Wired recently published an article with the sensational title “Move Over, Coders — Physicists Will Soon Rule Silicon Valley” primarily concerned with physicists or former physicists working in deep learning (neural networks), machine learning, and data science. The article implies that this exodus of physicists to the Silicon Valley and computer industry is a remarkable new phenomenon.
This exodus of physicists is not new. It has been true at least since the first big physics employment bubble crashed in the late 1960’s, early 1970’s. The post-Sputnik boom in physics degrees and grad students produced a huge surplus of physicists by the late 1960’s.
The article mentions Dennis Ritchie, creator of the widely used C programming language and co-creator of the Unix operating system, who had a joint degree in applied mathematics and physics from Harvard University. Dennis Ritchie started at Bell Labs in 1967, just as the post-Sputnik physics bubble was starting to crash. Back in the 60’s, 70’s, early 80’s a fair number of physicists decamped for Bell Labs, mostly to work on computer and telecommunications related activities.
A high profile example is Emanuel Derman, author of My Life as a Quant (2004) and later books, who worked at Bell Labs from 1980 to 1985 before moving on to Wall Street. He mentions quite a number of other physicists at Bell Labs at the same time.
Most physicists end up in some sort of software development. The high profile “quant” jobs are actually rather rare and hard to get. The Wall Street firms are typically going after very strong physicists, especially theoretical physicists like Derman.
The Large Hadron Collider
The Large Hadron Collider (LHC) at CERN, where the Higgs Boson was discovered, produced a huge surplus of experimental particle physics (high energy physics) Ph.D.’s with no jobs in the field. Experimental particle physics involves large amounts of software development for data acquisition, instrument monitoring and control, and data analysis, mostly in C and C++, although there is still some “legacy” FORTRAN software. The heyday of FORTRAN in physics was a long time ago.
Although there have been attempts to use neural networks and other machine learning methods in particle physics, the workhorse of data analysis in the field is Ronald Fisher’s maximum likelihood estimation and classification — primarily estimation of parameters such as the mass and width of the Higgs Boson. The discovery of the Higgs was a maximum likelihood analysis.
Although it is undoubtedly possible to map maximum likelihood onto neural networks, in practice they are different. Neural networks are an attempt to simulate the low level structure of the neurons in the brain and solve problems by brute force fitting of data to models with huge numbers of adjustable parameters. In contrast, maximum likelihood involves attempts to understand the phenomenon under study and model it as a small number of functions corresponding to higher level concepts such as the Higgs Boson. A neural net could exactly fit the Higgs Boson peak yet never produce or confirm a physical model of what causes the peak.
American Institute of Physics (AIP) Employment Studies
The American Institute of Physics (AIP) publishes a number of reports on physics employment, which if read carefully, show that very few Ph.D.’s in Physics stay in the field.
The referenced AIP report starts with the phrase:
Positions accepted by PhD degree recipients following receipt of their degrees fall into three categories: postdoctoral fellowships, potentially permanent positions and other temporary positions.
The figure on page two of the AIP report specifically lists a “private sector” block under “potentially permanent”. Table 1 on page 3 lists 70 percent of potentially permanent positions as “private sector.”
The use of the phrase “potentially permanent positions” in the AIP report for industry positions, especially in the computer industry, is highly misleading.
Academia, including some government labs and institutes, has tenured positions and other positions with guarantees of job security until retirement. “Potentially permanent positions” is an accurate description of “tenure track research jobs.”
The vast majority of industry employees are “at will” and can be laid off at any time for any reason or no reason. Senior executives often have employment contracts that take them out of the “at will” category but these rarely provide long term “permanent” status. Indeed, they can usually be fired by the Board of Directors.
The computer industry is notoriously unstable with jobs often quite short term in practice, with layoffs common. In addition, there is considerable anecdotal and statistical evidence that computer careers are often short-lived with many computer professionals leaving the field in their thirties — and later.
The primary audience for a report on outcomes for Ph.D.’s in Physics one year after graduation, getting their Ph.D. degree, is students (and parents of students) evaluating whether to pursue a Ph.D.. Obviously, they should investigate longer term outcomes, but young people often don’t, focusing the next step in their life/career. Potentially permanent position sounds like a general version of “tenure track research job.”
Students with little or no work experience often do not have an accurate impression of salaries, working conditions, career prospects and other aspects of the work world (or academia), specifically in the private sector computer industry where most Ph.D’s in Physics currently end up.
News coverage of computer companies like Google emphasizes many far out research-like projects such as the Google self-driving car, the AlphaGo deep learning project, and so forth. These sound like academic research, so why wouldn’t these companies have comparable positions to tenure track research jobs? Indeed, in very rare cases, they may have such positions.
However, the vast majority of industry jobs are at will full time jobs without a specified end date. Especially in the computer industry, they are quite insecure and often short-lived, very different from what potentially permanent position implies. They are not analogous to tenure track jobs.
Physicists have been migrating from Physics to the Silicon Valley and computer industry in large numbers for over forty years, since the post-Sputnik physics employment bubble burst in the late 1960’s.
Anyone considering pursuing a Ph.D. in Physics should consider that they are likely to end up as some sort of software engineer after several years of minimal pay as a graduate student. A Ph.D. in Physics in the United States typically takes at least five years. Six or seven years, even longer, is not uncommon in particle physics and some other sub-fields of Physics.
As the Wired article notes briefly, discoveries such as the Higgs Boson represent expected discoveries with little practical or revolutionary scientific impact. They are not comparable to unexpected discoveries like the neutron (1932) and nuclear fission in the 1930’s and 1940’s that drove the huge expansion of physics in the 1940’s and 1950’s, potentially providing jobs in physics for Ph.D.’s.
As I discussed briefly in my previous post Should You Get Started in Data Science? this torrent of physicists, many highly qualified in statistics and data analysis as well as computer programming, means that the competition for the relatively small number of data science positions is and likely will remain fierce.
The picture of computer pioneer Dennis Ritchie is from Wikipedia. It is a picture of him in 2011. It is licensed under the Creative Commons License 2.0 (license here)
© 2017 John F. McGowan
About the Author
John F. McGowan, Ph.D. solves problems using mathematics and mathematical software, including developing gesture recognition for touch devices, video compression and speech recognition technologies. He has extensive experience developing software in C, C++, MATLAB, Python, Visual Basic and many other programming languages. He has been a Visiting Scholar at HP Labs developing computer vision algorithms and software for mobile devices. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech). He can be reached at [email protected].