Just like how you teach a kid to walk, scientists are teaching AI rules and regulations to make it more efficient.
On socially compliant navigation: Researchers show how real-world RL-based finetuning can enable mobile robots to adapt on the fly to the behavior of humans, to obstacles, and other challenges associated with real-world navigation:
Abstract.
We propose an online reinforcement learning approach, SELFI, to fine-tune a control policy trained on model-based learning. In SELFI, we combine the best parts of data efficient model-based learning with flexible model-free reinforcement learning, alleviating both of their limitations. We formulate a combined objective: the objective of the model-based learning and the learned Q-value from model-free reinforcement learning. By maximizing this combined objective in the online learning process, we improve the performance of the pre-trained policy in a stable manner. Main takeaways from our method are.
On the way to rejuvenation using AI and multidisciplinary knowledge!
An AI-generated small-molecule inhibitor treats fibrosis in vivo and in phase I clinical trials.
At the heart of AI, matrix math has just seen its biggest boost “in more than a decade.”
A team of engineers, physicists, and data scientists from Princeton University and the Princeton Plasma Physics Laboratory (PPPL) have used artificial intelligence (AI) to predict—and then avoid—the formation of a specific type of plasma instability in magnetic confinement fusion tokamaks. The researchers built and trained a model using past experimental data from operations at the DIII-D National Fusion Facility in San Diego, Calif., before proving through real-time experiments that their model could forecast so-called tearing mode instabilities up to 300 milliseconds in advance—enough time for an AI controller to adjust operating parameters and avoid a tear in the plasma that could potentially end the fusion reaction.
Sandra Watcher, profiled as part of TechCrunch’s series on women in AI, is a professor of data ethics at the University of Oxford.
DENVER—(BUSINESS WIRE)—Palantir Technologies Inc. (NYSE: PLTR) today announced that the Army Contracting Command – Aberdeen Proving Ground (ACC-APG) has awarded Palantir USG, Inc. — a wholly-owned subsidiary of Palantir Technologies Inc. — a prime agreement for the development and delivery of the Tactical Intelligence Targeting Access Node (TITAN) ground station system, the Army’s next-generation deep-sensing capability enabled by artificial intelligence and machine learning (AI/ML). The agreement, valued at $178.4 million, covers the development of 10 TITAN prototypes, including five Advanced and five Basic variants, as well as the integration of new critical technologies and the transition to fielding.
“This award demonstrates the Army’s leadership in acquiring and fielding the emerging technologies needed to bolster U.S. defense in this era of software-defined warfare. Building on Palantir’s years of experience bringing AI-enabled capabilities to warfighters, Palantir is now proud to deliver the Army’s first AI-defined vehicle” Post this
TITAN is a ground station that has access to Space, High Altitude, Aerial, and Terrestrial sensors to provide actionable targeting information for enhanced mission command and long range precision fires. Palantir’s TITAN solution is designed to maximize usability for Soldiers, incorporating tangible feedback and insights from Soldier touch points at every step of the development and configuration process. Building off Palantir’s prior work delivering AI capabilities for the warfighter, Palantir is deploying the Army’s first AI-defined vehicle.
Couple things. Mr Johnson is self aware and has a sense of humor about it. And another argument you can use against the overpopulation people is that there is currently one acre of habitable land available for every person on the planet.
How much oxygen does Jupiter’s moon, Europa, produce, and what can this teach us about its subsurface liquid water ocean? This is what a study published today in Nature Astronomy hopes to address as an international team of researchers investigated how charged particles break apart the surface ice resulting in hydrogen and oxygen that feed Europa’s extremely thin atmosphere. This study holds the potential to help scientists better understand the geologic and biochemical processes on Europa, along with gaining greater insight into the conditions necessary for finding life beyond Earth.
For the study, the researchers used the Jovian Auroral Distributions Experiment (JADE) instrument onboard NASA’s June spacecraft to collect data on the amount of oxygen being discharged from Europa’s icy surface due to charge particles emanating from Jupiter’s massive magnetic field. In the end, the researchers found that oxygen production resulting from these charged particles interacting with the icy surface was approximately 26 pounds per second (12 kilograms per second), which is a much more focused number compared to previous estimates which ranged from a few pounds per second to over 2,000 pounds per second.
“Europa is like an ice ball slowly losing its water in a flowing stream. Except, in this case, the stream is a fluid of ionized particles swept around Jupiter by its extraordinary magnetic field,” said Dr. Jamey Szalay, who is a research scholar at Princeton University, a scientist on JADE, and lead author of the study. “When these ionized particles impact Europa, they break up the water-ice molecule by molecule on the surface to produce hydrogen and oxygen. In a way, the entire ice shell is being continuously eroded by waves of charged particles washing up upon it.”