Lifeboat Foundation

We study the question of how to decompose Hilbert space into a preferred tensor-product factorization without any pre-existing structure other than a Hamiltonian operator, in particular the case of a bipartite decomposition into “system” and “environment.” Such a decomposition can be defined by looking for subsystems that exhibit quasi-classical behavior. The correct decomposition is one in which pointer states of the system are relatively robust against environmental monitoring (their entanglement with the environment does not continually and dramatically increase) and remain localized around approximately-classical trajectories. We present an in-principle algorithm for finding such a decomposition by minimizing a combination of entanglement growth and internal spreading of the system. Both of these properties are related to locality in different ways.

Put your coding skills to the test as you work your way through multiple rounds of algorithmic coding puzzles for the title of Code Jam Champ and 15,000 USD.

Scientists have created synthetic organisms that can self-replicate. Known as “Xenobots,” these tiny millimeter-wide biological machines now have the ability to reproduce — a striking leap forward in synthetic biology.

Published in the Proceedings of the National Academy of Sciences 0, a joint team from the University of Vermont, Tufts University, and Harvard University used Xenopus laevis frog embryonic cells to construct the Xenobots.

Their original work began in 2020 when the Xenobots were first “built.” The team designed an algorithm that assembled countless cells together to construct various biological machines, eventually settling on embryonic skin cells from frogs.

A new method of identifying gravitational wave signals using quantum computing could provide a valuable new tool for future astrophysicists.

A team from the University of Glasgow’s School of Physics & Astronomy have developed a quantum algorithm to drastically cut down the time it takes to match gravitational wave signals against a vast databank of templates.

This process, known as matched filtering, is part of the methodology that underpins some of the gravitational wave signal discoveries from detectors like the Laser Interferometer Gravitational Observatory (LIGO) in America and Virgo in Italy.

AI will completely take over game development by the early 2030s. To a point where there will be almost no human developers. Just people telling AI what they want to play and it builds it in real time.

Over the past few years we’ve seen massive improvements in AI technology, from GPT-3, AI picture generation to self-driving cars and drug discovery. But can machine learning progress change games?

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Local consciousness, or our phenomenal mind, is emergent, whereas non-local consciousness, or universal mind, is immanent. Material worlds come and go, but fundamental consciousness is ever-present, according to the Cybernetic Theory of Mind. From a new science of consciousness to simulation metaphysics, from evolutionary cybernetics to computational physics, from physics of time and information to quantum cosmology, this novel explanatory theory for a deeper understanding of reality is combined into one elegant theory of everything.

#CyberneticTheoryofMind #Consciousness #Evolution #Mind #Documentary

Continue reading “Towards The Cybernetic Theory of Mind” »

Why do industrial robots require teams of engineers and thousands of lines of code to perform even the most basic, repetitive tasks while giraffes, horses, and many other animals can walk within minutes of their birth?

My colleagues and I at the USC Brain-Body Dynamics Lab began to address this question by creating a robotic limb that learned to move, with no prior knowledge of its own structure or environment [1,2]. Within minutes, G2P, our reinforcement learning algorithm implemented in MATLAB^®, learned how to move the limb to propel a treadmill (Figure 1).

System sidesteps computing bottleneck in tuning artificial intelligence algorithms.

Closing Gaps in Geometrically Frustrated Symmetric Clusters: Local Equivalence between Discrete Curvature and Twist Transformations.

Reward maximisation is one strategy that works for reinforcement learning to achieve general artificial intelligence. However, deep reinforcement learning algorithms shouldn’t depend on reward maximisation alone.

Identifying dual-purpose therapeutic targets implicated in aging and disease will extend healthspan and delay age-related health issues.