Artificial intelligence (AI) models trained on published scientific findings have been used to invent valuable materials and targeted therapies, but they typically ignore the human scientists who continually alter the landscape of discovery. Here we show that incorporating the distribution of human expertise by training unsupervised models on simulated inferences cognitively accessible to experts dramatically improves (up to 400%) AI prediction of future discoveries beyond those focused on research content alone, especially when relevant literature is sparse. These models succeed by predicting human predictions and the scientists who will make them. By tuning human-aware AI to avoid the crowd, we can generate scientifically promising "alien" hypotheses unlikely to be imagined or pursued without intervention until the distant future, which hold promise to punctuate scientific advance beyond questions currently pursued. Accelerating human discovery or probing its blind spots, human-aware AI enables us to move toward and beyond the contemporary scientific frontier.

翻译：基于已发表科学发现训练的人工智能模型已被用于发明有价值材料及靶向疗法，但这些模型通常忽略了持续重塑科学发现格局的人类科学家本身。本研究表明，通过将人类专业知识分布纳入考量——在专家认知可达的模拟推理数据上训练无监督模型——能显著提升（最高达400%）AI对科学发现的预测能力，其效果远超仅关注研究内容的模型，尤其在相关文献稀疏时表现突出。这类模型通过预测人类科学家的研究偏好及具体科学家身份实现成功。通过引导具有人类感知能力的AI避开研究热点，我们能够生成科学潜力巨大的"异类"假说，这类假说在无干预情况下可能需长期才能被人类想象或追踪。这些假说有望突破当前研究的边界，加速科学进步。无论是加速人类发现进程还是探测其盲区，具有人类感知能力的人工智能将助力我们迈向并超越当代科学前沿。