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.”
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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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.
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.
Like many other industries, the space exploration process is also adopting Artificial Intelligence (AI) and robotics to fast-track its mission. This leads us to great lengths and never explored places. Emerging technologies like machine learning and deep learning are organizations working in space programs an opportunity to embrace AI and robotics. Robots help the space exploration process through the mechanical design of planetary rovers, the mechanical design of space manipulators, actuators and sensors of space robots, end-effector/tools of space robots, reconfigurable robots, and robot mobility. Here are some exemplary cases of robotics and AI in space tech.
1. Canadian Space Association launched Dextre, a robotic arm designed to install and replace small equipment such as exterior cameras or the 100-kg batteries used on the Space Station, and to test new tools and robotics techniques.
2. Voyager 1 and Voyager 2, were designed to conduct close-up studies of Jupiter and Saturn, Saturn’s rings, and both Jupiter and Saturn’s largest moon. These robots are still communicating information via the Deep Space Network (DSN) and are presently closer to Pluto than to Earth or the sun.
Thousand brain model of human intelligence.
A Thousand Brains by Jeff Hawkins unveils a theory of intelligence that will revolutionize our understanding of the brain and the future of AI and humanity. With a foreword by Richard Dawkins, A Thousand Brains tackles one of science’s greatest mysteries: what is intelligence and how does the brain create it? In this video series, Jeff talks to Numenta VP of Marketing Christy Maver about his motivations behind the book, what readers can expect and what each of the book’s three sections cover.
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.
Sign in Welcome! Log into your account your username your password Forgot your password? Get help Default Kit Password recovery Recover your password your email A password will be e-mailed to you. HometechA celebrated AI has learned a new…
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?
