In this episode, I explore why we need a broader kind of science alongside traditional, reductionistic research. I respond to listener Ance’s comment that science must remain falsifiable and narrowly focused, agreeing mainstream science should keep its identity. However, I propose parallel research communities—like how mainstream and alternative medicine coexist—so fringe topics can be studied rigorously yet independently. Examples include the Monroe Institute’s work on consciousness and Avi Loeb’s Galileo Project on UAPs. I also highlight the importance of testing top-down effects, citing my own biophysics work on immune cells and hints from evolutionary studies like Lenski’s long-term E. coli experiments.

I delve into the concept of 'truth' — how my understanding has evolved over the years, particularly after my shift to idealism in 2020.

I reflect on my earlier, reductionist views as a physics student, where truth seemed like a set of universal rules waiting to be uncovered. I contrast this with the insights I gained from philosophy and spiritual traditions, which pointed to a more subjective and elusive understanding of truth.

Drawing on perspectives from thinkers like Immanuel Kant, Donald Hoffman, and Bernardo Kastrup, I explore the idea that what we perceive as the physical world might be merely "renderings" of a deeper reality, or noumenon, filtered through our subjective consciousness.

I also discuss the implications of this perspective on science, suggesting a balance between conventional approaches and more open-minded explorations at the fringes of knowledge.

Finally, I share how this shift has influenced my personal relationship with truth, allowing me to embrace a wider range of ideas—from mainstream science to unconventional and "woo" topics—with curiosity and excitement.

Here two short blog posts on similar topics:

Becoming naive again

https://www.tumblr.com/cm-shorts/736227453152985088/becoming-naive-again?source=share

Pragmatic theories of truth

https://www.tumblr.com/cm-shorts/734446932599209984/pragmatic-theories-of-truth-navigating-belief-and?source=share

I explore the concept of deep learned correlations, drawing parallels between random processes in nature, human behavior, and advanced systems like GPT. I explain how GPT generates coherent, meaningful outputs by learning complex, long-term correlations from vast amounts of data, combining determinism, randomness, and learning. This analogy is extended to human creativity, personal behavior, and even evolution, suggesting that life might not rely solely on random mutations but on an intelligent mechanism that has learned deep correlations of successful life forms. I propose that evolution, like GPT, could generate well-adapted organisms efficiently through these learned correlations, offering a fresh perspective on the process of life's diversification.