Lifeboat Foundation

“We hope that this soft robotic arm exemplifies a future where machines assist, complement, and understand human needs more deeply than ever before.”

Drawing inspiration from the movements of elephant trunks and octopus tentacles, researchers at the CREATE lab of t. It ishe Swiss Federal Institute of Technology Lausanne (EPFL) has developed a revolutionary robotic structure, the “trimmed helicoid.”

Set to usher in greater compliance and control in robotic design, this structure ensures safer interactions between humans and robots and is a result of blending computational modeling with astute biological observations.

Startup ‘paradoxal surfboards’ integrates sargassum algae into its 3D-printed surfboard as a substitute for petroleum to make the board.

Modern computer models—for example for complex, potent AI applications—push traditional digital computer processes to their limits. New types of computing architecture, which emulate the working principles of biological neural networks, hold the promise of faster, more energy-efficient data processing.

A team of researchers has now developed a so-called event-based architecture, using photonic processors with which data are transported and processed by means of light. In a similar way to the brain, this makes possible the continuous adaptation of the connections within the neural network. This changeable connections are the basis for learning processes.

For the purposes of the study, a team working at Collaborative Research Center 1,459 (Intelligent Matter)—headed by physicists Prof. Wolfram Pernice and Prof. Martin Salinga and computer specialist Prof. Benjamin Risse, all from the University of Münster—joined forces with researchers from the Universities of Exeter and Oxford in the UK. The study has been published in the journal Science Advances.

High-resolution maps of biological annotations in the brain are increasingly generated and shared. In this Review, Bazinet and colleagues discuss how brain connectomes can be enriched with biological annotations to address new questions about brain network organization.

Jailbroken large language models (LLMs) and generative AI chatbots — the kind any hacker can access on the open Web — are capable of providing in-depth, accurate instructions for carrying out large-scale acts of destruction, including bio-weapons attacks.

An alarming new study from RAND, the US nonprofit think tank, offers a canary in the coal mine for how bad actors might weaponize this technology in the (possibly near) future.

In an experiment, experts asked an uncensored LLM to plot out theoretical biological weapons attacks against large populations. The AI algorithm was detailed in its response and more than forthcoming in its advice on how to cause the most damage possible, and acquire relevant chemicals without raising suspicion.

“For AI to be motivated towards a goal, it must know what it wants.”

The possible board combinations in a game of Go are more than the number of atoms in the known universe, but it’s still a finite number. In the real world, there are infinite possibilities for what might happen next, and uncertainty is rampant. How realistic, then, is AGI?

A recent research paper published in Frontiers in Ecology and Evolution explores obstacles toward AGI. Biological systems with degrees of general intelligence — organisms ranging from the humble microbes to the humans reading this — are capable of improvising to meet their goals. What prevents AI from improvising?

Scanning tunnelling microscopy images of simple glycoconjugates and glycosaminoglycans and their corresponding structures.

Scanning tunnelling microscopy has enabled researchers to directly image important sugar molecules attached to lipids and proteins. The experiments provide a picture at the single-molecule level of the sequences and locations of glycans bound to important biomolecules, offering new insight into the role they play in biology.

WASHINGTON, Oct 16 (Reuters) — When British naturalist Charles Darwin sketched out his theory of evolution in the 1,859 book “On the Origin of Species” — proposing that biological species change over time through the acquisition of traits that favor survival and reproduction — it provoked a revolution in scientific thought.

Now 164 years later, nine scientists and philosophers on Monday proposed a new law of nature that includes the biological evolution described by Darwin as a vibrant example of a much broader phenomenon, one that appears at the level of atoms, minerals, planetary atmospheres, planets, stars and more.

It holds that complex natural systems evolve to states of greater patterning, diversity and complexity.

Are humans progressing morally as well as materially? What does it mean to be human in the cosmos? On a new episode of ID the Future, we bring you the second half of a stimulating conversation between Dr. David Berlinski and host Eric Metaxas on the subject of Berlinski’s book Human Nature.

In Human Nature, Berlinski argues that the utopian view that humans are progressing toward evolutionary and technological perfection is wishful thinking. Men are not about to become like gods. “I’m a strong believer in original sin,” quips Berlinski in his discussion with Metaxas. In other words, he believes not only that humans are fundamentally distinct from the rest of the biological world, but also that humans are prone to ignorance and depravity as well as wisdom and nobility. During this second half of their discussion, Berlinski and Metaxas compare and contrast the ideas of thinkers like psychologist Steven Pinker, author Christopher Hitchens, and physicist Steven Weinberg. The pair also spar gracefully over the implications of human uniqueness. Berlinski, though candid and self-critical, is unwilling to be pigeonholed. Metaxas, drawing his own conclusions about the role of mind in the universe, challenges Berlinski into moments of clarity with his usual charm. The result is an honest, probing, and wide-ranging conversation about the nature of science and the human condition. Download the podcast or listen to it here.

This is Part 2 of a two-part interview. If you missed it, listen to Part 1.