In this AI based Science article, we explore How Netflix adopted an Open Source Model to improve their Entertainment Recommender Systems. First, let us discuss in brief, what Machine Learning basically means. In simple terms, Machine Learning is a technique by which a computer can "learn" from data, without using a complex set of different rules. This approach is mainly based on training a model from datasets. The better the quality of the datasets, the better the accuracy of the Machine Learning Model.

I recently overheard my 2-year-old daughter talking to Amazon's voice assistant Alexa, and two things struck me. First, she doesn't distinguish the disembodied voice from that of a regular human. Second, she barks orders at Alexa in a way that would be considered rude by any social convention. I was suddenly aware and troubled that Alexa is setting a terrible example for my daughter--that women are subservient, should accept rudeness, and belong in the home. All four of the major in-home artificial intelligence, or AI, assistants--Alexa, Apple's Siri, Google Assistant, and Microsoft's Cortana--speak by default with a female voice.

The key message from a new policy report from the University of Manchester is that future-state artificial intelligence needs to be socially responsible. This is because the current path of development of new artificial intelligence technology has been shown, through experimentation, to contain bias. For example, Joy Buolamwini has challenged inherent ethnic and female bias in many facial recognition systems. This issue of bias becomes serious when used in the business setting and by governments, such as when assessing welfare claims. Here many developed systems can be discriminatory.

At Google's annual developer conference this past week, CEO Sundar Pichai played a clip of what seemed to be a mundane human interaction: Someone calling to make an appointment with their hairdresser. The two voices negotiated the date and time, with the "assistant" providing the client's name for the reservation. Instead, Pichai said, it was Google's artificial intelligence making the call to the unwitting hair salon receptionist. The technology is called Google Duplex, a system that combines natural language processing and speech generation that allegedly allows Google to accomplish customer service tasks in a number of limited situations. Right now, the company is only testing the technology internally, focusing on booking reservations at restaurants and hair salons, as well as inquiring about holiday hours for businesses.

At Google's annual developer conference this past week, CEO Sundar Pichai played a clip of what seemed to be a mundane human interaction: Someone calling to make an appointment with their hairdresser. The two voices negotiated the date and time, with the "assistant" providing the client's name for the reservation. Instead, Pichai said, it was Google's artificial intelligence making the call to the unwitting hair salon receptionist. The technology is called Google Duplex, a system that combines natural language processing and speech generation that allegedly allows Google to accomplish customer service tasks in a number of limited situations. Right now, the company is only testing the technology internally, focusing on booking reservations at restaurants and hair salons, as well as inquiring about holiday hours for businesses.

Explainable Recommendation refers to the personalized recommendation algorithms that address the problem of why -- they not only provide the user with the recommendations, but also make the user aware why such items are recommended by generating recommendation explanations, which help to improve the effectiveness, efficiency, persuasiveness, and user satisfaction of recommender systems. In recent years, a large number of explainable recommendation approaches -- especially model-based explainable recommendation algorithms -- have been proposed and adopted in real-world systems. In this survey, we review the work on explainable recommendation that has been published in or before the year of 2018. We first high-light the position of explainable recommendation in recommender system research by categorizing recommendation problems into the 5W, i.e., what, when, who, where, and why. We then conduct a comprehensive survey of explainable recommendation itself in terms of three aspects: 1) We provide a chronological research line of explanations in recommender systems, including the user study approaches in the early years, as well as the more recent model-based approaches. 2) We provide a taxonomy for explainable recommendation algorithms, including user-based, item-based, model-based, and post-model explanations. 3) We summarize the application of explainable recommendation in different recommendation tasks, including product recommendation, social recommendation, POI recommendation, etc. We devote a chapter to discuss the explanation perspectives in the broader IR and machine learning settings, as well as their relationship with explainable recommendation research. We end the survey by discussing potential future research directions to promote the explainable recommendation research area.

Amazon's Echo Dot and its accompanying version of Alexa for kids called FreeTime raised eyebrows and questions about children's privacy from the start. Now, Sen. Edward J. Markey (Massachusetts) and Rep. Joe Barton (Texas) want to know what the e-commerce giant is doing to ensure the privacy of kids who use the speaker and the voice assistant. The lawmakers have penned a letter to Amazon asking if kids' interactions with the speaker are saved and shared with third parties. They also want to know if the company worked with child development experts when they designed the device. The tech giant answered some of the lawmakers' concerns in a statement, telling CNET that "Amazon takes privacy and security seriously, and FreeTime on Alexa is no different."

Over the years of teaching and research, I have gradually standardized the notation that I use for describing the math of recommender systems. This is the notation that I use in my classes, Joe Konstan and I have adopted for our MOOC, and that I use in most of my research papers. If you haven't already settled on a notation, perhaps you would consider adopting this one. I have tried to strike a balance between clarity and clutter. I slightly overload the meaning of some symbols; in particular, I am loose with distinctions between sets and matrices, because it is generally clear from context which is being invoked; I do not overload external referents, however.

For wealth managers, losing clients can be detrimental. When longstanding client relationships dissolve, it can be a blow to advisors' revenue and reputation. Not to mention, replacing them can cost significant money and time. As financial advisors aim to strengthen relationships, retain valued clients and attract new business, artificial intelligence is an overlooked and underutilized tool. Emerging technology continues to dominate the conversation in the wealth management community, and the jury is still out on how the advent of robo advisors and similar technology will influence the advisory industry for better or worse.

Google's Duplex could make Assistant the most lifelike AI yet Richard Nieva, CNET Google has big plans for Assistant, and some of what it showed at I/O this week is equal parts fascinating and worrisome. CNET took a closer look at the so-called Duplex technology that will allow Google Assistant to do things like make phone calls on your behalf. The company has since said it will alert the person on the other end that they're speaking to AI, but for many, questions remain. If there's one thing I've learned over the last few years, it's that when Coates publishes something, it's going to be worth your time. This piece about the current state of Yeezus is no exception.