We study the problem of learning probabilistic first-order logical rules for knowledge base reasoning.

Mass Effect is some of the best science fiction ever made. That may sound like a grandiose comment, but it's true. As a trilogy, the original games from 2007-2013 effortlessly plucked the most cerebral ideas from the sci-fi genre and slotted them into a memorable military role-playing game that had players invested from beginning to controversial end. Whether you prefer the hopeful, optimistic outlook of Asimov, the dark and reflective commentary of Shelley, the accessible thought experiments of Star Trek, or the arch melodrama of Battlestar Galactica, Mass Effect has it all. The trilogy is as happy grazing on the western-inspired tropes of Star Wars as the "hard" sci-fi of Iain M Banks, blending all its moods and micro-stories into a compelling, believable galaxy that somehow walks a line between breathless optimism and suffocating bleakness.

We study the problem of learning probabilistic first-order logical rules for knowledge base reasoning. This learning problem is difficult because it requires learning the parameters in a continuous space as well as the structure in a discrete space. We propose a framework, Neural Logic Programming, that combines the parameter and structure learning of first-order logical rules in an end-to-end differentiable model. This approach is inspired by a recently-developed differentiable logic called TensorLog [5], where inference tasks can be compiled into sequences of differentiable operations. We design a neural controller system that learns to compose these operations. Empirically, our method outperforms prior work on multiple knowledge base benchmark datasets, including Freebase and WikiMovies.

Science fiction is full of characters, set pieces, and scenarios that few other genres could ever get away with. Due to its often speculative nature, the most accomplished sci-fi movies can sometimes require a bit of work on the part of the viewer. Yet as fans of the genre understand, when it's done right, a great sci-fi film is well worth the mental gymnastics that watching it might demand. Speaking of sci-fi done right: Whether you're a lifelong genre devotee or have never even sat through a Star Wars movie to the end, a little guidance can go a long way--and that's exactly what we've got for you. When you're ready to take your mind on a cinematic journey, check out any one (or all) of our picks for the very best science fiction movies you can watch right now.

From London's Big Ben to Edinburgh's castle, many UK cities are instantly recognisable thanks to their distinctive buildings. But these famous landmarks could be hidden away behind bulky transport systems in just 26 years, according to artificial intelligence (AI). Brighton-based film editor, Duncan Thomsen, used AI to imagine what five of the UK's largest cities could look like in 2050. The resulting images feature a range of futuristic tranport systems running through the cities, which resemble scenes from Blade Runner. 'I like the idea of this Blade Runner future - it brought a smile to my face,' Mr Thomsen said.

AI expert Marva Bailer explains how, even though there are currently laws in place, the average person has more access than ever to create deepfakes of celebrities. Ridley Scott, director of sci-fi classics like "Alien" and "Blade Runner," is terrified about AI technology running away with society. In an interview with Rolling Stone promoting his film "Napoleon," Scott was asked if artificial intelligence worried him, and the answer was an emphatic yes. "We have to lock down AI. And I don't know how you're gonna lock it down," he told the outlet.

There are countless science-fiction movies about humanoid robots, but so far robot actors are yet to step up and star in their own films. Luckily for human actors, that future may still be far away as'the world's most advanced' humanoid robot shows off its acting'skills' in this uncanny clip. Ameca, a product of the engineers at Cornwall-based startup Engineered Arts, was asked to provide an impression from a film. 'All those moments will be lost in time, like tears in rain', Ameca said, quoting Blade Runner as it moved through a series of human-like expressions. In the background, a faint movie soundtrack could even be heard playing, adding some much-needed drama to the robot's delivery.

Annapurna Interactive is developing a game based on the iconic science fiction film Blade Runner. The game's set between the events of Blade Runner and Blade Runner 2049, so you can get some closure as to what Deckard was doing before meeting up with Ryan Gosling in an abandoned casino or whatever. Blade Runner 2033: Labyrinth follows a Blade Runner -- the name on their ID is blanked out in the trailer -- as they explore a mysterious location called the "land of the dead." You can't tell much from the trailer, but we see footage of what looks like an early version of the memory-crafting technology seen in Blade Runner 2049. Annapurna says this game is actually canon and it takes place just one year after the events of the original film, which would put it directly in the crosshairs of some big events alluded to in the sequel. It's always good to see more Blade Runner in gaming, especially after the criminally underrated and recently remastered 1997 adventure title.

Opinion Of late, the deepfake detection research community, which has since late 2017 been occupied almost exclusively with the autoencoder-based framework that premiered at that time to such public awe (and dismay), has begun to take a forensic interest in less stagnant architectures, including latent diffusion models such as DALL-E 2 and Stable Diffusion, as well as the output of Generative Adversarial Networks (GANs). For instance, in June, UC Berkeley published the results of its research into the development of a detector for the output of the then-dominant DALL-E 2. What seems to be driving this growing interest is the sudden evolutionary jump in the capability and availability of latent diffusion models in 2022, with the closed-source and limited-access release of DALL-E 2 in spring, followed in late summer by the sensational open sourcing of Stable Diffusion by stability.ai. GANs have also been long-studied in this context, though less intensively, since it is very difficult to use them for convincing and elaborate video-based recreations of people; at least, compared to the by-now venerable autoencoder packages such as FaceSwap and DeepFaceLab – and the latter's live-streaming cousin, DeepFaceLive. In either case, the galvanizing factor appears to be the prospect of a subsequent developmental sprint for video synthesis. The start of October – and 2022's major conference season – was characterized by an avalanche of sudden and unexpected solutions to various longstanding video synthesis bugbears: no sooner had Facebook released samples of its own text-to-video platform, than Google Research quickly drowned out that initial acclaim by announcing its new Imagen-to-Video T2V architecture, capable of outputting high resolution footage (albeit only via a 7-layer network of upscalers).

StyleGAN is the open-sourced TensorFlow implementation made by NVIDIA. It has revolutionized high quality facial image generation. However, this democratization of Artificial Intelligence / Machine Learning (AI/ML) algorithms has enabled hostile threat actors to establish cyber personas or sock-puppet accounts in social media platforms. These ultra-realistic synthetic faces. This report surveys the relevance of AI/ML with respect to Cyber & Information Operations. The proliferation of AI/ML algorithms has led to a rise in DeepFakes and inauthentic social media accounts. Threats are analyzed within the Strategic and Operational Environments. Existing methods of identifying synthetic faces exists, but they rely on human beings to visually scrutinize each photo for inconsistencies. However, through use of the DLIB 68-landmark pre-trained file, it is possible to analyze and detect synthetic faces by exploiting repetitive behaviors in StyleGAN images. Project Blade Runner encompasses two scripts necessary to counter StyleGAN images. Through PapersPlease acting as the analyzer, it is possible to derive indicators-of-attack (IOA) from scraped image samples. These IOAs can be fed back into AmongUs acting as the detector to identify synthetic faces from live operational samples. The opensource copy of Blade Runner may lack additional unit tests and some functionality, but the open-source copy is a redacted version, far leaner, better optimized, and a proof-of-concept for the information security community. The desired end-state will be to incrementally add automation to stay on-par with its closed-source predecessor.