The Reinforcement Gap — or why some AI skills improve faster than others  

The article discusses the "reinforcement gap" in AI, where certain tasks are improving rapidly due to the use of reinforcement learning, while other tasks are lagging behind. Reinforcement learning, which involves an AI system learning through trial-and-error interactions with its environment, has proven to be highly effective for tasks such as playing games like chess and Go. However, many other AI tasks, such as natural language processing and computer vision, do not benefit as much from reinforcement learning and are instead relying on other approaches, such as supervised learning and unsupervised learning. This has led to a widening gap between the capabilities of AI systems in reinforcement-friendly tasks and those in other domains. The article suggests that this reinforcement gap could have significant implications for the future of AI, as the rapidly improving skills in certain areas could lead to a concentration of power and influence among the companies and researchers working on those tasks. The article highlights the need for a more balanced approach to AI development to ensure that progress is made across a wide range of applications.