During the last decade, one of the biggest issues in the gaming industry is the explosive growth of the AAA video games production cost. Studios are always on the look-up for technologies that could help bring down the cost of game development. Recent advances in the neural image generation models bring some hope that the realization of this dream may be not so far away.
Can computers think? Can AI models be conscious? These and similar questions often pop up in discussions of recent AI progress, achieved by natural language models GPT-3, LAMDA and other transformers. They are nonetheless still controversial and on the brink of a paradox, because there are usually many hidden assumptions and misconceptions about how the brain works and what thinking means. There is no other way, but to explicitly reveal these assumptions and then explore how the human information processing could be replicated by machines.
Now you won’t surprise anyone with filters that improve the quality of photos. But the restoration of old portraits still leaves much to be desired. Older photos tend to be too blurry, so normal image sharpening methods won't work on them.
Facebook has released the NLLB project (No Language Left Behind). The main feature of this development is the coverage of more than two hundred languages, including rare languages of African and Australian peoples. In addition, Facebook has applied a new approach to the machine learning model, where the translation is carried out directly from one language to another, without intermediate translation into English.
A group of scientists using machine learning "rediscovered" the law of universal gravitation.
Animated avatars have long become a part of our lives. But realistic modeling of closing animation still remained an open challenge.
On the one hand, modern physical modeling techniques can generate realistic clothing geometry at interactive speed. On the other hand, modeling a photorealistic appearance usually requires physical rendering, which is too expensive for interactive applications.
A group of scientists using machine learning "rediscovered" the law of universal gravitation.
To do this, they trained a "graph neural network" to simulate the dynamics of the Sun, planets and large moons of the solar system from 30 years of observations. Then they used symbolic regression to discover the analytical expression for the force law implicitly learned by the neural network.
