Warner Bros. is turning to artificial intelligence company Cinelytic to help decide which movies it should make Finally, Warner Bros. is acknowledging its continued reactionary decision making and mishandling of some of its most valuable IPs, and is partnering with an AI analytics firm to get a clearer picture of what works and what doesn't. Up until recently, the studio had made a series of blunders with its tent-pole DC superhero properties that garnered nothing but divisiveness among critics and fans, and led to its Avengers-style team-up film Justice League costing the studio more than $100 million. In addition, the rate at which new projects were being announced and shelved, the amount of directors signing on and then leaving in-development films, and the handling of relationships with big Hollywood names like Ben Affleck and Zack Snyder resulted in wave after wave of negative headlines. Big changes were clearly needed. After the financial failings of Justice League, WB appointed Walter Hamada as the head of DC Films, and under his leadership the studio has seen increased success with its DCEU titles, such as Aquaman, and more recently its standalone film Joker.

LAS VEGAS -- Ambarella, Inc. (Nasdaq: AMBA), an artificial intelligence (AI) vision silicon company, today announced that Ambarella and Amazon Web Services, Inc. (AWS) customers can now use Amazon SageMaker Neo to train machine learning (ML) models once and run them on any device equipped with an Ambarella CVflow -powered AI vision system on chip (SoC). Until now, developers had to manually optimize ML models for devices based on Ambarella AI vision SoCs. This step could add considerable delays and errors to the application development process. Ambarella and AWS collaborated to simplify the process by integrating the Ambarella toolchain with the Amazon SageMaker Neo cloud service. Now, developers can simply bring their trained models to Amazon SageMaker Neo and automatically optimize the model for Ambarella CVflow-powered SoCs.

He nailed it in his high-school tests, and nailed it again in his university entrance examination. But, according to headlines, "he wants to be a playwright, not a scientist." Oh, how meaningful a contraposition can be! His was a wonderful achievement, but the bodies of the news stories were not dedicated to how he had been able to accomplish it, what his study techniques are or his motivations. No, they were primarily focused on what he wants and doesn't want to be.

Danielle Ling is an experienced video journalist and business reporter. As Associate Editor of NU Property & Casualty magazine and NU Propertycasualty360.com, Danielle manages all multimedia and reports on industry news and risk-related coverage, managing all weather-related content. Danielle heads PC360's video production, producing news packages, video interviews, and animated videos. A University of Maryland and Philip Merrill College of Journalism alum, prior to this role, Danielle served as a video journalist for Verizon FiOS 1 News NJ on the show "Push Pause."

We are looking for a smart and creative Machine Learning Engineer. With your expertise, you will bring new ideas to the team and improve our music recommendation services. Your role will be to to solve real world problems by applying machine learning technics. From the simplest heuristic rule to the most advanced state of the art model, you have only one goal: provide the best listening experience to our users.

We address the challenging open problem of learning an effective latent space for symbolic music data in generative music modeling. We focus on leveraging adversarial regularization as a flexible and natural mean to imbue variational autoencoders with context information concerning music genre and style. Through the paper, we show how Gaussian mixtures taking into account music metadata information can be used as an effective prior for the autoencoder latent space, introducing the first Music Adversarial Autoencoder (MusAE). The empirical analysis on a large scale benchmark shows that our model has a higher reconstruction accuracy than state-of-the-art models based on standard variational autoencoders. It is also able to create realistic interpolations between two musical sequences, smoothly changing the dynamics of the different tracks. Experiments show that the model can organise its latent space accordingly to low-level properties of the musical pieces, as well as to embed into the latent variables the high-level genre information injected from the prior distribution to increase its overall performance. This allows us to perform changes to the generated pieces in a principled way.

Last March, at the South by Southwest tech conference in Austin, Texas, Tesla and SpaceX founder Elon Musk issued a friendly warning: "Mark my words," he said, billionaire casual in a furry-collared bomber jacket and days' old scruff, "AI is far more dangerous than nukes." No shrinking violet, especially when it comes to opining about technology, the outspoken Musk has repeated a version of these artificial intelligence premonitions in other settings as well. "I am really quite close… to the cutting edge in AI, and it scares the hell out of me," he told his SXSW audience. "It's capable of vastly more than almost anyone knows, and the rate of improvement is exponential." Musk, though, is far from alone in his exceedingly skeptical (some might say bleakly alarmist) views. A year prior, the late physicist Stephen Hawking was similarly forthright when he told an audience in Portugal that AI's impact could be cataclysmic unless its rapid development is strictly and ethically controlled.

Despite what the phrase "artificial intelligence" seems to imply, machines do not "think," as discussed at length in a previous article. However, computers do have a useful machine equivalent to "thinking" and that is math. One way humans think is with math, while the only way computers think is with math. Bits are binary, assembly language is based on mathematical logic and everything from regressions to neural networks is based on mathematical foundations. All of computer science is applied mathematics and Artificial Intelligence is no different.

Amazon Echo can do seven things that Google Home can't. Amazon and Google have long competed for the No. 1 smart speaker spot. And while Google Home has features that Echo doesn't, like listening to multiple commands at a time, Amazon still has a set of unique skills that Google can't yet do. Only Amazon Echo ($100 at Amazon) can guard your house and alert you if it hears something suspicious while you're gone. It also lets you know when your packages are out for delivery.

Does anyone know of scientific literature that shows that, even in cases in which we have enough parallel data (English-French), use of monolingual data can be beneficial? To me it seems reasonable that if we, for instance, added monolingual data to the decoder, it would be better at scoring candidate predictions in terms of fluency. That being said, I cannot find peer-reviewed articles that show this.