arXiv:2101.06573cs.AI
Understanding in Artificial Intelligence
Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.
Abstract
Current Artificial Intelligence (AI) methods, most based on deep learning, have facilitated progress in several fields, including computer vision and natural language understanding. The progress of these AI methods is measured using benchmarks designed to solve challenging tasks, such as visual question answering. A question remains of how much understanding is leveraged by these methods and how appropriate are the current benchmarks to measure understanding capabilities. To answer these questions, we have analysed existing benchmarks and their understanding capabilities, defined by a set of understanding capabilities, and current research streams. We show how progress has been made in benchmark development to measure understanding capabilities of AI methods and we review as well how current methods develop understanding capabilities.
I do! So long as we don’t appeal to consciousness as the basis for understanding but measure it with benchmarks, LLMs understand many things. Maetschke, S., Iraola, D. M., Barnard, P., Shafiei Bavani, E., Zhong, P., Xu, Y., & Yepes, A. J. (2021). Understanding in artificial…
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The author says: “Large Language Models don’t understand anything — and they can’t get better at something they can’t do.” Yet, LLM’s can understand (unless one claims that understanding requires consciousness): https://arxiv.org/abs/2101.06573 And, strangely enough, LLM’s can…
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