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A new study has revealed how the glass-like shells of diatoms help these microscopic organisms perform photosynthesis in dim conditions. A better understanding of how these phytoplankton harvest and interact with light could lead to improved solar cells, sensing devices and optical components.

“The and toolkit we developed could pave the way toward mass-manufacturable, sustainable optical devices and more efficient harvesting tools that are based on shells,” said research team member Santiago Bernal from McGill University in Canada. “This could be used for biomimetic devices for sensing, new telecommunications technologies or affordable ways to make clean energy.”

Diatoms are found in most bodies of water. Their shells are covered in holes that respond to light differently depending on their size, spacing and configuration. In the journal Optical Materials Express, the researchers, led by McGill University’s David V. Plant and Mark Andrews, report the first optical study of an entire diatom shell. They analyzed how different sections of the shell, or frustule, respond to sunlight and how this response is connected to photosynthesis.

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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 .

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.

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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 (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.

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Deep Learning AI Specialization: https://imp.i384100.net/GET-STARTED
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.

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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.”

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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.

“The clarity of the signals from a number of different molecules in the data is remarkable,” says Mercedes López-Morales, an astronomer at the Center for Astrophysics | Harvard & Smithsonian and one of the scientists who contributed to the new results.

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