Climate change: I am inevitable. Robert Downey Jr.: I am Iron Man. That's basically what went down during the opening keynote at Amazon's new re:MARS tech and innovation conference in Las Vegas. Avengers actor Robert Downey Jr. made the very Tony Stark-like announcement that he'd be launching a new organization focused on solving environmental woes using artificial intelligence and other advanced technologies. "Between robotics and technology, we could probably clean up the planet significantly, if not entirely, within a decade," he said on Tuesday night.

Symbol detection for Massive Multiple-Input Multiple-Output (MIMO) is a challenging problem for which traditional algorithms are either impractical or suffer from performance limitations. Several recently proposed learning-based approaches achieve promising results on simple channel models (e.g., i.i.d. Gaussian). However, their performance degrades significantly on real-world channels with spatial correlation. We propose MMNet, a deep learning MIMO detection scheme that significantly outperforms existing approaches on realistic channels with the same or lower computational complexity. MMNet's design builds on the theory of iterative soft-thresholding algorithms and uses a novel training algorithm that leverages temporal and spectral correlation to accelerate training. Together, these innovations allow MMNet to train online for every realization of the channel. On i.i.d. Gaussian channels, MMNet requires two orders of magnitude fewer operations than existing deep learning schemes but achieves near-optimal performance. On spatially-correlated channels, it achieves the same error rate as the next-best learning scheme (OAMPNet) at 2.5dB lower SNR and with at least 10x less computational complexity. MMNet is also 4--8dB better overall than a classic linear scheme like the minimum mean square error (MMSE) detector.

We focus on the problem of streaming recommender system and explore novel collaborative filtering algorithms to handle the data dynamicity and complexity in a streaming manner. Although deep neural networks have demonstrated the effectiveness of recommendation tasks, it is lack of explorations on integrating probabilistic models and deep architectures under streaming recommendation settings. Conjoining the complementary advantages of probabilistic models and deep neural networks could enhance both model effectiveness and the understanding of inference uncertainties. To bridge the gap, in this paper, we propose a Coupled Variational Recurrent Collaborative Filtering (CVRCF) framework based on the idea of Deep Bayesian Learning to handle the streaming recommendation problem. The framework jointly combines stochastic processes and deep factorization models under a Bayesian paradigm to model the generation and evolution of users' preferences and items' popularities. To ensure efficient optimization and streaming update, we further propose a sequential variational inference algorithm based on a cross variational recurrent neural network structure. Experimental results on three benchmark datasets demonstrate that the proposed framework performs favorably against the state-of-the-art methods in terms of both temporal dependency modeling and predictive accuracy. The learned latent variables also provide visualized interpretations for the evolution of temporal dynamics.

"I have songwriting credits…even though I don't know how to write a song." 1 The speaker of this statement is not a musician and has no musical training. He helped create an app called Endel, which is self-described as "a cross-platform audio ecosystem." 2 Endel is part of a larger part of the current hot debate over works of art being "created" by computers using programs employing "artificially intelligent" modes of computer learning, or AI for short. "Dmitry Evgrafov, Endel's composer and head of sound design, says all 600 tracks were made'with a click of a button.' There was minimal human involvement outside of chopping up the audio and mastering it for streaming. Endel even hired a third-party company to write the track titles." 3 What makes this notable is that Endel has a record deal with Warner Bros. Music. 4 "Five Endel albums have already been released, and 15 more are coming this year -- all of which will be generated by code. In the future, Endel will be able to make infinite ambient tracks." 5 But didn't the Endel engineers create the software in question?

The acquisition brings Tableau's analytics and Salesforce's AI-powered Einstein platform together, which the press release claims "will deliver the most intelligent and intuitive analytics and visualization platform for every department and every user at any company". "We are bringing together the world's #1 CRM with the #1 analytics platform. Tableau helps people see and understand data, and Salesforce helps people engage and understand customers. It's truly the best of both worlds for our customers--bringing together two critical platforms that every customer needs to understand their world." Accenture (NYSE:ACN), along with digital services provider Avanada, has been recognized in a recently published report from IDC (International Data Corporation) for its leadership in worldwide Microsoft implementation services.

Cisco helps IT teams better understand network behavior and predict issues with new artificial intelligence and machine learning capabilities. Since its introduction two years ago, Cisco's intent-based networking has reinvented how networks are built and managed. Cisco is furthering this effort through multidomain integrations designed to provide end-to-end security, segmentation and application experience. Cisco is delivering these new software advancements via software subscriptions, granting customers access to ongoing innovation. SAN DIEGO, California – Cisco Live U.S. – Today, Cisco announces software innovations designed to make managing and securing networks easier.

Cisco Live U.S. – Today, Cisco announces software innovations designed to make managing and securing networks easier. As today's businesses increasingly invest in digital technologies, IT teams are struggling under the amplified workload. To alleviate this burden and allow IT to focus on delivering innovation, Cisco is introducing new artificial intelligence and machine learning capabilities to allow IT teams to function at machine speed and scale through personalized network insights. As part of its broadened capabilities offering, Cisco is also unveiling innovations to more effectively manage users and applications across the entire enterprise network – from campus networks and wide-area networks, to data centers and the IoT edge. IT teams currently face a daunting challenge.

Over a decade ago, I was sitting in a college math physics course and my professor spelt out an idea that kind of blew my mind. I think it isn't a stretch to say that this is one of the most widely applicable mathematical discoveries, with applications ranging from optics to quantum physics, radio astronomy, MP3 and JPEG compression, X-ray crystallography, voice recognition, and PET or MRI scans. This mathematical tool--named the Fourier transform, after 18th-century French physicist and mathematician Joseph Fourier--was even used by James Watson and Francis Crick to decode the double helix structure of DNA from the X-ray patterns produced by Rosalind Franklin. You probably use a descendant of Fourier's idea every day, whether you're playing an MP3, viewing an image on the web, asking Siri a question, or tuning in to a radio station. In addition to his work in theoretical physics and math, he was also the first to discover the greenhouse effect.)

The rapid improvement of language models has raised the specter of abuse of text generation systems. This progress motivates the development of simple methods for detecting generated text that can be used by and explained to non-experts. We develop GLTR, a tool to support humans in detecting whether a text was generated by a model. GLTR applies a suite of baseline statistical methods that can detect generation artifacts across common Figure 1: The top-k overlay within GLTR. It is easy sampling schemes. In a human-subjects study, to distinguish sampled from written text. The real text we show that the annotation scheme provided is from the Wikipedia page of The Great British Bake by GLTR improves the human detection-rate Off, the fake from GPT-2 large with temperature 0.7. of fake text from 54% to 72% without any prior training.

Abstract: Filters in convolutional neural networks are sensitive to their initialization. The random numbers used to initialize filters are a bias and determine if you will "win" and converge to a satisfactory local minimum so we call this The Filter Lottery. We observe that the 28x28 Inception-V3 model without Batch Normalization fails to train 26% of the time when varying the random seed alone. This is a problem that affects the trial and error process of designing a network. Because random seeds have a large impact it makes it hard to evaluate a network design without trying many different random starting weights.