And one in which Matthew McConaughey took the Leo role in Titanic. And Saved by the Bell's Tiffani Thiessen played Rachel in Friends. The entertainment industry isn't exactly short on "what if?" scenarios in which actors came close to, but were ultimately passed over, playing iconic roles. For more than 99% of movie history, fans have been able to do little more than squirrel away this trivia for use in pop quizzes. That is until the arrival of deepfakes.

Grover's architecture is based on OpenAI's GPT-2, a powerful pre-training model. The training has been performed on randomly-sampled sequences from RealNews dataset and the Newspaper Python library has been used to extract the body and metadata from each article. The dataset used for building this model is called as RealNews which is a fairly large corpus of news articles from Common Crawl. For training the model, the researchers construct a large corpus of news articles with metadata from Common Crawl which includes 5,000 news domains indexed by Google News. News from Common Crawl from December 2016 through March 2019 were used as training data and the news articles published in April 2019 from the April 2019 were used as test data.

A Behavior Modification Empire is an organization that establishes the means of human interaction via highly addictive technologies that elicit Pavlovian reactions to social rewards and punishments and then sells the right to manipulate participants to third-parties. Jaron Lanier, a Virtual Reality pioneer and important Internet luminary, begins a 2018 TED talk by referring to an early computer scientist named Norbert Wiener. Weiner wrote a book called "The Human Use of Human Beings," in which he envisioned a dystopian future governed by a computer system that would gather "data from people and [provide] feedback to those people in real time in order to put them . . . in a Skinner box, in a behaviorist system." One could imagine a global computer system where everybody has devices on them all the time, and the devices are giving them feedback based on what they did, and the whole population is subject to a degree of behavior modification. And such a society would be insane, could not survive, could not face its problems. Of course, Weiner's notion proved to be eerily prophetic, and now that it's happened, Lanier believes we have to figure out how to survive it.

New technology sometimes makes the world around us look unrecognizable. But human nature--even over the centuries--remains remarkably familiar. Again and again, as we've researched the stories that go into these podcast episodes, we've seen that the past is full of hints about what's coming around the corner. We hope you'll join us as we reveal The Secret History of the Future.

NEW YORK - In 1964, Stanley Kubrick, on the recommendation of the science-fiction author Arthur C. Clarke, bought a telescope. "He got this Questar and he attached one of his cameras to it," said Katharina Kubrick, the filmmaker's stepdaughter. "On a night where there was a lunar eclipse, he dragged us all out onto the balcony and we were able to see the moon like a big rubber ball. I don't think I've seen it as clearly since. He looked at it all the time."

The current crop also includes applications of on-demand manufacturing, augmented reality, music-assisted learning, interactive video, and online music creation. The Aussie team got the nod for their knack at using AI to separate, classify and up-res audio "for the purpose of audio search, discovery, recommendation, personalization, and quality enhancement," which works with studio, UGC audio and, maybe, live mobile communication, a statement from Techstars Music reads. Software engineer and chief of Mawson and Popgun Stephen Phillips paid tribute to his fellow Brisbanites with a tweet. Two more AI startups from Mawson are ready for take off. Both Replica and SuperRes have joined the Techstars Music 2019 program in LA. White says she's "excited and super grateful" to be a member of Techstars' 2019 class of music-based startups.

"Alexa, why is my baby crying?" Scientists say they have developed a new artificial intelligence (AI) tool that can reliably interpret the cries of babies.

Neural processes combine the strengths of neural networks and Gaussian processes to achieve both flexible learning and fast prediction of stochastic processes. However, neural processes do not consider the temporal dependency structure of the underlying processes and thus are limited in modeling a large class of problems with temporal structure. In this paper, we propose Sequential Neural Processes (SNP). By incorporating temporal state-transition model into neural processes, the proposed model extends the potential of neural processes to modeling dynamic stochastic processes. In applying SNP to dynamic 3D scene modeling, we also introduce the Temporal Generative Query Networks. To our knowledge, this is the first 4D model that can deal with the temporal dynamics of 3D scenes. In experiments, we evaluate the proposed methods in dynamic (non-stationary) regression and 4D scene inference and rendering.

To make music composition more approachable, we designed the first AI-powered Google Doodle, the Bach Doodle, where users can create their own melody and have it harmonized by a machine learning model Coconet (Huang et al., 2017) in the style of Bach. For users to input melodies, we designed a simplified sheet-music based interface. To support an interactive experience at scale, we re-implemented Coconet in TensorFlow.js (Smilkov et al., 2019) to run in the browser and reduced its runtime from 40s to 2s by adopting dilated depth-wise separable convolutions and fusing operations. We also reduced the model download size to approximately 400KB through post-training weight quantization. We calibrated a speed test based on partial model evaluation time to determine if the harmonization request should be performed locally or sent to remote TPU servers. In three days, people spent 350 years worth of time playing with the Bach Doodle, and Coconet received more than 55 million queries. Users could choose to rate their compositions and contribute them to a public dataset, which we are releasing with this paper. We hope that the community finds this dataset useful for applications ranging from ethnomusicological studies, to music education, to improving machine learning models.

Gonna need some more details here. Is this for personal hobby projects, your own business, contracting work, an employer, etc? Do you have a plan for who owns that work, what rights each party has, and a contract encoding all of that? If this is for anything other than a personal, open source, and free project I'd strongly caution you to get that sorted first, and if it's for work with others you're going to need to get a lawyer involved in the process to cover your ass at minimum. Professionally I do almost all of my work in software engineering these days but I want to make sure I stay fresh enough to take advantage of my academic background in applied math and research experience in ML. I'd be open to the idea but I'd need much more information.