972 research outputs found
CoCoMo: Computational Consciousness Modeling for Generative and Ethical AI
The CoCoMo model proposes a computational solution to the challenge of
incorporating ethical and emotional intelligence considerations into AI
systems, with the aim of creating AI agents that combine knowledge with
compassion. To achieve this goal, CoCoMo prioritizes fairness, beneficence,
non-maleficence, empathy, adaptability, transparency, and critical and
exploratory thinking abilities. The model employs consciousness modeling,
reinforcement learning, and prompt template formulation to support these
desired traits. By incorporating ethical and emotional intelligence
considerations, a generative AI model can potentially lead to improved
fairness, reduced toxicity, and increased reliability.Comment: 9 pages, 3 figures, 5 table
Knowledge-Guided Data-Centric AI in Healthcare: Progress, Shortcomings, and Future Directions
The success of deep learning is largely due to the availability of large
amounts of training data that cover a wide range of examples of a particular
concept or meaning. In the field of medicine, having a diverse set of training
data on a particular disease can lead to the development of a model that is
able to accurately predict the disease. However, despite the potential
benefits, there have not been significant advances in image-based diagnosis due
to a lack of high-quality annotated data. This article highlights the
importance of using a data-centric approach to improve the quality of data
representations, particularly in cases where the available data is limited. To
address this "small-data" issue, we discuss four methods for generating and
aggregating training data: data augmentation, transfer learning, federated
learning, and GANs (generative adversarial networks). We also propose the use
of knowledge-guided GANs to incorporate domain knowledge in the training data
generation process. With the recent progress in large pre-trained language
models, we believe it is possible to acquire high-quality knowledge that can be
used to improve the effectiveness of knowledge-guided generative methods.Comment: 21 pages, 13 figures, 4 table
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