Revolutionizing Machine Learning Models: A New Approach to Language Understanding
In a groundbreaking development in the field of artificial intelligence, a team of computer scientists has created a new type of machine learning model that is more nimble and flexible than traditional models. The key to this innovation? The model must periodically forget what it knows.
While this new approach may not replace the massive models that power the biggest apps, it has the potential to provide valuable insights into how these programs understand language. According to Jea Kwon, an AI engineer at the Institute for Basic Science in South Korea, the new research represents “a significant advance in the field.”
Most AI language engines today rely on artificial neural networks, where each “neuron” in the network processes information and passes it along to other neurons through multiple layers. Through training with large amounts of data, these networks adapt and improve their ability to understand language.
However, this training process requires significant computing power and can be challenging to adapt if the model needs to learn new languages or information. Mikel Artetxe, a coauthor of the new research, and his colleagues sought to address these limitations by erasing and retraining specific parts of the model.
By erasing the information related to the building blocks of words in one language and retraining the model on a second language, the researchers found that the model could effectively learn and process the new language. This suggests that the deeper layers of the network store more abstract information about the concepts behind human languages, allowing the model to adapt and learn new languages more efficiently.
Lead author Yihong Chen explained, “We live in the same world. We conceptualize the same things with different words in different languages. That’s why you have this same high-level reasoning in the model. An apple is something sweet and juicy, instead of just a word.”
This innovative approach to machine learning has the potential to revolutionize how AI models are trained and adapted, opening up new possibilities for language understanding and beyond.
