The banking industry will benefit from adopting the latest technology advancements that include artificial intelligence and cognitive computing. These technologies provide the opportunity to mine the massive amounts of transactional data that banks have collected over the past decades to better...

Big data analysis has been used to improve the healthcare industry in several ways, such as developing personalized medicines, working to fight cancer and making pharmaceutical trials more efficient. Another exciting area that big data is ameliorating is the ability to forecast admission rates. With the help of machine learning, big data is able to predict the number of admissions a healthcare facility will have at any given time. This predicted number will allow for these facilities to better prepare for their anticipated number of patients by having enough staff ready to work, surgeries scheduled at the most opportune times, and the right type and amount of supplies stocked. This new application of big data analysis and machine learning to predict admission rates is based on both internal and external data.

It's still rough around the edges, but FaceApp demonstrates the scary future of neural network-based image editing. Neural networks are revolutionizing computing, and image editing is one area that will experience drastic changes. Already, apps like Prisma are demonstrating this power, but a new company is using neural networks in a different way with an app called FaceApp. Unlike Prisma, which keeps the content of a source photo but changes the style, FaceApp looks to change the content while maintaining photorealism. Available now as a free download for iOS, with an Android version coming soon, FaceApp currently offers six filters: Smile, Hot, Young, Old, Male, and Female.

Enter the Trump and Dump Trading Platform. Using artificial intelligence, the Trump and Dump Platform identifies Trump's tweets mentioning publicly traded companies. Tweets likely to trigger a stock drop are immediately shorted, with notifications delivered via Slack. As we close the short, any captured profits are donated to the ASPCA. So the whole thing actually saves puppies.

"I think that everyone recognizes that for IoT to really realize its full potential and value that it is about creating ecosystems and marketplaces and that no single vendor is able to support what is required," explained Esmeralda Swartz, VP, Marketing Enterprise and Cloud at Ericsson, in this SYS-CON.tv Internet of @ThingsExpo, taking place June 6-8, 2017 at Javits Center, New York City, is co-located with 20th International @CloudExpo and will feature technical sessions from a rock star conference faculty and the leading industry players in the world. The Internet of Things (IoT) is the most profound change in personal and enterprise IT since the creation of the Worldwide Web more than 20 years ago. All major researchers estimate there will be tens of billions devices - computers, smartphones, tablets, and sensors - connected to the Internet by 2020. This number will continue to grow at a rapid pace for the next several decades.

The internet strongly agrees there's an artificial intelligence (AI) revolution going on, and it's come to pharma marketing. IBM Watson, the AI brain being infused all over healthcare, will form the backend of an interactive display ad for GSK Consumer Healthcare's Theraflu. The cold-and-flu product was one of seven brands, across four beta partners, to sign on with Watson Ads, an initiative announced this past summer by the Weather Co. -- an IBM property -- aiming to develop a new wave of ads that people will be able to communicate with. According to the firm, it marks the first consumer use of IBM's cognitive technology for advertising. "This is a whole new foray into marketing," says D. J. Reali, SVP, national ad sales for the Weather Co.

This blog has been talking a lot about Machine Learning (ML) with regard to tabular data. That makes sense because predictive algorithms based on tabular data are often easy to implement and have a lot of potential to improve outcomes. Also, we have access to a lot of tabular data from the EHR. However, ML is capable of doing a lot more than predicting probabilities on tabular data, and there are incredible opportunities in other areas of healthcare. One in particular is in Radiology and Pathology departments.

I published a post about the current status of "Data Scientist" in Japan, as a periodic follow-up analysis since two years ago. Its trend still remains, but it's beyond my anticipation at that time. Indeed growing trend of "Artificial Intelligence" in Japan is steeper than that in English, and "Data Scientist" is now getting to be forgotten by people, although in the global market data scientist is still a major role spreading data science including both statistics and machine learning across industries. Although I did not explicitly mention in the post, I guess that Japanese people may think that data scientist is a professional for statistical analysis although artificial intelligence engineer is one for machine learning or artificial intelligence as a misleading technology. Two years ago, already I've had seen some disappointment at "Data Science" powered by statistics and supported by data scientists.

Recently, I have been working on the Neural Networks for Machine Learning course offered by Coursera and taught by Geoffrey Hinton. Overall, it is a nice course and provides an introduction to some of the modern topics in deep learning. However, there are instances where the student has to do lots of extra work in order to understand the topics covered in full detail. One of the assignments in the course is to study the Neural Probabilistic Language Model (The related article can be downloaded from here). An example dataset, as well as a code written in Octave (equivalently Matlab) are provided for the assignment.

With the extraordinary change that technology has brought to the news and information landscape, the future of news depends on journalists working alongside smart machines. The first wave of this symbiosis was news automation, where artificial intelligence systems generate written stories and alerts directly from data. The goal is not to displace journalists from their jobs -- it's about freeing up their time from labor-intensive tasks so they can do higher-order journalism. Following the direction set in motion by automation, the next evolution will be about leveraging smart tools that can help journalists augment their own writing. This means AI-powered interfaces capable of providing context to topics in real time and even optimizing a news report based on its dateline and subject matter.