Cassie Kozyrkov

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.

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.

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.

Despite the rise of data engineering and data science functions in today’s corporations, leaders report difficulty in extracting value from data. 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. Let’s talk about how you can change that!

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.

Despite the meteoric rise of data science in today’s corporations, leaders still report difficulty realizing value from their investment in ML. Many organizations aren’t aware that they have a blindspot with respect to their lack of data effectiveness and hiring PhDs doesn’t seem to help. Cassie will talk about three problems she sees within data science today and how to solve them.

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.

Businesses collect more data than one person can understand and evaluate. As information increases, decision-making can become more complex. Every industry is affected by this, from retail to financial services to healthcare. So how can you use decision intelligence to turn information into better actions at any scale? In this lively talk, you will learn how to use technology, processes, and people in order to make better decisions at scale.

Every story about wishes – from Midas to magic lamps – reminds us of the same thing: it’s not the genie that’s dangerous, it’s the unskilled wisher. Today, the arms race is heating up between the wisher and the genie: between human capabilities and technological complexity… and we’re losing. Forget AGI, we’re not even equipped to handle complex technology in general. But we can change that! In this talk, we’ll discover how to harness technology to cognitively enhance the wisher and keep up with the genie.

What lurks just beyond the buzz and hype of generative AI? A new landscape of threats and opportunities that will transform our experiences, our businesses, and society as a whole. While most perspectives on AI are mired in a long history of science fiction, in this keynote we’ll cut through the noise to take a brutally honest look at what generative AI means not only for your bottom line but also for our collective future.

Today, technology allows an unprecedented number of people to wield impact on a scale once reserved for kings and popes. When we enlarge ourselves with technology, it’s easier to step on the people around us. How do those of us who have found ways to scale our reach do so responsibly? Is the world ready for the new era of uneven acceleration? What are the tools, techniques, and teachers available for augmenting our decision intelligence to avoid unintended consequences and leave the world better off than we found it?

How your life turns out boils down to only two things: luck and the quality of your decisions, so decision-making is the most important skill you can learn, yet society doesn’t treat decision-making as a skill… Once way this shows up is in outcome bias, which is the #1 thing preventing you from learning the right lessons from your own decisions (and everyone else’s too). Tolerance of outcome bias is a way of institutionalizing cowardice – if we work together to fix it, the impact could be civilization-changing. Meanwhile, confirmation bias is the #1 thing preventing you from turning information into better actions. You can’t be a data-driven leader if you don’t take steps to fix yours. The vaccine for both outcome bias and confirmation bias is the same and starts by asking the most powerful question there is: “What would it take to change your mind?”