Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Category: information science

First-principles simulations reveal quantum entanglement in molecular polariton dynamics

This is what fun looks like for a particular set of theoretical chemists driven to solve extremely difficult problems: Deciding whether the electromagnetic fields in molecular polaritons should be treated classically or quantum mechanically.

Graduate student Millan Welman of the Hammes-Schiffer Group is first author on a new paper that presents a hierarchy of first principles simulations of the dynamics of molecular polaritons. The research is published in the Journal of Chemical Theory and Computation.

Originally 67 pages long, the paper is dense with von Neumann equations and power spectra. It explores dynamics on both electronic and vibrational energy scales. It makes use of time-dependent density functional theory (DFT) in both its conventional and nuclear-electronic orbital (NEO) forms. It spans semiclassical, mean-field-quantum, and full-quantum approaches to simulate dynamics.

Tesla’s Momentum Can’t Be Stopped

Tesla continues to advance and solidify its momentum in the electric vehicle market through significant technological innovations, expansions, and achievements in autonomous driving, AI-powered technologies, and overall growth.

## Questions to inspire discussion.

Robo Taxi Service Expansion.

🚕 Q: How has Tesla’s robo taxi service in California expanded its operations? A: Tesla’s robo taxi service now operates until 2 a.m. with only 4 hours of downtime, indicating operational readiness and confidence in the system’s performance.

🌎 Q: What hiring moves suggest Tesla’s plans for global robo taxi expansion? A: Tesla is hiring a senior software engineer in Fremont to develop backend systems for real-time pricing and fees for robo taxi rides worldwide.

🌙 Q: How is Tesla preparing for expanded robo taxi coverage across the US? A: Tesla is hiring autopilot data collection supervisors for night and afternoon shifts in Arizona, Florida, Texas, and Nevada, indicating planned expansion of services.

Continue reading “Tesla’s Momentum Can’t Be Stopped” | >

The Role of Bioelectrical Patterns in Regulative Morphogenesis: An Evolutionary Simulation and Validation in Planarian Regeneration

Endogenous bioelectrical patterns are an important regulator of anatomical pattern during embryogenesis, regeneration, and cancer. While there are three known classes of instructive bioelectric patterns: directly encoding, indirectly encoding, and binary trigger, it is not known how these design principles could be exploited by evolution and what their relative advantages might be. To better understand the evolutionary role of bioelectricity in anatomical homeostasis, we developed a neural cellular automaton (NCA). We used evolutionary algorithms to optimize these models to achieve reliable morphogenetic patterns driven by the different ways in which tissues can interpret their bioelectrical pattern for downstream anatomical outcomes. We found that: All three types of bioelectrical codes allow the reaching of target morphologies; Resetting of the bioelectrical pattern and the change in duration of the binary trigger alter morphogenesis; Direct pattern organisms show an emergent robustness to changes in initial anatomical configurations; Indirect pattern organisms show an emergent robustness to bioelectrical perturbation; Direct and indirect pattern organisms show a emergent generalizability competency to new (rotated) bioelectrical patterns; Direct pattern organisms show an emergent repatterning competency in post-developmental-phase. Because our simulation was fundamentally a homeostatic system seeking to achieve specific goals in anatomical state space (the space of possible morphologies), we sought to determine how the system would react when we abrogated the incentive loop driving anatomical homeostasis. To abrogate the stress/reward system that drives error minimization, we used anxiolytic neuromodulators. Simulating the effects of selective serotonin reuptake inhibitors diminished the ability of artificial embryos to reduce error between anatomical state and bioelectric prepattern, leading to higher variance of developmental outcomes, global morphological degradation, and induced in some organisms a bistability with respect to possible anatomical outcomes. These computational findings were validated by data collected from in vivo experiments in SSRI exposure in planarian flatworm regeneration.

Smells interpreted as taste!

When we eat or drink, we don’t just experience taste, but rather a ‘flavor’. This taste experience arises from a combination of taste and smell, where aromas from food reach the nose via the oral cavity, known as retronasal odor. Researchers have now shown that the brain integrates these signals earlier than previously thought – already in the insula, a brain region known as the taste cortex – before the signals reach the frontal cortex, which controls our emotions and behavior.

