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The NASA/ESA/CSA James Webb Space Telescope is showing off its capabilities closer to home with its first image of Neptune. Not only has Webb captured the clearest view of this peculiar planet’s rings in more than 30 years, but its cameras are also revealing the ice giant in a whole new light.

Most striking about Webb’s new image is the crisp view of the planet’s dynamic rings — some of which haven’t been seen at all, let alone with this clarity, since the Voyager 2 flyby in 1989. In addition to several bright narrow rings, the Webb images clearly show Neptune’s fainter dust bands. Webb’s extremely stable and precise image quality also permits these very faint rings to be detected so close to Neptune.

Neptune has fascinated and perplexed researchers since its discovery in 1846. Located 30 times farther from the Sun than Earth, Neptune orbits in one of the dimmest areas of our Solar System. At that extreme distance, the Sun is so small and faint that high noon on Neptune is similar to a dim twilight on Earth.

Scientists have long studied the work of Subrahmanyan Chandrasekhar, the Indian-born American astrophysicist who won the Nobel Prize in 1983, but few know that his research on stellar and planetary dynamics owes a deep debt of gratitude to an almost forgotten woman: Donna DeEtte Elbert.

From 1948 to 1979, Elbert worked as a “computer” for Chandrasekhar, tirelessly devising and solving mathematical equations by hand. Though she shared authorship with the Nobel laureate on 18 papers and Chandrasekhar enthusiastically acknowledged her seminal contributions, her greatest achievement went unrecognized until a postdoctoral scholar at UCLA connected threads in Chandrasekhar’s work that all led back to Elbert.

Elbert’s achievement? Before anyone else, she predicted the conditions argued to be optimal for a planet or star to generate its own magnetic field, said the scholar, Susanne Horn, who has spent half a decade building on Elbert’s work.

Many biomedical researchers spend their careers searching for big discoveries – the next wonder drug, vaccine, or device that’s going to solve the greatest challenges in modern medicine.

But many monumental findings have small beginnings, routed in foundational R&D and a genuine curiosity about basic biology. Just look at the history of Nobel Prize-worthy discoveries, such as CRISPR-Cas or GFP: These discoveries are, at first, not appreciated for the dramatic, long-term impact that they end up having on biotechnology and medicine.1,2

Dr. Joscha Bach (MIT Media Lab and the Harvard Program for Evolutionary Dynamics) is an AI researcher who works and writes about cognitive architectures, mental representation, emotion, social modeling, and multi-agent systems.

He is founder of the MicroPsi project, in which virtual agents are constructed and used in a computer model to discover and describe the interactions of emotion, motivation, and cognition of situated agents.

Bach’s mission to build a model of the mind is the bedrock research in the creation of Strong AI, i.e. cognition on par with that of a human being. He is especially interested in the philosophy of AI and in the augmentation of the human mind.

July 25th, 2017

Eleven-year-old Simons only took a year to complete his bachelor’s degree, which usually takes at least three years.

In a conversation with the Dutch daily De Telegraaf, Simons said that, “I don’t really care if I’m the youngest.” “It’s all about getting knowledge for me.”

“This is the first puzzle piece in my goal of replacing body parts with mechanical parts,” Simons said.

How do you find novel materials with very specific properties—for example, special electronic properties which are needed for quantum computers? This is usually a very complicated task: various compounds are created, in which potentially promising atoms are arranged in certain crystal structures, and then the material is examined, for example in the low-temperature laboratory of TU Wien.

Now, a cooperation between Rice University (Texas), TU Wien and other international research institutions has succeeded in tracking down suitable materials on the computer. New theoretical methods are used to identify particularly promising candidates from the vast number of possible materials. Measurements at TU Wien have shown the materials do indeed have the required properties and the method works. This is an important step forward for research on quantum materials. The results have now been published in the journal Nature Physics.

UNIVERSITY PARK, Pa.— A person’s distrust in humans predicts they will have more trust in artificial intelligence’s ability to moderate content online, according to a recently published study. The findings, the researchers say, have practical implications for both designers and users of AI tools in social media.

“We found a systematic pattern of individuals who have less trust in other humans showing greater trust in AI’s classification,” said S. Shyam Sundar, the James P. Jimirro Professor of Media Effects at Penn State. “Based on our analysis, this seems to be due to the users invoking the idea that machines are accurate, objective and free from ideological bias.”

The study, published in the journal of New Media & Society also found that “power users” who are experienced users of information technology, had the opposite tendency. They trusted the AI moderators less because they believe that machines lack the ability to detect nuances of human language.

Watch NVIDIA CEO Jensen Huang unveil the new Ada Lovelace GPU architecture, new advances to its computing platforms, and new cloud services to further the era of AI and the metaverse, and transform every industry.

Dive into the announcements and discover more content at https://www.nvidia.com/gtc.

00:00 GeForce Beyond: A Special Broadcast at GTC
19:34 NVIDIA Omniverse.
37:07 NVIDIA Robotics Platforms: Isaac, DRIVE, Clara Holoscan, Metropolis.
57:22 NVIDIA AI
1:08:48 Large Language Models.
1:15:39 Hopper and Grace Hopper.
1:21:47 AI & Omniverse Services to Enterprise.
1:30:25 Closing Summary.

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