Research at the Dell EMC and Intel HPC and AI Innovation Lab is demonstrating how organizations can build better, faster neural networks to drive recommendation engines. If you go to Netflix to look for the next movie you want to watch, a recommendation engine will give you suggestions tailored to your interests and past viewing experiences. When you visit a website, you're likely to see ads based on your browsing history and past purchases. If you shop on Amazon, you will get all kinds of recommendations based on your purchasing history and the purchasing history of other customers with similar interests. As Amazon explains, "We examine the items you've purchased, items you've told us you own, items you've rated, and items you've told us you like. Based on those interests, we make recommendations."

IoT steadily remains on the march, setting tech trends and reformatting life as we know it. In fact, personal finance is only one of the things that it's going to drastically change. But although attacks on IoT devices were up 280% in 2017, its increasing momentum and strong influence have led analysts to consider it a primary transforming agent for financial services, according to Deloitte. Here are the three major ways IoT is doing just that. Ultimately, the limiting factor that defines what we can do with our personal finances is the personal data that is available.

A BSTRACT Most generative models of audio directly generate samples in one of two domains: time or frequency. While sufficient to express any signal, these representations are inefficient, as they do not utilize existing knowledge of how sound is generated and perceived. A third approach (vocoders/synthesizers) successfully incorporates strong domain knowledge of signal processing and perception, but has been less actively researched due to limited expressivity and difficulty integrating with modern auto-differentiation-based machine learning methods. In this paper, we introduce the Differentiable Digital Signal Processing (DDSP) library, which enables direct integration of classic signal processing elements with deep learning methods. Focusing on audio synthesis, we achieve high-fidelity generation without the need for large autoregressive models or adversarial losses, demonstrating that DDSP enables utilizing strong inductive biases without losing the expressive power of neural networks. Further, we show that combining interpretable modules permits manipulation of each separate model component, with applications such as independent control of pitch and loudness, realistic extrapolation to pitches not seen during training, blind dereverberation of room acoustics, transfer of extracted room acoustics to new environments, and transformation of timbre between disparate sources. In short, DDSP enables an interpretable and modular approach to generative modeling, without sacrificing the benefits of deep learning. The library is publicly available 1 and we welcome further contributions from the community and domain experts. 1 I NTRODUCTION Neural networks are universal function approximators in the asymptotic limit (Hornik et al., 1989), but their practical success is largely due to the use of strong structural priors such as convolution (Le-Cun et al., 1989), recurrence (Sutskever et al., 2014; Williams & Zipser, 1990; Werbos, 1990), and self-attention (V aswani et al., 2017). These architectural constraints promote generalization and data efficiency to the extent that they align with the data domain. From this perspective, end-to-end learning relies on structural priors to scale, but the practitioner's toolbox is limited to functions that can be expressed differentiably. Here, we increase the size of that toolbox by introducing the Differentiable Digital Signal Processing (DDSP) library, which integrates interpretable signal processing elements into modern automatic differentiation software (TensorFlow). While this approach has broad applicability, we highlight its potential in this paper through exploring the example of audio synthesis.

To support human decision making with machine learning models, we often need to elucidate patterns embedded in the models that are unsalient, unknown, or counterintuitive to humans. While existing approaches focus on explaining machine predictions with real-time assistance, we explore model-driven tutorials to help humans understand these patterns in a training phase. We consider both tutorials with guidelines from scientific papers, analogous to current practices of science communication, and automatically selected examples from training data with explanations. We use deceptive review detection as a testbed and conduct large-scale, randomized human-subject experiments to examine the effectiveness of such tutorials. We find that tutorials indeed improve human performance, with and without real-time assistance. In particular, although deep learning provides superior predictive performance than simple models, tutorials and explanations from simple models are more useful to humans. Our work suggests future directions for human-centered tutorials and explanations towards a synergy between humans and AI.

Fox News Flash top headlines for Jan. 13 are here. Check out what's clicking on Foxnews.com "Star Wars" actor Mark Hamill was recently reunited with a long-lost piece of memorabilia from the iconic movie franchise that was gifted to him many years ago. Bookmans Entertainment Exchange in Flagstaff, Ariz., returned the soundtrack for the 1977 film, "Star Wars: A New Hope," to the actor known to many as Luke Skywalker. The record was a gift to Hamill from film composer John Williams.

I just dragged my sorry sack of cells around the Consumer Electronics Show 2020 for two days; it was hard work. Here are some notes of the things I saw during my visit. It's not intended to be a comprehensive review (nor an unbiased one), I'll leave that up to the professional journalists. This posting is just about what I happened to stroll into and piqued my interest. For the last few years I've done a write up on the show. Here are links to some of the previous years' reviews: What is better than a 4k TV? I think every major manufacturer had an 8k TV on display.

Welcome to TechTalks' AI book reviews, a series of posts that explore the latest literature on AI. It wouldn't be an overstatement to say that artificial intelligence is one of the most confusing and least understood fields of science. On the one hand, we have headlines that warn of deep learning outperforming medical experts, creating their own language and spinning fake news stories. On the other hand, AI experts point out that artificial neural networks, the key innovation of current AI techniques, fail at some of the most basic tasks that any human child can perform. Artificial intelligence is also marked with some of the most divisive disputes and rivalries.

Warner Bros are going to start using artificial intelligence (AI) to help predict how successful their films could be. The platform will allow them to test profitability outcomes depending on different factors including casting, distributor and release date. Warner Bros. Pictures International - which is one of the "big six" in Hollywood - has signed a deal with LA start-up Cinelytic to use their project management system, according to The Hollywood Reporter. The forecasting tool was launched last year and offers a unique set of industry insights. It claims the platform is able to accurately predict box office returns using a variety of data.

Chinese tech giant Tencent has published content produced by automated software called Dreamwriter for the past five years, with an emphasis on business and financial stories. An online platform run by a company called Shanghai Yingxun Technology Company reproduced Tencent's AI-generated financial report on its own website in 2018. While the defendant had already deleted the report from its own website, a fine of 1,500 yuan ($217) was still payable. Over recent years, AI has more and more invaded most sectors and the creative arts are no different. For example, Google has created an AI-powered tool called AutoDraw that identifies what someone is trying to draw, and then digitally recreates it.

AI is about to play more of a role in the movie-making process. Warner Bros. Pictures has unveiled plans to use Cinelytic's AI project management system to assist in making decisions on movies during the "greenlight process." No, it won't have the final say on whether or not a movie goes forward. Rather, this will help the studio predict a movie's revenue, gauge the value of stars and determine when a title should premiere. Studio execs would ultimately have the final say, but the AI could help determine whether a movie is treated as a summer blockbuster or early-in-the-year filler material.