Lifeboat Foundation

Large language models may enable real-time communication with extraterrestrial civilizations despite the vast distances between stars. We need to start thinking about what to tell them about us.

By Franck Marchis & Ignacio G. López-Francos

Sam Altman, CEO of OpenAI,… said some kind of national payments would likely be needed as technology killed more jobs even as it generated massive wealth for others.

Many tech entrepreneurs have long suggested that guaranteed income could cushion job losses from AI and automation. The latest and largest study of the idea was spearheaded by the man behind ChatGPT.

AI models can easily generate essays and other types of text. However, they’re nowhere near as good at solving math problems, which tend to involve logical reasoning—something that’s beyond the capabilities of most current AI systems.

But that may finally be changing. Google DeepMind says it has trained two specialized AI systems to solve complex math problems involving advanced reasoning. The systems—called AlphaProof and AlphaGeometry 2—worked together to successfully solve four out of six problems from this year’s International Mathematical Olympiad (IMO), a prestigious competition for high school students. They won the equivalent of a silver medal.

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Research develops an automated system using machine learning to quantify motor symptoms in Parkinson’s disease and predict disease progression, offering new therapeutic insights.

Futurology: The global demand for AI computing has data centers consuming electricity like frat houses chug beer. But researchers from the University of Minnesota might have a wildly innovative solution to curb AI’s growing thirst for power with a radical new device that promises vastly superior energy efficiency.

The researchers have designed a new “computational random-access memory” (CRAM) prototype chip that could reduce energy needs for AI applications by a mind-boggling 1,000 times or more compared to current methods. In one simulation, the CRAM tech showed an incredible 2,500x energy savings.

Traditional computing relies on the decades-old von Neumann architecture of separate processor and memory units, which requires constantly moving data back and forth in an energy-intensive process. The Minnesota team’s CRAM completely upends that model by performing computations directly within the memory itself using spintronic devices called magnetic tunnel junctions (MTJs).

Brighter with Herbert.

It is clear that specific memories are not stored in individual specific neurons.

But the epigenetic state of neurons influences whether they become part of memory…

Memories are encoded by sparse populations of neurons but how such sparsity arises remains largely unknown. We found that a neuron’s eligibility to be recruited into the memory trace depends on its epigenetic state prior to encoding. Principal neurons in the mouse lateral amygdala display intrinsic chromatin plasticity, which when experimentally elevated favors neuronal allocation into the encoding ensemble. Such chromatin plasticity occurred at genomic regions underlying synaptic plasticity and was accompanied by increased neuronal excitability in single neurons in real time. Lastly, optogenetic silencing of the epigenetically altered neurons prevented memory expression, revealing a cell-autonomous relationship between chromatin plasticity and memory trace formation. These results identify the epigenetic state of a neuron as a key factor enabling information encoding.

An algorithm developed by Washington State University researchers can better find data anomalies than current anomaly-detection software, including in streaming data.

The work, reported in the Journal of Artificial Intelligence Research, makes fundamental contributions to artificial intelligence (AI) methods that could have applications in many domains that need to quickly find anomalies in large amounts of data, such as in cybersecurity, power grid management, misinformation, and medical diagnostics.

Being able to better find the anomalies would mean being able to more easily discover fraud, disease in a medical setting, or important unusual information, such as an asteroid whose signals overlap with the light from other stars.

A team of computer scientists and roboticists with members from Texas A&M University in the U.S., and the Mohamed Bin Zayed University of Artificial Intelligence in Abu Dhabi, working with a colleague from Boston Dynamics, has configured a robot made by Boston Dynamics to seek out and stun weeds using a small blowtorch. The team has posted a paper describing their efforts to the arXiv preprint server.

Boston Dynamics, maker of the well-known quadruped Big Dog, has been working on technology to improve both the robot’s agility and processing ability. Its latest quadruped is Spot, a robot with increased agility, highly accurate sensors and a brain that includes AI capabilities. In this new effort, the research team used some of Spot’s abilities to tame weeds growing on cropland.

The researchers trained Spot to recognize weeds among a field of regular crops. They also strapped a small tank filled with propane to its back that is used to fuel a small blow torch held by the robot’s arm. The idea is for Spot to wander around cropland looking for weeds and upon finding them, stun them by blasting their central parts with burning gas. The blowtorch is not used to incinerate the weed, but to heat its core to such an extent that the growth of the weed is stunted for several weeks.