The power of artificial intelligence (AI) and the usage of new digital technologies are providing the banking industry an opportunity for new forms of engagement. The movement from traditional channels to conversational banking is upon us. Subscribe to The Financial Brand via email for FREE!Messaging platforms that include voice and text-based interfaces and being increasingly used by digital consumers as their preferred method of engagement, according to a research study from Accenture. The Amazon Echo, Google Home and Apple HomePod (as well as other devices) use technologies such as AI, machine learning and natural-language processing (NLP) to enable voice engagement. An increasing nuber of financial organizations are beginning to take notice.

It's fair to say that artificial intelligence has become a part of our daily lives. The average person doesn't think twice about letting an algorithm provide music recommendations or directions on how to get from A to B. According to McKinsey, big data strategies could save the US healthcare system up to $100 billion a year thanks to AI-assisted efficiencies in trials, research, and clinical practice. So, how is AI being utilised, and will the general public embrace it? Image classification – which refers to the process of extracting information from multiple images – has so far been one of the main uses of AI and deep learning within healthcare. While the practice does not replace doctors, it essentially makes their jobs much quicker and easier (and lessens the chances of human error).

Supporters of President Donald Trump got another place to find love with the launch of a new website – Trump.dating. The website launched in February helps people find friends and partners. However, it allows only straight men. Gay men and women are barred from registering on the website. When signing up on the site, users are provided with two options -- "straight man" or "straight woman."

A while back I was watching the well-renowned presentation by Facebook Messenger VP, David Marcus. He told that Facebook (FB) is embracing bots full heartedly and even encourage the use of bots. Frankly, I found it refreshing when a big entity like FB was giving their blessing to bots, and particularly the part where it is used by us, slightly smaller businesses, who are so desperately trying to make our way through the endless sea of information. So, I just had to watch it again. Then it struck me, what about my own daily business, and the needs I have?

Do smart assistants demonstrate, or just mimic, intelligence? In 1950, British computer scientist and mathematician Alan Turing conceived of a test to answer the question, "can machines think?" If you carefully read his proposal in the paper Computing Machinery and Intelligence, according to Jim Hendler, director of Rensselaer Polytechnic Institute's Data Exploration and Applications, Turing believed language differentiated humans from animals, so if a computer could convincingly use language, then it could be considered intelligent. Today, there are plenty of voice recognition programs, such as Nuance's Dragon and Google's Voice Search, as well as voice-recognizing smart home assistants like Amazon's Alexa and Apple's Siri, but none of them are even trying to fulfill Turing's goal of thinking computers. Rather, they provide quick, transparent access to the vast storehouse of online data. "So far, all these attempts are just computerized idiot savants," says Hendler. "We are still no closer to understanding what intelligence is."

In December, I converted my one-bedroom apartment in San Francisco into a "smart home." I connected as many of my appliances and belongings as I could to the internet: an Amazon Echo, my lights, my coffee maker, my baby monitor, my kid's toys, my vacuum, my TV, my toothbrush, a photo frame, a sex toy, and even my bed. "Our bed?" asked my husband, aghast. "What can it tell us?" "Our breathing rate, heart rate, how often we toss and turn, and then it will give us a sleep report each morning," I explained. "Sounds creepy," he said, as he plopped down on that bed, not bothered enough to relax instead on our non-internet-connected couch. I soon discovered that the only thing worse than getting a bad night's sleep is to subsequently get a report from my bed telling me I got a low score and "missed my sleep goal." Thanks, smart bed, but I know that already. Why would I do this? It was appealing to imagine living like the Beast in the Disney movie, with animated objects around my home taking care of my every need and occasionally serenading me.

"The next big step will be for the very concept of the'device' to fade away… The computer itself will be an intelligent assistant helping you through your day." - Google CEO, Sundar Pichai Soon, most of our interaction with the digital world will be done in the easiest most natural way – by voice. Virtual assistants, such as Amazon's Alexa and Google Assistant are in a race to provide the most valuable service to us. To do this - and to secure our dependence on them - virtual assistants will become highly capable, near omniscient, and masters of the rapidly-growing internet of things (IoT). The more we open up to these'voice first' machines the more they will help us. The most useful assistants will combine a deep understanding of the circumstances, attitudes and needs that govern our individual lives with the machines' access to the world's information, products and services.

A substantial progress in development of new and efficient tensor factorization techniques has led to an extensive research of their applicability in recommender systems field. Tensor-based recommender models push the boundaries of traditional collaborative filtering techniques by taking into account a multifaceted nature of real environments, which allows to produce more accurate, situational (e.g. context-aware, criteria-driven) recommendations. Despite the promising results, tensor-based methods are poorly covered in existing recommender systems surveys. This survey aims to complement previous works and provide a comprehensive overview on the subject. To the best of our knowledge, this is the first attempt to consolidate studies from various application domains in an easily readable, digestible format, which helps to get a notion of the current state of the field. We also provide a high level discussion of the future perspectives and directions for further improvement of tensor-based recommendation systems.

We propose a hybrid algorithm for top-$n$ recommendation task that allows to incorporate both user and item side information within the standard collaborative filtering approach. The algorithm extends PureSVD -- one of the state-of-the-art latent factor models -- by exploiting a generalized formulation of the singular value decomposition. This allows to inherit key advantages of the classical algorithm such as highly efficient Lanczos-based optimization procedure, minimal parameter tuning during a model selection phase and a quick folding-in computation to generate recommendations instantly even in a highly dynamic online environment. Within the generalized formulation itself we provide an efficient scheme for side information fusion which avoids undesirable computational overhead and addresses the scalability question. Evaluation of the model is performed in both standard and cold-start scenarios using the datasets with different sparsity levels. We demonstrate in which cases our approach outperforms conventional methods and also provide some intuition on when it may give no significant improvement.

One of the 2017 trends in CRM was to figure out how to provide users with that next-best lead, up-sell opportunity or marketing prospect. Those insights were achievable due to the implementation of AI, which analysts expect will continue into other aspects of sales, marketing and service. "You're starting to see more logical use cases across the CRM spectrum with AI," said Michael Fauscette, principal analyst at G2 Crowd. "You'll start seeing more intelligent assistants to take things off your plate, like scheduling meetings or finding the right PowerPoint -- those types of things will keep getting better." AI assistants have already found their way into the consumer space, but analysts see business users also benefitting from an algorithmic assistant automating the monotonous parts of the sales or marketing cycle.