Lifeboat Foundation

NVIDIA workflows connect real and synthetic data

Training foundation models for humanoid and other robots typically requires large amounts of data, noted NVIDIA. Teleoperation is one way to capture human demonstration data, but it can be expensive and time-consuming, it said.

Continue reading “NVIDIA adds tools it says will accelerate development of humanoid robots” »

We’re using AI and Google Maps driving trends to optimize traffic light patterns and improve traffic flow. Stop-and-go traffic in urban areas causes 29 times more emissions than on open roads. Researchers at Google are partnering with cities around the globe, from Rio to Jakarta. So far, local governments have saved fuel and lowered emissions for nearly 30 million car rides every month. Learn more about this research at: https://g.co/research/greenlight.

If you are a city representative or traffic engineer and are interested in joining the waiting list, please complete this form: https://docs.google.com/forms/d/e/1FA…

Continue reading “How AI is fixing traffic lights | Project Green Light” »

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.