A part of what we see in science fiction movies will soon become a reality, thanks to artificial intelligence. Every time you saw people talking to holograms in sci-fi movies and thought to yourself "that would be awesome to have", you just might be closer to that future. Smartphones will soon be able to create photorealistic 3D holograms with an AI model developed by a research team at MIT. This system determines the best way to generate holograms from a sequence of input images. This fascinating technology could have applications for VR and AR headsets.

The framework leverages the Python scripting language and its associated tools and libraries, including Jupyter notebooks and NumPy.

Configuring one's expectations before settling down to watch the latest big (and small) screen adaptation of Mortal Kombat is something of a process. The largely wretched game-to-movie subgenre carries with it little-to-no hope at this stage, even the so-called "best" examples are seen as just about tolerable, and the last two attempts to translate Midway's long-running fighting game failed to justify why watching these characters battle it out would be preferable to playing as them instead. As popular as the game still is (the most recent iteration has sold over 8m copies worldwide), transporting it to film is still a rather dated prospect, almost 25 years after the last version, the result of a torturous period in development hell. So while the odds might seem stacked against it, the film also arrives at an opportune time, as cinemas are opening up again and audiences are craving bigger, gaudier events to lure them back. Just weeks after their record Godzilla vs Kong success (a hit proving that after a year of misery, appealing to our basest, silliest instincts is a surefire win right now), Warners is using the same hybrid release for Mortal Kombat, chucking it up on HBO Max and out in cinemas at the same time.

BRUSSELS (AFP) – The European Union unveils a plan on Wednesday (April 21) to regulate the sprawling field of artificial intelligence, aimed at …

In 1969, as revolutionary fires burned, the Academy gave its Best Picture award to "Oliver!" Hollywood, still ruled by the crumbling studio system, was almost willfully blind to the nineteen-sixties; even breakthrough films such as "2001: A Space Odyssey" and "Rosemary's Baby" were left off the Best Picture list, which included representatives of such superannuated genres as the big-budget musical ("Funny Girl") and the medieval costume drama ("The Lion in Winter"). Under the newly devised rating system, "Oliver!" became the first G-rated film to win Best Picture, and it remains the last. By the next year, movies like "Midnight Cowboy" and "Easy Rider" finally injected the ceremony with a dose of sixties counterculture--but the decade was over. Two of this year's eight Best Picture nominees are set largely in 1969, and they show what Hollywood wouldn't bring itself to see back then. "The Trial of the Chicago 7" dramatizes the politicized court proceedings against activists who, the year before, protested the Democratic National Convention in Chicago.

I have been reading some good examples that explain (in layman's terms) what is the curse of dimensionality. These examples first considers a circle inside a square (2 dimensions: example 1) - and then considers a sphere inside a cube (3 dimensions: example 2). This is to illustrate the fact that the cube in example 2 is a lot more "emptier" (ratio of volume between sphere and cube) compared to the square in example 1. As the number of dimensions increase (e.g. the cube becomes a hypercube in 4 dimensions), it can be mathematically shown that the ratio of emptiness increases more and more. In this analogy, the sphere represents the data and the cube represents the space which the data belongs to. These examples show us that in higher dimensions, we need exponentially more and more data to fill this space - thus, in higher dimensions, data becomes more "sparse", and this sparsity makes it harder to fit machine learning algorithms (I understand this is intuitively, but I don't know if there is a mathematical explanation behind why sparsity gives machine learning algorithms a hard time - perhaps sparsity makes some of the matrix calculations harder to calculate?

Originally published at Ross Dawson. Shortly after the new year 2021, the Media Synthesis community at Reddit began to become more than usually psychedelic. The board became saturated with unearthly images depicting rivers of blood, Picasso's King Kong, a Pikachu chasing Mark Zuckerberg, Synthwave witches, acid-induced kittens, an inter-dimensional portal, the industrial revolution and the possible child of Barack Obama and Donald Trump. The bizarre images were generated by inputting short phrases into Google Colab notebooks (web pages from which a user can access the formidable machine learning resources of the search giant), and letting the trained algorithms compute possible images based on that text. In most cases, the optimal results were obtained in minutes. Various attempts at the same phrase would usually produce wildly different results. In the image synthesis field, this free-ranging facility of invention is something new; not just a bridge between the text and image domains, but an early look at comprehensive AI-driven image generation systems that don't need hyper-specific training in very limited domains (i.e. NVIDIA's landscape generation framework GauGAN [on which, more later], which can turn sketches into landscapes, but only into landscapes; or the various sketch face Pix2Pix projects, that are likewise'specialized'). Example images generated with the Big Sleep Colab notebook [12].

Having access to multi-modal cues (e.g. vision and audio) empowers some cognitive tasks to be done faster compared to learning from a single modality. In this work, we propose to transfer knowledge across heterogeneous modalities, even though these data modalities may not be semantically correlated. Rather than directly aligning the representations of different modalities, we compose audio, image, and video representations across modalities to uncover richer multi-modal knowledge. Our main idea is to learn a compositional embedding that closes the cross-modal semantic gap and captures the task-relevant semantics, which facilitates pulling together representations across modalities by compositional contrastive learning. We establish a new, comprehensive multi-modal distillation benchmark on three video datasets: UCF101, ActivityNet, and VGGSound. Moreover, we demonstrate that our model significantly outperforms a variety of existing knowledge distillation methods in transferring audio-visual knowledge to improve video representation learning. Code is released here: https://github.com/yanbeic/CCL.

You're probably reading this on a browser built by Apple or Google. If you're on a smartphone, it's almost certain those two companies built the operating system. You probably arrived from a link posted on Apple News, Google News or a social media site like Facebook. And when this page loaded, it, like many others on the Internet, connected to one of Amazon's ubiquitous data centers. Amazon, Apple, Facebook and Google -- known as the Big 4 -- now dominate many facets of our lives. But they didn't get there alone. They acquired hundreds of companies over decades to propel them to become some of the most powerful tech behemoths in the world.

In the games and the original movies, Kano has always been portrayed as a thieving, conniving villain, and little else (besides a mortal rival for Kombatant Sonya Blade). In this film, he is the engine that keeps the story moving, as he steals every scene he's in and gets the biggest laughs. And like Cole, he is the audience's other surrogate in understanding "Mortal Kombat's" bizarre, violent, supernatural world, and the beings that live in it, including the thunder god Raiden.