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Senzing’s Software for Real-Time AI for Entity Resolution to Fight Financial Crime

Senzing, a new artificial intelligence-based (AI) software company, announced its Senzing software product to address the $14.37 billion financial fraud market. Senzing is an IBM spinout that has reinvented entity resolution, which senses who is who in real time across multiple big data sources.

High School Students Win Top Two Prizes for Congressional Data Challenge at the Library of Congress

The Library of Congress announced the winners of the Congressional Data Challenge, a competition asking participants to use legislative data sets sourced from Congress.gov and other platforms to develop digital projects that analyze, interpret or share congressional data in user-friendly ways. High school students won the top-two prizes for the data-based competition, which was open to the general public.

Big Data and the Psychologist’s Role on the Analytical Team

Now that the role of the data scientist has become essential in the workplace, more roles in data analytics are opening up in large companies—including jobs for psychologists. The infographic below from our friends over at the University of Southern California Dornsife shows us why psychologists can have such a powerful role in leveraging big data effectively.

Advancements in Dynamic and Efficient Deep Learning Systems

We’re seeing much hype in the marketplace about the potential of AI, especially with respect to computer vision systems and its ability accelerate the development of everything from self-driving cars to autonomous robots. To create more dynamic and efficient deep learning systems, that don’t compromise accuracy, IBM Research is exploring new and novel computer vision techniques from both a hardware and software angle.

Deep Reinforcement Learning: From Board Games to the Boardroom

In this contributed article, Andrew Vaziri, a Senior Artificial Intelligence Engineer at Bonsai, highlights how we are now entering the age of profit-making Deep Reinforcement Learning (DRL), and why it took so long for this technology to make the leap from board games to the boardroom?

Big Data in Financial Services

In this special guest feature, David Friend, co-founder & CEO of Wasabi Technologies, takes a look at the big data and cloud storage technology stack as it relates to the finance industry. The financial services industry is highly competitive, with products fighting for the smallest differentiation to make an impact in the market. Big data and the cloud serve to provide important competitive advantage to this important industry.

AI Ethics and The New Digital Divide

In this contributed article, Paulo Malvar, Chief Computational Scientist at Codeq LLC, advises that the entire AI field needs to engage in serious conversations around the ethics of the products we create or we’ll face the consequences. Another AI Winter is very possible, but this time it wouldn’t be triggered by our over-promises, but by society’s perception of us and our creations.

Want to Build an AI Product? Data is Key

In this contributed article, Mahe Bayireddi, CEO and co-founder of Phenom People, explains the different waves of AI (both current and future) and to help readers understand the importance of labeled data. Further, In order for artificial intelligence to truly reach goal-based AI in the future, we need entrepreneurs, innovators, and disruptors to continue to build AI-driven products.

Interview: Dr. Bhushan Desam, Director, Global AI Business at Lenovo

I recently caught up with Dr. Bhushan Desam, AI global business leader for Lenovo’s Data Center Group to discuss how the digital transformation of business isn’t truly possible without incorporating machine learning. Digital transformation is underway. As the C-suite demands better insights from data, enterprises will be tasked to make data-driven decisions based on those insights.

The Evolution of Data Lakes

In this special guest feature, Glenn Graney, Director – Industrial & High Tech for QAD, suggests that manufacturers should be planning for all enterprise data sets to be part of the greater data lake. The transition to a data lake emphasizes flexible access to analysis tools and is less centered on data preparation. By definition, the data lake will be made up of a variety of data sources and the accessibility requirements and effort will only be defined at the time of the query.