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Category: robotics/AI – Page 1,500

Anton van den Hengel’s journey from intellectual property law to computer vision pioneer

The world’s most-cited researcher in visual question-answering, Anton van den Hengel, is also Amazon’s director of applied science. Learn how his journey to computer vision started with law—and how his work is supporting Amazon’s business through the development and application of state-of-the-art computer vision and scalable machine learning.

#ComputerVision #CVPR2022

Amazon’s director of applied science in Adelaide, Australia, believes the economic value of computer vision has “gone through the roof”.

MIT researchers have built a new LEGO-like AI chip

With a more sustainable world goal, MIT researchers have succeeded in developing a new LEGO-like AI chip. Imagine a world where cellphones, smartwatches, and other wearable technologies don’t have to be put away or discarded for a new model. Instead, they could be upgraded with the newest sensors and processors that would snap into a device’s internal chip – similar to how LEGO bricks can be incorporated into an existing structure. Such reconfigurable chips might keep devices current while lowering electronic waste. This is really important because green computing is the key to a sustainable future.

MIT engineers have developed a stackable, reprogrammable LEGO-like AI chip. The chip’s layers communicate thanks optically to alternating layers of sensing and processing components, as well as light-emitting diodes (LEDs). Other modular chip designs use conventional wiring to transmit signals between layers. Such intricate connections are difficult, if not impossible, to cut and rewire, making stackable configurations nonreconfigurable.

Rather than relying on physical wires, the MIT design uses light to transfer data across the AI chip. As a result, the chip’s layers may be swapped out or added upon, for example, to include extra sensors or more powerful processors.

AI Shakespeare and AI Oscar Wilde Debate Machine Creativity at Oxford

If that reads a little like Shakespeare defending humans’ innate superiority over artificial intelligence hundreds of years ahead of his time, it’s not.

But it is something almost as far out: an AI system trained to express itself like the bard. The AI assimilated his style and perspective by ingesting his plays—educating itself to give an opinion on AI creativity in iambic pentameter.

“Shakespeare” was speaking as part of a debate held in the University of Oxford Union featuring AI versions of classic writers and literary characters.

Generative AI to Help Humans Create Hyperreal Population in Metaverse

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.

Single brain scan can diagnose Alzheimer’s disease

The research uses machine learning technology to look at structural features within the brain, including in regions not previously associated with Alzheimer’s. The advantage of the technique is its simplicity and the fact that it can identify the disease at an early stage when it can be very difficult to diagnose.

Although there is no cure for Alzheimer’s disease, getting a diagnosis quickly at an early stage helps patients. It allows them to access help and support, get treatment to manage their symptoms and plan for the future. Being able to accurately identify patients at an early stage of the disease will also help researchers to understand the that trigger the disease, and support development and trials of new treatments.

The research is published in the Nature Portfolio Journal, Communications Medicine.

Artificial intelligence has reached a threshold. And physics can help it break new ground

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 LIMoE — A Step Towards Goal Of A Single AI

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.

Microsoft Lasers Music into Glass for 1000 Years of Storage

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.”

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