Even the most complex topics can be segregated in easy to consume buckets. Let me try to explain them and let's start with the evolution: Sometimes it's easy to understand, when you ask Siri, Cortana or OK Google to get you directions. Sometimes it's less obvious, like when you make an abnormal purchase on your credit card and don't get a fraud alert from your bank. AI, ML and DL are everywhere and Data Science is the interdisciplinary field of methonds to extract the knowledge needed. All these technologies are making a huge difference in our lives every day and evoloving fast by a magnitude of people working to improve them consistently.

Whether you realize it or not, artificial intelligence (AI) is already shaping our world. Whenever you use Siri or Alexa, you are already communicating with digital assistance – a form of AI that is pegged as an "intelligent digital assistant." These AI assistants are designed to make your life easier. Now, it is clear they are headed to the classroom as well. According to Artificial Intelligence Market in the US Education Sector 2017-2021, experts expect AI in education to grow by "47.50% during the period 2017-2021."

Siri and Alexa are clearly far from perfect, but there is hope that steady progress in machine learning will turn them into articulate helpers before long. A new test, however, may help show that a fundamentally different approach is required for AI systems to actually master language. Developed by researchers at the Allen Institute for AI (AI2), a nonprofit based in Seattle, the AI2 Reasoning Challenge (ARC) will pose elementary-school-level multiple-choice science questions. Each question will require some understanding of how the world works. The project is described in a related research paper (pdf).

IBM is hoping to create a new voice assistant for the business world -- and before Amazon can create one first. At its Think conference in Las Vegas, IBM on Tuesday took the wraps off Watson Assistant, a virtual helper it's been developing with its customers for over a year. Don't expect to start saying, "Watson, what's the weather?" or "Watson, play They Might be Giants" to an IBM-branded speaker anytime soon. Instead, Watson Assistant will function as the behind-the-scenes brains for a variety of new digital helpers made by a variety of businesses for your home and in stores, airports, hotels and cars. For example, Watson Assistant is already in use at Munich Airport to power a robot that can tell you directions and gate information.

Recommender systems (RSs) provide an effective way of alleviating the information overload problem by selecting personalized items for different users. Latent factors based collaborative filtering (CF) has become the popular approaches for RSs due to its accuracy and scalability. Recently, online social networks and user-generated content provide diverse sources for recommendation beyond ratings. Although {\em social matrix factorization} (Social MF) and {\em topic matrix factorization} (Topic MF) successfully exploit social relations and item reviews, respectively, both of them ignore some useful information. In this paper, we investigate the effective data fusion by combining the aforementioned approaches. First, we propose a novel model {\em \mbox{MR3}} to jointly model three sources of information (i.e., ratings, item reviews, and social relations) effectively for rating prediction by aligning the latent factors and hidden topics. Second, we incorporate the implicit feedback from ratings into the proposed model to enhance its capability and to demonstrate its flexibility. We achieve more accurate rating prediction on real-life datasets over various state-of-the-art methods. Furthermore, we measure the contribution from each of the three data sources and the impact of implicit feedback from ratings, followed by the sensitivity analysis of hyperparameters. Empirical studies demonstrate the effectiveness and efficacy of our proposed model and its extension.

As we move into another new year there are once again a new set of predictions, hopes and concerns as to the direction of digital marketing over the next twelve months and beyond. There are so many exciting developments set to take place and I'm really looking forward to seeing how these take hold in 2018. One of the interesting areas I've observed across many trends is the recurrence of two themes: artificial intelligence (AI) and machine learning. These two closely related subjects seem to be taking hold across a number of areas, including marketing automation, digital creative, personalization and marketing technology. However, it's the influence of AI and machine learning on the future of search that I'd like to explore in some detail in this article and the potential implications on business and marketing.

It's another step in the march toward advice that erodes investors' needs for human help: A robo advisor focused on serving the high-net-worth client, powered by artificial intelligence and designed to automate their specific tax management concerns. Meeting the complicated needs of the wealthy requires a deep knowledge of tax rules and regulations says Hedgeable CEO Mike Kane. "Technology and AI systems can interpret and learn from these rules better than humans can, without our emotional biases." Doubling down on its embrace of Asian themes, Hedgeable's newest offering in its robo platform is a feature called'Tax Samurai,' run by an AI bot called'Katana.' For 30 basis points, it will work for client accounts with a minimum of $1 million to analyze their securities, aggregate all of their financial data, create tax efficient transfers, apply automated downside protection on any current holdings, and perform tax efficient trading and tax-loss harvesting.

Since Apple developed Siri there have been great strides made in the science of voice recognition. Will we soon be throwing away our mice and keyboards and simply talking to our computers? Or will the problems I have with Alexa continue to haunt voice recognition? My wife and I are like all married couples at breakfast. We do not speak to each other.

That's fine, as far as it goes. But it doesn't rule out the possibility that digital assistants think human beings are a joke. Nor that Alexa is laughing at jokes other digital assistants may have just cracked. In a society as technologically out of control as this one, anything is possible. In recent months, thousands of people have written to me (mostly by postcards, often filled to their edges) about an epidemic of belly laughs directed at them by their household appliances.

Mike Chen was working on a health care start-up, he and his colleagues had one of those light bulb moments regarding digital assistance. "There should be something where you can just text it, and it just, like, does it for you," he said. Three years later, his remote personal-assistant company, Magic, has employees in the United States and the Philippines. Its promise is bold: to do "anything" for customers who send requests over text or email, Mr. Chen said, 24 hours a day and 365 days a year. Fin, another new virtual personal-assistant company, is the brainchild of Sam Lessin and Andrew Kortina, a founder of the popular mobile payment service Venmo.