"Grokking" is a term that's important to understand in this new world of AI. Two years ago, researchers Yuri Burda and Harri Edwards from OpenAI aimed to teach a large language model basic arithmetic but initially faced challenges as the modes struggled to generalize. However, extended training periods revealed unexpected success in training a model to perform arithmetic tasks. This accidental discovery prompted further investigation, leading to the identification of a phenomenon termed "grokking," where models suddenly grasp a task after prolonged exposure. This finding challenges traditional understanding of deep learning mechanisms and highlights the potential for unforeseen breakthroughs in AI research.

Our friends over at Silverchair took a historical journey through the evolution of AI. The history of AI traces back to ancient philosophies of mechanized human thought, progressing through periods marked by advancement in automatons, formalized mathematical reasoning, and ambitious predictions in the 1960s and 1970s. This journey, fraught with setbacks and critiques, led to a focus on narrow AI applications in the 1980s and 1990s, characterized by expert systems and neural networks. Today, the AI landscape is dominated by deep learning and big data, emphasizing the importance of experimentation and focused application to tackle specific challenges while retaining the collaborative spirit that has defined AI research throughout history. Enjoy Samantha Green's (Director of Product Marketing at Silverchair) reflection back as she puts historical context on the future.