问HN：目前非大型语言模型（non-LLM）人工智能工作的热门领域有哪些？
十多年前，“人工智能”可能主要指的是强化学习、进化算法/遗传算法等领域。如今，大多数关注点似乎集中在大语言模型（LLMs）、卷积神经网络（CNNs）以及其他依赖于人工标注或至少依赖于人类创建数据的方法上，并且在“学习”和“推理”之间存在静态的分隔。
我知道目前在强化学习和其他领域仍然有非大语言模型、非卷积神经网络、非以人为中心的人工智能发展主题。您认为今天哪些是最突出或最有前景的，或者最有可能实现的？
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10+ years ago, "AI" would likely refer to work in RL, evolutionary/genetic algorithms, etc.<p>Nowadays most of the spotlight seems centered on LLMs, CNNs, and other methods that are either human-labeled or at least reliant on human-created data, and have a static separation between "learning" and "inference".<p>I know that there are still non-LLM, non-CNN, non-anthropocentric topics of AI development currently, in RL and in other areas. Which would you say are the most prominent or promising today, or likeliest to come to fruition?