Next month, the brightest minds in machine learning, artificial intelligence, and advanced analytics are descending on San Jose, California for the 2019 O'Reilly Artificial Intelligence Conference. Perennially one of the top conferences in the field, the O'Reilly AI Conference is unique in its focus on bridging tech and business to "push the boundaries of AI" and transform industries. We're looking forward to the show and joining the discussion about how businesses can put their data to work more effectively - without having to hire highly specialized talent. If you're headed to San Jose, we'd love to meet and learn how you're tackling these very challenges. There are multiple ways to engage with the Sisu team at the O'Reilly AI Conference, from our booth in the Expo Hall (#217) to an Executive Briefing on usable machine learning from our CEO Peter Bailis.

Many industries are now investing heavily in data science and automation to replace manual tasks and/or to help with decision making, especially in the realm of leveraging computer vision to automate many monitoring, inspection, and surveillance tasks. This has resulted in the emergence of the 'data scientist' who is conversant in statistical thinking, machine learning (ML), computer vision, and computer programming. However, as ML becomes more accessible to the general public and more aspects of ML become automated, applications leveraging computer vision are increasingly being created by non-experts with less opportunity for regulatory oversight. This points to the overall need for more educated responsibility for these lay-users of usable ML tools in order to mitigate potentially unethical ramifications. In this paper, we undertake a SWOT analysis to study the strengths, weaknesses, opportunities, and threats of building usable ML tools for mass adoption for important areas leveraging ML such as computer vision. The paper proposes a set of data science literacy criteria for educating and supporting lay-users in the responsible development and deployment of ML applications.

Despite incredible recent advances in machine learning, building machine learning applications remains prohibitively time-consuming and expensive for all but the best-trained, best-funded engineering organizations. This expense comes not from a need for new and improved statistical models but instead from a lack of systems and tools for supporting end-to-end machine learning application development, from data preparation and labeling to productionization and monitoring. In this document, we outline opportunities for infrastructure supporting usable, end-to-end machine learning applications in the context of the nascent DAWN (Data Analytics for What's Next) project at Stanford.