Google's AI model can help improve neural networks in medical research

The key conflict that this research tried to solve was to make deep neural networks more robust and efficient in crucial medical applications. In various medical research tasks such as cancer, practitioners do not always have ample data sets that are clearly labelled in terms of what they constitute. This has typically made it difficult for medical AI researchers to create efficient training models for deep neural networks to identify medical data with high accuracy. Called Multi-Instance Contrastive Learning (MICLe), Azizi and his team have created what is called a'self supervised learning' model. The key postulate of self supervised machine learning models is that they are trained on unlabelled data, thereby enabling the application of AI in niche areas where collection of clearly defined data sets may be difficult – such as in cancer research itself.