Lifeboat Foundation

A team from the University of Geneva (UNIGE) has succeeded in modeling an artificial neural network capable of this cognitive prowess. After learning and performing a series of basic tasks, this AI was able to provide a linguistic description of them to a “sister” AI, which in turn performed them. These promising results, especially for robotics, are published in Nature Neuroscience.

Performing a new task without prior training, on the sole basis of verbal or written instructions, is a unique human ability. What’s more, once we have learned the task, we are able to describe it so that another person can reproduce it. This dual capacity distinguishes us from other species which, to learn a new task, need numerous trials accompanied by positive or negative reinforcement signals, without being able to communicate it to their congeners.

A sub-field of artificial intelligence (AI)—Natural language processing—seeks to recreate this human faculty, with machines that understand and respond to vocal or textual data. This technique is based on artificial neural networks, inspired by our biological neurons and by the way they transmit electrical signals to one another in the brain. However, the neural calculations that would make it possible to achieve the cognitive feat described above are still poorly understood.

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Joint research led by Yu Toyoshima and Yuichi Iino of the University of Tokyo has demonstrated individual differences in, and successfully extracted commonalities from, the whole-brain activity of roundworms. The researchers also found that computer simulations based on the whole-brain activity of roundworms more accurately reflect real-brain activity when they include so-called “noise,” or probabilistic elements. The findings were published in the journal PLOS Computational Biology.

The roundworm Caenorhabditis elegans is a favorite among neuroscientists because its 302 neurons are completely mapped. This gives a fantastic opportunity to reveal their neural mechanism at a systems level. Thus far, scientists have been making progress in revealing the different states and patterns of each neuron and the assemblies they form. However, how these states and patterns are generated has been a less explored frontier.

Continue reading “Scientists demonstrate how individual differences in ‘whole-brain’ activity are generated in roundworms” »

The Schwartz Reisman Institute for Technology and Society and the Department of Computer Science at the University of Toronto, in collaboration with the Vector Institute for Artificial Intelligence and the Cosmic Future Initiative at the Faculty of Arts \& Science, present Geoffrey Hinton on October 27, 2023, at the University of Toronto.

0:00:00 — 0:07:20 Opening remarks and introduction.
0:07:21 — 0:08:43 Overview.
0:08:44 — 0:20:08 Two different ways to do computation.
0:20:09 — 0:30:11 Do large language models really understand what they are saying?
0:30:12 — 0:49:50 The first neural net language model and how it works.
0:49:51 — 0:57:24 Will we be able to control super-intelligence once it surpasses our intelligence?
0:57:25 — 1:03:18 Does digital intelligence have subjective experience?
1:03:19 — 1:55:36 Q\&A
1:55:37 — 1:58:37 Closing remarks.

Continue reading “Will digital intelligence replace biological intelligence?” »

A critical molecule for the metabolism of living organisms has been synthesized for the first time by University of Hawaiʻi at Mānoa researchers at low temperatures (10 K) on ice coated nanoparticles mimicking conditions in deep space, marking a “cool” step in advancing our understanding of the origins of life.

Engineers have unveiled an encodable multifunctional material that can dynamically tune its shape and mechanical properties in real time. Inspired by the remarkable adaptability observed in biological organisms like the octopus, a breakthrough has been achieved in soft machines. A research team, led by Professor Jiyun Kim in the Department of Materials Science and Engineering at UNIST has successfully developed an encodable multifunctional material that can dynamically tune its shape and mechanical properties in real-time. This groundbreaking metamaterial surpasses the limitations of existing materials, opening up new possibilities for applications in robotics and other fields requiring adaptability.

Current soft machines lack the level of adaptability demonstrated by their biological counterparts, primarily due to limited real-time tunability and restricted reprogrammable space of properties and functionalities.

In order to bridge this gap, the research team introduced a novel approach utilizing graphical stiffness patterns.

Organic materials discovered on Mars may have originated from atmospheric formaldehyde, according to new research, marking a step forward in our understanding of the possibility of past life on the Red Planet.

Scientists from Tohoku University have investigated whether the early atmospheric conditions on Mars had the potential to foster the formation of biomolecules – organic compounds essential for biological processes.

Their findings, published in Scientific Reports, offer intriguing insights into the plausibility of Mars harboring life in its distant past.

Drawing inspiration from the extraordinary adaptability seen in biological entities such as the octopus, a significant advancement in the field of soft robotics has been made. Under the guidance of Professor Jiyun Kim from the Department of Materials Science and Engineering at UNIST, a research team has successfully developed an encodable multifunctional material that can dynamically tune its shape and mechanical properties in real-time.

This groundbreaking metamaterial surpasses the limitations of existing materials, opening up new possibilities for applications in robotics and other fields requiring adaptability.

Current soft machines lack the level of adaptability demonstrated by their biological counterparts, primarily due to limited real-time tunability and restricted reprogrammable space of properties and functionalities. In order to bridge this gap, the research team introduced a novel approach utilizing graphical stiffness patterns. By independently switching the digital binary stiffness states (soft or rigid) of individual constituent units within a simple auxetic structure featuring elliptical voids, the material achieves in situ and gradational tunability across various mechanical qualities.

The FIT4NANO project has mapped out the expansive applications and future directions of focused ion beam technology, emphasizing its critical role in advancing research and development across multiple disciplines, from microelectronics to life sciences.

Processing materials on the nanoscale, producing prototypes for microelectronics, or analyzing biological samples: The range of applications for finely focused ion beams is huge. Experts from the EU collaboration FIT4NANO have now reviewed the many options and developed a roadmap for the future. The article, published in Applied Physics Review, is aimed at students, users from industry and science as well as research policymakers.

Discovery and Applications.