Lifeboat Foundation

One of the cornerstone challenges in machine learning, time series forecasting has made groundbreaking contributions to several domains. However, forecasting models can’t generalize the distribution shift that changes with time because time series data is inherently non-stationary. Based on the assumptions about the inter-instance and intra-instance temporal distribution shifts, two main types of techniques have been suggested to address this issue. Both stationary and nonstationary dependencies can be separated using these techniques. Existing approaches help reduce the impact of the shift in the temporal distribution. Still, they are overly prescriptive because, without known environmental labels, every sequence instance or segment might not be stable.

Before learning about the changes in the stationary and nonstationary states throughout time, there is a need to identify when the shift in the temporal distribution takes place. By assuming nonstationarity in observations, it is possible to theoretically identify the latent environments and stationary/nonstationary variables according to this understanding.

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A new study into life on Mars has forced scientists to change what we thought we know about the Red Planet.

Europe’s Mars Express orbiter snapped the gorgeous shots.

Generative AI can be hijacked by self-replicating worms.

“Enriched cosmic dust, on the other hand, I think makes for a plausible source.”

Dr. Walton’s team now plans to test their theory experimentally, using large reaction vessels to recreate the conditions that might have prevailed in the primeval melt holes, then setting the initial conditions to those that probably existed in a cryoconite hole four billion years ago before waiting to see whether any chemical reactions of the kind that produce biologically relevant molecules do indeed develop.

The post Scientists say cosmic dust may have kick-started life on Earth appeared first on Talker.

The James Webb Space Telescope (JWST) is helping scientists uncover how planets form by advancing understanding of their birthplaces and the circumstellar disks surrounding young stars.

In a paper published in The Astronomical Journal, a team of scientists, led by Naman Bajaj of the University of Arizona and including Dr. Uma Gorti at the SETI Institute, images for the first time winds from an old planet-forming disk (still very young relative to the sun) which is actively dispersing its gas content. The disk has been imaged before, but winds from old disks haven’t. Our knowing when the gas disperses is important, as it constrains the time left for nascent planets to consume the gas from their surroundings.

At the heart of this discovery is the observation of TCha, a young star (relative to the sun) enveloped by an eroding disk notable for its vast dust gap, approximately 30 astronomical units in radius. For the first time, astronomers have imaged the dispersing gas (aka winds) using the four lines of the noble gases neon (Ne) and argon (Ar), one of which is the first detection in a planet-forming disk. The images of [Ne II] show that the wind is coming from an extended region of the disk.

Getting into space is difficult, but it may be that other worlds have even harder times at it than we do, imprisoned by orbital debris, high gravity, or even being quarantined by alien civilizations.

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Continue reading “The Fermi Paradox: Imprisoned Planets” »

The great mystery of where all the aliens are in our vast Universe contemplates ancient interstellar civilizations building enormous megastructures that rival worlds or even stars in the immensity… and asks why we can’t see these giant alien artifacts.

David Brin on Event Horizon with John Michael Godier: • A.I. Wars, The Fermi Paradox and Grea…
This Week in Space with Rod Pyle: • Alien Megastructures — Isaac Arthur a…

Continue reading “The Fermi Paradox: Absent Megastructures” »

Johns Hopkins electrical and computer engineers are pioneering a new approach to creating neural network chips—neuromorphic accelerators that could power energy-efficient, real-time machine intelligence for next-generation embodied systems like autonomous vehicles and robots.

Electrical and computer engineering graduate student Michael Tomlinson and undergraduate Joe Li—both members of the Andreou Lab—used natural language prompts and ChatGPT4 to produce detailed instructions to build a spiking neural network chip: one that operates much like the human brain.

Through step-by-step prompts to ChatGPT4, starting with mimicking a single biological neuron and then linking more to form a network, they generated a full chip design that could be fabricated.