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Category: information science – Page 72

As object identification and three-dimensional (3D) reconstruction techniques become essential in various reverse engineering, artificial intelligence, medical diagnosis, and industrial production fields, there is an increasing focus on seeking vastly efficient, faster speed, and more integrated methods that can simplify processing.

In the current field of object identification and 3D , extracting sample contour information is primarily accomplished by various computer algorithms. Traditional computer processors suffer from multiple constraints, such as high-power consumption, low-speed operation, and complex algorithms. In this regard, there has recently been growing attention in searching for alternative to perform those techniques.

The development of optical computing theory and has provided a more complete theoretical basis for object identification and 3D reconstruction techniques. Optical methods have received more attention as an alternative paradigm than traditional mechanisms in recent years due to their enormous advantages of ultra-fast operation speed, high integration, and low latency.

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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.

Other topics covered in this sci-fi documentary video include: the building of super projects made possible by advancing fusion energy, the possibilities of brain chips, new age space technology and spacecraft such as a hover bike developed for the Moon in 2050, Mars colonization, and technology predictions based on black holes, biotechnology, and when will humanity become a Kardashev Type 1, and then Type 2 Civilization.

To see more of Venture City and to access the ‘The Future Archive Files’…

• Timelapse of Future Technology (Master List)
• Encyclopedia of the Future (Entries)

…visit my Patreon here: / venturecity.

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

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.

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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.

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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

The researchers started in the simulation world, running billions of scenarios in Isaac Gym, a high-performance GPU-based physics simulation environment. The algorithm in the simulator rewarded actions that mimicked human-like walking while punishing the ones that didn’t. Once the simulation perfected the task, it was transferred to a real-world humanoid bot that did not require further fine-tuning.

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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.

“I believe we have found one of the brain’s prototypes for building sequences,” says Professor Edvard Moser. He describes the activity pattern as “a fundamental algorithm that is intrinsic to the brain and independent of experience.”

The breakthrough discovery was published in Nature.

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This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

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.

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