Source: Google AI Blog

2 links > Lora Aroyo and Praveen Paritosh
Uncovering Unknown Unknowns in Machine Learning
11 feb. 2021 - The performance of machine learning (ML) models depends both on the learning algorithms, as well as the data used for training and evaluation. The role of the algorithms is well studied and the focus of a multitude of challenges, such as SQuAD, GLUE, ImageNet, and many others. In addition, there have been efforts to also improve the data, including a series of workshops addressing issues for ML evaluation. In contrast, research and challenges that focus on the data used for evaluation of ML models are not commonplace. Furthermore, many evaluation datasets contain items that are easy to evaluate, e.g., photos with a subject that is easy to identify, and thus they miss the natural ambiguity of real world context. The absence of ambiguous real-world examples in evaluation undermines the ability to reliably test machine learning performance, which makes ML models prone to develop “weak spots”, i.e., classes of examples that are difficult or impossible for a model to accurately evaluate, because that class of examples is missing from the evaluation set.
 · adversarial-ai · crowd-sourcing · generative-adversarial-networks · machine-learning · research > Daniel Freedman and Ehud Rivlin
Quantum Supremacy Using a Programmable Superconducting Processor
28 aug. 2020 - Colorectal cancer (CRC) is a global health problem and the second deadliest cancer in the United States, resulting in an estimated 900K deaths per year. While deadly, CRC can be prevented by removing small precancerous lesions in the colon, called polyps, before they become cancerous. In fact, it is estimated that a 1% increase in the adenoma detection rate (ADR, defined as the fraction of procedures in which a physician discovers at least one polyp) can lead to a 6% decrease in the rate of interval CRCs (a CRC that is diagnosed within 60 months of a negative colonoscopy).
 · quantum-computing