Researchers propose bias fix for GPT-3 and other language models

Few-shot learning, or the ability to learn tasks from a few examples, is a key aspect of human intelligence. Large AI natural language models like OpenAI’s GPT-3 can perform few-shot learning without fine-tuning. But despite the promise of few-shot learning, new research finds that the accuracy of language models — particularly GPT-3 — can be […]

Researchers propose LEAF, a frontend for developing AI classification algorithms

In machine learning, mel-filterbanks — fixed, hand-engineered representations of sound — are often used to train algorithms that classify sound. Decades after the design of mel-filterbanks, research shows that they exhibit desirable mathematical properties for representation learning; in other words, they represent strong audio feature. But the design of mel-filterbanks is also flawed by biases, […]

Researchers propose ‘safe’ reinforcement learning algorithm for dangerous scenarios

Researchers have proposed a method for allowing reinforcement learning algorithms to accumulate knowledge while erring on the side of caution. The team, which hails from the University of Toronto, the Vector Institute, and the University of California, Berkeley,  claims this approach can achieve competitive performance while incurring lower catastrophic failure rates during training compared to […]