Artificial intelligence takes center stage as the discussion traces how modern neural networks evolved from simple stacked linear models into transformative systems capable of complex reasoning. The conversation demystifies foundational terms—AI, machine learning, NLP, and neural networks—before exploring the leap created by transformers and generative pre-training, which enabled large language models to learn emergent behaviours from massive text corpora. With OpenAI, Google, and Meta driving rapid progress, capabilities such as integrated image–text analysis now redefine expectations for scientific work. Alongside opportunity, the speakers highlight ethical risks including fabricated data, manuscript misuse, and the challenge of detecting AI-generated text. They stress the need for vigilance, transparency, and collaborative editorial safeguards to protect research integrity as these tools reshape evidence creation, synthesis, and dissemination.