Schedule: Executive Briefing/Best Practices sessions

While the role of the manager doesn't require deep knowledge of ML algorithms, it does require understanding how ML-based products should be developed. Ira Cohen explores what it takes to manage ML-based products, the cycle of developing ML-based capabilities (or entire products), and the role of the (product) manager in each step of the cycle. Read more.

AI product managers (PMs) are rising. With the shift from the digital revolution to the AI revolution, the old product management workflow and frameworks are crumbling down. Mayukh Bhaowal explores new ways to manage AI products and outlines how AI executive roles are different and what toolbox you'll need to succeed in the era of artificial intelligence. Read more.

Developments in ML and DL provided remarkable advances in the predictive capabilities of AI. However, the black box nature of the modern models creates challenges for those looking to adopt these techniques. Bahman Bahmani examines a framework and presents design and operating principles, recommendations, and best practices for human-AI integration in enterprise workflows, products, and services. Read more.

Consumers want everything now, at their fingertips, with very little effort. To meet these demands and compete, companies need to fundamentally rethink how they operate. Yi Zhang explores some predictions on how conversational technology will evolve from its current state in 2019. She outlines some common misunderstandings about the technologies and provides case studies from several industries. Read more.

Despite a meteoric rise in data volumes within modern enterprises, enabling nontechnical users to put this data to work in diagnostic and predictive tasks remains a fundamental challenge. Peter Bailis details the lessons learned in building new systems to help users leverage the data at their disposal, drawing on production experience from Facebook, Microsoft, and the Stanford DAWN project. Read more.

Machine learning has enabled the move from manually programming robots to allowing machines to learn from and adapt to changes in the environment. Bastiane Huang examines how AI-enabled robots are used in warehouse automation, including recent progress in deep reinforcement learning, imitation learning, and real-world requirements for various industrial problems. Read more.

New AI solutions in question answering, chatbots, structured data extraction, text generation, and inference all require deep understanding of the nuances of human language. David Talby outlines challenges, risks, and best practices for building NLU-based systems, drawing on examples and case studies from products and services built by Fortune 500 companies and startups over the past seven years. Read more.

Though Nara Logics doesn't always follow them, it has developed great best practices for designing, developing, and delivering great software. Jana Eggers is here to explore what happens when you start adding AI to great software by covering six key features of software development that are similar when adding AI, six that are different, and how to adjust. Read more.

Join Lindsay Hiebert and Vikrant Viniak as they explore challenges for developers as they design a product that solves a real-world problem using the power of AI and IoT. To unlock the potential of AI at the edge, Intel launched its Intel AI: In Production ecosystem to accelerate prototype to production at the edge with Intel and partner offerings. Read more.

Embeddings have emerged as an exciting by-product of the deep neural revolution and now apply universally to images, words, documents, and graphs. Many algorithms only require unlabeled datasets, which are plentiful in businesses. Mayank Kejriwal examines what these embeddings really are and how businesses can use them to bolster their AI strategy. Read more.