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The fine structure constant is one of the most important natural constants of all. At TU Wien, a remarkable way of measuring it has been found—it shows up as a rotation angle.

One over 137: This is one of the most important numbers in physics. It is the approximate value of the so-called fine structure constant—a physical quantity that is of outstanding importance in atomic and particle physics.

There are many ways to measure the fine structure constant—usually it is measured indirectly, by measuring other physical quantities and using them to calculate the fine structure constant. At TU Wien, however, an experiment has now been performed, in which the fine structure constant itself can be directly measured—as an angle.

‘Friendly consultation’

Marcos wants note verbale sent.

A multi-institution research team has developed an optical chip that can train machine learning hardware. Their research is published today in Optica.

Machine learning applications have skyrocketed to $165 billion annually, according to a recent report from McKinsey. But before a machine can perform intelligence tasks such as recognizing the details of an image, it must be trained. Training of modern-day artificial intelligence (AI) systems like Tesla’s autopilot costs several million dollars in electric power consumption and requires supercomputer-like infrastructure.

This surging AI “appetite” leaves an ever-widening gap between computer hardware and demand for AI. Photonic integrated circuits, or simply optical chips, have emerged as a possible solution to deliver higher computing performance, as measured by the number of operations performed per second per watt used, or TOPS/W. However, though they’ve demonstrated improved core operations in machine intelligence used for data classification, photonic chips have yet to improve the actual front-end learning and machine training process.

Anguera, J.A., Volponi, J.J., Simon, A.J. et al. Integrated cognitive and physical fitness training enhances attention abilities in older adults. npj Aging 8, 12 (2022). https://doi.org/10.1038/s41514-022-00093-y.

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Alphabet’s X unit spun up a tool that teaches code to write itself. It has been moved into Google Labs, signaling its growing importance.

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

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WASP-39b could be key to finding much calmer, more habitable worlds.

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. Watch now.

Like Rodin’s The Thinker, there was plenty of thinking and pondering about the large language model (LLM) landscape last week. There were Meta’s missteps over its Galactica LLM public demo and Stanford CRFM’s debut of its HELM benchmark, which followed weeks of tantalizing rumors about the possible release of OpenAI’s GPT-4 sometime over the next few months.

The online chatter ramped up last Tuesday. That’s when Meta AI and Papers With Code announced a new open-source LLM called Galactica, that it described in a paper published on Arxiv as “a large language model for science” meant to help scientists with “information overload.”

The James Webb Space Telescope (JWST) has just scored another first: a detailed molecular and chemical portrait of a distant world’s skies.

The telescope’s array of highly sensitive instruments was trained on the atmosphere of a “hot Saturn”—a planet about as massive as Saturn orbiting a star some 700 light-years away—known as WASP-39 b. While JWST and other space telescopes, including Hubble and Spitzer, have previously revealed isolated ingredients of this broiling planet’s atmosphere, the new readings provide a full menu of atoms, molecules, and even signs of active chemistry and clouds.

Continue reading “James Webb Space Telescope reveals an exoplanet atmosphere as never seen before” »