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Harnessing the Power of Large Language Models For Economic and Social Good: Foundations

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In his 2016 book, The Fourth Industrial Revolution, Klaus Schwab, the founder of the World Economic Forum, predicted the advent of the next technological revolution, underpinned by artificial intelligence (AI). He argued that, like its predecessors, the AI revolution would wield global socio-economic repercussions. Schwab’s writing was prescient. In November 2022, OpenAI released ChatGPT, a large language model (LLM) embedded with a conversation agent. The reception was phenomenal, with more than 100 million people accessing it during the first two months.  Not only did ChatGPT garner widespread personal use, but hundreds of corporations promptly incorporated it and other LLMs to optimize their processes and to enable new products. In this blog post, adapted from our recent whitepaper, we examine the capabilities and limitations of LLMs. In a future post, we will present four case studies that explore potential applications of  LLMs. Despite the consensus that this technology revolution will have global consequences, experts differ on whether the impact will be positive or negative. On one hand, OpenAI’s stated mission is to create systems that benefit all of humanity. On the other hand, following the release of ChatGPT, more than a thousand researchers and technology leaders signed an open letter calling for a six-month hiatus on the development of such systems out of concern for societal welfare. As we navigate the fourth industrial revolution, we find ourselves at a juncture where AI, including LLMs, is reshaping sectors. But with new  technologies come new challenges and risks. In the case of LLMs, these include disuse - the untapped potential of opportune LLM applications; misuse - dependence on LLMs where their usage may be unwarranted; and abuse - exploitation of LLMs for malicious intent.  To harness the advantages of LLMs while mitigating potential harms, it is imperative to address these issues. This post begins by describing the fundamental principles underlying LLMs.  We then delve into a range of practical applications, encompassing data science, training and education, research, and strategic planning. Our objective is to demonstrate high leverage use-cases and identify strategies to curtail misuse, abuse, and disuse, thus paving the way for more informed and effective use of this transformative technology.


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This material is based upon work funded and supported by the Department of Defense under Contract No. FA8702-15-D-0002 with Carnegie Mellon University for the operation of the Software Engineering Institute, a federally funded research and development center. The view, opinions, and/or findings contained in this material are those of the author(s) and should not be construed as an official Government position, policy, or decision, unless designated by other documentation. References herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by Carnegie Mellon University or its Software Engineering Institute. This report was prepared for the SEI Administrative Agent AFLCMC/AZS 5 Eglin Street Hanscom AFB, MA 01731-2100. NO WARRANTY. THIS CARNEGIE MELLON UNIVERSITY AND SOFTWARE ENGINEERING INSTITUTE MATERIAL IS FURNISHED ON AN "AS-IS" BASIS. CARNEGIE MELLON UNIVERSITY MAKES NO WARRANTIES OF ANY KIND, EITHER EXPRESSED OR IMPLIED, AS TO ANY MATTER INCLUDING, BUT NOT LIMITED TO, WARRANTY OF FITNESS FOR PURPOSE OR MERCHANTABILITY, EXCLUSIVITY, OR RESULTS OBTAINED FROM USE OF THE MATERIAL. CARNEGIE MELLON UNIVERSITY DOES NOT MAKE ANY WARRANTY OF ANY KIND WITH RESPECT TO FREEDOM FROM PATENT, TRADEMARK, OR COPYRIGHT INFRINGEMENT. [DISTRIBUTION STATEMENT A] This material has been approved for public release and unlimited distribution. Please see Copyright notice for non-US Government use and distribution.

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