For Glovo, being a fast growing startup has meant that for years we have prioritized productionizing ML models over engaging frequently with the research community. In the last year, however, we felt like we reached the maturity stage needed to start engaging with the rest of the AI community. To help with this, we founded a new team within our Central Data Science organization: the CORE (Collaboration and Research) team. CORE's mission is to explore high-risk high-reward R&D projects in the AI space, foster scientific publications and increase conference attendance. A key part of this initiative is sponsoring and participating in relevant AI conferences.

Computer scientists in AI are beginning to refuse to review Google AI research until Google changes its stance on former AI ethics co-lead Timnit Gebru. Reviewers who select research for publication at academic conferences work on a voluntary basis to support the scientific community and are typically chosen based on their experience and expertise. According to an analysis of papers published last year and updated this summer, Google is the largest contributor in the world to AI research conferences. Gebru said she was fired last week following an email expressing frustration over a lack of progress on diversity at Google and interference in the process of publishing a paper for a research conference. If you aren't going to follow academic norms, I'm not going to peer-review your org's publications (which we all do for free).

COVID-19 officially became a global pandemic on Wednesday. As public health officials and governments respond; businesses brace for losses; and events like trade shows, SXSW, and Google's I/O shutter around the world, the disease is also impacting scientific conferences. Ironically, a coronavirus conference got canceled this week, and on Tuesday the International Conference on Learning Representations (ICLR), one of the fastest-growing machine learning conferences in the world, shared that it will now be a virtual event held entirely online. Papers will be presented in prerecorded five-minute videos with a slide deck, while researchers invited to make longer presentations can submit 15-minute videos. In a post about the change to an all-digital conference, organizers called the cancellation of an in-person event an "… opportunity to innovate on how to host an effective remote conference."

NeurIPS 2019, the latest incarnation of the Neural Information Processing Systems conference, wrapped up just over a week ago. Multiple great blog posts have already summarized various talks and key trends, so the goal of this piece is more humble: to reflect on the experience of attending the conference, and in particular whether its vast size is harmful to its purpose as a research conference. Thirteen thousand attendees, 1,428 accepted papers, and 57 workshops vast. This is 9 minutes condensed down to 15 seconds, and this is not even close to all the attendees! Is that a Rolling Stones concert?

The world's largest AI research conference is underway in Vancouver, Canada. Researchers are presenting more than 1,400 papers at the Neural Information Processing Systems (NeurIPS) conference, ranging from work that organizers believe has had the greatest impact over the past decade to Yoshua Bengio's continued march toward consciousness for deep learning. But even as the conference showed theoretical research and neuroscience-related papers on the rise alongside categories like algorithms and deep learning, the mushrooming of the event itself -- and the associated growing pains -- was a constant theme, and it speaks to the growth of the AI field in general. Organizers said that at the start of the conference Sunday, they expected about 400 people to show up for registration. All told, NeurIPS 2019 welcomed 13,000 attendees, up 40% from the prior year.

As a combat veteran and more recently an industry technologist and university professor, I have observed with concern the increasing automation--and dehumanization--of warfare. Sarah Underwood's discussion of autonomous weapons in her news story "Potential and Peril" (June 2017) highlighting this trend also reminded me of the current effort to update the ACM Code of Ethics, which says nothing about the responsibilities of ACM members in defense industries building the software and hardware in weapons systems. Underwood said understanding the limitations, dangers, and potential of autonomous and other warfare technologies must be a priority for those designing such systems in order to minimize the "collateral damage" of civilian casualties and property/infrastructure destruction. Defense technologists must be aware of and follow appropriate ethical guidelines for creating and managing automated weapons systems of any kind. Removing human control and moral reasoning from weapons will not make wars less likely or less harmful to humans.

You might go to a cutting-edge machine learning research conference like NIPS hoping to find some mathematical insight that will help you take your deep learning system's performance to the next level. Unfortunately, as Andrew Ng reiterated to a live crowd of 1,000 attendees this past Monday, there is no secret AI equation that will let you escape your machine learning woes. All you need is some rigor, and much of what Ng covered is his remarkable NIPS 2016 presentation titled "The Nuts and Bolts of Building Applications using Deep Learning" is not rocket science. Andrew Ng delivers a powerful message at NIPS 2016. Andrew Ng's lecture at NIPS 2016 in Barcelona was phenomenal -- truly one of the best presentations I have seen in a long time.