(BURLINGTON, Vermont) – To persist, life must reproduce. Over billions of years, organisms have evolved many ways of replicating, from budding plants to sexual animals to invading viruses.
Now scientists at the University of Vermont, Tufts University, and the Wyss Institute for Biologically Inspired Engineering at Harvard University have discovered an entirely new form of biological reproduction—and applied their discovery to create the first-ever, self-replicating living robots.
We’ve combined lab-grown neurons with silicon chips and made it available to anyone, for first time ever.
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There could soon be a future where criminals serve their time inside an AI prison and have ‘artificial memories’ implanted into their brains.
The future is looking hella crazy. A dystopian virtual imprisonment system called Cognify places offenders in high-tech pods where they’re fitted with headsets and fed a stream of AI content. The idea is that prisoners would ‘live’ through artificial memories designed to reform their behaviour.
Violent offenders, for example, could be forced to experience their crime from the victim’s perspective.
Researchers from the Department of Physics have managed to experimentally develop a new magnetic state: a magneto-ionic vortex or “vortion.” The research, published in Nature Communications, allows for an unprecedented level of control of magnetic properties at the nanoscale and at room temperature, and opens new horizons for the development of advanced magnetic devices.
The use of Big Data has multiplied the energy demand in information technologies. Generally, to store information, systems utilize electric currents to write data, which dissipates power by heating the devices. Controlling magnetic memories with voltage, instead of electric currents, can minimize this energy expenditure.
One way to achieve this is by using magneto-ionic materials, which allow for the manipulation of their magnetic properties by adding or removing ions through changes in the polarity of the applied voltage. So far, most studies in this area have focused on continuous films, rather than on controlling properties at the nanometric scale in discrete “bits,” essential for high-density data storage.
Neural networks inspired by the brain are reshaping AI with memory retention, continuous learning, and greater efficiency.
In The Ouroboros Code, Antonin Tuynman, PhD, delivers a groundbreaking exploration of the ouroboric structure of existence, where science, philosophy, and spirituality converge into a singular, self-referential system of reality. This book is a cerebral deep-dive into the essence of consciousness, artificial intelligence, pancomputationalism, and the paradoxical nature of reality as both the creator and the created—a cosmic ouroboros biting its own tail. What makes The Ouroboros Code truly exceptional is its ability to bridge the gap between technological futurism and esoteric wisdom. It boldly addresses questions such as: Is intelligence an emergent phenomenon or an intrinsic property of the universe? Does AI have the potential to become self-aware, and what are the philosophical implications? How do information theory, consciousness, and quantum mechanics interconnect? Tuynman’s writing is dense yet poetic, rigorous yet deeply intuitive—a must-read for thinkers at the intersection of philosophy, science, and spirituality. This book serves as an intellectual feast for those who have pondered the Simulation Hypothesis, the Omega Point, or the nature of computational reality.
#OuroborosCode #DigitalPhysics #SimulationHypothesis #cybernetics #panpsychism #idealism #consciousness #ontology #SelfSimulation #OmegaPoint #eschatology
Ecstadelic Media Group releases The Ouroboros Code: Reality’s Digital Alchemy Self-Simulation Bridging Science and Spirituality by Antonin Tuynman, PhD as an Audible audiobook in addition to previously released Kindle eBook and paperback (Press Release, Burlingame, CA, USA, February 28, 2025 10.25 PM PST)
We introduce PokéChamp, a minimax agent powered by Large Language Models (LLMs) for Pokémon battles. Built on a general framework for two-player competitive games, PokéChamp leverages the generalist capabilities of LLMs to enhance minimax tree search. Specifically, LLMs replace three key modules: player action sampling, opponent modeling, and value function estimation, enabling the agent to effectively utilize gameplay history and human knowledge to reduce the search space and address partial observability. Notably, our framework requires no additional LLM training. We evaluate PokéChamp in the popular Gen 9 OU format. When powered by GPT-4o, it achieves a win rate of 76% against the best existing LLM-based bot and 84% against the strongest rule-based bot, demonstrating its superior performance. Even with an open-source 8-billion-parameter Llama 3.1 model, PokéChamp consistently outperforms the previous best LLM-based bot, Pokéllmon powered by GPT-4o, with a 64% win rate. PokéChamp attains a projected Elo of 1300–1500 on the Pokémon Showdown online ladder, placing it among the top 30%-10% of human players. In addition, this work compiles the largest real-player Pokémon battle dataset, featuring over 3 million games, including more than 500k high-Elo matches. Based on this dataset, we establish a series of battle benchmarks and puzzles to evaluate specific battling skills. We further provide key updates to the local game engine. We hope this work fosters further research that leverage Pokémon battle as benchmark to integrate LLM technologies with game-theoretic algorithms addressing general multiagent problems. Videos, code, and dataset available at this https URL.
