In forthcoming years, everyone will get to observe how beautifully Metaverse will evolve towards immersive experiences in hyperreal virtual environments filled with avatars that look and sound exactly like us. Neil Stephenson’s Snow Crash describes a vast world full of amusement parks, houses, entertainment complexes, and worlds within themselves all connected by a virtual street tens of thousands of miles long. For those who are still not familiar with the metaverse, it is a virtual world in which users can put on virtual reality goggles and navigate a stylized version of themselves, known as an avatar, via virtual workplaces, and entertainment venues, and other activities. The metaverse will be an immersive version of the internet with interactive features using different technologies such as virtual reality (VR), augmented reality (AR), 3D graphics, 5G, hologram, NFT, blockchain, haptic sensors, and artificial intelligence (AI). To scale personalized content experiences to billions of people, one potential answer is generative AI, the process of using AI algorithms on existing data to create new content.
In computing, procedural generation is a method of creating data algorithmically as opposed to manually, typically through a combination of human-generated assets and algorithms coupled with computer-generated randomness and processing power. In computer graphics, it is commonly used to create textures and 3D models.
The algorithmic difficulty is typically seen in Diablo-style RPGs and some roguelikes which use instancing of in-game entities to create randomized items. Less frequently it can be used to determine the relative difficulty of hand-designed content to be subsequently placed procedurally, as can be seen with the monster design in Unangband. For example, the designer can rapidly create content, but leaves it up to the game to determine how challenging that content is to overcome, and consequently where in the procedurally generated environment this content will appear. Notably, the Touhou series of bullet hell shooters use algorithmic difficulty. Though the users are only allowed to choose certain difficulty values, several community mods enable ramping the difficulty beyond the offered values.
For years, physicists have been making major advances and breakthroughs in the field using their minds as their primary tools. But what if artificial intelligence could help with these discoveries?
Last month, researchers at Duke University demonstrated that incorporating known physics into machine learning algorithms could result in new levels of discoveries into material properties, according to a press release by the institution. They undertook a first-of-its-kind project where they constructed a machine-learning algorithm to deduce the properties of a class of engineered materials known as metamaterials and to determine how they interact with electromagnetic fields.
Google announced a new technology called LIMoE that it says represents a step toward reaching Google’s goal of an AI architecture called Pathways.
Pathways is an AI architecture that is a single model that can learn to do multiple tasks that are currently accomplished by employing multiple algorithms.
LIMoE is an acronym that stands for Learning Multiple Modalities with One Sparse Mixture-of-Experts Model. It’s a model that processes vision and text together.
Philip Glass to release a short silence on the matter.
The music vault is a parallel project to the Global Seed Vault (opens in new tab), which keeps the seeds of today’s trees and plants safe for the future, just in case we need to rebuild agriculture for any reason. The vault is located on the island of Spitsbergen, Norwegian territory, within the Arctic circle. It lacks tectonic activity, is permanently frozen, is high enough above sea level to stay dry even if the polar caps melt, and even if the worst happens, it won’t thaw out fully for 200 years. Just to be on the safe side, the main vault is built 120m into a sandstone mountain, and its security systems are said to be robust. As of June 2021, the seed vault had conserved 1,081,026 different crop samples.
The music is to be stored in a dedicated vault in the same mountain used by the seed vault. The glass used is an inert material, shaped into platters 75mm (3 inches) across and 2mm (less than 1/8th of an inch) thick. A laser encodes data in the glass by creating layers of three-dimensional nanoscale gratings and deformations. Machine learning algorithms read the data back by decoding images and patterns created as polarized light shines through the glass. The silica glass platters are fully resistant to electromagnetic pulses and the most challenging of environmental conditions. It can be baked, boiled, scoured and flooded without degradation of the data written into the glass. Tests to see if it really does last many thousands of years, however, can be assumed to be ongoing.
Jurgen Willis, Vice President of Program Management at Microsoft, said, “In this proof of concept, Microsoft and Elire Group worked together to demonstrate how Project Silica can help achieve the goal of preserving and safeguarding the world’s most valuable music for posterity, on a medium that will stand the test of time, using innovative archival storage in glass.”
The use of fire was a key factor in the evolution of Homo sapiens, not only for the creation of more sophisticated tools but also for making food safer, which in turn aided brain development.
To date, only five sites with fire evidence dating back 500,000 years have been found worldwide, including Wonderwerk Caves and Swartkrans in South Africa, Chesowanja in Kenya, Gesher Benot Ya’aqov in Israel, and Cueva Negra in Spain.
Now, a n Israeli research team has used artificial intelligence algorithms to discover a sixth site that shows traces of human fire! The study revealed evidence of human use of fire at a late Paleolithic site in Israel. The research results have been published in the journal PNAS.
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Artificial intelligence has altered the practise of science by enabling researchers to examine the vast volumes of data generated by current scientific instruments. Using deep learning, it can learn from the data itself and can locate a needle in a million haystacks of information. AI is advancing the development of gene searching, medicine, medication design, and chemical compound synthesis.
Scientists Detect Fastest-Growing Black Hole in the Universe
To extract information from fresh data, deep learning employs algorithms, often neural networks trained on massive volumes of data. With its step-by-step instructions, it is considerably different from traditional computing. It instead learns from data. Deep learning is far less transparent than conventional computer programming, leaving vital concerns unanswered: what has the system learnt and what does it know?
Incorporating established physics into neural network algorithms helps them to uncover new insights into material properties
According to researchers at Duke University, incorporating known physics into machine learning algorithms can help the enigmatic black boxes attain new levels of transparency and insight into the characteristics of materials.
Researchers used a sophisticated machine learning algorithm in one of the first efforts of its type to identify the characteristics of a class of engineered materials known as metamaterials and to predict how they interact with electromagnetic fields.