Credible sources of accurate information about AI19 Sep 2017 Rachel Thomas [
There is a lot of misleading and even false information about AI out there, ranging from apallingly bad journalism to overhyped marketing materials to quotes from misinformed celebrities. Last month, it even got so bad that Snopes had to debunk a story about Facebook research that was inaccurately covered by a number of outlets.
AI is a complex topic moving at an overwhelming pace (even as someone working in the field, I find it impossible to keep up with everything that is happening). Beyond that, there are those who stand to profit off overhyping advances or drumming up fear.
I want to recommend several credible sources of accurate information. Most of the writing on this list is intended to be accessible to anyone—even if you aren’t a programmer or don’t work in tech:
Tom Simonite’s writing for Wired.
Jack Clark’s email newsletter, Import AI, provides highlights and summaries of a selection of AI news and research from the previous week. You can check out previous issues (or sign up) here. Jack Clark is Strategy & Communications Director at OpenAI.
Mariya Yao’s writing on Topbots and on Forbes. Mariya is CTO and head of R&D for Topbots, a strategy and research firm for applied artificial intelligence and machine learning. Fun fact: Mariya worked on the LIDAR system for the 2nd place winner in the DARPA grand challenge for autonomous vehicles.
Dave Gershgorn’s writing at Quartz.
Interactions between AI and society
Zeynep Tufekci, a professor at UNC-Chapel Hill, is an expert on the interactions between technology and society. She shares a lot of important ideas on twitter, or read her New York Times op-eds here.
Kate Crawford is a professor at NYU, principal researcher at Microsoft, and co-founder of the AI Now Research institute, dedicated to studying the social impacts of AI. You can follow her on twitter here.
I also want to highlight a few great examples of AI researchers thoughtfully deconstructing the hype around some high-profile stories in the past few months, in an accessible way:
Denny Britz provides a balanced perspective on OpenAIs bot for Defense of the Ancients (a popular computer game).
Stephen Merity gives a thoughtful deconstruction of how the DeepCoder story degraded in accuracy.
Jeremy Howard addresses the highly controversial research on whether deep learning can detect sexual orientation.
A brief note about Twitter
Twitter is quite useful for keeping up on machine learning news and many people share surprisingly deep insights (that I often can’t find elsewhere). I was skeptical of Twitter before I started using it. The whole idea seemed weird: you can only write a 140 characters at a time? I already had Facebook and Linkedin, did I really need another social media account? It now occupies a useful and distinct niche for me. The hardest part is getting started; feel free to take a look at my twitter or Jeremy’s favorites to look for interesting accounts. Whenever I read an article I like or hear a talk I like, I always look up the author/speaker on twitter and see if I find their tweets interesting. If so, I follow them.