“There is significant interest in the development and application of foundation models for scientific discovery,” said Foundation Models for Scientific Discovery and Innovation,” a recent report from the National Academies. “Foundation models possess the capacity to generate outputs or findings and discern patterns within extensive data sets with data volumes that are considered overwhelming for classical modes of inquiry. Efforts are under way to use these models to accelerate various aspects of scientific work flows (including streamlining literature reviews, planning experiments, data analysis, and code development) and generating novel findings and hypotheses that can then spur further research directions. However, significant challenges remain in the effective use of these models in scientific applications, including issues with flawed or limited training data and limited verification, validation, and uncertainty quantification capabilities.”
High performance computing has been a major part of my education and subsequent Pdcareer. In the late 1960s I was doing atomic and molecular calculations as a PhD physics student at the University of Chicago. Then in the early 1990s, I was the general manager of IBM’s new Scalable Powerparallel (SP) family of parallel supercomputers.
The advances of supercomputers over the past several decades have been remarkable. The machines I used as a graduate student in the 1960s probably had a peak performance of a few million floating point calculations per second (megaflops). Every year since 1993, the TOP500 project has been publishing a list of the 500 most powerful supercomputers in the world. In the latest such list, the fastest supercomputer surpassed 1.8 billion billion floating point calculation per second (exaflops).
AI is now taking high performance computing to a whole new level of capabilities. A September, 2023 issue of The Economist, “How AI Can Revolutionize Science,” included a number of articles on the impact of AI on scientific discovery. “Debate about artificial intelligence (AI) tends to focus on its potential dangers: algorithmic bias and discrimination, the mass destruction of jobs and even, some say, the extinction of humanity,” noted the issue’s lead article. “As some observers fret about these dystopian scenarios, however, others are focusing on the potential rewards. AI could, they claim, help humanity solve some of its biggest and thorniest problems. And, they say, AI will do this in a very specific way: by radically accelerating the pace of scientific discovery, especially in areas such as medicine, climate science and green technology.” (more…)