Schedule: Executive Briefing/Best Practices sessions

CB Insights tracks over 3,000 AI startups across 25+ verticals. While every vertical has benefited from deep learning and better hardware processing, the bottlenecks and opportunities are unique to each sector. Deepashri Varadharajan explores what's driving AI applications in different verticals like healthcare, retail, and security and analyzes emerging business models. Read more.

Larry Carin, one of the world’s most published machine learning researchers, discusses the state of the art in machine learning and how it translates to business impact. Along the way, Larry shares examples of how modern machine learning is transforming business in several sectors, including healthcare delivery, security, and back-office business processing. 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 shares 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.

Broader AI adoption and gaining trust from customers requires AI systems to be fair, interpretable, robust, and safe. Anand Rao synthesizes the current research in FAT (fairness, accountability, and transparency) into a step-by-step methodology to address these issues—illustrated with case studies from the financial services and healthcare industries. Read more.

Anna Gressel, Jim Pastore, and Anwesa Paul lead a crash course on the emerging legal and regulatory frameworks governing AI, including GDPR and the California Consumer Privacy Act. They also explore key lawsuits challenging AI in US courts and unpack the implications for companies going forward, helping you mitigate legal and regulatory risks and position your AI products for success. Read more.

How does Salesforce make data science an Agile partner to over 100,000 customers? Sarah Aerni shares the nuts and bolts of the platform and details the Agile process behind it. From open source autoML library TransmogrifAI and experimentation to deployment and monitoring, Sarah covers the tools that make it possible for data scientists to rapidly iterate and adopt a truly Agile methodology. Read more.

Angelo Calvello explains why asset managers will inevitably (but slowly and haltingly) incorporate AI into their investment processes in a meaningful manner and argues that this incorporation could be accelerated by the entrance of an external AI-based actor or the success of AI-based investment startups. Read more.

Three elements will control the AI market: technology, data, and IP rights. Leveraging rich patent data, Thomas Marlow uncovers the companies with the top patent holdings across the world in groundbreaking research and implementation technologies, surfacing insights into the sources and owners of AI technology as well as the hurdles and opportunities that those entering the field today face. Read more.

The significant hype bubble building up around AI has convinced many executives that if they’re not already tech savvy, they might not be ready for AI’s “transformative power.” However, the reality is that AI is just another tool that can help your business, and you’re probably not that far behind. Vinay Seth Mohta explains how to evaluate AI as you would any other strategic investment. Read more.

Jana Eggers explores explainability and transparency as both required and unachievable goals for AI, with a focus on helping teams structure discussions about levels of explainability possible and needed for both user trust and regulatory requirements. Read more.

Effective data governance is foundational for AI adoption in enterprise, but it's an almost overwhelming topic. Paco Nathan offers an overview of its history, themes, tools, process, standards, and more. Join in to learn what impact machine learning has on data governance and vice versa. Read more.

Quantum computers will enable us to efficiently compute things never thought possible, but how will this impact artificial intelligence? Jennifer Fernick explains how to filter signal from noise in discussions surrounding quantum machine learning by exploring how quantum computers work, what types of AI problems they may be good at, and which industries and use cases will (and won't) benefit. Read more.