Collaborating Authors

Information Technology

How AI Helps Digital Enterprises Streamline Operations


Artificial intelligence (AI) is transforming how enterprises analyze and process information. It is also shifting from theoretical to real-world technology.

How Machine Learning and AI are Transforming the Online Gambling Industry


Artificial intelligence offers boundless possibilities. Despite currently being used for solving practical tasks, experts believe we are only viewing the tip of the iceberg. But even the tip looks incredible already. Imagine playing a game of strategy and having a completely customized scenario, instead of following one of the scripted few. A personalized gaming reality, with unique conversations and decisions made right there and then.

Council Post: Five Steps To Developing A Data Science Culture


You've developed a platform that's gaining significant customer traction and enabling you to collect vast amounts of transaction and user data. Word gets out about your software, you acquire more users and feature requests start rolling in. As you develop and deliver those new features, you engage more users and collect even more data! There's tremendous value in that data, but limited thinking may be limiting your ability to mine it for the insights you need to further improve your product or even develop new ones that better meet the needs of your user base. Perhaps you've only gotten as far as creating simple plots and histograms around events, fault detection and other simple rules-based alerting and reporting.

Does the Human Touch + AI = The Future of Work?


Artificial intelligence has long caused fear of job loss across many sectors as companies look for ways to cut costs, support workers and become more profitable. But new research suggests that even in STEM-based sectors like cybersecurity, AI simply can't replace some traits found only in humans, such as creativity, intuition and experience. There's no doubt, AI certainly has its place. And most business leaders agree that AI is important to the future success of their company. A recent survey found CEOs believe the benefits of AI include creating better efficiencies (62 percent), helping businesses remain competitive (62 percent), and allowing organizations to gain a better understanding of their customers, according to Ernst and Young.

Council Post: As Data Grows, What Could The Future Of AI Look Like In Banking?


As of now, AI in banking is discussed primarily in connection with specific features, but this is only the basic level of AI's potential.

Top Artificial Intelligence Solution Companies


BPU Holdings is a global company, headquartered in Korea that pioneers in the development of Artificial Emotional Intelligence (AEI). The mission of the company is to generate the most advanced, secure usable, and innovative Artificial Emotional Intelligence technology in the world. BPU has developed the first Artificial Emotional Intelligent (AEI) platform -- AEI Framework, which emulates how people think and feel. BPU improves the human condition by offering rigorous tools to improve emotional intelligence. Tracking and handling emotions enable the management of professional and interpersonal relationships, empathetically and judiciously.

Building A Simple Convolution Layer From Scratch


A convolution layer provides a method of producing a feature map from a two-dimensional input. This is accomplished by running a filter over the input data. The filter is just a set of weights that must be trained to identify a feature in regions of the input data. These features can be things like edges, points, or more complex information. The filter will have dimensional constraints that indicate width and height, and it will scan over the input data.

Is artificial intelligence changing art?


Hannah Seo is a science journalist based in New York City and the managing editor of Scienceline. She loves writing about the intersections of science, tech and culture. As an ethnically Korean Canadian raised in Qatar, she also considers herself an international nomad.

On Moving from Statistics to Machine Learning, the Final Stage of Grief


I've spent the last few months preparing for and applying for data science jobs. It's possible the data science world may reject me and my lack of both experience and a credential above a bachelors degree, in which case I'll do something else. Regardless of what lies in store for my future, I think I've gotten a good grasp of the mindset underlying machine learning and how it differs from traditional statistics, so I thought I'd write about it for those who have a similar background to me considering a similar move.1 This post is geared toward people who are excellent at statistics but don't really "get" machine learning and want to understand the gist of it in about 15 minutes of reading. If you have a traditional academic stats backgrounds (be it econometrics, biostatistics, psychometrics, etc.), there are two good reasons to learn more about data science: The world of data science is, in many ways, hiding in plain sight from the more academically-minded quantitative disciplines.

NEC and Japanese research agency to use AI for automatic plastic waste detection


NEC and the Japan Agency Marine-Earth Science and Technology (JAMSTEC) have developed a system that uses artificial intelligence (AI) imaging recognition techniques to automatically detect microplastics from seawater and sediment samples. The system, according to NEC, has been developed using its Rapid machine learning technology in combination with JAMESTEC's method for staining microplastics using fluorescent dyes in samples, before capturing videos of the dyed microplastics. The software then automatically extracts image data for each microplastic that appears in the video and uses AI recognition technology to sort microplastics based on sizes and shapes at a processing speed of 60 per minute, NEC said. The Japanese conglomerate touted the new system could improve the current method that is used to analyse microplastics to determine the impact plastic waste has on marine life. Typically, the process of analysing microplastics involves scooping seawater and sediment with a fine mesh, before using a microscope to pick and analyse each microplastic manually to determine the number, size, and types that exist in the ocean.