good way to reduce bias in the model.

WIT is a very useful tool for analysis of model performance. Ability to inspect models in a simple no-code environment will be of great help especially in the business perspective.

It also gives insights to factors beyond training the model like understanding why and how that model was created and how the dataset is fitting in the model.

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Demonstration Of What-If Tool For Machine Learning Model Investigation - Analytics India Magazine

Fort Collins, Colorado The Global Machine Learning & Big Data Analytics Education Market research report offers insightful information on the Global Machine Learning & Big Data Analytics Education market for the base year 2019 and is forecast between 2020 and 2027. Market value, market share, market size, and sales have been estimated based on product types, application prospects, and regional industry segmentation. Important industry segments were analyzed for the global and regional markets.

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Global Machine Learning & Big Data Analytics Education Market Segmentation:

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Machine Learning & Big Data Analytics Education Market Size is Thriving Worldwide 2020 | Growth and Profit Analysis, Forecast by 2027 - The Daily...