Correctly labelling training data for AI models is vital to avoid serious problems, as is using sufficiently large datasets. However, manually labelling massive amounts of data is time-consuming and laborious. Using pre-labelled datasets can be problematic, as evidenced by MIT having to pull its 80 Million Tiny Images datasets. For those unaware, the popular dataset was found to contain thousands of racist and misogynistic labels that could have been used to train AI models. AI News caught up with Devang Sachdev, VP of Marketing at Snorkel AI, to find out how the company is easing the laborious process of labelling data in a safe and effective way. AI News: How is Snorkel helping to ease the laborious process of labelling data?

The paper [5] also gives updates on Snorkel's industry use cases with even more applications at scale, for example, Google in Snorkel Drybell to scientific work in MRI classification and automated Genome-wide association study (GWAS) curation, both accepted in Nature Comms.

You can read the web-friendly version of the book here. You can find the source code on GitHub. The Discord to discuss the answers to the questions in the book is here. As a candidate, I've interviewed at a dozen big companies and startups. I've got offers for machine learning roles at companies including Google, NVIDIA, Snap, Netflix, Primer AI, and Snorkel AI. I've also been rejected at many other companies.

Today I'm excited to announce Snorkel AI's launch out of stealth! Snorkel AI, which spun out of the Stanford AI Lab in 2019, was founded on two simple premises: first, that the labeled training data machine learning models learn from is increasingly what determines the success or failure of AI applications. And second, that we can do much better than labeling this data entirely by hand. At the Stanford AI lab, the Snorkel AI founding team spent over four years developing new programmatic approaches to labeling, augmenting, structuring, and managing this training data. We were fortunate to develop and deploy early versions of our technology with some of the world's leading organizations like Google, Intel, Apple, Stanford Medicine, resulting in over thirty-six peer-reviewed publications on our findings; innovations in weak supervision modeling, data augmentation, multi-task learning, and more; inclusion in university computer science curriculums; and deployments in popular products and systems that you've likely interacted with in the last few hours.