Letters
Last week, I talked about how best practices for machine learning projects are not one-size-fits-all, and how they vary depending on whether a project uses structured or unstructured data, and whether the dataset is small or big.
Letters
Experience gained in building a model to solve one problem doesn’t always transfer to building models for other problems. How can you tell whether or not intuitions honed in one project are likely to generalize to another?
Letters
Last Wednesday, the U.S. Capitol building was overrun by insurrectionists at the moment when members of Congress were certifying the results of a national election.
Letters
In my letter last week, I alluded to the way AI tends to concentrate power and wealth. This tendency worries me, and I believe it deserves more attention. The U.S. government has been looking into these winner-take-most dynamics at a few leading technology companies...
Letters
Happy New Year! As we enter 2021, I want to share with you three wishes I have for AI in the upcoming year. I hope we can: Narrow the gap between proofs-of-concept and production.
Letters
Every year for the past decade, I flew to Singapore or Hong Kong to celebrate my mother’s birthday with her on December 22. This year, for the first time, we did it via Zoom.
Letters
When a researcher works for a company, what rights should they have to publish their work, and what rights should the company that sponsored the work have? This issue has come up many times in the AI community across many companies...
Letters
Like many people in the AI community, I am saddened by the sudden departure from Google of ethical AI researcher Timnit Gebru. Timnit is a tireless champion of diversity and fairness in AI.
Letters
The rise of AI creates opportunities for new startups that can move humanity forward. In the 1990s, the internet was embraced successfully by incumbent companies including Apple and Microsoft, but it also inspired hugely impactful...
Letters
Over the last two weeks, I described the importance of clean, consistent labels and how to use human-level performance (HLP) to trigger a review of whether labeling instructions need to be reviewed.
Letters
Last week, I wrote about the limitation of using human-level performance (HLP) as a metric to beat in machine learning applications for manufacturing and other fields. In this letter, I would like to show why beating HLP isn’t always the best way to improve performance.
Letters
Beating human-level performance (HLP) has been a goal of academic research in machine learning from speech recognition to X-ray diagnosis. When your model outperforms humans, you can argue that you’ve reached a significant milestone and publish a paper!