Trends in AI -- August 2022

While blockbuster research has slowed down slightly in the past month, probably because of the summer season, conferences are back at full speed in person: NAACL in Seattle, SIGIR in Madrid, and also ICML, for which we created a special guide with the help of GPT-3. Other news we'd like to highlight, to begin with are: Every month we analyze the most recent research literature and select a varied set of 10 papers you should know of. Why Scaling laws¹ is a pervasive empirical phenomenon in modern Neural Networks, where the error is observed to off as a power of the training set size, model size, or both. While some have embraced this fact to devise a research agenda focused on scaling up, many think there must be ways to build better models without the need for outrageous scale. This paper explores a technique -- data pruning -- that can improve the learning efficiency of NNs "beating" scaling laws.