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In this episode I am looking forward to exploring more about alternate interpretations of Quantum Mechanics. In previous episodes exploring consciousness, I’ve encountered several people who believe that Quantum Mechanics is at the root of consciousness. My current thinking is that it replaces one mystery with another one without really providing an explanation for consciousness. We are still stuck with the options of consciousness being a pre-existing property of the universe or some aspect of it, vs. it being an emergent feature of a processing network. Either way, quantum mechanics is an often misunderstood brilliant theory at the root of physics. It tells us that basic particles don’t exist at a specific position and momentum—they are, however, represented very accurately as a smooth wavefunction that can be used to calculate the distribution of a set of measurements on identical particles. The process of observation seems to cause the wavefunction to randomly collapse to a localized spot. Nobody knows for certain what causes this collapse. This is known as the measurement problem. The many worlds theorem says the wavefunction doesn’t collapse. It claims that the wavefunction describes all the possible universes that exist and the process of measurement just tells us which universe we are living in.

My guest is a leading proponent of transactional quantum mechanics.

Dr. Ruth E. Kastner earned her M.S. in Physics and Ph.D. in History and Philosophy of Science from the University of Maryland. Since that time, she has taught widely and conducted research in Foundations of Physics, particularly in interpretations of quantum theory. She was one of three winners of the 2021 Alumni Research Award at the University of Maryland, College Park (https://tinyurl.com/2t56yrp2). She is the author of 3 books: The Transactional Interpretation of Quantum Theory: The Reality of Possibility (Cambridge University Press, 2012; 2nd edition just published, 2022), Understanding Our Unseen Reality: Solving Quantum Riddles (Imperial College Press, 2015); and Adventures In Quantumland: Exploring Our Unseen Reality (World Scientific, 2019). She has presented talks and interviews throughout the world and in video recordings on the interpretational challenges of quantum theory, and has a blog at transactionalinterpretation.org. She is also a dedicated yoga practitioner and received her 200-Hour Yoga Alliance Instructor Certification in February, 2020.

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Creating and sustaining fusion reactions—essentially recreating star-like conditions on Earth—is extremely difficult, and Nathan Howard, Ph.D., a principal research scientist at the MIT Plasma Science and Fusion Center (PSFC), thinks it’s one of the most fascinating scientific challenges of our time.

“Both the science and the overall promise of fusion as a clean energy source are really interesting. That motivated me to come to grad school [at MIT] and work at the PSFC,” he says.

Howard is member of the Magnetic Fusion Experiments Integrated Modeling (MFE-IM) group at the PSFC. Along with MFE-IM group leader Pablo Rodriguez-Fernandez, Howard and the team use simulations and machine learning to predict how plasma will behave in a fusion device. MFE-IM and Howard’s research aims to forecast a given technology or configuration’s performance before it’s piloted in an actual fusion environment, allowing for smarter design choices. To ensure their accuracy, these models are continuously validated using data from previous experiments, keeping their simulations grounded in reality.

A few decades later, the neuropsychologists Roger Sperry and Michael Gazzaniga studied more of these so-called split-brain patients and discovered that each half of the brain processed information independently. Each could make its own decisions and control its own behaviours. In a sense, the surgery had created two separate selves. In some of these patients, one side of their body (controlled by one hemisphere) would do one thing, while the other half (controlled by the other hemisphere) would do the opposite. For example, one hand would button their shirt while the other hand would unbutton it.

So why didn’t these split-brain patients, post-surgery, feel like they had two selves? The answer is that their brains fooled them into thinking that only one self existed and that it was in charge. When one of their hands did something unexpected, they made up a story to explain why. I changed my mind. I didn’t like the way that shirt looked.

These stories or confabulations show the power of the illusion of selfhood – a feeling that evolutionary psychologists believe evolved because it is adaptively useful. What better way to ensure that the physical package carrying and protecting the information in our DNA – namely, our bodies – survives long enough to pass on that code to the next generation? The illusion of the self makes us feel unique and provides us with a goal-oriented purpose to our lives.

