From CFACT

By David Wojick

I recently had the honor to write CFACT comments in response to an Energy Department (DOE) request for information (RFI). The topic is “Transformational Artificial Intelligence Models” where the transformation in question is that of improving scientific and engineering research. DOE is one of the world’s biggest funders of physical and computer science research, so this is potentially an important opportunity to make a big difference.

The focus of their RFI is on using AI models to do research. But we pointed out that they can also be used to understand what is going on in research across the huge number of publications that are often published on a major topic. Our point is on the “I” side of AI. It is a specific form of intelligence that AI models can emulate.

Here are the CFACT comments:

Transformational AI Models for Analyzing Science and Engineering Research

By David Wojick, Senior Analyst, CFACT

Here are CFACT comments in response to the U.S. Energy Department’s “Request for Information (RFI) on Partnerships for Transformational Artificial Intelligence Models.“

DOE proposes “to curate DOE scientific data across the National Laboratory complex for use in artificial intelligence (AI) models and to develop self-improving AI models for science and engineering using this data.“

Our basic comment is that DOE’s Transformational AI Program should include the use of AI models to analyze ongoing research. A major research area typically produces far more publications than a human can read. Those models focused on language and reasoning capabilities can be configured to effectively analyze these massive bodies of information, where the data is textual.

For example, plasma physics is a major topic of DOE research, both scientific and engineering. According to Google Scholar the number of journal articles specifically addressing plasma is on the order of 40,000 a year or more. Articles at least mentioning it number in the hundreds of thousands a year. These numbers are well beyond what a human can read and analyze.

A properly designed and trained AI model can read all of these articles and answer well formulated questions about the entire corpus. Here are some of the sorts of questions the answers to which could dramatically assist American researchers.

1. Who is doing what, including by country? For example China now rivals America in research output. In which topics are they most active?

2. How do the articles on a given topic all fit together? This includes mapping the cognitive structure of the research.

3. How does that cognitive structure changes over time? Examples include the dynamics of the findings, methods, applications and questions being addressed. All change over time, sometimes rapidly emerging. Early identification could be crucial.

4. What are the competing hypotheses and schools of thought regarding a specific problem or question? The frontier of science and engineering is a land of conjecture and debate. These debates often generate central questions for new research.

A model or models that can answer these crucial questions will give new meaning to the concept of keeping up with one’s field of research. This new power can both speed up the diffusion of knowledge and lead to new discoveries.

Regarding curation this AI research analysis will of course require access to a large number of journals, conference proceedings, research reports and other sources. DOE’s Office of Scientific and Technical Information (OSTI) has already made considerable progress in this regard. DOE should also consider partnering with major journal publishers. A small number of leading publishers account for a large fraction of the journals.

Initial efforts could focus on a specific research topic, or a specific community like DOE funded research, or both.

By way of background I was Senior Consultant for Innovation at OSTI for almost ten years under director Walt Warnick, where I did a lot of research on mapping the cognitive structure and dynamics of scientific and engineering research. This research has continued at CFACT and CFACT has also done research on AI cognition. I will be happy to provide additional information on these comments.

The singular ability of language and reasoning models to analyze huge bodies of technical information opens up new ways to understand and advance research.