I work on HR systems, so I deal with HR a lot and hear a lot of stories. They hate dealing with employees as much as employees hate dealing with them. There are a lot more behind the scenes work they'd rather be doing. The only people I hear bitching about HR in the workplace are 1) the ones that HR has to handhold through forms (government or benefits vendor required, forms aren't for fun, ever) or 2) constantly causing issues in the workplace (and it's never the employees fault - "everyone else is the issue").

You can calculate every machine lewrning algorithm also by hand at a very small scale. For example an ANN with one single hidden layer that has to learn the "XOR" function. An other example is the support vector machine for which you can crate nice visualizations of the inital idea. At a large scale everything becomes quite infeasible to do by hand, but we dont have to since we have computers. If you understand small examples like mentioned above, you got the idea, no meed to do it by hand at a large scale.

This is a bitter pill to swallow, but leveraging Artificial Intelligence (AI) can help CPGs and retailers achieve better promotional outputs.

Do AI and machine learning affect consumer product safety?

Sony Corp. on Thursday renamed itself Sony Group Corp. in the first name change in over six decades, a symbolic change for the conglomerate that encompasses financial services, gaming and entertainment. The name change represents Sony's diversification of its business from its well-known electronics segment that rolled out Walkman portable music players and TVs that symbolized the "It's a Sony" slogan. Sony Group will seek to maximize synergies among its group companies and foster growth areas. Sony Electronics Corp. took on the name Sony Corp. from Thursday. Sony has been buoyed in recent years by its PlayStation game consoles and robust demand for its image sensors that are used in cameras and smartphones.

Media bias can lead to increased political polarization, and thus, the need for automatic mitigation methods is growing. Existing mitigation work displays articles from multiple news outlets to provide diverse news coverage, but without neutralizing the bias inherent in each of the displayed articles. Therefore, we propose a new task, a single neutralized article generation out of multiple biased articles, to facilitate more efficient access to balanced and unbiased information. In this paper, we compile a new dataset NeuWS, define an automatic evaluation metric, and provide baselines and multiple analyses to serve as a solid starting point for the proposed task. Lastly, we obtain a human evaluation to demonstrate the alignment between our metric and human judgment.

The next blockbuster to grace cinema screens after lockdown could be written by a computer, according to the authors of a new study examining movie tropes. They discovered that big movies tend to stick to the same core tropes, with clusters of storytelling devices appearing repeatedly in the biggest films every year. Researchers from the University of Granada, and the University of Cádiz looked for common tropes in more than 10,000 movies, their ratings, genre and reviews. The Spanish researchers identified over 25,000 tropes that formed 42 distinct clusters commonly found within similar genres by looking at IMDB and TVTrope data. The team also discovered modern society was sending some once common movie tropes into steep decline, including Damsel in Distress and Racist Jokes.

So, you recently bought or were gifted an Echo, Echo Dot, or another Echo device, and it's sitting in your kitchen, silently awaiting your next order. Before you can ask your Alexa-powered Echo to play your favorite Spotify playlist or to turn on your living room lights, you'll need to tweak a few key settings. Get the scoop on how to train Alexa to recognize your voice, keep her from letting just anyone buy stuff on Amazon, tell her where you live and work, and more. As soon as your new Echo is up and running, Alexa can start answering your questions and doing your bidding. That said, it's a good idea to help Alexa get accustomed to your voice as soon as possible.

It doesn't take a scientist to detect that artificial intelligence is rippling through pop culture. The future concept (and, in limited form, today's reality) has ping-ponged from the wild twists of HBO's Westworld to Grimes's latest acclaimed album Miss Anthropocene taking the perspective of an "anthropomorphic goddess of climate change" (whatever that means, the music's great). Hollywood has been working through these ideas for nearly a century, moving in fascinatingly different directions. Starting with Fritz Lang's revolutionary 1927 Metropolis, movies have been obsessed with the alternate intrigue and terror of computerized beings with unknown intelligence and understanding of the world. Now we have AI movies as diverse as Her and Ex Machina, ranging from melancholy love story to funny-scary freakout and everywhere in between.

We're living in a phenomenal moment for machine learning (ML), what Sonali Sambhus, head of developer and ML platform at Square, describes as "the democratization of ML." It's become the foundation of business and growth acceleration because of the incredible pace of change and development in this space. But for engineering and team leaders without an ML background, this can also feel overwhelming and intimidating. I regularly meet smart, successful, highly competent and normally very confident leaders who struggle to navigate a constructive or effective conversation on ML -- even though some of them lead teams that engineer it. I've spent more than two decades in the ML space, including work at Apple to build the world's largest online app and music store. As the senior director of engineering, anti-evil, at Reddit, I used ML to understand and combat the dark side of the web.