Lifeboat Foundation

On this episode, neuroscientist and author Robert Sapolsky joins Nate to discuss the structure of the human brain and its implication on behavior and our ability to change. Dr. Sapolsky also unpacks how the innate quality of a biological organism shaped by evolution and the surrounding environment — meaning all animals, including humans — leads him to believe that there is no such thing as free will, at least how we think about it today. How do our past and present hormone levels, hunger, stress, and more affect the way we make decisions? What implications does this have in a future headed towards lower energy and resource availability? How can our species manage the mismatch of our evolutionary biology with our modern day challenges — and navigate through a ‘determined’ future?

About Robert Sapolsky:

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Physicists at Rice University and their collaborators have made a discovery that sheds new light on magnetism and electronic interactions in advanced materials, with the potential to transform technologies like quantum computing and high-temperature superconductors.

Led by Zheng Ren and Ming Yi, the research team’s study on iron-tin (FeSn) thin films reshapes scientific understanding of kagome magnets — materials named after an ancient basket-weaving pattern and structured in a unique, latticelike design that can create unusual magnetic and electronic behaviors due to the quantum destructive interference of the electronic wave function.

The findings, published in Nature Communications, reveal that FeSn’s magnetic properties arise from localized electrons, not the mobile electrons scientists previously thought. This discovery challenges existing theories about magnetism in kagome metals in which itinerant electrons were assumed to drive magnetic behavior. By providing a new perspective on magnetism, the research team’s work could guide the development of materials with tailored properties for advanced tech applications such as quantum computing and superconductors.

Researchers hope to detect gravitons using quantum sensing to link quantum mechanics with Einstein’s theory of relativity.

This complete shell structure results in enhanced stability compared to isotopes with different configurations.

“¹⁰⁰ Sn is also the heaviest nucleus comprising protons and neutrons in equal numbers — a feature that enhances the contribution of the short-range proton–neutron pairing interaction and strongly influences its decay via the weak interaction,” CERN researchers remarked in a previous study.

“Understanding the nuclear properties in the vicinity of ¹⁰⁰ Sn, which has been suggested to be the heaviest doubly magic nucleus with proton number Z (50) equal to neutron number N (50), has been a long-standing challenge for experimental and theoretical nuclear physics,” said the research team in the study.

About a year and a half ago, quantum control startup Quantum Machines and Nvidia announced a deep partnership that would bring together Nvidia’s DGX Quantum computing platform and Quantum Machine’s advanced quantum control hardware. We didn’t hear much about the results of this partnership for a while, but it’s now starting to bear fruit and getting the industry one step closer to the holy grail of an error-corrected quantum computer.

In a presentation earlier this year, the two companies showed that they are able to use an off-the-shelf reinforcement learning model running on Nvidia’s DGX platform to better control the qubits in a Rigetti quantum chip by keeping the system calibrated.

Yonatan Cohen, the co-founder and CTO of Quantum Machines, noted how his company has long sought to use general classical compute engines to control quantum processors. Those compute engines were small and limited, but that’s not a problem with Nvidia’s extremely powerful DGX platform. The holy grail, he said, is to run quantum error correction. We’re not there yet. Instead, this collaboration focused on calibration, and specifically calibrating the so-called “π pulses” that control the rotation of a qubit inside a quantum processor.

A decade after the discovery of the “amplituhedron,” physicists have excavated more of the timeless geometry underlying the standard picture of how particles move.

As the rivalry between quantum and classical computing intensifies, scientists are making unexpected discoveries about quantum systems.

Classical computers outperformed a quantum computer in simulations of a two-dimensional quantum magnet system, showing unexpected confinement phenomena. This discovery by Flatiron Institute researchers redefines the practical limits of quantum computing and enhances understanding of quantum-classical computational boundaries.

Classical computer triumphs over quantum advantage.

Once relegated to theory, a newly discovered quantum object could be used to create new devices that will outpace modern electronics.

A new kind of quantum object called orbital angular momentum monopole has been identified that could revolutionize the emerging field of orbitronics, which leverages the rotational quantum states of electrons for next-generation computing devices that are faster, more efficient, and with dramatically lower power consumption.

As a result, orbitronics is seen as a potential successor to traditional electronics, where data is stored, transferred, and manipulated by controlling electric currents within transistors. As transistor sizes approach the atomic scale in order to fit more components onto a single computer ship, there will eventually be a limit where a transistor cannot become any smaller.

The question of the conditions under which Artificial Intelligence (AI) can transcend the threshold of consciousness can only be answered with certainty if we manage to unravel the mechanism underlying conscious systems. The most promising strategy to approach this goal is to unveil the brain’s functional principle involved in the formation of conscious states and to transfer the findings to other physical systems. Empirical evidence suggests that the dynamical features of conscious brain processes can be ascribed to self-organized criticality and phase transitions, the deeper understanding of which requires methods of quantum electrodynamics (QED). QED-based model calculations reveal that both the architectural structure and the chemical composition of the brain are specifically designed to establish resonant coupling to the ubiquitous electromagnetic vacuum fluctuations, known as zero-point field (ZPF). A direct consequence of resonant brain-ZPF coupling is the selective amplification of field modes, which leads us to conclude that the distinctive feature of conscious processes consists in modulating the ZPF. These insights support the hypothesis that the ZPF is a foundational field with inherent phenomenal qualities, implying that the crucial condition for AI consciousness lies in a robot’s capacity to tap into the phenomenal spectrum immanent in the ZPF.

Full Title: The Path to Sentient Robots: AI Consciousness in the Light of New Insights into the Functioning of the Brain.