Language is a hallmark of human cognition. It allows for efficient communication between humans and is a building block for high-level cognition. The study of how linguistic meaning is represented in the brain and produced by it is of great interest to multiple disciplines that model human behavior. In recent studies, deep-learning models demonstrated how the next word prediction task can promote high-order linguistic abilities. In my talk I argue that deep learning language models are an effective and insightful way to capture cognition better than most existing models, therefore promising potential as a way of predicting real-life behavior. In my talk, I will explain how this deep learning model (as well as other concepts related to deep learning) relates to language, human behavior, and brain activity. It offers an alternative framework for understanding language in general and the relationship between language and the brain. I will illustrate this paradigm with two of my research projects. My study collected intracranial neural responses to two instances of natural language processing: (1) participants listening to stories and (2) participants engaging in real-life conversations. Using this method, large, high-quality neural datasets aligned with natural language processing and comprehension were collected. I then examined brain dynamics during free conversations with this novel dataset using advanced deep learning methods. This approach enables an in-depth understanding of real-life processes related to speech production and comprehension. The results will show how a deep-learning framework can shed light on human behavior in naturalistic environments, which can be applied to predict, mimic and modify human behavior.