Adam Tornhill introduces novel techniques to uncover both problematic code and the social dimension of the teams that build your software. This combination lets you prioritize the parts of your system that benefit the most from improvements, detect organizational issues, and make practical decisions guided by data.

Conversations about designing for artificial intelligence often start and end with replacing the humans, but few explore the potential of AI working alongside people to augment our intelligence. Aye Moah shares lessons learned from the design process behind Boomerang Respondable, the worlds first AI assistant that works with people to help them write better emails.

Daphne Koller explains how Coursera is using large-scale data processing and machine learning in online education. Building on Coursera's wealth of online learning data, Daphne discusses the role of automation in scaling access to education that is personalized and efficient at connecting people with skills and knowledge throughout their lives.

Vijay Narayanan takes you on an inspiring journey exploring how the cloud, data, and artificial intelligence are powering and accelerating the genomic revolutionsaving and changing lives in the process.

Artificial intelligence will accelerate both cancer research and the development of autonomous vehicles. Jason Waxman explains why the ultimate potential of AI will be realized through its societal benefits and positive impact on our world. Collaboration between industry, government, and academia are required to drive this societal innovation and deliver the scale and promise of AI to everyone.

Much of Apache Sparks power comes from lazy evaluation along with intelligent pipelining, which can make debugging more challenging than on traditional distributed systems. Holden Karau and Joey Echeverria explore how to debug Apache Spark applications, the different options for logging in Sparks variety of supported languages, and some common errors and how to detect them.

David Talby and Claudiu Branzan offer a live demo of an end-to-end system that makes nontrivial clinical inferences from free-text patient records. Infrastructure components include Kafka, Spark Streaming, Spark, and Elasticsearch; data science components include spaCy, custom annotators, curated taxonomies, machine-learned dynamic ontologies, and real-time inferencing.

Data is powering a machine-learning renaissance. Understanding our data helps save lives, secure our personal and business information, and engage our customers with better relevance. However, as Mike Olson explains, without big data and a platform to manage big data, machine learning and artificial intelligence just dont work.

Wee Hyong Tok and Danielle Dean explain how the global, trusted, and hybrid Microsoft platform can enable you to do intelligence at scale, describing real-life applications where big data, the cloud, and AI are making a difference and how this is accelerating the digital transformation for these organizations at a lighting pace.