 This is our fourth annual predict event and we couldn't be more excited that the day has finally come. This is the place to be for the predictors, the data scientists, the technologists and the business decision makers of Ireland, Europe and beyond. We have people here from all over the world, from all over Europe, the US and even the Middle East have come here to be here today. So you're all very welcome. Thanks for coming. This year's predict is even bigger and better. We have the usual parallel streams throughout the day. We've talked during lunchtime in the experience zone and we go all the way through the evening with a drinks reception with the Camembert Quartet. So please do make sure you stay for that. That's going to be a great treat. We started predict in 2015 for a few reasons. We really wanted to push the boundaries of a technology predict of analytics and business decision making conference. We wanted to create an event which went beyond the traditional conference where people can come, learn, connect and be inspired with the possibilities of data science and technology. And to do that our focus is on great speakers, great content and great experiences that span technology, data science, design and business decision making. So there's going to be a whole breadth of coverage of all of that. We'll see great technologies, great products and services in the experience zone throughout the day and you'll see great speakers and panel discussions throughout the day. Predictive analytics, in 2015 predictive analytics was really starting to come into its own. It was starting to become move beyond the kind of the big corporates and become more mainstream where all companies could really access the power of data using cloud computing and technology. What was once the kind of reserve of large corporates was truly becoming democratized. So we wanted to kind of move with that and create an event where people could really understand the possibilities and be educated and inspired as what could happen. The conversations became more nuanced where the scare stories about robots taking our jobs became more nuanced into augmented intelligence rather than artificial intelligence. But in summary, like the key goals were to develop and support the data science community here in Ireland to try to help demystify data science for the business community and to help position Ireland as leaders in this emerging and important field of predictive analytics. And I suppose for selfish reasons, this was the conference we always wanted to go to and it didn't exist so therefore we created it. So I'd like to say a few quick words before we get on with the business stream, the business of data science. Firstly, thanks to all our partners and supporters for your engagement and contribution to this event. Obviously without this community engagement this event wouldn't be possible and you can see the incredible breadth and power of the brands and the names up here. So please do engage with these organizations in the experience zone throughout the day. They've got a lot to offer, they'll be on stage, they'll be talking about different aspects of data science, e-learning, design, really, really interesting stuff. So in 2015 we started Predict and since then the conversation has evolved. We started out talking about the disruptive power of predictive analytics, about the third industrial revolution. IBM demoed their Watson robot and they talked about augmenting not replacing human intelligence. John Elder explained how number crunching and industrial scale has become possible because of modern software libraries and computing power. We talked about data protection and having a trust strategy to be aligned with your data strategy and we explored the new frontier of unstructured data. In 2016, interestingly to me, I found that the technology was almost been taken as a given and people were more concerned about how to get their projects into production and adopted by the end user. So it was, I think Steve Collins at one point from Swerve said we just pushed the data into our machine learning algorithm and out comes a recommendation as if that was the easy part but actually the hard part is all the bits that become before and after that. We were reminded in 2016 that our models are mere opinions kind of formed and created in code and that these models are mostly created by white middle class males and this can result in a biased view of the world sometimes in the selection of data sets or the selection of training criteria for these models and this can have a negative impact on the world. So we talked about how to have a diverse input into these systems, as diverse as possible and some potential solutions and we'll go to that again. We talked about winning the trust of people in our models and again this comes back to the ethics and the trust in data science and models and this has become huge over the last few years. In 2017 John Elder gave a keynote speech and he talked about computers pointing out the flaws in us human beings. He talked about how we are soft thinkers and how our brains make huge mistakes and can be biased in many ways. We talked about AI and we talked about the singularity but it's not all one-way traffic it was pointed out that human beings also boost or boast huge creative capacity and common sense that data models often lack and therefore it's actually in a collaboration between humans and machines where the real power of data science arises. A recurring message from predict 2017 was that every company needs to become a data company and we saw it firsthand from BMW and their shift from being an engineering company to being an e-mobility and data company. So moving on then to 2018 the key themes this year again are artificial intelligence, technology, data science and business. We're taking the usual approach of talking right through the value chain from data, data structuring, data gathering, model creation, model validation all the way to better decision making and understanding that value chain all the way through. We'll be exploring this team in lots of sessions, the MIMANI sessions throughout the day, you'll have a schedule on your seat, it's also available on your mobile, just go to predict.ie, hit schedule and hit the mobile accessible version, it's very easy to use. So we'll be talking about AI in action, IoT, sport, health, lots of very interesting topics happening here and on the other stage. I will be finishing back here at 5pm with a panel discussion on lessons from the day and some key takeaways from the day. So just briefly to talk about how today will work. We're in the blue stage right now so there's an experience zone if you haven't already been through. We'll have our coffee breaks and lunch in there. There's the yellow stage which will be running in parallel throughout most of the day. There's nothing in parallel right now so don't worry, you don't have a choice to make right now. So there'll be networking events, drinks receptions in the evening, all in the experience zone. So there's also a workshop tomorrow on data science from John Elder so there's still opportunities to book that if you haven't done so already. But sit back, relax, take it all in, tweet away, the hashtag is hash predict conf. We have Ian Campbell here again this year who'll be writing up the whole proceedings into the predict book four. Everyone who is hence this year has access to the predict passport and in the predict passport you have access to all of the previous talks, books and slides from the previous three years and plus all of the content from this year will be available. So that'll be sent out by email after the event. So you can relax and focus on tweeting. So if you're going to tweet, if you want to ask a question throughout any of the sessions, simply tweet your question with the hashtag predict conf and I will try to pick it up and ask the speakers during the panel discussion. So let's move on. We're on to our first session and the first session is predict and the business of data science. So this session we are, it'll come up now, this session we're looking at real world application of data science to business decision making. We're not going to shy away from the technical details but the emphasis is going to be on demystifying data science for business decision makers and how to set up teams to collaborate, create the models and put the business decision making to practice using data science.