SEONGNAM, SOUTH KOREA--At the new headquarters of South Korea's largest internet company, a fleet of self-driving robots whirl around delivering coffee, lunchboxes and mailed packages. More than 100 of these autonomous robots, which look a bit like the droid R2-D2 from the "Star Wars" films and go by the name "Rookie," are operational at Naver high-rise office building. The tower is specifically designed with bump-free flooring and handle-free doors that open via sensors to help the robots move around more easily. Powering the robots is a critical technology: a private 5G network that gives them a stable connection to the cloud, or virtual server, where their computing takes place, and where each unit's learned intelligence is stored and shared. Having the hardware for this in the cloud keeps the robots small and less expensive to build, key requirements for broader adoption.

"Luke, I am your father" are five of the most famous words ever spoken on screen. When Darth Vader shattered Luke Skywalker's world in "The Empire Strikes Back," he sent shivers down the spines of audiences everywhere--in large part because of actor James Earl Jones' famous baritone. Now, Jones, 91, has announced he is hanging up the mask and retiring as the voice of one of the most infamous cinematic villains. But don't despair: Although Jones will no longer record new lines for Star Wars projects, the character--and Jones' voice--will live on thanks to artificial intelligence. As first reported by Vanity Fair, Respeecher, a Ukrainian voice synthesis company, will use a combination of archival recordings, voice acting and AI technology to continue bringing Darth Vader to the screen.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. AudioLM is Google's new model, capable of generating music in the same style as the prompt.

With the midterm elections just weeks away, the political vitriol and rhetoric are about to heat up. One Arizona State University professor thinks most of the hyperbolic chatter will come from malicious bots spreading racism and hate on social media and in the comments section on news sites. Victor Benjamin, assistant professor of information systems at the W. P. Carey School of Business, has been researching this phenomenon for years. He says the next generation of AI is a reflection of what's going on in society. Benjamin says that as AI learning becomes increasingly dependent on public data sets, such as online conversations, it is vulnerable to influence from cyber adversaries injecting disinformation and social discord. They are swaying public opinion on issues such as presidential elections, public health and social tensions.

Plenoptic imaging offers not only 2-D projections but also adds light array directions, thus supporting single-shot all-in-focus imaging. While its poor spatial resolution becomes an obstacle to high-quality all-in-focus imaging performance. Although various super-resolution (SR) methods have been designed and combined with multifocus image fusion (MFIF), high-quality multi-focus fused super-resolution images can be reconstructed for various applications, almost all of them deal with MFIF and SR separately. To our best knowledge, we first unify MFIF and SR problems as the multi-focus image super-resolution fusion (MFISRF) in the optical perspective and thus propose a novel dataset-free unsupervised framework named deep fusion prior (DFP) to address such MFISRF, particularly for plenoptic super-resolution all-in-focus imaging. Both numerical and practical experiments have proved that our proposed DFP approaches or even outperforms those state-of-the-art MFIF and SR method combinations. Therefore, we believe DFP can be potentially used in various computational photography applications.

Nowadays, People prefer to follow the latest news on social media, as it is cheap, easily accessible, and quickly disseminated. However, it can spread fake or unreliable, low-quality news that intentionally contains false information. The spread of fake news can have a negative effect on people and society. Given the seriousness of such a problem, researchers did their best to identify patterns and characteristics that fake news may exhibit to design a system that can detect fake news before publishing. In this paper, we have described the Fake News Challenge stage #1 (FNC-1) dataset and given an overview of the competitive attempts to build a fake news detection system using the FNC-1 dataset. The proposed model was evaluated with the FNC-1 dataset. A competitive dataset is considered an open problem and a challenge worldwide. This system's procedure implies processing the text in the headline and body text columns with different natural language processing techniques. After that, the extracted features are reduced using the elbow truncated method, finding the similarity between each pair using the soft cosine similarity method. The new feature is entered into CNN and DNN deep learning approaches. The proposed system detects all the categories with high accuracy except the disagree category. As a result, the system achieves up to 84.6 % accuracy, classifying it as the second ranking based on other competitive studies regarding this dataset.

