Every day we hear and read about how machine learning is changing the face of technology. From social media to virtual assistants like Siri and Alexa, IoT, and even automobiles, algorithms analyze terabytes of data and make faster decisions. Storing data and harnessing technology to make life easier has become cheaper. In an article for HBR magazine, Andrew Ng, the founder of Google Brain wrote, "If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future." People often think of Artificial Intelligence (AI) and Machine Learning (ML) as the same.

Artificial intelligence is a concept that was first introduced as a storytelling device in the portrayal of robots and other'thinking' machines. What was a futuristic work of fiction before is now today's buzzword. The progress we've made has produced virtual assistants such as Amazon's Alexa and Tesla's self-driving cars. And by the looks of it, we've just scratched the surface. Advancements in the field of artificial intelligence have slowly been revolutionizing processes across different industries.

Recommender systems in academia are not widely available. This may be in part due to the difficulty and cost of developing and maintaining recommender systems. Many operators of academic products such as digital libraries and reference managers avoid this effort, although a recommender system could provide significant benefits to their users. In this paper, we introduce Mr. DLib's "Recommendations as-a-Service" (RaaS) API that allows operators of academic products to easily integrate a scientific recommender system into their products. Mr. DLib generates recommendations for research articles but in the future, recommendations may include call for papers, grants, etc. Operators of academic products can request recommendations from Mr. DLib and display these recommendations to their users. Mr. DLib can be integrated in just a few hours or days; creating an equivalent recommender system from scratch would require several months for an academic operator. Mr. DLib has been used by GESIS Sowiport and by the reference manager JabRef. Mr. DLib is open source and its goal is to facilitate the application of, and research on, scientific recommender systems. In this paper, we present the motivation for Mr. DLib, the architecture and details about the effectiveness. Mr. DLib has delivered 94m recommendations over a span of two years with an average click-through rate of 0.12%.

Recommender systems are growing progressively more popular in online retail because of their ability to offer personalized experiences to unique users. Mean Average Precision at K (MAP@K) is typically the metric of choice for evaluating the performance of a recommender systems. However, the use of additional diagnostic metrics and visualizations can offer deeper and sometimes surprising insights into a model's performance. This article explores Mean Average Recall at K (MAR@K), Coverage, Personalization, and Intra-list Similarity, and uses these metrics to compare three simple recommender systems. If you would like to use any of the metrics or plots discussed in this article, I have made them all available in a python library recmetrics.

Does your mopping robot or your food processor have names? Do you ascribe a character to them? Unless you're a robot yourself, you probably do. The entire history of engineering has been about making the most human-like machines possible. We humans are hardwired to process social interactions, so we subconsciously expect machines to be at least somewhat human.

One of the great wonders of the online experience is when we actually fall for someone we meet on social media. One moment they're in our notifications and the next, you two are talking and texting till the A.M. All the while the anticipation builds for a face to face encounter. And after putting apprehension aside, we go on a date with a worthy online connection…..and things go great! Turns out, the person you met the'Net' is actually interesting and charming in person. And most of all their physical appearance aligns with the photos associated with their social media accounts!

The next disruptive technology phase is already upon here and it includes a technology designed to emulate human conversation – chatbots programmed with artificial intelligence. It will have implications for nonprofits way beyond simply setting up a Facebook Messenger bot for your nonprofit's Facebook Brand Page. With over 100,000 bots created on the Facebook Messenger platform and the rise of AI and conversational user interfaces (think SIRI), Gartner analysts predict by 2020 the average person will have more conversations with bots than their spouse. Opportunities to chat with robots are growing, whether it through our smartphones, tablets, home appliances, virtual personal assistants or our cars.And, while we might think being addicted to chatting with a virtual chatbot is more like fodder for an episode of Black Mirror, it isn't. Gartner analyst suggests this trend will have far more impact on our lives, work, and society than social media and connectivity did a decade ago. Think about how you've interacted with SIRI.

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For years, advocates have hyped Artificial Intelligence's (AI) potential to do for healthcare what it's doing for other industries – personalize recommendations, prioritize searches, and tag pictures. Investments have reflected the excitement, with the healthcare AI market expected to reach $6.6B by 2021. "People see smart computers all around them – Apple's Siri, Amazon's Alexa, Tesla's self-driving car – and they think healthcare should be the same. Obviously, healthcare is far more complex, requires much higher accuracy, and has less margin for error," said Dr. Michael Blum, Associate Vice Chancellor for Informatics, University of California, San Francisco (UCSF), speaking on the topic ahead of last year's Radiological Society of North America (RSNA) annual conference. However, disparate efforts, different systems, massive amounts of data and layers of complexity have meant the industry remains largely unchanged. Fortunately, most hospitals have at least one thing in common: imaging machines – the technology radiologists use to scan, diagnose and treat patients and the largest generator of healthcare data.

Artificial Intelligence and digital marketing have become the talk of the town and are trending and making immense news in distinct fields with its demands. With the ability to gather data, analyze it, apply it and then comprehend it, AI is working towards transforming the digital strategies. Here are three ways in which AI is disrupting the digital marketing sector. The most crucial aspect of a successful digital marketing campaign is an excellent customer experience. If the content delivered to users is relevant to them, they would more likely become recurring customers and have brand loyalty.