For example, Real Entrepreneur Women, a career coaching service, has developed an AI coach called Skye. “Skye is designed to help female coaches cut through overwhelm by providing actionable strategies and personalized support to grow their businesses,” said founder Sophie Musumeci. “She’s like having a dedicated business strategist in your pocket – streamlining decision-making, creating tailored content, and helping clients stay consistent. It’s AI with heart, designed to scale human connection in industries where trust and relationships are everything.”

In the next few years, Musumeci predicted, “I see democratized AI creating new business models where the gap between big and small players closes entirely, giving more entrepreneurs the confidence and capability to thrive.”

Education is another area ripe for AI disruption, and it’s possibilities for hyper-personalization in learning. “The current education system in USA is designed to educate the masses,” said Andy Thurai, principle analyst with Constellation Research. “It assumes everyone is at the same skill level and same interest in areas of topic and same expertise. It tries to push the information down our throats, and forces us to learn in a certain way.”

Artificial Intelligence (AI) is revolutionizing industries globally, and medical education is no exception. For a nation like India, where the healthcare system faces immense pressure, AI integration in medical learning is more than a convenience, it’s a necessity. AI-powered tools offer medical students transformative benefits: personalized learning pathways that adapt to individual knowledge gaps, advanced clinical simulation platforms for risk-free practice, intelligent tutoring systems that provide immediate feedback, and sophisticated diagnostic training algorithms that enhance clinical reasoning skills. From offering personalized guidance to transforming clinical training, chatbots and digital assistants are redefining how future healthcare professionals prepare for their complex and demanding roles, enabling more efficient, interactive, and comprehensive medical education.

Personalized learning One of AI’s greatest contributions to medical education is its ability to create and extend personalized learning experiences. Conventional methods, on the other hand, often utilize a one-size-fits-all approach, leaving students to fend for themselves when they struggle. AI has the power to change this by analyzing a student’s performance and crafting study plans tailored to their strengths and weaknesses. This means students can focus on areas where they need the most help, saving time and effort.

Using a series of more than 1,000 X-ray snapshots of the shapeshifting of enzymes in action, researchers at Stanford University have illuminated one of the great mysteries of life—how enzymes are able to speed up life-sustaining biochemical reactions so dramatically. Their findings could impact fields ranging from basic science to drug discovery, and provoke a rethinking of how science is taught in the classroom.

“When I say enzymes speed up reactions, I mean as in a trillion-trillion times faster for some reactions,” noted senior author of the study, Dan Herschlag, professor of biochemistry in the School of Medicine. “Enzymes are really remarkable little machines, but our understanding of exactly how they work has been lacking.”

There are lots of ideas and theories that make sense, Herschlag said, but biochemists have not been able to translate those ideas into a specific understanding of the chemical and physical interactions responsible for enzymes’ enormous reaction rates. As a result, biochemists don’t have a basic understanding and, therefore, have been unable to predict rates or design new enzymes as well as nature does, an ability that would be impactful across industry and medicine.

A study from Nagoya University.

Nagoya University, sometimes abbreviated as NU, is a Japanese national research university located in Chikusa-ku, Nagoya. It was the seventh Imperial University in Japan, one of the first five Designated National University and selected as a Top Type university of Top Global University Project by the Japanese government. It is one of the highest ranked higher education institutions in Japan.

An AI system developed by Google DeepMind, Google’s leading AI research lab, appears to have surpassed the average gold medalist in solving geometry problems in an international mathematics competition.

The system, called AlphaGeometry2, is an improved version of a system, AlphaGeometry, that DeepMind released last January. In a newly published study, the DeepMind researchers behind AlphaGeometry2 claim their AI can solve 84% of all geometry problems over the last 25 years in the International Mathematical Olympiad (IMO), a math contest for high school students.

Why does DeepMind care about a high-school-level math competition? Well, the lab thinks the key to more capable AI might lie in discovering new ways to solve challenging geometry problems — specifically Euclidean geometry problems.

Uniquely human features of neocortical development and maturation are not only intriguing for their implications in human-specific cognitive abilities, but they are also vulnerable to dysregulation which could cause or contribute to distinctly human brain disorder pathophysiology. The human cerebral cortex is essential for both cognition and emotional processing and dysregulation of these processes of the cortex are associated with a wide range of brain disorders including schizophrenia (SZ), autism spectrum disorder (ASD), Parkinson’s disease (PD), and Alzheimer’s disease (AD) (Berman and Weinberger, 1991; Rubenstein, 2011; Xu et al., 2019). Much remains to be learned about the mechanisms governing cortical expansion and responses to pathogenesis between human and non-human primates (NHPs) (Otani et al., 2016). Understanding these differences could shed light on the underlying mechanisms responsible for human-specific brain disorders and lead to the identification of key targets for the development of effective therapies.

