Lifeboat Foundation: Safeguarding Humanity
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Category: robotics/AI – Page 36

GOOD LUCK, HAVE FUN, DON’T DIE — Welcome To The Perfect Prison

Gore Verbinski’s Good Luck, Have Fun, Dont Die hits like a nasty mirror held up at the worst possible angle. On paper, the setup sounds almost playful: a “Man From the Future” drops into a diner in Los Angeles and has to recruit the exact combination of disgruntled strangers for a one-night mission to stop a rogue AI. But the horror isn’t metal skeletons and laser fire. It’s the idea that the end of humanity doesn’t arrive with an explosion. It arrives with an upgrade. A perfectly tuned stream of algorithmic entertainment that doesn’t merely distract people—it replaces them. A manufactured paradise so frictionless, so gratifying, so chemically rewarding, that the messy, strenuous, inconvenient act of being human starts to feel obsolete.

#goodluckhavefundontdie #samrockwell #ai #algorithm.

Check out my playlists on film here — Film Explored — • Film Explored.

Check out my playlist on Alien here — • New to Aliens? Start Here.

Check out my playlist on Predator here — • New to Predator? Start Here.

Quantum reservoir computing peaks at the edge of many-body chaos, study suggests

Reservoir computing is a promising machine learning-based approach for the analysis of data that changes over time, such as weather patterns, recorded speech or stock market trends. Classical reservoir computing techniques are known to perform best at the “edge of chaos,” or in simpler terms, at a “sweet spot” in which the behavior of systems is neither entirely predictable (i.e., order) nor completely unpredictable (i.e., chaos).

In recent years, some physicists and quantum engineers have been exploring the possibility of realizing a quantum equivalent of classical reservoir computing, known as quantum reservoir computing (QRC). These approaches enable the processing of temporal data and the prediction of events unfolding over time, leveraging high-dimensional quantum states.

Researchers at the University of Tokyo carried out a study investigating how QRC would behave when applied to complex quantum many-body systems, which consist of several interacting quantum particles. Their paper, published in Physical Review Letters, introduces a physics-based framework that could inform the future development of QRC systems.

Nanodevice produces continuous electricity from evaporation

A nanodevice developed at EPFL produces an autonomous, stable current from evaporating saltwater by using heat and light to control the movement of ions and electrons. Previously, researchers in the Laboratory of Nanoscience for Energy Technology (LNET) in EPFL’s School of Engineering reported a platform for studying the hydrovoltaic (HV) effect—a phenomenon that allows electricity to be harvested when fluid is passed over the charged surface of a nanodevice. Their platform consisted of a hexagonal network of silicon nanopillars, the space between which created channels for evaporating fluid samples.

Now the LNET team, led by Giulia Tagliabue, has developed this platform into a hydrovoltaic system with a power output that matches or exceeds similar technologies—with a major advantage. Instead of relying on heat and light to simply boost evaporation, the EPFL system generates current by harnessing heat and light to control the movement of ions in evaporating saltwater, and the flow of electrons in the silicon nanodevice.

“Heat and light imbalances will always affect a hydrovoltaic device, but we have discovered how these can be leveraged to our advantage,” explains LNET researcher Tarique Anwar.

These Billionaires Plan To Bring Self-Driving Tech To Everything That Moves

Applied Intuition’s cofounders are building software that can drive everything from planes to tanks to automobiles. But to expand beyond its $800 million business selling tech for cars, they will have to take on Tesla, Google, Nvidia and a host of other startups jostling for pole position in the autonomy race.

‘Learn-to-Steer’ method improves AI’s ability to understand spatial instructions

Researchers from the Department of Computer Science at Bar-Ilan University and from NVIDIA’s AI research center in Israel have developed a new method that significantly improves how artificial intelligence models understand spatial instructions when generating images—without retraining or modifying the models themselves. Image-generation systems often struggle with simple prompts such as “a cat under the table” or “a chair to the right of the table,” frequently placing objects incorrectly or ignoring spatial relationships altogether. The Bar-Ilan research team has introduced a creative solution that allows AI models to follow such instructions more accurately in real time.

The new method, called Learn-to-Steer, works by analyzing the internal attention patterns of an image-generation model, effectively offering insight into how the model organizes objects in space. A lightweight classifier then subtly guides the model’s internal processes during image creation, helping it place objects more precisely according to user instructions. The approach can be applied to any existing trained model, eliminating the need for costly retraining.

The results show substantial performance gains. In the Stable Diffusion SD2.1 model, accuracy in understanding spatial relationships increased from 7% to 54%. In the Flux.1 model, success rates improved from 20% to 61%, with no negative impact on the models’ overall capabilities.

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