Andrew Barto (barto@cs.umass.edu) is Professor Emeritus of computer science at the University of Massachusetts Amherst. He is also a co-recipient of the 2024 ACM A.M Turing Award.

In the context of unprecedented U.S. Department of Defense (DoD) budgets, this paper examines the recent history of DoD funding for academic research in algorithmically based warfighting. We draw from a corpus of DoD grant solicitations from 2007 to 2023, focusing on those addressed to researchers in the field of artificial intelligence (AI). Considering the implications of DoD funding for academic research, the paper proceeds through three analytic sections. In the first, we offer a critical examination of the distinction between basic and applied research, showing how funding calls framed as basic research nonetheless enlist researchers in a war fighting agenda. In the second, we offer a diachronic analysis of the corpus, showing how a 'one small problem' caveat, in which affirmation of progress in military technologies is qualified by acknowledgement of outstanding problems, becomes justification for additional investments in research. We close with an analysis of DoD aspirations based on a subset of Defense Advanced Research Projects Agency (DARPA) grant solicitations for the use of AI in battlefield applications. Taken together, we argue that grant solicitations work as a vehicle for the mutual enlistment of DoD funding agencies and the academic AI research community in setting research agendas. The trope of basic research in this context offers shelter from significant moral questions that military applications of one's research would raise, by obscuring the connections that implicate researchers in U.S. militarism.

Third, key advances in knowledge have always been placed in the public domain. Knowledge is what economists call a public good. Thomas Jefferson put it well when he said that knowledge is like a candle: When one candle lights another, it doesn't diminish the first. That's why basic research has to be funded publicly, and knowledge should be disseminated as widely as possible. While that was true for the basic research underlying AI, an increasing fraction of critical AI research is being done within private organizations, without the full knowledge-sharing that was so important, for instance, in the development of the mRNA vaccines.

Most of the public discourse around artificial intelligence (AI) policy focuses on one of two perspectives: how the government can support AI innovation, and how the government can deter its harmful or negligent use. Yet there can also be a role for government in making it easier to use AI beneficially--in this niche, the National Science Foundation (NSF) has found a way to contribute. Through a grant-making program called Fairness in Artificial Intelligence (FAI), the NSF is providing $20 million in funding to researchers working on difficult ethical problems in AI. The program, a collaboration with Amazon, has now funded 21 projects in its first two years, with an open call for applications in its third and final year. This is an important endeavor, furthering a trend of federal support for the responsible advancement of technology, and the NSF should continue this important line of funding for ethical AI.

At the beginning of June, the Armenian Prime Minister, Tigran Avinyan, spoke about the artificial intelligence strategy that Armenia wishes to put in place. Several topics were discussed: fundamental research, applied research, infrastructure, public sector, private sector, training and financing. On the Yerevan side, we now wish to give priority to AI in order to join, in the long term, the countries already well advanced in the sector. In the state of play mentioned by the Prime Minister of Armenia, he wants to focus his strategy on basic research, which he believes would be the point requiring the least investment: a stable internet connection and supercomputers may be enough to conduct research or training on artificial neural networks. The government also wants to build on its strengths in mathematics and experimental and natural sciences.

Natural language processing (NLP) research combines the study of universal principles, through basic science, with applied science targeting specific use cases and settings. However, the process of exchange between basic NLP and applications is often assumed to emerge naturally, resulting in many innovations going unapplied and many important questions left unstudied. We describe a new paradigm of Translational NLP, which aims to structure and facilitate the processes by which basic and applied NLP research inform one another. Translational NLP thus presents a third research paradigm, focused on understanding the challenges posed by application needs and how these challenges can drive innovation in basic science and technology design. We show that many significant advances in NLP research have emerged from the intersection of basic principles with application needs, and present a conceptual framework outlining the stakeholders and key questions in translational research. Our framework provides a roadmap for developing Translational NLP as a dedicated research area, and identifies general translational principles to facilitate exchange between basic and applied research.

Nowadays the term computing is very broad. The definition covers everything that is necessary to handle information with computers, e.g. Whole disciplines like Computer Engineering, Information Technology or Cybersecurity also belong to this term. I want to start this article with a very brief history of computing and based on this extrapolate where the journey could go. Computing is as old as mankind.

Is China pulling ahead of the US in AI? Not quite, argues Dieter Ernst of CIGI in a recent report entitled "Competing in artificial intelligence chips: China's challenge amid technology war." In addition to the hard engineering, Ernst reveals a social story of a global AI community on the verge of fracture. These new restrictions will likely bring the best out of some Chinese firms, while putting others out to pasture. All the while, basic research is likely to suffer worldwide as ties that bound the Chinese and western academic communities fray.

A "masterpiece" is how then-Deputy Defense Secretary Patrick Shanahan infamously described the Fiscal Year 2020 budget request. It would, he said, align defense spending with the U.S. National Defense Strategy -- both funding the future capabilities necessary to maintain an advantage over near-peer powers Russia and China, and maintaining readiness for ongoing counter-terror campaigns. While research and development funding increased in 2020, it did not represent the funding shift toward future capabilities that observers expected. Despite its massive size, the budget was insufficient to address the department's long-term challenges. Key emerging technologies identified by the department -- such as hypersonic weapons, artificial intelligence, quantum technologies, and directed-energy weapons -- still lacked a "clear and sustained commitment to investment." It was clear that the Department of Defense did not make the difficult tradeoffs necessary to fund long-term modernization.

The White House released a budget proposal this week that at first glance, looks like a big win for the fields of artificial intelligence and machine learning. The budget for fiscal year 2021 (which begins in October) would ramp up spending for AI research at DARPA (the Pentagon's research arm) and the National Science Foundation by roughly $549 million. The budget request, which still needs to be approved by Congress, increases AI funding from $50 million to $249 million at DARPA, and from $500 million to $850 million at NSF. But while technologists applaud the increased investment in AI, the White House budget proposal is giving many in the science community pause. Overall, the budget proposes $142.2 billion in spending for research and development, a 9% cut from current levels.