Have you ever wanted to learn more about transfer learning and the idea of universal language modeling? I'm David Shaw, and in this episode of AI News, we'll dive right in.
Transfer learning is focused on storing gained knowledge, and then leveraging that knowledge to solve a similar problem. Until now, transfer learning was somewhat limited in computer vision. However, recent research has shown it can have an impact on natural language processing, or NLP, and also reinforcement learning, or RL.
Within NLP, text classification is a key component. It's concerned with real life scenarios like bots, assistants, fraud, and even document classification. In the past, academic research relied on embedding to train various models. The issue with this, however, is that there are limitations when it comes to embedding. For example, words that rarely occur in vocabulary, dealing with shared representations, co-occurrence statistics, and support for new languages can create barriers. But these challenges can be overcome.
If you want to learn more about universal language modeling and get a step-by-step breakdown, then check out the article in the links provided.
Thanks for watching AI News. We'll see you next week.