A new artificially intelligent ‘Ramanujan Machine’ can generate hundreds of new mathematical conjectures, which might lead to new math proofs and theorems.
It’s 2050 and you’re due for your monthly physical exam. Times have changed, so you no longer have to endure an orifices check, a needle in your vein, and a week of waiting for your blood test results. Instead, the nurse welcomes you with, “The doctor will sniff you now,” and takes you into an airtight chamber wired up to a massive computer. As you rest, the volatile molecules you exhale or emit from your body and skin slowly drift into the complex artificial intelligence apparatus, colloquially known as Deep Nose. Behind the scene, Deep Nose’s massive electronic brain starts crunching through the molecules, comparing them to its enormous olfactory database. Once it’s got a noseful, the AI matches your odors to the medical conditions that cause them and generates a printout of your health. Your human doctor goes over the results with you and plans your treatment or adjusts your meds.
Compute costs, spurious noise, and privacy problems all mean that we can’t keep moving toward bigger and bigger datasets to fuel AI progress.
A novel computer algorithm, or set of rules, that accurately predicts the orbits of planets in the solar system could be adapted to better predict and control the behavior of the plasma that fuels fusion facilities designed to harvest on Earth the fusion energy that powers the sun and stars.
Excellent hand and hand conversation between David Sinclair and Bracken Darrell. David is an expert in longevity and life extension, and Bracken is an experienced successful businessman, CEO of multinational Logitech.
The encounter took place on February 92021, during an online scientific symposium organized by the American Federation of Aging Research (AFAR).
OEC promoting AI in Africa.
Ranjan KC
Artificial intelligence (AI) is learning more about how to work with (and on) humans. A recent study has shown how AI can learn to identify vulnerabilities in human habits and behaviours and use them to influence human decision-making.
A novel computer algorithm, or set of rules, that accurately predicts the orbits of planets in the solar system could be adapted to better predict and control the behavior of the plasma that fuels fusion facilities designed to harvest on Earth the fusion energy that powers the sun and stars.
The algorithm, devised by a scientist at the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL), applies machine learning, the form of artificial intelligence (AI) that learns from experience, to develop the predictions. “Usually in physics, you make observations, create a theory based on those observations, and then use that theory to predict new observations,” said PPPL physicist Hong Qin, author of a paper detailing the concept in Scientific Reports. “What I’m doing is replacing this process with a type of black box that can produce accurate predictions without using a traditional theory or law.”
Qin (pronounced Chin) created a computer program into which he fed data from past observations of the orbits of Mercury, Venus, Earth, Mars, Jupiter, and the dwarf planet Ceres. This program, along with an additional program known as a “serving algorithm,” then made accurate predictions of the orbits of other planets in the solar system without using Newton’s laws of motion and gravitation. “Essentially, I bypassed all the fundamental ingredients of physics. I go directly from data to data,” Qin said. “There is no law of physics in the middle.”
Researchers at the Max Planck Institute for Intelligent Systems (MPI-IS) and ETH Zürich have recently created HuggieBot 2.0, a robot that can hug users at their request. This robot, set to be presented at the ACM/IEEE International Conference on Human-Robot Interaction (HRI) in March, builds on a previous robotic system created by Alexis E. Block, one of the authors, during her Master’s degree.
57:03 “A tool that would be used for millenia.”
Foresight biotech & health extension group sponsored by 100 plus capital.
The toolset runs with Q-CTRL’s flagship BOULDER OPAL software for developers and R&D teams, automated closed-loop hardware optimization is also trained to obtain new experimental data/results from quantum computers while simultaneously running optimizations for algorithms. It can be used as a standalone tool or in tandem with a machine-learner online optimization package (M-LOOP) that manages quantum experiments autonomously.
