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WASHINGTON — NASA selected Rocket Lab to launch a pair of cubesats in 2024 to monitor energy entering and exiting the polar regions of the planet.

NASA announced Aug. 14 it awarded a task order through its Venture-class Acquisition of Dedicated and Rideshare (VADR) contract to Rocket Lab for the launch of two 6U cubesats for the Polar Radiant Energy in the Far-InfraRed Experiment, or PREFIRE mission.

The NASA announcement did not disclose the value of the task order. The agency stated in past awards done under VADR that the pricing is considered “proprietary” because the awards are competed in a closed environment, with only companies on the VADR contract eligible to bid on launches of taxpayer-funded missions.

The physics of cell communication: ISTA scientists successfully model cell dynamics.

Like us, cells communicate. Well, in their own special way. Using waves as their common language, cells tell one another where and when to move. They talk, they share information, and they work together – much like the interdisciplinary team of researchers from the Institute of Science and Technology Austria (ISTA) and the National University of Singapore (NUS). They conducted research on how cells communicate – and how that matters to future projects, e.g. application to wound healing.

Biology may evoke images of animals, plants, or even theoretical computer models. The last association might not immediately come to mind, yet it is crucial in biological research. Complex biological phenomena, even the minutest details, can be understood through precise calculations. ISTA Professor Edouard Hannezo utilizes these calculations to comprehend physical principles in biological systems. His team’s recent work provides new insights into how cells move and communicate within living tissue.

For a half century, mathematicians have tried to define the exact circumstances under which a black hole is destined to exist. A new proof shows how a cube can help answer the question.

The robot’s memory is so large that it can memorise all Jeppesen navigation charts, a task that is impossible for human pilots.

Both artificial intelligence (AI) and robotics have made significant strides in recent years, meaning most human jobs could soon be overtaken by technology — on the ground and even in the skies above us.

A team of engineers and researchers from the Korea Advanced Institute of Science & Technology (KAIST) is currently developing a humanoid robot that can fly aircraft without needing to modify the cockpit.

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A research team has developed an advanced aqueous zinc-ion battery with an enhanced cycle lifespan using a weak magnetic field and a new VS2 material. The breakthrough addresses the challenges of zinc dendrite growth and cathode material limitations. Credit: Mao Yunjie.

A research team at the Hefei Institutes of Physical Science (HFIPS) of Chinese Academy of Sciences (CAS), led by Prof. Zhao Bangchuan, developed a high-performance aqueous zinc-ion battery with ultralong cycle lifespan in a weak magnetic field.

The findings were recently published in the journal Materials Horizons.

UC San Diego’s Q-MEEN-C is developing brain-like computers through mimicking neurons and synapses in quantum materials. Recent discoveries in non-local interactions represent a critical step towards more efficient AI hardware that could revolutionize artificial intelligence technology.

We often believe that computers are more efficient than humans. After all, computers can solve complex math equations in an instant and recall names that we might forget. However, human brains can process intricate layers of information rapidly, accurately, and with almost no energy input. Recognizing a face after seeing it only once or distinguishing a mountain from an ocean are examples of such tasks. These seemingly simple human functions require considerable processing and energy from computers, and even then, the results may vary in accuracy.

How close the measured value conforms to the correct value.

Smaller language models can be based on a billion parameters or less—still pretty large, but much smaller than foundational LLMs like ChatGPT and Bard. They are pre-trained to understand vocabulary and human speech, so the incremental cost to customize them using corporate and industry-specific data is vastly lower. There are several options for these pre-trained LLMs that can be customized internally, including AI21 and Reka, as well as open source LLMs like Alpaca and Vicuna.

Smaller language models aren’t just more cost-efficient, they’re often far more accurate, because instead of training them on all publicly available data—the good and the bad—they are trained and optimized on carefully vetted data that addresses the exact use cases a business cares about.

That doesn’t mean they’re limited to internal corporate data. Smaller language models can incorporate third-party data about the economy, commodities pricing, the weather, or whatever data sets are needed, and combine them with their proprietary data sets. These data sources are widely available from data service providers who ensure the information is current, accurate, and clean.