North Carolina Agricultural and Technical State University
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New Farmers of America Association
No full textA crowd of men eating and standing in the sand.https://digital.library.ncat.edu/photos/1050/thumbnail.jp
New Farmers of America Association
No full textA group of men and a woman standing in front of the Dr. H. O. Sargent Award exhibit. Standing fifth from the left is Dr. A. P. Bell, former distinguished professor and chairperson of agriculture at North Carolina Agriculture and Technical State University. On the far left is MeeCee Baker, a member of the National Vocational Agriculture Teachers Association.https://digital.library.ncat.edu/photos/1136/thumbnail.jp
Evidence Quality and Validity Framework and Metrics for Large Language Models
No full textGenerative AI along with its language models (referred to as Large Language Models - LLMs) are able to perform human-like tasks such as answering questions, perform text-to-text, text-to-speech, text-to-image, and text-to-code generation on topics and images they have been pre-trained using real-world data. Given that LLMs are trained using different kinds of data sources, inherent biases and imperfection present in these data sources might cause the LLMs to produce unintended results. LLMs are also prone to generating incorrect responses or hallucinate which may question the accuracy, validity, and reliability of LLM generated responses.
Ascertaining the validity and quality of LLMs and comparing the results generated from different LLMs are formidable tasks because there is no de-facto gold standard LLM to measure the existing LLMs against. Factors such as the huge corpus of information and data used to pre-train the LLMs, the engineered prompts, the vector embeddings, and the fine-tuning of the models vary across different LLMs. Given the popularity, the acceptance, the productivity-gains that come with using them, as well as reported biases and misuses of LLMs, it is imperative that there should be an objective way to ascertain and validate the quality and truthfulness of LLM outputs.
Meaningfulness and interpretability of LLM outputs are important for trust, transparency, and for informing human decision-making. There is on-going research to develop metrics for determining the accuracy and reliability of LLM outputs. We propose use of the evaluation metrics commonly used in statistics, medicine, and social science domains to assess the quality and reliability of outputs generated by LLMs. The metrics are repeatability score, reproducibility score, generalizability score, robustness score, and replicability score. Using these metrics and scores when designing LLM-based projects can help to ensure their trustworthiness and reliability
Curated by Artificial Intelligence: AI-Human Collaboration in the Art Museum
No full textIn 2023 the Nasher Museum of Art at Duke University embarked on an experiment to use artificial intelligence to curate an exhibition from the museum’s collection. This was, to our knowledge, the first time a museum had done so. Using tools provided by OpenAI and several other data science frameworks, we transformed publicly-accessible information on our collection into statistical representations of “relatedness.” This dataset included fields such as artist, title, date, medium, cultural group, keywords, and description for the nearly 14,000 objects in the Nasher’s collection. This custom version of ChatGPT was thus “educated” about the Nasher’s collection and able to access, select and make connections among specific works. Our team further developed a series of prompts and instructions for ChatGPT that asked it to select artworks for the exhibition. We followed a similar process in the creation of the accompanying wall and label texts and the gallery\u27s design. The resulting exhibition, Dreams of Tomorrow: Utopian and Dystopian Visions, curated by AI, featured 21 artworks from the Nasher’s collection and was on display in our Incubator gallery from September 9, 2023, to February 18, 2024.
This presentation explores the capabilities and limitations of AI as curator, the use of the exhibition as a pedagogical tool, and the reception of the project by our university and community visitors. We will also share our technical process for creating a custom ChatGPT interface, as well as recent advances in this technology that would have provided easier ways to enlist AI as a curatorial counterpart. While museum professionals are far from relinquishing control of exhibition making and interpretation, this exercise was a powerful way to explore the applications of AI in the creative realm as related to curatorial authorship and expertise, the subjectivity of the selection process, and the future impact of technology on museums. We ultimately discovered that although AI was not a substitute for the human curator, it could be a powerful curatorial collaborator
New Farmers of America Association
No full textWooden building with bell tower, surrounded by trees and a rose arbor. Labeled \u27after\u27.https://digital.library.ncat.edu/photos/1192/thumbnail.jp
New Farmers of America Association
No full textRaymond Hanse and wife.https://digital.library.ncat.edu/photos/1183/thumbnail.jp
New Farmers of America Association
No full textThree men and a woman making sausage links.https://digital.library.ncat.edu/photos/1154/thumbnail.jp
New Farmers of America Association
No full textGroup of men and women standing in a field of crops.https://digital.library.ncat.edu/photos/1152/thumbnail.jp
New Farmers of America Association
No full textGeorge Canady\u27s old house. Canady lived in this house 10 years.https://digital.library.ncat.edu/photos/1144/thumbnail.jp
New Farmers of America Association
No full textThree men standing, S. B. Simmons standing to the far right with NFA member and another person.https://digital.library.ncat.edu/photos/1210/thumbnail.jp