Lifeboat Foundation

SPEAKING at the University of Cambridge in 1980, Stephen Hawking considered the possibility of a theory of everything that would unite general relativity and quantum mechanics – our two leading descriptions of reality – into one neat, all-encompassing equation. We would need some help, he reckoned, from computers. Then he made a provocative prediction about these machines’ growing abilities. “The end might not be in sight for theoretical physics,” said Hawking. “But it might be in sight for theoretical physicists.”

Artificial intelligence has achieved much since then, yet physicists have been slow to use it to search for new and deeper laws of nature. It isn’t that they fear for their jobs. Indeed, Hawking may have had his tongue firmly in his cheek. Rather, it is that the deep-learning algorithms behind AIs spit out answers that amount to a “what” rather than a “why”, which makes them about as useful for a theorist as saying the answer to the question of life, the universe and everything is 42.

The information security landscape is rapidly changing in response to quantum computing technology, which is capable of cracking modern encryption techniques in minutes, but a promising US government encryption algorithm for the post-quantum world was just cracked in less than an hour thanks to a decades-old math theorem.

In July 2022, the US National Institute of Standards and Technology (NIST) chose a set of encryption algorithms that it hoped would stand up to the encryption-cracking power of quantum computers and tasked researchers with probing them for vulnerabilities, offering a $50,000 prize for anyone who was able to break the encryption.

Astronomers at Caltech have used a machine learning algorithm to classify 1,000 supernovae completely autonomously. The algorithm was applied to data captured by the Zwicky Transient Facility, or ZTF, a sky survey instrument based at Caltech’s Palomar Observatory.

“We needed a helping hand, and we knew that once we trained our computers to do the job, they would take a big load off our backs,” says Christoffer Fremling, a staff astronomer at Caltech and the mastermind behind the new algorithm, dubbed SNIascore. “SNIascore classified its first supernova in April 2021, and, a year and a half later, we are hitting a nice milestone of 1,000 supernovae.”

Continue reading “Machine learning tools autonomously classify 1,000 supernovae” »

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As we head through hard times things can seem rather bleak, but there’s lots of amazing and beneficial technologies on the horizon.

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Nvidia unveils its new artificial intelligence 3D model maker for game design uses text or photo input to output a 3D mesh and can also edit and adjust 3D models with text descriptions. New video style transfer from Nvidia uses CLIP to convert the style of 3D models and photos. New differential equation-based neural network machine learning AI from MIT solves brain dynamics.

AI News Timestamps:
0:00 Nvidia AI Turns Text To 3D Model Better Than Google.
2:03 Nvidia 3D Object Style Transfer AI
4:56 New Machine Learning AI From MIT

#nvidia #ai #3D

https://www.youtube.com/watch?v=QrXnYHubFPc

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AI News Timestamps:
0:00 New AI Robot Dog Beats Human Soccer Skills.
2:34 Breakthrough Humanoid Robotics & AI Tech.
5:21 Google AI Makes HD Video From Text.
8:41 New OpenAI DALL-E Robotics.
11:31 Elon Musk Reveals Tesla Optimus AI Robot.
16:49 Machine Learning Driven Exoskeleton.
19:33 Google AI Makes Video Game Objects From Text.
22:12 Breakthrough Tesla AI Supercomputer.
25:32 Underwater Drone Humanoid Robot.
29:19 Breakthrough Google AI Edits Images With Text.
31:43 New Deep Learning Tech With Light waves.
34:50 Nvidia General Robot Manipulation AI
36:31 Quantum Computer Breakthrough.
38:00 In-Vitro Neural Network Plays Video Games.
39:56 Google DeepMind AI Discovers New Matrices Algorithms.
45:07 New Meta Text To Video AI
48:00 Bionic Tech Feels In Virtual Reality.
53:06 Quantum Physics AI
56:40 Soft Robotics Gripper Learns.
58:13 New Google NLP Powered Robotics.
59:48 Ionic Chips For AI Neural Networks.
1:02:43 Machine Learning Interprets Brain Waves & Reads Mind.

Researchers have investigated the capability of known quantum computing algorithms for fault-tolerant quantum computing to simulate the laser-driven electron dynamics of excitation and ionization processes in small molecules. Their research is published in the Journal of Chemical Theory and Computation.

“These quantum computer algorithms were originally developed in a completely different context. We used them here for the first time to calculate electron densities of molecules, in particular their dynamic evolution after excitation by a light pulse,” says Annika Bande, who heads a group on theoretical chemistry at Helmholtz Association of German Research Centers (HZB). Bande and Fabian Langkabel, who is doing his doctorate with her, show in the study how well this works.

“We developed an algorithm for a fictitious, completely error-free quantum computer and ran it on a classical server simulating a quantum computer of ten qubits,” says Langkabel. The scientists limited their study to smaller molecules in order to be able to perform the calculations without a real quantum computer and to compare them with conventional calculations.

They then used QUARTZ to analyze retinal images from 7,411 more people, these aged 48 to 92, and combined this data with information about their health history (such as smoking, statin use, and previous heart attacks) to predict their risk of heart disease. Participants’ health was tracked for seven to nine years, and their outcomes were compared to Framingham risk score (FRS) predictions.

A common tool for estimating heart disease risk, the FRS looks at age, gender, total cholesterol, high density lipoprotein cholesterol, smoking habits, and systolic blood pressure to estimate the probability someone will develop heart disease within a given span of time, usually 10 to 30 years.

The QUARTZ team compared their data to 10-year FRS predictions and said the algorithm’s accuracy was on par with that of the conventional tool.

Circa 2020 Basically this means a magnetic transistor can have not only quantum properties but also it can have nearly infinite speeds for processing speeds. Which means we can have nanomachines with near infinite speeds eventually.

Abstract The discovery of spin superfluidity in antiferromagnetic superfluid 3He is a remarkable discovery associated with the name of Andrey Stanislavovich Borovik-Romanov. After 30 years, quantum effects in a magnon gas (such as the magnon Bose–Einstein condensate and spin superfluidity) have become quite topical. We consider analogies between spin superfluidity and superconductivity. The results of quantum calculations using a 53-bit programmable superconducting processor have been published quite recently[1]. These results demonstrate the advantage of using the quantum algorithm of calculations with this processor over the classical algorithm for some types of calculations. We consider the possibility of constructing an analogous (in many respecys) processor based on spin superfluidity.