Dow and S&P 500 are currently on track to close out the second quarter positive on the year. The markets remained flat to slightly higher this morning, during the last trading day of the quarter. Investors remain cautious amidst concerns of mixed economic data and a looming threat from the coronavirus pandemic. All eyes will be set on Federal Reserve Chairman Jerome Powell and Treasury Secretary Steve Mnuchin as they look to testify before the House Financial Services Committee. Amidst this uncertainty, our deep learning algorithms have parsed through the data and used Artificial Intelligence ("AI") to help you spot the Top Buys for today.

Recently, Microsoft announced that it was terminating dozens of journalists and editorial workers at its Microsoft News and MSN organizations. Instead, the company said, it will rely on artificial intelligence to curate and edit news and content that is presented on MSN.com, inside Microsoft's Edge browser, and in the company's Microsoft News apps. Explaining the decision, Microsoft issued a statement to the Verge. The statement reads: "Like all companies, we evaluate our business on a regular basis. This can result in increased investment in some places and, from time to time, re-deployment in others. These decisions are not the result of the current pandemic."

Anonymous sources in documentaries have often been reduced to a shadowy, voice-distorted figure -- or worse, a pixelated blur. But a new documentary premiering Tuesday on HBO has, with the aid of advanced digital technology, gone to greater lengths to preserve the secrecy of its sources while still conveying their humanity. "Welcome to Chechnya," directed by David France, is about an underground pipeline created to rescue LGBTQ Chechens from the Russian republic where the government has for several years waged a crackdown on gays. In the predominantly Muslim region in southern Russia ruled by strongman Ramzan Kadyrov, LGBTQ Chechens have been detained, tortured and killed. France, the filmmaker behind "How to Survive a Plague" and "The Death and Life of Marsha P. Johnson," worked in secrecy with the Russian LGBT Network, a group formed to help save gay Chechens and find them asylum abroad.

Shot more than a century ago, a scene showing "Buffalo Bill" as he conducts an interview with an Oglala Lakota leader looks as if it were filmed yesterday. This old film clip was recently remastered using artificial intelligence (AI), and the result lookslike high-definition video. The artist behind this transformation is giving Live Science readers a first look at the astonishing result. Though still black and white, the remastered footage no longer appears jittery and sped-up, as silent films usually do. Motion in very old movies looks unnaturally fast because the hand-cranked film cameras of the day captured fewer frames per second (fps) than cameras do now.

Other key takeaways include: VCs devoted $34.3 billion to AI and machine learning deals in 2019, a 23.7% year-over-year drop due largely to declines …

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Artificial intelligence identifies, locates seizures in real-time.

Dr. Rothman said the health system is using artificial intelligence and machine learning in its platform and aims to collaborate with others to …

Many active learning and search approaches are intractable for industrial settings with billions of unlabeled examples. Existing approaches, such as uncertainty sampling or information density, search globally for the optimal examples to label, scaling linearly or even quadratically with the unlabeled data. However, in practice, data is often heavily skewed; only a small fraction of collected data will be relevant for a given learning task. For example, when identifying rare classes, detecting malicious content, or debugging model performance, the ratio of positive to negative examples can be 1 to 1,000 or more. In this work, we exploit this skew in large training datasets to reduce the number of unlabeled examples considered in each selection round by only looking at the nearest neighbors to the labeled examples. Empirically, we observe that learned representations effectively cluster unseen concepts, making active learning very effective and substantially reducing the number of viable unlabeled examples. We evaluate several active learning and search techniques in this setting on three large-scale datasets: ImageNet, Goodreads spoiler detection, and OpenImages. For rare classes, active learning methods need as little as 0.31% of the labeled data to match the average precision of full supervision. By limiting active learning methods to only consider the immediate neighbors of the labeled data as candidates for labeling, we need only process as little as 1% of the unlabeled data while achieving similar reductions in labeling costs as the traditional global approach. This process of expanding the candidate pool with the nearest neighbors of the labeled set can be done efficiently and reduces the computational complexity of selection by orders of magnitude.

In a world where new technology emerges at exponential rates, and our daily lives are increasingly mediated by speakers and sound waves, text to speech technology is the latest force evolving the way we communicate. Text to speech technology refers to a field of computer science that enables the conversion of language text into audible speech. Also known as voice computing, text to speech (TTS) often involves building a database of recorded human speech to train a computer to produce sound waves that resemble the natural sound of a human speaking. This process is called speech synthesis. The technology is trailblazing and major breakthroughs in the field occur regularly.