Attaining ‘sentience’ is not easy for an AI robot. Thousands of experts and researchers are working on AI and robots to crack the code and finally attain the one innovation that can change the entire course of the tech industry.
Category: robotics/AI – Page 1,531
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 brain changes 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.”
Did Google’s A.I. Just Become Sentient? Two Employees Think So
Can an A.I. think and feel? It seems like the answer is always no, but to two Google engineers think this isn’t the case. Join me as we look at the wild story of Google LaMDA and the engineer who thinks the AI system has come to life.
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Tour of Real-World Machine Learning Problems
Mike LorreyThe arguments I put into my article in The Space Review for the Space Force are valid to this discussion. https://www.thespacereview.com/article/3576/1
Real-world examples make the abstract description of machine learning become concrete.
In this post you will go on a tour of real world machine learning problems. You will see how machine learning can actually be used in fields like education, science, technology and medicine.
Each machine learning problem listed also includes a link to the publicly available dataset. This means that if a particular concrete machine learning problem interest you, you can download the dataset and start practicing immediately.
Joscha Bach — Agency in an Age of Machines
Synopsis: The arrival of homo sapiens on Earth amounted to a singularity for its ecosystems, a transition that dramatically changed the distribution and interaction of living species within a relatively short amount of time. Such transitions are not unprecedented during the evolution of life, but machine intelligence represents a new phenomenon: for the first time, there are agents on earth that are not part of the biosphere. Instead of competing for a niche in the ecosystems of living systems, AI might compete with life itself.
How can we understand agency in the context of the cooperation and competition between AI, humans and other organisms?
This talk was part of the ‘Stepping Into the Future‘conference.
Bio: Joscha Bach, Ph.D. is an AI researcher who worked and published about cognitive architectures, mental representation, emotion, social modeling, and multi-agent systems. He earned his Ph.D. in cognitive science from the University of Osnabrück, Germany, and has built computational models of motivated decision making, perception, categorization, and concept-formation. He is especially interested in the philosophy of AI and in the augmentation of the human mind.
Joscha has taught computer science, AI, and cognitive science at the Humboldt-University of Berlin and the Institute for Cognitive Science at Osnabrück.
His book “Principles of Synthetic Intelligence – PSI: An Architecture of Motivated Cognition” (Oxford University Press) is available on amazon.