In academia, there are rare and talented individuals who can seamlessly bridge several scientific disciplines. Professor Leon Cooper was one of those people. Instantly recognizable for his rustic New York accent, perfectly coiffed hair, and fine Italian suits, Cooper was a Nobel laureate in physics and a master of interdisciplinary inquiry.
When I first entered graduate school in 1993, I witnessed his talent in the lecture hall and elsewhere. In the elevator of the physics department, he had an inquisitive air, fearlessly asking young researchers sharp questions. “Do you really believe in what you’re working on?” he asked, leading the conversation beyond the technical to the philosophical.
Our elevator rides became a canvas for passionate discussions as we bonded over a shared love of music. During these interactions, I discovered Cooper’s significant contributions to the electrical phenomenon called superconductivity.
What is this? Basically, at room temperature, the current encounters resistance, but a fascinating change occurs near absolute zero (-273.15°C). At this temperature, current flows through the superconductor, which has zero resistance. This allows you to levitate magnets, for example. This superconducting effect could play a vital role in future clean energy, medical and technological innovations.
But what? cause Superconductivity? Mr Cooper claimed that the concept now known as ‘Cooper pairs’ was behind it. These are pairs of electrons that can effectively combine, changing their properties and allowing them to pass through the wire unhindered. This insight earned Cooper the 1972 Nobel Prize in Physics, along with John Bardeen and John Robert Schriefer.
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But what sets Cooper apart is not just his mastery of quantum physics, but his ability to transcend disciplines.
A year after winning the Nobel Prize, he took the helm of Brown University’s Brain and Nervous Systems Laboratory, applying theoretical approaches from particle physics to mysterious areas of the brain. By then, this was his mid-90s, and the machine learning landscape was developing rapidly.
Although computers back then were not as powerful as they are today, the basic theory behind machine learning existed. And Cooper tended to integrate physics with neuroscience, making significant contributions to the creation of his neural network, an algorithm inspired by the structure and function of the human brain.
One of his key tools for developing such networks was the Ising model, a theory derived from the physics of atomic spin.
To understand this, imagine atoms in a slab of metal placed on the points of a grid. Now imagine that each of these atoms is a small magnet, either pointing up (representing a positive “spin”) or pointing down (representing a negative “spin”). The collective behavior of how all these atomic magnets interact determines whether a metal becomes a magnet or not.
Machine learning and neurons in the brain hold many similarities between this Ising model of atomic spin, the small magnetic field that atoms have. The complex dance of atomic spins finds a counterpart in how neurons communicate and form networks.
Extending this analogy, a wide variety of machine learning architectures draw parallels between the atoms in the Ising model and neurons in the brain. The complex dance of atomic spins finds a counterpart in how neurons communicate and form networks.
why? In the Ising model, neighboring atoms communicate with each other through the mutual energy between their spins. And when adjacent spins “match” each other, this atomic energy decreases. This is similar to how the brain works. Neurons can send or not send signals to each other based on the signals of neighboring neurons.
new scientific field
Inspired by Cooper’s spirit of mixing disciplines, I worked with cosmologists to spur bold new scientific endeavors. robert brandenberger. Since 1997, we have been using neural networks (and hence Ising models) to try to understand the very structure of the universe.
Thirty years later, Cooper’s ideas could be used to overcome new challenges in music. More specifically, using machine learning to compose music. Artificial intelligence is great at analyzing recorded tracks into their key components, but actually creating original music has proven to be a formidable frontier.
Behind all music there are certain rules, laws that determine how chord progressions are formed. And right now, AI models have a hard time understanding these rules.
In cooperation with robert lowe, a music machine learning composer at New York University, embarked on an innovative journey to overcome specific challenges in music composition. Our solution, Pentahelix, a geometric and physical model inspired by jazz improvisation, offers a ray of hope in addressing this problem.
Imagine the pentahelix as a lattice similar to the Ising model but with a honeycomb-like structure. The grid points do not represent spins, but instead correspond to potential musical tones. Within this geometric framework, a large number of tones can encode musical chords and melodic patterns. Basically, it provides an organized way of musical elements.
This geometric model serves as a tool for understanding how jazz improvisers navigate the vast space of musical tones and chords. By using Pentahelix, you can gain insight into the strategies and patterns they employ in their creative process. This not only increases your understanding of musical improvisation, but also opens up new possibilities for music composition and performance.
Jazz, with its subtle interplay of instruments and layered chord changes that occur over time, could be a source of training for this innovative approach to music composition in the realm of AI.
While we await results, we have a deep sense that Cooper will be proud of his ability to seamlessly navigate diverse areas of knowledge and continue to pursue innovative ideas and perspectives. There is.
This journey from superconductivity to jazz improvisation demonstrates the enduring power of interdisciplinary inquiry. It shows what is possible in the realm of science and technology and where the next limits will be broken.
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Source: www.sciencefocus.com
