## Why is AI hard and Physics simple?

We discuss why AI is hard and why physics is simple. We discuss how physical intuition and the approach of theoretical physics can be brought to bear on the field of artificial intelligence and specifically machine learning. We suggest that the underlying project of machine learning and the underlying project of physics are strongly coupled through the principle of sparsity, and we call upon theoretical physicists to work on AI as physicists. As a first step in that direction, we discuss an upcoming book on the principles of deep learning theory that attempts to realize this approach…. Read more at

## The quest for the proton charge radius

A slight anomaly in optical spectra of the hydrogen atom led Willis E. Lamb to the search for the proton size. As a result, he found the shift of the 2S1/2 level, the first experimental demonstration of quantum electrodynamics. In return, a modern test of QED yielded a new value of the charge radius of the proton. This sounds like Baron Muenchausens tale: to pull oneself out from the marsh by seizing his own hair. An independent method was necessary. Muonic hydrogen spectroscopy came to the aid. However, the high-precision result significantly differed from the previous, electronic, values: this is the proton radius puzzle. This puzzle produced a decade-long activity both in experimental work and in theory. Even if the puzzle seems to be solved, the precise determination of the proton charge radius requires further efforts in the future.

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## An Analysis of the Concept of Inertial Frame

The concept of inertial frame of reference is analysed. It has been shown that this fundamental concept of physics is not clear enough. A definition of inertial frame of reference is proposed which expresses its key inherent property. The definition is operational and powerful. Many other properties of inertial frames follow from the definition or it makes them plausible. In particular, the definition shows why physical laws obey space and time symmetries and the principle of relativity, it resolves the problem of clock synchronization and the role of light in it, as well as the problem of the geometry of inertial frames.

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## James D. Bjorken:“Why Do We Do Physics? Because Physics Is Fun!”

In this informal memoir, the author describes his passage through a golden age of elementary particle physics. It includes not only his career trajectory as a theoretical physicist but also his excursions into experimental physics and particle accelerator theory. While his successes are highlighted, some unsuccessful efforts are included in the narrative as well. Those “losers” were arguably as pleasurable as the less-frequent “winners.” Since retirement, the author has become interested in gravitation theory and cosmology—a new golden age. This activity is also briefly described ….. Read more at: https://www.annualreviews.org/doi/full/10.1146/annurev-nucl-101918-023359

## Reality as a Vector in Hilbert Space

**Sean M. Carroll**

I defend the extremist position that the fundamental ontology of the world consists of a vector in Hilbert space evolving according to the Schrödinger equation. The laws of physics are determined solely by the energy eigenspectrum of the Hamiltonian. The structure of our observed world, including space and fields living within it, should arise as a higher-level emergent description. I sketch how this might come about, although much work remains to be done.

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## On modelling bicycle power-meter measurements

**Tomasz Danek, Michael A. Slawinski, Theodore Stanoev**

We combine power-meter measurements with GPS measurements to study the model that accounts for the use of power by a cyclist. The model takes into account the change in elevation and speed along with adverse effects of air, rolling and drivetrain resistance. The focus is on estimating the resistance coefficients using numerical optimization techniques to maintain an agreement between modelled and measured power-meter values, which accounts for the associated uncertainties. The estimation of coefficients is performed for two typical scenarios of road cycling under windless conditions, along a course that is mainly flat as well as a course of near constant inclination. Also, we discuss relations between different combinations of two model parameters, where other quantities are constant, by the implicit function theorem. Using the obtained estimates of resistance coefficients for the two courses, we use the mathematical relations to make inferences on the model and physical conditions. Along with a discussion of results, we provide two appendices. In the first appendix, we illustrate the importance of instantaneous cadence measurements. In the second, we consider the model in constrained optimization using Lagrange multipliers.

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