Machine learning and artificial intelligence are no longer science fiction, but what does it take to harness their potential at scale and what does this mean for the future of work? Let's strip away the jargon in machine learning and AI to take a look at what’s easy, what’s hard, how to spot opportunities to improve your business, and what you need to know to avoid the two biggest threats in AI.

To automate a task reliably, it’s necessary to understand the solution first... or is it? Does AI free us to create groundbreaking solutions or are such notions mere hype? What does it mean to automate beyond human expression? What are the opportunities and dangers involved? Which jobs is AI designed to automate and will those careers disappear? How does data fit into the story and where does AI bias come from? Let’s talk about how to navigate new technological frontiers to approach AI safely and responsibly for a brighter future.

AI and data science aren't just passing fads, they're the future of software and technology. Unfortunately, they're also fields with some of the worst reputations when it comes to diversity. Let's talk about the role that our choice of language plays in the data professions, why most people are tackling AI bias the wrong way, and why diversity in AI matters even more than you think. Come find out the real reasons to be both worried and excited for humanity's AI future... and how you can help make sure that future is bright.

Many organizations aren’t aware that they have a blindspot with respect to their lack of data effectiveness and hiring experts doesn’t seem to help. Is shifting focus to data product leadership the solution? It might be… if we can solve three big problems. This session examines what it takes to build a truly data-driven organizational culture and presents a series of suggestions for leaders facing these challenges.

Machine learning and artificial intelligence are no longer science fiction, but what does it take to harness their potential effectively, responsibly, and reliably? This talk gives you actionable advice based on lessons learned at Google that will help you adopt an AI safety mindset.

Machine learning and artificial intelligence are no longer science fiction, but what does it take to harness their potential effectively, responsibly, and reliably? How should leaders think about their roles and responsibilities in a future where complex algorithms and black box models are the norm? What do developers need to know to keep their skills relevant? And how can regular citizens ensure that they're informed and empowered to participate in decisions based on their data. This talk gives you actionable advice based on lessons learned at Google that will help you adopt an AI safety mindset to be a responsible leader and citizen in an increasingly data-fueled future.

Machine learning and artificial intelligence are no longer science fiction, but what does it take to be the kind of innovator that is able to harness their potential at scale? Let's strip away the jargon in machine learning and AI to take a look at what’s easy, what’s hard, how to spot opportunities to improve your business and the world around you.

Machine learning and artificial intelligence are no longer science fiction, but what does it take to be the kind of innovator that can harness their potential? Let’s strip away the jargon in machine learning and AI to look at what’s easy and hard and how to spot opportunities to improve your business and the products, services, and support experiences you deliver for your customers.