How can we detect outliers, both scattered and clustered, and also explicitly assign them to respective micro-clusters, without knowing apriori how many micro-clusters exist? How can we perform both tasks in-house, i.e., without any post-hoc processing, so that both detection and assignment can benefit simultaneously from each other? Presenting outliers in separate micro-clusters is informative to analysts in many real-world applications. However, a na\"ive solution based on post-hoc clustering of the outliers detected by any existing method suffers from two main drawbacks: (a) appropriate hyperparameter values are commonly unknown for clustering, and most algorithms struggle with clusters of varying shapes and densities; (b) detection and assignment cannot benefit from one another. In this paper, we propose D.MCA to $\underline{D}$etect outliers with explicit $\underline{M}$icro-$\underline{C}$luster $\underline{A}$ssignment. Our method performs both detection and assignment iteratively, and in-house, by using a novel strategy that prunes entire micro-clusters out of the training set to improve the performance of the detection. It also benefits from a novel strategy that avoids clustered outliers to mask each other, which is a well-known problem in the literature. Also, D.MCA is designed to be robust to a critical hyperparameter by employing a hyperensemble "warm up" phase. Experiments performed on 16 real-world and synthetic datasets demonstrate that D.MCA outperforms 8 state-of-the-art competitors, especially on the explicit outlier micro-cluster assignment task.

Zoltan Istvan is a former journalist, political candidate, entrepreneur, bestselling author, and founder of the US Transhumanist Party. He has been on this podcast twice before when we discussed Istvan's presidential campaign and his bestselling novel The Transhumanist Wager. During this 1-hour conversation with Zoltan Istvan, we cover a variety of interesting topics such as the challenge of doing graduate school at Oxford, Quantum Archaeology; Trump, transhumanism, politics, and conflict; the Immortality or Bust documentary; microchipping refugees and selling off public lands; the ethics of doing damage now in the hope of fixing it later; technosolutionism and why Technology is Not Enough; longevity, entrepreneurship, and healthcare; the distinction between a body with a brain vs a brain with a body; the timeline to AGI, mind-uploading and indefinite life extension. As always you can listen to or download the audio file above or scroll down and watch the video interview in full. To show your support you can write a review on iTunes, make a direct donation, or become a patron on Patreon.

Aliens, astronauts, time travel--you name it, there's a dazzling sci-fi film about it. That makes compiling a list of the best sci-fi nearly impossible. It's almost impossible to know where to start--or where to stop. To understand where sci-fi films came from, you need to head back to the dawn of the cinema age. Right at the beginning, Metropolis, released in 1927, used groundbreaking visuals to create a reference point for all future urban dystopias--it's no fluke, for example, that the aesthetic of Blade Runner bears more than a passing resemblance to Fritz Lang's prophetic city hellscape. Then along came War of the Worlds (1953), a gripping tale of alien invasion adapted from H. G. Wells' classic novel. In 1964, Dr. Strangelove did more than most films before or since to ossify the fear of a nuclear holocaust. Below is WIRED's ever-evolving selection of the sci-fi movies everyone should watch, from the obscure to the hugely influential. You may also enjoy our guides to the best sci-fi books of all time and the best space movies. This content can also be viewed on the site it originates from. When Alfonso Cuarón wrote the screenplay for Gravity, he wasn't setting out to make a film about space itself. Rather, he was interested in exploring the concepts of adversity and human resilience, with space as a secondary background. But it was hard for audiences to not be wowed by the visuals in this Oscar-winning film about two scientists (George Clooney and Sandra Bullock) who find themselves stranded in space, and what they must endure in order to get safely back to Earth.

Artificial intelligence (AI) is essential for high-tech, future-proof armed forces.