BLOGS

Two researchers have revised the Drake equation, a mathematical formula for the probability of finding life or advanced civilizations in the universe.

University of Rochester. Are We Alone in the Universe? Revisiting the Drake Equation, NASA

Topics: Astrobiology, Astrophysics, Artificial Intelligence, Civilization, SETI

See: Britannica.com/The-Fermi-Paradox/Where-Are-All-The-Aliens

Abstract
This study examines the hypothesis that the rapid development of Artificial Intelligence (AI), culminating in the emergence of Artificial Superintelligence (ASI), could act as a "Great Filter" that is responsible for the scarcity of advanced technological civilizations in the universe. It is proposed that such a filter emerges before these civilizations can develop a stable, multiplanetary existence, suggesting the typical longevity (L) of a technical civilization is less than 200 years. Such estimates for L, when applied to optimistic versions of the Drake equation, are consistent with the null results obtained by recent SETI surveys, and other efforts to detect various techno-signatures across the electromagnetic spectrum. Through the lens of SETI, we reflect on humanity's current technological trajectory – the modest projections for L suggested here, underscore the critical need to quickly establish regulatory frameworks for AI development on Earth and the advancement of a multiplanetary society to mitigate against such existential threats. The persistence of intelligent and conscious life in the universe could hinge on the timely and effective implementation of such international regulatory measures and technological endeavors.

Is artificial intelligence the great filter that makes advanced technical civilizations rare in the universe? Michael A. Garrett, Acta Astronautica, Volume 219, June 2024, Pages 731-735

Plastic fantastic: this perovskite-based device can be reconfigured and could play an important role in artificial intelligence systems. (Courtesy: Purdue University/Rebecca McElhoe)

Topics: Artificial Intelligence, Biology, Computer Science, Materials Science

Researchers in the US have developed a perovskite-based device that could be used to create a high-plasticity architecture for artificial intelligence. The team, led by Shriram Ramanathan at Purdue University, has shown that the material’s electronic properties can be easily reconfigured, allowing the devices to function like artificial neurons and other components. Their results could lead to more flexible artificial-intelligence hardware that could learn much like the brain.

Artificial intelligence systems can be trained to perform a task such as voice recognition using real-world data. Today this is usually done in software, which can adapt when additional training data are provided. However, machine learning systems that are based on hardware are much more efficient and researchers have already created electronic circuits that behave like artificial neurons and synapses.

However, unlike the circuits in our brains, these electronics are not able to reconfigure themselves when presented with new training information. What is needed is a system with high plasticity, which can alter its architecture to respond efficiently to new information.

Device can transform into four components for artificial intelligence systems, Sam Jarman, Physics World

Rutgers researchers and their collaborators have found that learning - a universal feature of intelligence in living beings - can be mimicked in synthetic matter, a discovery that in turn could inspire new algorithms for artificial intelligence (AI). (Courtesy: Rutgers University-New Brunswick)

Topics: Artificial Intelligence, Computer Science, Materials Science, Quantum Mechanics

Quantum materials known as Mott insulators can “learn” to respond to external stimuli in a way that mimics animal behavior, say researchers at Rutgers University in the US. The discovery of behaviors such as habituation and sensitization in these non-living systems could lead to new algorithms for artificial intelligence (AI).

Neuromorphic, or brain-inspired, computers aim to mimic the neural systems of living species at the physical level of neurons (brain nerve cells) and synapses (the connections between neurons). Each of the 100 billion neurons in the human brain, for example, receives electrical inputs from some of its neighbors and then “fires” an electrical output to others when the sum of the inputs exceeds a certain threshold. This process, also known as “spiking”, can be reproduced in nanoscale devices such as spintronic oscillators. As well as being potentially much faster and energy-efficient than conventional computers, devices based on these neuromorphic principles might be able to learn how to perform new tasks without being directly programmed to accomplish them.

Quantum material ‘learns’ like a living creature, Isabelle Dumé, Physics World