Machine Learning Techniques for Data Mining

| July 27, 2019

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Technical basis for data mining: algorithms for acquiring structural descriptions from examples „ Structural descriptions represent patterns explicitly Can be used to predict outcome in new situation ‹ Can be used to understand and explain how prediction is derived (maybe even more important) „ Methods originate from artificial intelligence, statistics, and research on databases.

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Is Python storming ahead of Java in fintech?

Article | March 18, 2020

The use of Python is catching up to Java in banking and fintech applications, but what are the reasons behind the emergence of Python? While three million developers have joined the Java community in the past year, in the banking sector, Python is fast closing in on Java’s position in top spot. Python’s backstory in banking Across all sectors, Python has reached seven million active developers fuelled in part by a staggering 62% of machine learning developers and data scientists who now use the programming language.

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What Is An AI Model?

Article | May 24, 2021

Reveal makes extensive use of AI models. Generally, an AI model is a software program that has been trained on a set of data to perform specific tasks like recognizing certain patterns. Artificial intelligence models use decision-making algorithms to learn from the training and data and apply that learning to achieve specific pre-defined objectives. Reveal offers a Model Library which consists of a collection of pre-existing models you can use straight out of the box, extend or adapt to suit your specific needs, or stack and pack to achieve a larger objective. We give you the ability to create your own AI models, which you can use for your own purposes as well as make available to others via our Model Marketplace. We also will work with you to create custom models such as the ones that drive DLA Piper's Aiscension and those used by Epiq in its new AI Model Library program.

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THE FUTURE IS BOTH AUTOMATED AND INTELLIGENT

Article | June 2, 2021

Intelligent Automation (IA) is one of the trending buzzwords of our times. What makes automation smart? Is it new? Why the renewed focus? Bill Gates believed automation to be a double-edged sword when he said: “Automation applied to an efficient operation will magnify the efficiency. … Automation applied to an inefficient operation will magnify the inefficiency.” IA lies at the intersection of robotics, artificial intelligence (AI) and business process management (BPM). But before you think HAL from 2001: A Space Odyssey, J.A.R.V.I.S. from Iron Man or Terminator 2: Judgment Day scenarios, first, a little context. IA is not new; automated manual processes have been in existence since the dawn of the Industrial Revolution. It enabled speeding up go-to-market, reduced errors and improved efficiencies. Over time, automation made its way into software development, quality assurance processes, manufacturing, finance, health care and all aspects of daily life. “Intelligent” automation backed by robotics, AI and BPM creates smarter business processes and workflows that can incrementally think, learn and adapt as they go — for instance, processing millions of documents and applications in a day, finding errors and suggesting fixes or recommendations. What Intelligent Automation Does, Humans Can’t IA enables the automation of knowledge work by mimicking human workers’ capabilities. It includes four main capabilities: vision, execution, language, and thinking and learning. Each of these capabilities combines different technologies that are used as stand-alone or in combination to complement each other. One oft-quoted IA example is fraud detection and prevention in the BFSI sector. Robotic process automation (RPA) optimizes the speed and accuracy of the fraud identification process. Since RPA can go through months’ worth of data in a matter of hours and throws up exceptions, teams cannot keep up with the speed and scale needed to resolve the issues flagged. However, speed and efficiency are of the essence where fraud management is concerned. The answer lies in AI and BPM coupled with RPA. IA can streamline the process end-to-end. Pascal Bornet notes in his book, Intelligent Automation, that IA can help improve the overall automation rate to nearly 80%, and it can help improve the time to solve a fraud incident and obtain clients’ refunds by 50%. While RPA provides excellent benefits and quick solutions, cognitive technologies offer long-term value for businesses, employees and customers. IA And Digital Transformation IA adoption is growing swiftly across the enterprise, being fast adopted by more than 50% of the world’s largest companies. Its benefits are relevant to the majority of business processes. For example: • Industrial systems that sense and adapt based on rules. • Chatbots that learn from customer interactions to improve engagement. • Sales and marketing systems that predict buyer journeys and identify leads The Future Of Work: Bitter Or Better There is much speculation when it comes to IA and the future of work. The main contention is that robots will take away jobs from humans. My argument is that, while it will cause role changes, it doesn’t necessarily mean job losses. The Industrial Revolution helped automate “blue-collar” jobs in manufacturing and agriculture. Similarly, IA will automate many white-collar jobs that are tedious and tiring. A recent IBM report shows that 90% of executives in firms where IA is being used believe it creates higher-value work for employees. So, no, we will not be living in a dystopic world controlled by bots running amok! IA means better roles, the elimination of laborious tasks and improvements in employee well-being. The Promise Of The Better Life In 2018 alone, over $5 trillion (6% of global GDP) was lost due to fraud. Medical errors in the U.S. incur an estimated economic value of almost $1 trillion — and 86% of those mistakes are administrative. A 2017 Medliminal Healthcare Solutions study found that 80% of U.S. medical billings contain at least minor errors — creating unnecessary annual health care spending of $68 billion. The World Economic Forum cited an ILO report that “estimates that the annual cost to the global economy from accidents and work-related diseases alone is a staggering $3 trillion.” Now, let us imagine we can save $5 trillion globally through the deployment of IA. It means: • Global budgets allocated to education could more than double. • Global healthcare budgets could be increased by more than 70%. • Environmental investments could be multiplied almost twentyfold. Transitioning To Intelligent Automation However, adopting IA is not like flipping a switch. There are some key steps an organization must experience in its bid to be automating intelligently. • Planning. For the successful adoption of IA, business leaders must understand the relationship between people and machines. Enterprises must plan so as not to disrupt other parts of the business and integrate IA seamlessly into the existing programs. Instead of adopting IA across the processes, identify where it delivers the most value. Automating broken processes will not fix the problem. IA will only reap rewards on stable and mature processes • Change management. IA is not easy to implement. There will be a great deal of resistance to adopting IA in your organizations. Designing a change management strategy, an execution road map, an enterprise operating model and key metrics for ROI will help your cause. Invite key stakeholders from the outset to ensure buy-in and train your employees to work in collaboration with IA. • Governance framework. Establishing a governance framework helps determine who will watch the watchmen. The bigger the role of IA in your organization, the more critical governance becomes. Designing a framework will help you monitor performance as well as define exceptions and errors. It is a recipe for disaster if you don’t have a command and control center to ensure IA is making the right choices. Even more reason for humans with industry expertise to still “have their jobs” and excel at them. Future Of Intelligent Automation The future of IA will direct businesses to a more adaptive model that is beneficial for business leaders to uncover higher value and employees to do more satisfactory and creative roles. Preparing for an intelligent future means adapting our technology, skills and education to fit the future of the workforce. What are we waiting for? Disclaimer – This article was 1st published on Forbes.comEnable GingerCannot connect to Ginger Check your internet connection or reload the browserDisable in this text fieldRephraseRephrase current sentenceEdit in Ginger

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How AI Powers Industrial Inspections by Drone: the Neurala and Optelos Collaboration

Article | February 27, 2020

Artificial Intelligence (AI) definitely tops the list of important themes for the drone industry. From data management to flight automation, AI has been part of the description. But how exactly does AI power processes like industrial inspections? An integration between a drone data management platform and an AI company serves as an example. Drone inspections generate a lot of images, huge quantities of data. While not all of those images are useful, all of them need review in order to identify a potential defect – like finding a needle in a haystack. That’s where AI can help – significantly reducing the human effort required to find that one important image from thousands of others.

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