The minds at Respawn Entertainment are wizards when it comes to the action-adventure genre. Twenty-fourteen's Titanfall and its criminally underrated followup, 2016's Titanfall 2, challenged traditional boots-on-the-ground shooters with a heightened sense of scale and verticality, while the more recent Jedi: Fallen Order etched itself as one of the greatest Star Wars narratives told in any medium. The Los Angeles studio's fixation with exoskeletons, Blade Runner, and visuals that bleed Wachowski and Masamune Shirow's Ghost In The Shell is nothing new, but they are intertwined with world-building to create headier pockets of science fiction bliss. The free-to-play shooter set in the Titanfall universe first launched in February 2019. No extended gameplay reveals that cringe out with comms from Chad and the rest of the QA team.

This brand-new report is the third in EIT Digital's Policy Perspective series and addresses the important topic on how Europe should deal with Artificial Intelligence. It provides business and policy decision makers with a scenario-based impact assessment instrument for AI policy development. The report explores the impact of Artificial Intelligence in general as well as in more specific application domains strategic for Europe: Health, Manufacturing, Climate, and Mobility. In all of these areas, it identifies both general and sector specific opportunities for and concerns about the further deployment of AI. It concludes with an assessment addressing the impact on innovation potential, fairness, trust, and growth opportunities.

New computing technologies have facilitated a flurry of new stimuli in our daily lives. Everything from self-driving cars, to Netflix movie recommendations and fraud detection to shopping recommendations, embody the essence of machine learning. Machine Learning (ML), one of the mainstays of Information Technology (IT), can be defined as a subset of Artificial Intelligence. They are a powerful set of algorithm and model which gives computers the ability to learn without being programmed. Machine learning is being extensively used across diverse industries to gain business-critical insights to solve business problems.

Modern approaches to sound synthesis using deep neural networks are hard to control, especially when fine-grained conditioning information is not available, hindering their adoption by musicians. In this paper, we cast the generation of individual instrumental notes as an inpainting-based task, introducing novel and unique ways to iteratively shape sounds. To this end, we propose a two-step approach: first, we adapt the VQ-VAE-2 image generation architecture to spectrograms in order to convert real-valued spectrograms into compact discrete codemaps, we then implement token-masked Transformers for the inpainting-based generation of these codemaps. We apply the proposed architecture on the NSynth dataset on masked resampling tasks. Most crucially, we open-source an interactive web interface to transform sounds by inpainting, for artists and practitioners alike, opening up to new, creative uses.

Despite showing increasingly human-like conversational abilities, state-of-the-art dialogue models often suffer from factual incorrectness and hallucination of knowledge (Roller et al., 2020). In this work we explore the use of neural-retrieval-in-the-loop architectures - recently shown to be effective in open-domain QA (Lewis et al., 2020b; Izacard and Grave, 2020) - for knowledge-grounded dialogue, a task that is arguably more challenging as it requires querying based on complex multi-turn dialogue context and generating conversationally coherent responses. We study various types of architectures with multiple components - retrievers, rankers, and encoder-decoders - with the goal of maximizing knowledgeability while retaining conversational ability. We demonstrate that our best models obtain state-of-the-art performance on two knowledge-grounded conversational tasks. The models exhibit open-domain conversational capabilities, generalize effectively to scenarios not within the training data, and, as verified by human evaluations, substantially reduce the well-known problem of knowledge hallucination in state-of-the-art chatbots.

Recent years have seen the proliferation of disinformation and misinformation online, thanks to the freedom of expression on the Internet and to the rise of social media. Two solutions were proposed to address the problem: (i) manual fact-checking, which is accurate and credible, but slow and non-scalable, and (ii) automatic fact-checking, which is fast and scalable, but lacks explainability and credibility. With the accumulation of enough manually fact-checked claims, a middle-ground approach has emerged: checking whether a given claim has previously been fact-checked. This can be made automatically, and thus fast, while also offering credibility and explainability, thanks to the human fact-checking and explanations in the associated fact-checking article. This is a relatively new and understudied research direction, and here we focus on claims made in a political debate, where context really matters. Thus, we study the impact of modeling the context of the claim: both on the source side, i.e., in the debate, as well as on the target side, i.e., in the fact-checking explanation document. We do this by modeling the local context, the global context, as well as by means of co-reference resolution, and reasoning over the target text using Transformer-XH. The experimental results show that each of these represents a valuable information source, but that modeling the source-side context is more important, and can yield 10+ points of absolute improvement.

This goal, while laudable, remains elusive.

So goes the classic line from HBO's dystopian television series Westworld. The show depicts the growing consciousness, and later uprising, of android "hosts" from a western-themed amusement park. The phrase is the series' proverbial safeword, the recurring host admission that they are not, to the great relief of all Westworld guests, sentient beings. Westworld is the latest addition in the Hollywood tradition of sinister robots that gain intelligence, gain consciousness and go rogue. Blade Runner, The Terminator, The Matrix, Transcendence, Ex Machina... the list is long and, for many, a clear demonstration of why the full implications of artificial intelligence (AI) might not be worth the convenience it brings.

Mozart wowed audiences as a child. The Beatles blew away Ed Sullivan. Beyonce hypnotized Super Bowl crowds. The world has been enthralled by those we call musical geniuses. But what defines a musical genius? And how does society recognize it? We probe these questions as we examine the life and career of Aretha Franklin, a transformational figure in American music, and the rise of a young prodigy, Keedron Bryant. For more information on this episode, visit nationalgeographic.com/overheard. Want more? Watch the Genius: Aretha, a series about Aretha’s life, now streaming on Hulu. And check out the magazine piece about her and this journey through the career of the Queen of Soul.  Immerse yourself in the genius of Aretha Franklin and her music with this playlist https://lnk.to/ArethaGenius!NGE. Available on Spotify and Apple Music. And of course, check out the song that made Keedron viral and the opera performance that cemented Aretha’s genius.

The internet is terrified of the New York Police Department's newest "canine" on unit: Digidog, a robo-dog that the Netflix series "Black Mirror" warned of. After a video went viral of Digidog in action, the internet started comparing it to Series 4 Episode 5, "Metalhead," where human society is no longer in existence and has been overrun by robot dogs. Some fear that this new invention could eventually turn into something negative. It was first deployed in February when men were being held hostage in a Bronx apartment and the robot was able to see how safe it was and if it was safe for the police to enter, the New York Times reported. The creators of Digidog, Boston Dynamics, explained that these devices won't be used as a weapon, but a political art collective has shared a few examples of how easy it is for things to go downhill fast, including a handful of Muslim Americans being killed by drones, according to the Guardian.