Few-shot Learning

A new benchmark aims to raise the bar for large language models. Researchers at 132 institutions worldwide introduced the Beyond the Imitation Game benchmark (BIG-bench), which includes tasks that humans perform well but current state-of-the-art models don’t.

Pretrained language models like GPT-3 have shown notable proficiency in few-shot learning. Given a prompt that includes a few example questions and answers (the shots) plus an unanswered question (the task), such models can generate an accurate answer.

A new study examines a major strain of recent research: huge models pretrained on immense quantities of uncurated, unlabeled data and then fine-tuned on a smaller, curated corpus.

Getting high accuracy out of a classifier trained on a small number of examples is tricky. You might train the model on several large-scale datasets prior to few-shot training, but what if the few-shot dataset includes novel classes? A new method performs well even in that case.

A libel-detection system could help news outlets and social media companies stay out of legal hot water. CaliberAI, an Irish startup, scans text for statements that could be considered defamatory, Wired reported.

For children, associating a word with a picture that illustrates it helps them learn the word’s meaning. Research aims to do something similar for machine learning models. Researchers improved a BERT model’s performance on some language tasks by training it on a large dataset of image-word pairs.

A strategy manifesto from one of China’s biggest tech companies declares, amid familiar visions of ubiquitous AI, that deepfakes are more boon than bane.

Few-shot learning seeks to build models that adapt to novel tasks based on small numbers of training examples. This sort of learning typically involves complicated techniques, but researchers achieved state-of-the-art results using a simpler approach.

Robots are moving into a job that traditionally required the human touch.What’s new: A commercial warehouse that ships electrical supplies deployed AI-driven robotic arms from Covariant, a high-profile Silicon Valley robotics firm.