In exploratory programming, programmers often face a semantic gap between their high-level understanding and the low-level interfaces available for interacting with objects in a system. That is, technical object struc...
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ISBN:
(纸本)9798400712159
In exploratory programming, programmers often face a semantic gap between their high-level understanding and the low-level interfaces available for interacting with objects in a system. That is, technical object structure and behavior need to be interpreted as abstract domain concepts, which then increases cognitive load and thus impedes exploration progress. We propose semantic object interfaces that bridge this gap by enabling contextual, natural-language conversations with objects. Our approach leverages an exploratory programming agent powered by a large language model (LLM) to translate natural-language questions into low-level experiments and provide high-level answers. We describe a framework for integrating semantic object interfaces into existing exploratory programming systems, including a prototype implementation in Squeak/Smalltalk using GPT-4o. We showcase the potential of semantic object interfaces through case studies and discuss their feasibility, limitations, and impact on the programming experience. While challenges remain, our approach promises to reduce mental effort and empower programmers to explore and understand systems at a higher level of abstraction for a better programming experience.
Hidden Markov models and neural networks are enabling new approaches to understanding and translating spoken language. Google, Microsoft, AT&T, Nuance Communications, and Apple are among the companies driving rese...
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Hidden Markov models and neural networks are enabling new approaches to understanding and translating spoken language. Google, Microsoft, AT&T, Nuance Communications, and Apple are among the companies driving research in this area. Israeli researchers have developed a computational machine-learning model based on how bacteria communicate and act collectively. Their algorithm could enable development of robots that could form smart teams and also improve work in areas such as swarm computing. Paleontologists have turned to artificial intelligence to analyze satellite imagery and create a model of where best to find sites with fossils. The researchers trained their AI-based application to recognize the spectral signatures of different types of land cover and combined that data with elevation and slope information to identify likely fossil-bearing sites.
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