“We saw that the taste cortex reacts to taste-associated aromas as if they were real tastes,” explains the lead author. “The finding provides a possible explanation for why we sometimes experience taste from smell alone, for example in flavored waters. This underscores how strongly odors and tastes work together to make food pleasurable, potentially inducing craving and encouraging overeating of certain foods.”

The study involved 25 healthy adults who were first taught to recognize both a sweet taste and a savory taste through combinations of taste and smell. This was followed by two brain imaging sessions using functional magnetic resonance imaging (fMRI), in which the participants were given either a tasteless aroma or a taste without smell. The researchers trained an algorithm to recognize patterns in brain activity for sweet and savory tastes, and then tested whether the same patterns could be identified when the participants were only given aromas.

Tesla AI5 & AI6 Chips “Compressing Reality”?! What Did Elon See?!

Elon Musk has revealed Tesla’s new AI chips, AI5 and AI6, which will drive the company’s shift towards AI-powered services, enabling significant advancements in Full Self-Driving capabilities and potentially revolutionizing the self-driving car industry and beyond.

## Questions to inspire discussion.

Tesla’s AI Chip Advancements.

🚀 Q: What are the key features of Tesla’s AI5 and AI6 chips? A: Tesla’s AI5 and AI6 chips are inference-first, designed for high-throughput and efficient processing of AI models on devices like autos, Optimus, and Grok voice agents, being 40x faster than previous models.

💻 Q: How do Tesla’s AI5 and AI6 chips compare to previous models? A: Tesla’s AI5 chip is a 40x improvement over AI4, with 500 TOPS expanding to 5,000 TOPS, enabling excellent performance in full self-driving and Optimus humanoid robots.

🧠 Q: What is the significance of softmax in Tesla’s AI5 chip? A: AI5 is designed to run softmax natively in a few steps, unlike AI4 which relies on CPU and runs softmax in 40 steps in emulation mode.

Continue reading “Tesla AI5 & AI6 Chips “Compressing Reality”?! What Did Elon See?!” | >

Clocks created from random events can probe ‘quantumness’ of universe

A newly discovered set of mathematical equations describes how to turn any sequence of random events into a clock, scientists at King’s College London reveal. The paper is published in the journal Physical Review X.

The researchers suggest that these formulas could help to understand how cells in our bodies measure time and to detect the effects of quantum mechanics in the wider world.

Studying these timekeeping processes could have far-reaching implications, helping us to understand proteins with rhythmic movements which malfunction in motor neuron disease or chemical receptors that cells use to detect harmful toxins.

‘More than just an image’: New algorithm can extract hyperspectral info from conventional photos

Professionals in agriculture, defense and security, environmental monitoring, food quality analysis, industrial quality control, and medical diagnostics could benefit from a patent-pending innovation that opens new possibilities of conventional photography for optical spectroscopy and hyperspectral imaging.

Young Kim, Purdue University professor, University Faculty Scholar and Showalter Faculty Scholar, and postdoctoral research associate Semin Kwon of the Weldon School of Biomedical Engineering created an algorithm that recovers detailed spectral information from photographs taken by conventional cameras. The research combines computer vision, color science and optical spectroscopy.

“A photograph is more than just an image; it contains abundant hyperspectral information,” Kim said. “We are one of the pioneering research groups to integrate computational spectrometry and spectroscopic analyses for biomedical and other applications.”

What Is Superposition and Why Is It Important?

Imagine touching the surface of a pond at two different points at the same time. Waves would spread outward from each point, eventually overlapping to form a more complex pattern. This is a superposition of waves. Similarly, in quantum science, objects such as electrons and photons have wavelike properties that can combine and become what is called superposed.

While waves on the surface of a pond are formed by the movement of water, quantum waves are mathematical. They are expressed as equations that describe the probabilities of an object existing in a given state or having a particular property. The equations might provide information on the probability of an electron moving at a specific speed or residing in a certain location. When an electron is in superposition, its different states can be thought of as separate outcomes, each with a particular probability of being observed. An electron might be said to be in a superposition of two different velocities or in two places at once. Understanding superposition may help to advance quantum technology such as quantum computers.

One of the fundamental principles of quantum mechanics, superposition explains how a quantum state can be represented as the sum of two or more states.

/* */