Algorithmic Progress

Algorithmic progress in artificial intelligence focuses on improving the efficiency and effectiveness of algorithms, particularly within large language models and computer vision. Current research emphasizes developing more efficient training methods, exploring novel architectures like transformers and graph neural networks augmented with stacks for recursive reasoning, and analyzing the relative contributions of increased compute versus algorithmic innovations to performance gains. This research is crucial for mitigating the escalating computational costs of AI development and expanding the practical applications of powerful AI systems across various domains.