Collaborating Authors


The case against investing in machine learning: Seven reasons not to and what to do instead


The word on the street is if you don't invest in ML as a company or become an ML specialist, the industry will leave you behind. The hype has caught on at all levels, catching everyone from undergrads to VCs. Words like "revolutionary," "innovative," "disruptive," and "lucrative" are frequently used to describe ML. Allow me to share some perspective from my experiences that will hopefully temper this enthusiasm, at least a tiny bit. This essay materialized from having the same conversation several times over with interlocutors who hope ML can unlock a bright future for them. I'm here to convince you that investing in an ML department or ML specialists might not be in your best interest. That is not always true, of course, so read this with a critical eye. The names invoke a sense of extraordinary success, and for a good reason. Yet, these companies dominated their industries before Andrew Ng's launched his first ML lectures on Coursera. The difference between "good enough" and "state-of-the-art" machine learning is significant in academic publications but not in the real world. About once or twice a year, something pops into my newsfeed, informing me that someone improved the top 1 ImageNet accuracy from 86 to 87 or so. Our community enshrines state-of-the-art with almost religious significance, so this score's systematic improvement creates an impression that our field is racing towards unlocking the singularity. No-one outside of academia cares if you can distinguish between a guitar and a ukulele 1% better. Sit back and think for a minute.

A 20-Year Community Roadmap for Artificial Intelligence Research in the US Artificial Intelligence

Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.