I remember that morning going to the lab and I was thinking this is it, this is the last Jeopardy game. It became real to me when the music played and Johnny Gilbert said from IBM Research in Yorktown Heights, New York, this is Jeopardy and I just went. Hear it is one day. This is the culmination of all this work. To be honest with you I was emotional. Watson. What is Shoe? You are right. We actually took the lead. We were ahead of them, but then we start getting some questions wrong. Watson? What is Leg? No, I'm sorry. I can't accept that. What is 1920's? No. What is Chic? No, sorry, Brad. What is Class? Class. You got it. Watson. What is Sauron? Sauron is right and that puts you into a tie for the lead with Brad. The double Jeopardy round of the first game I thought was phenomenal. Watson went on a terror. Watson, who is Franz List? You are right. What is Violin? Who is the Church Lady? Yes. Watson. What is Narcolepsy? You are right and with that you move to $36,681. Now, we come to Watson. Who is Bram Stoker and the wager, hello $17,973 and a two day total of $77,147. We won Jeopardy. They are very justifiably proud of what they've done. I would've thought that technology like this was years away but it's here now. I have the bruised ego to prove it. I think we saw something important today. Wow, wait a second. This is history. The 60th Annual Grammy Awards, powered by IBM Watson. There's a tremendous amount of unstructured data that we process on Grammy Sunday. Our partnership with the recording Academy really is focused on helping them with some of their workflows for their digital production. My content team is responsible not only for taking all this raw stuff that's coming in, but curating it and publishing it. You're talking about five hours of red carpet coverage with 5,000 artists, making that trip down the carpet with a 100,000 photos being shot. For the last five hours, Watson has been using AI to analyze the colors, patterns, and silhouettes of every single outfit that has passed through. So we've been able to see all the dominant styles and compare them to Grammy shows in the past. Watson's also analyzing the emotions of Grammy nominated song lyrics over the last 60 years. Get this, it can actually identify the emotional themes in music and categorize them as joy, sadness, and everything else in between. It's very cool. Fantasy sports are an incredibly important and fun way that we serve sports fans. Our fantasy games drive tremendous consumption across ESPN digital properties, and they drive tune-in to our events and studio shows. But our users have a lot of different ways they can spend their time. So we have to continuously improve our game so they choose to spend that time with us. This year, ESPN teamed up with IBM to add a powerful new feature to their fantasy football platform. Fantasy football generates a huge volume of content - articles, blogs, videos, podcasts. We call it unstructured data - data that doesn't fit neatly into spreadsheets or databases. Watson was built to analyze that kind of information and turn it into usable insights. We train Watson on millions of fantasy football stories, blog posts, and videos. We taught it to develop a scoring range for thousands of players, their upsides and their downsides, and we taught it to estimate the chances of player will exceed their upside or fall below the downside. Watson even assesses a player's media buzz and their likelihood to play. This is a big win for our fantasy football players. It's one more tool to help them decide which running back or QB to start each week. It's a great complement to the award-winning analysts our fans rely on. As with any Machine Learning, the system gets smarter all the time. That means the insights are better, which means are you just can make better decisions and have a better chance to win their matchup every week. The more successful our fantasy players are, the more time they'll spend with us. The ESPN and IBM partnership is a great vehicle to demonstrate the power of enterprise-grade AI to millions of people, and it's not hard to see how the same technology applies to real life. There are thousands of business scenarios where you're assessing value and making trade-offs. This is what the future of decision-making is going to look like. Man and machine working together, assessing risk and reward, working through difficult decisions. This is the same technology IBM uses to help doctors mine millions of pages of medical research and investment banks fund market moving insights.