Subtle differences observed by comparing human neurodevelopment to that of our closest evolutionary relatives could reveal underlying mechanisms, including genomic or transcriptional differences, contributing to varied phenotypes (Pollen et al., 2019). Human-specific responses to pathogenesis might be elucidated in a similar manner; by comparing brain pathophysiology of humans to our non-human primate counterparts (Hof et al., 2004). Although rodent models have taught us much about basic mammalian brain development and disorders (Fernando and Robbins, 2011), comparing governing processes and responses to species more closely related to humans can reduce the number of variables allowing for the identification of specific mechanisms responsible for observed deviations. Studies analyzing induced pluripotent stem cells (iPSCs) derived from humans, chimpanzees, and bonobos (Pan paniscus) show large sets of differentially expressed genes between human and NHP iPSCs. Perhaps the most compelling differentially expressed genes are those related to increased long interspersed element-1 (LINE-1) mobility in chimpanzees and bonobos, which could have implications on the rates of genetic divergence among species, and alternative mechanisms of pluripotency maintenance in chimpanzees (Marchetto et al., 2013; Gallego Romero et al., 2015). Furthermore, when human and NHP iPSCs were differentiated to neurons, they displayed distinctive migratory patterns at the neural progenitor cell (NPC) stage followed by contrasting morphology and timing of maturation in neurons (Marchetto et al., 2019). Despite the ability of two-dimensional (2D) PSC-derived neural cultures to demonstrate basic organization and transcriptomic changes of early brain development (Yan et al., 2013), while retaining the genetic background of the somatic cells from which they are reprogrammed, they lack the ability to develop complex cytoarchitecture, recapitulate advanced spatiotemporal transcriptomics, and brain region interconnectivity (including migration and axon guidance) of ensuing primate brain development (Soldner and Jaenisch, 2019). Intricate cellular heterogeneity, complex architecture, and interconnectivity of neurodevelopment, in addition to pathogenic responses, could be observed by comparing human and NHP brain tissues; however, ethical concerns and the inaccessibility of pre-and postnatal primate brain tissues limits the feasibility of such studies.

While brain organoids might be a long way from forming or sharing thoughts with us, they could still teach us much about ourselves. Brain organoids are three-dimensional (3D), PSC-derived structures that display complex radial organization of expanding neuroepithelium following embedding in an extracellular matrix like Matrigel and can recapitulate some subsequent processes of neurodevelopment including neurogenesis, gliogenesis, synaptogenesis, heterogenous cytoarchitecture, cell and axon migration, myelination of axons, and spontaneously-active neuronal networks (Lancaster et al., 2013; Bagley et al., 2017; Birey et al., 2017; Quadrato et al., 2017; Xiang et al., 2017; Marton et al., 2019; Shaker et al., 2021). It is likely that all these features of neurodevelopment are governed by some degree of specifies-specific dynamics. Brain organoids can be generated from human and NHP PSCs and, since some pathways regulating neural induction and brain region specification are well conserved in primates, both unguided cerebral organoids and guided brain region specific organoids can be generated (Mora-Bermúdez et al., 2016; Field et al., 2019; Kanton et al., 2019). Additional protocols have been established for the derivation of brain region specific organoids from human PSCs (hPSCs), including dorsal forebrain, ventral forebrain, midbrain, thalamus, basal ganglia, cerebellum, and telencephalic organoids (Muguruma et al., 2015; Sakaguchi et al., 2015; Jo et al., 2016; Bagley et al., 2017; Birey et al., 2017; Watanabe et al., 2017; Xiang et al., 2017, 2019; Qian et al., 2018). With some modifications, these methods could prove to be successful in establishing brain region-specific organoids from a variety of NHP PSC lines allowing for the reproducible comparison of homogeneous, human-specific neurodevelopment and brain disorder pathophysiology in brain regions beyond the cortex.