 Live from Miami, Florida, it's theCUBE. Covering IBM's data and AI forums. Brought to you by IBM. Welcome everybody to the port of Miami. My name is Dave Vellante and you're watching theCUBE, the leader in live tech coverage. We go out to the events. We extract the signal from the noise and we're here at the IBM data and AI forum. The hashtag is data AI forum. This is IBM, this is formally known as the IBM Analytics University. It's a combination of learning, peer network, and really the focus is on AI and data. And there are about 1700 people here up from about half of that last year when it was the IBM Analytics University. About 600 customers, a few hundred partners, there's press here, there's analysts, and of course theCUBE is covering this event. We'll be here for one day, 128 hands-on sessions, or sessions, 35 hands-on labs. As they say, a lot of learning, a lot of technical discussions, and a lot of best practices. What's happening here? For decades, our industry has marched to the cadence of Moore's Law. The idea that you could double the processor performance every 18 months, doubling the number of transistors within the footprint. That's no longer what's driving innovation in the IT and technology industry. Today it's a combination of data with machine intelligence applied to that data and cloud. So data, we've been collecting data, we've always talked about all this data that we've collected, and over the past 10 years with the advent of lower-cost warehousing technologies in file stores like Hadoop with activity going on at the edge with new databases in lower-cost data stores that can handle unstructured data as well as structured data. We've amassed this huge amount of data that's growing at a non-linear rate. The curve is steepening, exponential. So there's all this data, and then applying machine intelligence or artificial intelligence with machine learning to that data is the sort of blending of a new cocktail. And then the third piece of that, third leg of that stool is the cloud. Why is the cloud important? Well, it's important for several reasons. One, is that's where a lot of the data lives. Two, it's where agility lives. So cloud, cloud native of DevOps and being able to spin up infrastructure as code really started in the cloud and it's sort of seeping to on-prem slowly and hybrid and multi-cloud architectures. But cloud gives you not only that data access, not only agility but also scale, global scale. So you can test things out very cheaply. You can experiment very cheaply with cloud and data and AI. And then once your POC is set and you know it's going to give you business value and the business outcomes you want, you can then scale it globally and that's really what cloud brings. So this forum here today with the big keynotes, Rob Thomas kicked it off. Actually, I'll take that back. A gentleman named Ray Zahab, he's an adventure and ultramarathoner kicked it off. This dude at one time ran 4,500 miles in 111 days with two ultramarathoner colleagues. They had no days off. They traveled through six countries. They traversed Africa, the continent and they took two showers in 111 days. And this whole mission is really talking about the power of human beings and the will of humans to really rise above any challenge with no limits. So that was the sort of theme that was set for this, the tone that was set for this conference that Rob Thomas came in and invoked the metaphor of superheroes and superpowers, of course AI and data being two of those three superpowers that I talked about in addition to cloud. So Rob talked about eliminating the good to find the great. He talked about some of the experiences with Disney's. Ward Kimball and Stan Lee. Ward Kimball went to Walt Disney with this amazing animation of Walt Disney. He said, I love it. It was so funny. It was so beautiful. It was so amazing. You worked 283 days on this. I'm cutting it out. So Rob talked about cutting out the good to find the great. Also talked about AI as penetrated only about four to 10% within organizations. Why is that? Why is it so low? He said there are three things that are blockers there. One is data. And he specifically is referring to data quality. The second is trust. And the third is skillsets. So he then talked about, of course dovetailed a bunch of IBM products and capabilities into those blockers, those challenges. He talked about two in particular IBM cloud pack for data, which is this way to sort of virtualize data across different clouds and on-prem and hybrid and basically being able to pull different data stores in, virtualize it, combine, join data and be able to act on it and apply machine learning and AI to it. And then auto AI. A way to basically machine intelligence for artificial intelligence. In other words, AI for AI. What's an example? How do I choose the right algorithm and that's the best fit for the use case that I'm using? Let machines do that. They've got experience and they can have models that are trained to actually get the best fit. So we talked about that. Talked about a customer panel, Miami-Dade County, Wonderman Thompson and the Standard Bank of South Africa. These are incumbents that are using machine intelligence and AI to actually charge a super, super charge their business. We heard a use case with Royal Bank of Scotland basically applying AI and driving their net promoter score. So we'll talk some more about that. And we're going to be here all day today interviewing executives from IBM, talking about what customers are doing with AI, getting the feedback from the analysts. So this is what we do. Keep it right there, buddy. We're in Miami all day long. This is Dave Vellante. You're watching theCUBE. Be right back right after this short break.