Many blogs are written by industry leaders, but some academics are also jumping on the bandwagon. Helen Qu, an AI researcher from the Flatiron Institute, came to hate the unrewarding grind of conference submissions so much that she has decided to publish her research only on her new blog (she’s currently working on using game theory to prevent AI from acting subversively).
One long-running blog, called the AI Alignment Forum, is written by a wide community of researchers, including many academics, who are concerned with how to make AI safe and beneficial to humanity. Instead of peer review, the AI Alignment Forum uses a mechanism that allows members to upvote or downvote content and post comments to explain their views.
Of course, there’s no guarantee that readers have put much, if any, thought into an up- or downvote. “That’s one of the big weaknesses of it,” acknowledged Oliver Habryka, the CEO of Lightcone Infrastructure, which runs the AI Alignment Forum.
On the other hand, posts tend to get many more reactions than the handful of peer reviews they would receive at a journal. And readers can alter their votes after reacting to comments. Another way the AI Alignment Forum tries to highlight quality work is by revisiting the year’s top content and asking members to write commentaries on how it’s aged and whether the claims have held up.
The system offers several advantages over traditional publishing. First, it’s much, much faster, which is especially important in a quickly moving field. Often, if a researcher submits to a traditional journal, “by the time your thing passes peer review, there’s a very substantial chance it’s already out of date,” Habryka said.
But perhaps an even stronger advantage of the AI Alignment Forum, in Habryka’s view, is that the voting system draws readers’ attention to both good research and to the counter-arguments against it. Without such a mechanism, many researchers would rely on social media to highlight what’s up-and-coming. But that puts a field’s focus at the whim of companies that use proprietary, potentially biased, and often opaque mechanisms to decide which content to feature prominently. “I really don’t want the attention of my field to be downstream of the Twitter algorithm,” Habryka said.
