One small step for a machine… one giant leap for the singularity.
This AI actually improved a key algorithm that makes it run even faster.
In this video I discuss new Deepmind’s AlphaTensor algorithm and why this work is so important for all the fields of Engineering!
Deepmind’s paper “Discovering faster matrix multiplication algorithms with reinforcement learning”:
https://www.nature.com/articles/s41586-022-05172-4
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“Machine learning provides a way of providing almost human-like intuition to huge data sets. One valuable application is for tasks where it’s difficult to write a specific algorithm to search for something—human faces, for instance, or perhaps ” something strange,” wrote astrophysicist and Director of the Penn State University Extraterrestrial Intelligence Center, Jason Wright in an email to The Daily Galaxy. ” In this case, you can train a machine-learning algorithm to recognize certain things you expect to see in a data set,” Wright explains, ” and ask it for things that don’t fit those expectations, or perhaps that match your expectations of a technosignature.
Crowdsourcing Alien Structures
For instance,’ Wright notes, theoretical physicist Paul Davies has suggested crowdsourcing the task of looking for alien structures or artifacts on the Moon by posting imaging data on a site like Zooniverse and looking for anomalies. Some researchers (led by Daniel Angerhausen) have instead trained machine-learning algorithms to recognize common terrain features, and report back things it doesn’t recognize, essentially automating that task. Sure enough, the algorithm can identify real signs of technology on the Moon—like the Apollo landing sites!
The project, known as DAF-MIT AI Accelerator, selected a pilot out of over 1,400 applicants.
The United States Air Force (DAF) and Massachusetts Institute of Technology (MIT) commissioned their lead AI pilot — a training program that uses artificial intelligence — in October 2022. The project utilizes the expertise at MIT and the Department of Air Force to research the potential of applying AI algorithms to advance the DAF and security.
The military department and the university created an artificial intelligence project called the Department of the Air Force-Massachusetts Institute of Technology Artificial Intelligence Accelerator (DAF-MIT AI Accelerator).
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The project and the pilot
A prototype of the project was signed with an executive order in 2019, and it had various strategies put into place in 2020. The collective team, known as the DAF-MIT AI Accelerator, commissioned their lead AI pilot last month. “In this pilot, [the cohort] will gain a practical grounding in AI and its business applications helping you transform your organizations into the workforce of the future,” said Major John Radovan, deputy director of the AIA.
Why does Facebook constantly change its algorithm? I can never keep up.” “We’re the ones giving them money. Why are they always making it harder on us to get our content to our audience?” You’re not alone if you’ve ever asked either of those questions. For years, marketers and advertisers have been noticing changes to Facebook’s algorithm. Each time, these changes only seemed to hurt their organic reach and ad performance with Facebook marketing. Though the Facebook algorithm isn’t the only thing affecting the reach of your content, it is one of the most important. That’s why all marketers must stay up to date on the changes and updates as Facebook rolls them out.
Find out about changes to the Facebook algorithm, how it works, and take away 19 clever tips you can try today to outsmart the algorithm.
Next, you seem to assume that when I catch a ball, my mind solves equations unconsciously, brining together inertia, gravity, air resistance to calculate my response. You may be right, but I don’t think most neuroscientist agree with you. That’s another computationalist prejudice. Rather than solving equations, my nervous system uses experience and extrapolation through repeated trial and improvement to hone a skill in extrapolating paths; no equations involved. As I say, I could be wrong, it’s an empirical question. But as far as I know, the balance of evidence and theory supports my interpretation.
The meaning of semantics is not just that it means something, but that it can be used to make statements about the world, beyond the formal system used to express that meaning. That, too, is definitional.
Your main argument seems like a really desperate move to sustain the computationalist faith that you assert at the beginning in the face of huge, perhaps insuperable difficulties.
When in 2015, Eileen Brown looked at the ETER9 Project (crazy for many, visionary for few) and wrote an interesting article for ZDNET with the title “New social network ETER9 brings AI to your interactions”, it ensured a worldwide projection of something the world was not expecting.
Someone, in a lost world (outside the United States), was risking, with everything he had in his possession (very little or less than nothing), a vision worthy of the American dream. At that time, Facebook was already beginning to annoy the cleaner minds that were looking for a difference and a more innovative world.
Today, after that test bench, we see that Facebook (Meta or whatever) is nothing but an illusion, or, I dare say, a big disappointment. No, no, no! I am not now bad-mouthing Facebook just because I have a project in hand that is seen as a potential competitor.
I was even a big fan of the “original” Facebook; but then I realized, it took me a few years, that Mark Zuckerberg is nothing more than a simple kid, now a man, who against everything and everyone, gave in to whims. Of him, initially, and now, perforce, of what his big investors, deluded by himself, of what his “metaverse” would be.
Associate Professor of the Department of Information Technologies and Computer Sciences at MISIS University, Ph.D., mathematician and doctor Alexandra Bernadotte has developed algorithms that significantly increase the accuracy of recognition of mental commands by robotic devices. The result is achieved by optimizing the selection of a dictionary. Algorithms implemented in robotic devices can be used to transmit information through noisy communication channels. The results have been published in the peer-reviewed international scientific journal Mathematics.
The task of improving the object (audio, video or electromagnetic signals) classification accuracy, when compiling so-called “dictionaries” of devices is faced by developers of different systems aimed to improve the quality of human life.
The simplest example is a voice assistant. Audio or video transmission devices for remote control of an object in the line-of-sight zone use a limited set of commands. At the same time, it is important that the commands classifier based on the neural network accurately understands and does not confuse the commands included in the device dictionary. It also means that the recognition accuracy should not fall below a certain value in the presence of extraneous noise.
An artificial intelligence system from Google’s sibling company DeepMind stumbled on a new way to solve a foundational math problem at the heart of modern computing, a new study finds. A modification of the company’s game engine AlphaZero (famously used to defeat chess grandmasters and legends in the game of Go) outperformed an algorithm that had not been improved on for more than 50 years, researchers say.
The new research focused on multiplying grids of numbers known as matrices. Matrix multiplication is an operation key to many computational tasks, such as processing images, recognizing speech commands, training neural networks, running simulations to predict the weather, and compressing data for sharing on the Internet.
Simulations indicate that postmerger gravitational waves from coalescing neutron stars could allow researchers to hear the phase transitions between exotic states of matter.
When you cut yourself, a mass migration begins inside your body: Skin cells flood by the thousands toward the site of the wound, where they will soon lay down fresh layers of protective tissue.
In a new study, researchers from the University of Colorado Boulder have taken an important step toward unraveling the drivers behind this collective behavior. The team has developed an equation learning technique that might one day help scientists grasp how the body rebuilds skin, and could potentially inspire new therapies to accelerate wound healing.
“Learning the rules for how individual cells respond to the proximity and relative motion of other cells is critical to understanding why cells migrate into a wound,” said David Bortz, professor of applied mathematics at CU Boulder and senior author of the new study.
