Lifeboat Foundation

This timelapse of future technology begins with 2 Starships, launched to resupply the International Space Station. But how far into the future do you want to go?

Tesla Bots will be sent to work on the Moon, and A.I. chat bots will guide people into dreams that they can control (lucid dreams). And what happens when humanity forms a deeper understanding of dark energy, worm holes, and black holes. What type of new technologies could this advanced knowledge develop? Could SpaceX launch 100 Artificial Intelligence Starships, spread across our Solar System and beyond into Interstellar space, working together to form a cosmic internet, creating the Encyclopedia of the Galaxy. Could Einstein’s equations lead to technologies in teleportation, and laboratory grown black holes.

Continue reading “Timelapse of Future Technology Vol. II (Sci-Fi Documentary)” »

The race to develop quantum computers has really heated up over the past few years. State-of-the-art systems can now run simple algorithms using dozens of qubits—or quantum bits—which are the building blocks of quantum computers.

Can machine learning predict chaos? This paper performs a large-scale comparison of modern forecasting methods on a giant dataset of 135 chaotic systems.

Chaos and unpredictability are traditionally synonymous, yet large-scale machine-learning methods recently have demonstrated a surprising ability to forecast chaotic systems well beyond typical predictability horizons. However, recent works disagree on whether specialized methods grounded in dynamical systems theory, such as reservoir computers or neural ordinary differential equations, outperform general-purpose large-scale learning methods such as transformers or recurrent neural networks. These prior studies perform comparisons on few individually chosen chaotic systems, thereby precluding robust quantification of how statistical modeling choices and dynamical invariants of different chaotic systems jointly determine empirical predictability.

The fusion of biological principles with technological innovation has resulted in significant advancements in artificial intelligence (AI) through the development of Brainoware. Developed by researchers at Indiana University, Bloomington, this innovative system leverages clusters of lab-raised brain cells to achieve elementary speech recognition and solve mathematical problems.

The crux of this technological leap lies in the cultivation of specialized stem cells that mature into neurons—the fundamental units of the brain. While a typical human brain comprises a staggering 86 billion neurons interconnected extensively, the team managed to engineer a minute organoid, merely a nanometer wide. This tiny but powerful structure was connected to a circuit board through an array of electrodes, allowing machine-learning algorithms to decode responses from the brain tissue.

Termed Brainoware, this amalgamation of biological neurons and computational circuits exhibited remarkable capabilities after a brief training period. It was discerned between eight subjects based on their diverse pronunciation of vowels with an accuracy rate of 78%. Impressively, Brainoware outperformed artificial networks in predicting the Henon map, a complex mathematical construct within chaotic dynamics.

Signal processing is key to communications and video image processing for astronomy, medical diagnosis, autonomous driving, big data and AI. Menxi Tan and colleagues report a photonic processor operating at 17Tb/s for ultrafast robotic vision and machine learning.

The robot is blind and cannot see its environment but can continue to balance and walk, even if an object is hurled at it.

UC researchers Ilija Radosavovic and Bike Zhang wondered if “reinforcement learning,” a concept made popular by large language models (LLMs) last year, could also teach the robot how to adapt to changing needs. To test their theory, the duo started with one of the most basic functions humans can perform — walking.

Transformer model for learning

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“I believe we have found one of the brain’s prototypes for building sequences” says Professor Edvard Moser.

Scientists at NTNU’s Kavli Institute for Systems Neuroscience in Norway have discovered a pattern of activity in the brain that serves as a template for building sequential experiences.

Continue reading “Scientists discover a framework in the brain for organizing the order of things” »

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This has been one of the craziest years in AI in a long time: endless product launches, boardroom coups, intense policy debates about AI doom, and a race to find the next big thing. But we’ve also seen concrete tools and policies aimed at getting the AI sector to behave more responsibly and hold powerful players accountable. That gives me a lot of hope for the future of AI.

Quantum computers could become more useful now researchers at Google have designed an algorithm that can translate complex physical problems into the language of quantum physics.

By Alex Wilkins