Rooted in the explosion of deep learning over the past decade, this thesis spans from AlphaGo to ChatGPT to empirically examine the fundamental concepts needed to realize the vision of an artificial scientist: a machine with the capacity to autonomously generate original research and contribute to the expansion of human knowledge. The investigation begins with {\sc Olivaw}, an AlphaGo Zero-like agent that discovers Othello knowledge from scratch but is unable to communicate it. This realization leads to the development of the Explanatory Learning (EL) framework, a formalization of the problem faced by a scientist when trying to explain a new phenomenon to their peers. The effective EL prescriptions allow us to crack Zendo, a board game simulating the scientific endeavor. This success comes with a fundamental insight: an artificial scientist must develop its own interpretation of the language used to explain its findings. This perspective then leads us to see modern multimodal models as interpreters, and to devise a new way to build interpretable and cost-effective CLIP-like models: by coupling two unimodal models using little multimodal data and no further training. Finally, we discuss what ChatGPT and its siblings are still missing to become artificial scientists, and introduce Odeen, a benchmark about interpreting explanations that sees LLMs going no further than random chance while being instead fully solved by humans.

翻译：本论文植根于过去十年深度学习的爆发式发展，从AlphaGo到ChatGPT，实证检验了实现人工科学家愿景所需的基本概念：一种能够自主生成原创研究并促进人类知识扩展的机器。研究始于{\sc Olivaw}——一个类似AlphaGo Zero的智能体，它从零开始发现奥赛罗知识但无法进行交流。这一认识促成了解释性学习（EL）框架的开发，该框架形式化了科学家向同行解释新现象时所面临的问题。有效的EL方案使我们成功破解了模拟科学探索的棋盘游戏Zendo。这一成功带来了一个根本性洞见：人工科学家必须发展出自己对解释发现所用语言的诠释。这一视角进而引导我们将现代多模态模型视为解释器，并设计出一种构建可解释且高性价比的CLIP类模型的新方法：通过少量多模态数据且无需额外训练来耦合两个单模态模型。最后，我们探讨了ChatGPT及其同类模型成为人工科学家尚存的不足，并介绍了Odeen基准测试——该测试关注解释理解任务，结果显示大型语言模型的表现仅与随机猜测相当，而人类却能完全解决该任务。