Artificial intelligence: advances and challenges

Abstract

Introductory talk on artificial intelligence: definitions and its history from Greek automata and Alan Turing to today’s leaders. It covers key concepts (computer science, AI vs AGI, subfields, machine learning), the scope, potential uses and ethical limitations of AI, and use cases such as computer vision, generative AI and LLMs (the Transformer, open vs closed models, Hugging Face, LangChain). It closes with applications in agriculture (geospatial predictive analytics with SoilGrids, smart farming, virtual reality) and resources to keep learning. It includes a hands-on R example: the law of the flowering plants (cherry blossom timing and growing degree days with NASA POWER data).