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English(EN) You know, machine learning as a whole is a good domain, and certainly very useful when there are domains where exhaustiveness is a problem and having 'smart' fu

AI批评者抱怨过度依赖Transformer模型处理不适合的任务

作者 PulseAugur 编辑部 · [1 个来源] ·

作者对当前将Transformer模型应用于不适合任务的趋势表示沮丧，认为这种方法被过度使用，并分散了对更小、领域特定模型的研发精力。他们强调，虽然机器学习很有价值，尤其是在复杂领域，但将重点放在大型Transformer模型上处理代码补全等任务，却忽视了它们在真正学习和推理方面的局限性。 AI

影响 批评当前的AI发展趋势，主张在广泛的Transformer应用之上，对专业化模型进行更多研究。

排序理由 该条目是一篇评论文章，表达了对Transformer模型在AI中应用的批判性观点。

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AI批评者抱怨过度依赖Transformer模型处理不适合的任务

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  1. Mastodon — mastodon.social TIER_1 English(EN) · [email protected] ·

    You know, machine learning as a whole is a good domain, and certainly very useful when there are domains where exhaustiveness is a problem and having 'smart' fu

    You know, machine learning as a whole is a good domain, and certainly very useful when there are domains where exhaustiveness is a problem and having 'smart' fuzzing capabilities would help (literal fuzzers, code coverage analyzers, code semantic analyzers, even dynamic snippet a…

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