In the last few years artificial intelligence (AI) and machine learning (ML) have both started to feature more prominently in technology. That is especially the case in video editing, where artificial intelligence is being integrated in more and more ways. Not long from now, AI may completely transform video editing – and in some ways it already is starting to do just that. One of the earliest examples of AI's use in video editing was as far back as 2016 when IBM used their Watson supercomputer to curate footage and create a trailer for the horror film Morgan. Essentially Watson used machine learning to analyze other trailers, and then applied what it learned to curate and select scenes from the film that it felt would be good for the trailer.

The summer of 2014 was a heady time in Silicon Valley. Cash was flowing as freely as Soylent as every Stanford graduate with a half-baked idea about a "pinch point" and a semi-plausible pitch book was lining up checks from venture capital firms. Into this mix came Washboard, a startup so utterly absurd that most of the news outlets that wrote about it (and boy did they write about it) took the trouble to clarify that it was, in fact, "real". Washboard was designed to solve a real, if insubstantial, problem: it can be difficult for those who rely on coin-operated laundry machines to acquire enough quarters to run a load. Banks have limited hours, small businesses are not always obliging, and most apartment buildings don't have change machines.

Disclaimer: This is a third-party implementation. The original authors stated that they will be releasing code soon. A recent research showed that fully-convolutional GAN called MelGAN can invert mel-spectrogram into raw audio in non-autoregressive manner. They showed that their MelGAN is lighter & faster than WaveGlow, and even can generalize to unseen speakers when trained on 3 male 3 female speakers' speech. I thought this is a major breakthrough in TTS reserach, since both researchers and engineers can benefit from this fast & lightweight neural vocoder.

Nanophotonic neural networks are an exciting emerging technology which promises low-energy, ultra high-throughput machine learning systems implemented purely optically. Our lab has previously done work on these devices, and our new paper which extends programmable photonics to the quantum domain is now on arXiv! In this paper, we describe a photonic architecture for a quantum programmable gate array (QPGA) which can be dynamically reprogrammed to perform any quantum computation. We show how to exactly prepare arbitrary quantum states and operators on the device, and we apply machine learning techniques to automatically implement highly compact approximations to important quantum circuits. Below is an animation of a simulated QPGA being trained to implement a quantum Fourier transform on five qubits.

Machine learning and data science development isn't exactly a walk in the park, but Netflix hopes to streamline the arduous bits with a new freely available platform. The tech giant today announced that it has open-sourced Polynote, a multi-language programming notebook environment that integrates with Apache Spark and offers robust support for Scala, Python, and SQL. In a blog post, Netflix said that Polynote -- which has seen "substantial" adoption among its personalization and recommendation teams -- was designed to enable data scientists and AI researchers to integrate Netflix's JVM-based machine learning framework with Python machine learning and visualization libraries. "On the Netflix personalization infrastructure team, our job is to accelerate machine learning innovation by building tools that can remove pain points and allow researchers to focus on research. Polynote originated from a frustration with the shortcomings of existing notebook tools, especially with respect to their support of Scala," said the company.

Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new "Colossal Clean Crawled Corpus", we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.

We investigate the use of a non-parametric independence measure, the Hilbert-Schmidt Independence Criterion (HSIC), as a loss-function for learning robust regression and classification models. This loss-function encourages learning models where the distribution of the residuals between the label and the model-prediction is statistically independent of the distribution of the instances themselves. This loss-function was first proposed by Mooij et al. [2009] in the context of learning causal graphs. We adapt it to the task of robust learning for unsupervised covariate shift: learning on a source domain without access to any instances or labels from the unknown target domain. We prove that the proposed loss is expected to generalize to a class of target domains described in terms of the complexity of their density ratio function with respect to the source domain. Experiments on tasks of unsupervised covariate shift demonstrate that models learned with the proposed loss-function outperform several baseline methods.

After years of pretty dull smartphone design, we're finally getting some interesting ideas, like foldable phones, that recall the weird, early days of rotating, flipping and docking devices. The new $799 Google Pixel 4 and larger $899 4 XL, however, are definitely in the "boring" category, at least on first glance. But that's because everything special about these phones lies under the hood. The Pixel 4's standout feature is its software, which closes the gap between itself and the competition, along with an improved pair of cameras that will show you everything your heart desires, even the stars in the night sky. No, really, this phone can take pictures of stars, as long as you're in the right place at the right time.

In 1962, The Jetsons premiered on ABC. The show combines a familiar mid-century style with imaginative notions of what the future might look like. Today, the series is still seen as a comparative reality to how rapidly technology is shaping our lives. On The Jetsons, technology promises a solution to everyday problems. When Mrs. Jetson is exhausted by housework, the family invests in a robotic maid named Rosey.