With two marquee names cast, the show will continue to center on Joel and Ellie, and is expected to retell the story of the first game, released in 2013. This may give the showrunners some creative leeway. Video games are sometimes described as telling the parts of a movie that would be skipped, like traveling a few miles to the destination just to get to the next story beat. A TV show isn't hamstrung by any obligation to gameplay. Luckily, the first game had plenty of time pass between its chapters, so there's ripe opportunity to tell stories the games didn't -- or couldn't.

The listing of Bumble, which also owns Badoo, makes a billionaire of 31-year-old founder Whitney Wolfe Herd.

Creating a movie trailer takes time, and most broadcasters and streaming platforms don't have enough resources to do it. Their creative team, responsible for putting together promotional material for digital and social media, spends very little time being creative. To produce a 30" rough edit for a 10-movie stunt is about 5 days of viewing and logging. They also use and manage external agencies, leading to bottlenecks where one department's output is heavily prioritized over another's. The whole process is onerous, time consuming, and inefficient. PromoMii, a UK startup, solves this problem with a unique blend of domain expertise and machine learning (ML). Their product Nova provides functionality to search for scenes or specific dialogues across their library. Productivity is supercharged with template queries, enabling creatives to finish their spot in minutes in terms of days. Nordic Entertainment Group, one of PromoMii's customers, found that it was 10 times cheaper and 20 times faster to create trailers with Nova. A promotion which would usually take two days to produce was completed within 2 hours. This blog post is the first in a series of startup ML stories, where we tell stories like PromoMii's in terms of three crucial ingredients to building a successful business with ML – team, product, and partnership. PromoMii was founded by two Danes from Copenhagen to help large broadcasters promote their shows. Over time and working backwards from their customers, the company pivoted toward using Artificial Intelligence (AI) to enable creatives to be creative. The technological challenge inspired Tigran Mnatskanyan, CTO, to join PromoMii with a mission of building a great engineering team and crafting the content creation platform of the future. In terms of domain expertise, PromoMii's Chairman is Lester Mordue, an award-winning creative director bringing experience from MTV, Sky, Disney, and Discovery. As a creative himself, Lester immediately saw the benefits of Nova and is in a unique position to open doors for the business and provide guidance on product-market fit. "In my career, I've sat in boardrooms looking at tech and marketing ROI as well as sitting in edit suites looking for inspiration and story hooks," said Lester. "Viewers enjoy on-demand services and streaming platforms, and so too should marketeers who help make viewing decisions.

SYDNEY-- Microsoft Corp. said the U.S. should copy Australia's controversial proposal that tech companies pay newspapers for content--putting it at odds with Alphabet Inc.'s Google and Facebook Inc. It isn't the first time Microsoft has stepped into feuds involving rivals--particularly in areas where they have an edge. Its Bing search engine lags behind Google in market share. Microsoft has urged governments to better regulate facial-recognition technology and last year sided with a videogame developer against Apple Inc. in a dispute about app-store fees. The Australian proposal, if enacted into law--it is now before a parliamentary committee--could prompt other countries to follow suit in a global transformation of the relationship between tech companies and traditional media.

The hoax seems harmless enough. A few thousand AI researchers have claimed that computers can read and write literature. They've alleged that algorithms can unearth the secret formulas of fiction and film. That Bayesian software can map the plots of memoirs and comic books. That digital brains can pen primitive lyrics1 and short stories--wooden and weird, to be sure, yet evidence that computers are capable of more. But the hoax is not harmless. If it were possible to build a digital novelist or poetry analyst, then computers would be far more powerful than they are now. They would in fact be the most powerful beings in the history of Earth. Their power would be the power of literature, which although it seems now, in today's glittering silicon age, to be a rather unimpressive old thing, springs from the same neural root that enables human brains to create, to imagine, to dream up tomorrows.

Game of Thrones actors Bella Ramsey and Pedro Pascal – who also stars in Disney's Star Wars spinoff The Mandalorian – have been cast in HBO's television adaptation of blockbusting video game series The Last of Us. They will take on the roles of Ellie and Joel in the forthcoming drama. The Hollywood Reporter broke news of the adaptation last March, and since then speculation over the casting has been rife. Fans on social media initially favoured Booksmart star Kaitlyn Dever for the role of Ellie, and the actor expressed interest. Meanwhile, True Detective's Mahershala Ali was rumoured to have been approached for the role of Joel.

Kate Daniels, from Willow Farm in Worcestershire, told BBC Radio 4's Today programme that while the scientists might have been impressed, "I don't think this will come as a surprise to anyone that works with pigs".

A sequential recommender system aims to recommend attractive items to users based on behaviour patterns. The predominant sequential recommendation models are based on natural language processing models, such as the gated recurrent unit, that embed items in some defined space and grasp the user's long-term and short-term preferences based on the item embeddings. However, these approaches lack fundamental insight into how such models are related to the user's inherent decision-making process. To provide this insight, we propose a novel recurrent cell, namely FaNC, from Freudian and Newtonian perspectives. FaNC divides the user's state into conscious and unconscious states, and the user's decision process is modelled by Freud's two principles: the pleasure principle and reality principle. To model the pleasure principle, i.e., free-floating user's instinct, we place the user's unconscious state and item embeddings in the same latent space and subject them to Newton's law of gravitation. Moreover, to recommend items to users, we model the reality principle, i.e., balancing the conscious and unconscious states, via a gating function. Based on extensive experiments on various benchmark datasets, this paper provides insight into the characteristics of the proposed model. FaNC initiates a new direction of sequential recommendations at the convergence of psychoanalysis and recommender systems.

We propose a novel pitch estimation technique called DeepF0, which leverages the available annotated data to directly learns from the raw audio in a data-driven manner. F0 estimation is important in various speech processing and music information retrieval applications. Existing deep learning models for pitch estimations have relatively limited learning capabilities due to their shallow receptive field. The proposed model addresses this issue by extending the receptive field of a network by introducing the dilated convolutional blocks into the network. The dilation factor increases the network receptive field exponentially without increasing the parameters of the model exponentially. To make the training process more efficient and faster, DeepF0 is augmented with residual blocks with residual connections. Our empirical evaluation demonstrates that the proposed model outperforms the baselines in terms of raw pitch accuracy and raw chroma accuracy even using 77.4% fewer network parameters. We also show that our model can capture reasonably well pitch estimation even under the various levels of accompaniment noise.

Platforms that support online commentary, from social networks to news sites, are increasingly leveraging machine learning to assist their moderation efforts. But this process does not typically provide feedback to the author that would help them contribute according to the community guidelines. This is prohibitively time-consuming for human moderators to do, and computational approaches are still nascent. This work focuses on models that can help suggest rephrasings of toxic comments in a more civil manner. Inspired by recent progress in unpaired sequence-to-sequence tasks, a self-supervised learning model is introduced, called CAE-T5. CAE-T5 employs a pre-trained text-to-text transformer, which is fine tuned with a denoising and cyclic auto-encoder loss. Experimenting with the largest toxicity detection dataset to date (Civil Comments) our model generates sentences that are more fluent and better at preserving the initial content compared to earlier text style transfer systems which we compare with using several scoring systems and human evaluation.