"The customer is always right." My dad, a successful entrepreneur and businessman, must have said those words ten thousand times. While we can debate the accuracy of that statement, how customers believe they are treated and feel about a brand, is one of the strongest indicators of a company's success. Delivering a positive customer experience requires acquiring and applying knowledge to personalize interactions and manage expectations at every single customer touch point. Today's consumers are more than simply customers.

Roundup Here's our latest summary of AI news beyond what we've already covered. Over 40 festivals pledge to not use facial recognition: A campaign against facial recognition led by the nonprofit Fight for the Future has led to over 40 music festivals publicly committing that they would not use the technology. Evan Greer, deputy director, and Tom Morello, a musician and guitarist for rock band Rage Against the Machine, teamed up to pen an op-ed celebrating the efforts to push back on the smart AI cameras. "Over the last month, artists and fans waged a grassroots war to stop Orwellian surveillance technology from invading live music events," they wrote on Buzzfeed News. Our campaign pushed more than 40 of the world's largest music festivals -- like Coachella, Bonnaroo, and SXSW -- to go on the record and state clearly that they have no plans to use facial recognition technology at their events." Musicians and fans were invited to write to their favorite festival organizers, urging them to not support facial recognition. Now, the list of festivals that have confirmed they won't be using the tech has grown. There are still a few top names that have yet to respond, however, including Burning Man and Outside Lands. You can see the complete list here. Amazon's facial recognition tool fails on black athletes: Amazon's controversial Rekognition software mistook the faces of 27 black athletes competing in American football, baseball, basketball, and hockey, as suspected criminals in a mugshot database. An experiment by the American Civil Liberties Union (ACLU) revealed the dangers of relying on facial recognition technology like Rekognition. "This technology is flawed," said Duron Harmon, a football player for the New England Patriots safety whose face was false identified in the experiment. "If it misidentified me, my teammates, and other professional athletes in an experiment, imagine the real-life impact of false matches.

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Artificial intelligence is getting personal. Advances in massive data gathering and processing, coupled with wholesale leaps in machine processing power are setting the stage for a world where AI could closely simulate the human brain and become ingrained in many aspects of daily life. How soon that becomes reality could depend a lot on the humans who control it, because the ability to run basic AI initiatives has evolved to a point where generating a machine-learning model is as easy as planting roses in the backyard. "People like Google and Facebook have made it so easy for the average person to actually do an AI project," said Bob Friday (pictured, second from right), vice president and chief technology officer of Mist, a Juniper company. "Anyone in your audience could actually train a machine-learning model over the weekend. You personally could become a data scientist over the weekend."

On the heels of several new acquisitions and product announcements in recent months, Motorola Solutions is announcing a new radio equipped with a voice assistant, which the company says is the first of its kind. The public safety radio is called APX NEXT, building upon the company's prior APX two-way radios, and the virtual assistant that controls it has been dubbed ViQi (pronounced "Vicky"). The company's news release on Thursday said the radio is FirstNet-ready, built with LTE connectivity, and is the first APX radio to feature a touchscreen, designed for field use including with rain or gloves. Motorola Solutions Chief Technology Officer Mahesh Saptharishi said that besides being able to control the radio, the virtual assistant responds to commands like "ViQi, run a license plate," and can also look up driver's license information and vehicle identification numbers. He said other functions will come with future updates.

In the battle of the buds, our "taste test" was striking. A friend and I both compared listening to the Foo Fighters "Learn to Fly" on Amazon's new Echo Buds and the product it aims to emulate, what Apple calls the best-selling headphone "in the world," the AirPods. The Amazon product "sounded tinnier," said Jan Schreiber, a Laguna Beach, California-based photographer. The AirPods had richer bass and a fuller sound. We switched to other songs, and the verdict didn't change.

According to an old fable, if you sat a monkey at a keyboard and gave it enough time, it would eventually type out the entire works of Shakespeare. The idea behind the thought experiment is a simple one, but it is an outdated way of thinking about artificial intelligence (AI). For example, consider the predictive text function on a phone -- unlike the hypothetical monkey, it does not randomly generate suggested words, but detects patterns in the way we write to select the likeliest options. "We've gone from predicting the next letters in a word to predicting the next word to being able to autogenerate whole sentences, and sometimes whole articles," Adelaide-based AI researcher, Dr John Flackett, said. Last year, Google launched Smart Compose for Gmail users which anticipates potential endings to sentences as you type them out, with the company promising it would "save you time".

Can't get your dog or that tiger at the zoo to smile for your Instagram? A new artificially intelligent program developed by researchers from Nvidia can take the expression from one animal and put it on the photo of another animal. Called GANimal -- after generative adversarial networks, a type of A.I. -- the software allows users to upload an image of one animal to re-create the pet's expression and pose on another animal. GAN programs are designed to convert one image to look like another, but are typically focused on more narrow tasks like turning horses to zebras. GANimal, however, applies several different changes to the image, adjusting the expression, the position of the animal's head, and in many cases, even the background, from the inspiration image onto the source image.

Notebooks are the data scientist best friend and can also be a nightmare to work with. For someone accustomed to work with modern integrated develop environments(IDEs), working with notebooks feels like going back decades. Furthermore, modern notebook environments is mostly constrained to Python programs and lack first-class support for other programming languages. Polynote was born out of the necessity to accelerate data science experimentation at Netflix. Over the years, Netflix has built a world-class machine learning platform mostly based on JVM languages like Scala.

Matrix completion is often applied to data with entries missing not at random (MNAR). For example, consider a recommendation system where users tend to only reveal ratings for items they like. In this case, a matrix completion method that relies on entries being revealed at uniformly sampled row and column indices can yield overly optimistic predictions of unseen user ratings. Recently, various papers have shown that we can reduce this bias in MNAR matrix completion if we know the probabilities of different matrix entries being missing. These probabilities are typically modeled using logistic regression or naive Bayes, which make strong assumptions and lack guarantees on the accuracy of the estimated probabilities. In this paper, we suggest a simple approach to estimating these probabilities that avoids these shortcomings. Our approach follows from the observation that missingness patterns in real data often exhibit low nuclear norm structure. We can then estimate the missingness probabilities by feeding the (always fully-observed) binary matrix specifying which entries are revealed or missing to an existing nuclear-norm-constrained matrix completion algorithm by Davenport et al. [2014]. Thus, we tackle MNAR matrix completion by solving a different matrix completion problem first that recovers missingness probabilities. We establish finite-sample error bounds for how accurate these probability estimates are and how well these estimates debias standard matrix completion losses for the original matrix to be completed. Our experiments show that the proposed debiasing strategy can improve a variety of existing matrix completion algorithms, and achieves downstream matrix completion accuracy at least as good as logistic regression and naive Bayes debiasing baselines that require additional auxiliary information.