Lifeboat Foundation

Trey Parker and Matt Stone’s AI studio Deep Voodoo said Wednesday that it has secured a $20 million in an investment round led by Connect Ventures. The South Park creators’ startup said it will use the capital to accelerate its development of deep-fake technology, VFX services and original synthetic media projects.

Connect Ventures is an investment partnership between CAA and venture capital firm New Enterprise Associates and represents the first outside capital raise for Deep Voodoo, which was previously funded by Parker and Stone’s entertainment company Park County.

Parker and Stone originally began building out their deep fake technology in early 2020, assembling a team of artists for a feature film they had developed. When the film was suspended amid the Covid shutdown, they pivoted to building out those deep-fake tools.

For some, automation will usher in a labor-free utopia; for others, it signals a disastrous age-to-come. Yet whether seen as dream or nightmare, automation, argues Munn, is ultimately a fable that rests on a set of triple fictions. There is the myth of full autonomy, claiming that machines will take over production and supplant humans. But far from being self-acting, technical solutions are piecemeal; their support and maintenance reveals the immense human labor behind autonomous processes. There is the myth of universal automation, with technologies framed as a desituated force sweeping the globe. But this fiction ignores the social, cultural, and geographical forces that shape technologies at a local level. And, there is the myth of automating everyone, the generic figure of the human at the heart of automation claims. But labor is socially stratified and so automation’s fallout will be highly uneven, falling heavier on some (immigrants, people of color, women) than others. Munn moves from machine minders in China to warehouse pickers in the United States to explore the ways that new technologies do (and don’t) reconfigure labor. Combining this rich array of human stories with insights from media and cultural studies, Munn points to a more nuanced, localized, and racialized understanding of the future of work.

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Technology has given us everything from smart TVs that can hear you talking to self-driving cars, but before we became the digitally-driven society we are today, fear of new technology commonly served as one of the greatest threats to innovation. What we see as dated and relatively harmless inventions of the past were once the new technology that people freaked out about. Without an efficient way to educate the masses about the latest, hottest new inventions of their era, paranoia and confusion quickly took the place of logic and curiosity for many consumers. While many of these inventions are now seen as revolutionary and their modern counterparts are a part of our daily lives, there was once a time when these gadgets were some of the most frightening topics of discussion.

This transformer robot can stand on two legs and navigate a staircase. Its developers expect it to be a key member of rescue teams by 2030.

The next breakthrough to take the AI world by storm might be 3D model generators. This week, OpenAI open sourced Point-E, a machine learning system that creates a 3D object given a text prompt. According to a paper published alongside the code base, Point-E can produce 3D models in one to two minutes on a single Nvidia V100 GPU.

Point-E doesn’t create 3D objects in the traditional sense. Rather, it generates point clouds, or discrete sets of data points in space that represent a 3D shape — hence the cheeky abbreviation. (The “E” in Point-E is short for “efficiency,” because it’s ostensibly faster than previous 3D object generation approaches.) Point clouds are easier to synthesize from a computational standpoint, but they don’t capture an object’s fine-grained shape or texture — a key limitation of Point-E currently.

To get around this limitation, the Point-E team trained an additional AI system to convert Point-E’s point clouds to meshes. (Meshes — the collections of vertices, edges and faces that define an object — are commonly used in 3D modeling and design.) But they note in the paper that the model can sometimes miss certain parts of objects, resulting in blocky or distorted shapes.

You d still have human kitchen staff, but could quietly be automated once tec was ready.

McDonald’s has begun testing its first-ever robot restaurant in Texas, sparking debate and intrigue in equal measure.

In Forth Worth, Texas, the branch is fully automated and requires no human contact to order and pick up your favorite meal.

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Robots and autonomous cars will have eyes that see much more than the human eye is capable of, a review of the growing field of meta-optics has found.

Meta-optics is advancing science and technology far beyond the 3,000-year-old optical paradigm that we rely on for the visual human-machine interface, such as through cameras in our mobile phones, the lenses in microscopes, drones, and telescopes. Optical components are the technology bottleneck that meta-optics aims to transform, bringing the stuff of science-fiction stories into everyday devices.

The field, which blossomed after the early 2000s thanks to the conceptualization of a material with negative refractive index that could form a perfect lens, has grown rapidly in the last five years and now sees around 3,000 publications a year.

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AI art is amazing and is changing the world for the better. In this video, I explain why we should be embracing AI rather than trying to hold it back.

Continue reading “AI Art Is Getting Scary… — YouTube” »

Summary: Using advances in machine learning, researchers have created a new model that predicts the ease with which individuals produce and comprehend complex sentences.

Source: MIT

Cognitive scientists have long sought to understand what makes some sentences more difficult to comprehend than others. Any account of language comprehension, researchers believe, would benefit from understanding difficulties in comprehension.