Lifeboat Foundation

Florida Atlantic Center for Connected Autonomy and Artificial Intelligence (CA-AI.fau.edu) researchers have “cracked the code” on interference when machines need to talk with each other—and people.

Electromagnetic waves make wireless connectivity possible but create a lot of unwanted chatter. Referred to as “electromagnetic interference,” this noisy byproduct of wireless communications poses formidable challenges in modern day dense IoT and AI robotic environments. With the demand for lightning-fast data rates reaching unprecedented levels, the need to quell this interference is more pressing than ever.

Equipped with a breakthrough algorithmic solution, researchers from FAU Center for Connected Autonomy and AI, within the College of Engineering and Computer Science, and FAU Institute for Sensing and Embedded Network Systems Engineering (I-SENSE), have figured out a way to do that.

Read & tell me what you think 🙂

There is a rift between near and long-term perspectives on AI safety – one that has stirred controversy. Longtermists argue that we need to prioritise the well-being of people far into the future, perhaps at the expense of people alive today. But their critics have accused the Longtermists of obsessing on Terminator-style scenarios in concert with Big Tech to distract regulators from more pressing issues like data privacy. In this essay, Mark Bailey and Susan Schneider argue that we shouldn’t be fighting about the Terminator, we should be focusing on the harm to the mind itself – to our very freedom to think.

There has been a growing debate between near and long-term perspectives on AI safety – one that has stirred controversy. “Longtermists” have been accused of being co-opted by Big Tech and fixating on science fiction-like Terminator-style scenarios to distract regulators from the real, more near-term, issues, such as algorithmic bias and data privacy.

Continue reading “The real long-term dangers of AI” »

In this study, we explore the accelerated expansion of the universe within the framework of modified f(Q) gravity. The investigation focus on the role of bulk viscosity in understanding the universe’s accelerated expansion. Specifically, a bulk viscous matter-dominated cosmological model is considered, with the bulk viscosity coefficient expressed as $$\zeta = \zeta _0 \rho H^{-1} + \zeta _1 H $$ ζ = ζ 0 ρ H — 1 + ζ 1 H. We consider the power law f(Q) function $$f(Q)=\alpha Q^n $$ f (Q ) = α Q n, where $$\alpha $$ α and n are arbitrary constants and derive the analytical solutions for the field equations corresponding to a flat FLRW metric. Subsequently, we used the combined Cosmic Chronometers (CC)+Pantheon+SH0ES sample to estimate the free parameters of the obtained analytic solution.

Four-legged animals are innately capable of agile and adaptable movements, which allow them to move on a wide range of terrains. Over the past decades, roboticists worldwide have been trying to effectively reproduce these movements in quadrupedal (i.e., four-legged) robots.

Computational models trained via reinforcement learning have been found to achieve particularly promising results for enabling agile locomotion in quadruped robots. However, these models are typically trained in simulated environments and their performance sometimes declines when they are applied to real robots in real-world environments.

Alternative approaches to realizing agile quadruped locomotion utilize footage of moving animals collected by motion sensors and cameras as demonstrations, which are used to train controllers (i.e., algorithms for executing the movements of robots). This approach, dubbed “imitation learning,” was found to enable the reproduction of animal-like movements in some quadrupedal robots.

Small-angle scattering (SAS) is a powerful technique for studying nanoscale samples. So far, however, its use in research has been held back by its inability to operate without some prior knowledge of a sample’s chemical composition. Through new research published in The European Physical Journal E, Eugen Anitas at the Bogoliubov Laboratory of Theoretical Physics in Dubna, Russia, presents a more advanced approach, which integrates SAS with machine learning algorithms.

One of the variables in TD algorithms is called reward prediction error (RPE), which is the difference between the discounted predicted reward at the current state and the discounted predicted reward plus the actual reward at the next state. TD learning theory gained traction in neuroscience once it was demonstrated that firing patterns of dopaminergic neurons in the ventral tegmental area (VTA) during reinforcement learning resemble RPE^5,9,10.

Implementations of TD using computer algorithms are straightforward, but are more complex when they are mapped onto plausible neural machinery^11,12,13. Current implementations of neural TD assume a set of temporal basis-functions^13,14, which are activated by external cues. For this assumption to hold, each possible external cue must activate a separate set of basis-functions, and these basis-functions must tile all possible learnable intervals between stimulus and reward.

In this paper, we argue that these assumptions are unscalable and therefore implausible from a fundamental conceptual level, and demonstrate that some predictions of such algorithms are inconsistent with various established experimental results. Instead, we propose that temporal basis functions used by the brain are themselves learned. We call this theoretical framework: Flexibly Learned Errors in Expected Reward, or FLEX for short. We also propose a biophysically plausible implementation of FLEX, as a proof-of-concept model. We show that key predictions of this model are consistent with actual experimental results but are inconsistent with some key predictions of the TD theory.

The performance of artificial intelligence (AI) tools, including large computational models for natural language processing (NLP) and computer vision algorithms, has been rapidly improving over the past decades. One reason for this is that datasets to train these algorithms have exponentially grown, collecting hundreds of thousands of images and texts often collected from the internet.

Training data for robot control and planning algorithms, on the other hand, remains far less abundant, in part because acquiring it is not as straightforward. Some computer scientists have thus been trying to create larger datasets and platforms that could be used to train computational models for a wide range of robotics applications.

In a recent paper, pre-published on the server arXiv and set to be presented at the Robotics: Science and Systems 2024 conference, researchers at the University of Texas at Austin and NVIDIA Research introduced one of these platforms, called RoboCasa.

This is the first lesson that will eventually lead to Grover’s Algorithm and Rotation (Phase) Estimation. We talk about the task of distinguishing between two qubit states, with \.

The concept of traveling beyond the speed of light has been given theoretical grounding through the Alcubierre warp drive. Proposed by physicist Miguel Alcubierre in the 1990s, this warp drive involves creating a space-time bubble around a spacecraft. By contracting space in front of the spacecraft and expanding it behind, the ship can ride a wave of space-time, seemingly achieving faster-than-light travel without breaking the cosmic speed limit. In essence, it’s the space around the ship that moves, not the ship itself, allowing for rapid traversal of vast distances.

The theoretical feasibility of the Alcubierre warp drive hinges on generating an immense amount of energy, currently beyond our technological capabilities. The ship’s warp core, similar to a nuclear reactor, would utilize matter and antimatter collisions to produce the necessary energy for warping space. While this concept was initially fictional, Alcubierre proposed a solution to Einstein’s Field Equation that aligned with the principles of the Star Trek Warp Drive.

Continue reading “How Can Warp Drive Travel Faster Than Light? Alcubierre Warp Drive Explained” »