Mike Rhodin – SVP, Watson Group
Good morning. Good morning. And welcome to a very deep crowd of standing people at the back. This is great. This is an exciting point. I couldn’t be prouder, I couldn’t be happier, and I couldn’t be more honored to be asked to work with 2,000 of our best and brightest colleagues on how we can take this forward. Working with our partners, great companies, great institutions, that see the same vision that we see on where we’re going to take this technology as it evolves.
The formation of a new group is a big deal. But it’s part of a journey, it all starts with the germ of an idea. Someone had the grand challenge idea of, could we really answer the world’s hardest questions? Right? IBM Research did an incredible piece of work, culminating in a pretty daring display on live television. So, you start to look at television and you start to realize if it had gone the other way, right, it might not be so much fun.
But an incredible piece of work: 27 core researchers dedicated four years built on the shoulders of decades of research and technology. What we did next, we took that team and we built a team to start looking at, how would you commercialize it? We built a team under Manoj Saxena that, as many of you have met, embodied the essence of what it means to be an innovator, to be a startup. We intentionally hid them, right? A tiny group, protected. Based them out of Austin, let them play, let them experiment, let them learn. Learn in the market, working with many of you, our clients.
Now, any startup goes through many phases, right? They had to learn how to fix bugs and make it better and improve it every day. And over the last two years, working in the market, cocreating, collaborating with clients, with partners, we believe we’ve created something that is ready to go. Ready to go mainstream, and mainstream is where we’re headed.
With the creation of the new group, we’re going to take this from those 27 researchers, to the few hundred people that have been working in startup mode for the last two to three years, and we’re going to move on to the next phase: 2,000 people. That’s a lot, right? You’re going to see today examples of technology that are going to come out. New products, new capabilities that are going to really improve what we mean by cognitive systems, what Watson really is.
I think you’ll see that what you knew Watson as was merely the tip of the iceberg. The depths of our IBM researchers that have been working in parallel to the commercialization team have built a whole new wave of technology that now today is moving over to the new group. That technology is going to be rapidly commercialized and put in market and you’ll hear about some of those advances as we go through the morning.
We’re going to take some technology from our world leading software business, stuff that will help us move this along much faster and join the group. Right, so today we’ve gone from a few hundred to several hundred and over the course of the year that will continue to expand up to 2,000. Rapid growth environment.
Now, many of us have, you know, heard about Watson, we’ve read about Watson, we see articles about Watson, we see people speak about Watson. There’s YouTube on Watson, right?
So, what is Watson?
Right. If you take it at its essence, at its core, it’s a system that understand natural language. You don’t have to write programs, you don’t have to learn things like Fortran or Java. You just ask it questions. It reads. Think about it as reading, right? When it reads a lot, it adapts and it learns. It gets smarter.
When it gets smarter, you can start to ask it questions. When you ask it questions, it will generate and evaluate hypothesis, potential answers with a level of confidence. When you think about that, that’s how we work: we read, we learn. We start to answer questions. That’s how we know whether we’ve learned that or not.
Watson learns like our children do. How do you know when your children are learning a new subject? How do you know they’ve actually learned it? You ask them a question. You see whether it gets the right answer. And when it doesn’t get the right answer, you help them discover the right answer, and it learns. It gets smarter.
And the next time, it gets that right answer and it builds upon things. But it just doesn’t learn from what it knows today. You can add more data to it. It reads new books, every day. And as it reads new books, it learns. It connects the dots with what it just read with what it already knew. Sometimes the new reading contradicts what it already knew. It has to sort that out. The same way we do. Right? It has to understand new information in the context of its relevance — the connection — to the old information that it had, and how important is this new piece of information.
So Watson has come a long way. But this is really, think of this really as just an engine in a cognitive system. It’s not the end state; it’s the beginning state. So as we start to move forward, Watson is getting smarter, we’re adding new capabilities to it. It’s learning to reason, to think through things. To help people using it move along a journey to come up with the right answer, the right diagnosis. It’s using that first engine I talked about as a subroutine, as something that it calls. That it asks questions to. That is new technology from IBM Research called IBM Paths, WatsonPaths.