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On October 28, 2026, The Conversation published an article proposing that AI faces three major challenges in moving from the laboratory to the real world: the generalization gap, the human behavior gap, and the resource gap. Taking AI vision systems as an example, the article points out that most models are trained only on well-lit, high-quality images, and their human pose estimation performance drops noticeably in low-light or nighttime scenes; using an unsupervised domain adaptation method, a research team, without requiring manual joint annotations for low-light images, greatly improved the model on two low-light benchmarks in a 2026 study on low-light human pose estimation. The article also notes that the number of action combinations in human-object interaction is enormous and difficult to collect and annotate one by one, so the research instead uses large vision-language models to recognize new interactions unseen during training. Currently, this article has been reposted and reported by AIhub, and there are no further developments yet.
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Oct 28
AIhub
Why AI Is Difficult to Move from the Lab to the Real World: Three Major Challenges of Generalization, Human Behavior, and Resources
When AI vision systems move from the lab to homes, hospitals, and streets, they face three major challenges: generalization, human behavior, and resources. Most models are trained only on well-lit, high-quality images, and their human pose estimation performance drops noticeably in low-light or nighttime scenes. Using an unsupervised domain adaptation method, the authors' team, in a 2026 study on low-light human pose estimation, substantially improved model performance on two low-light benchmarks without requiring manual joint annotations for low-light images. The number of action combinations in human-object interaction is enormous and difficult to collect and annotate one by one, so the research instead uses large vision-language models to recognize new interactions unseen during training.