Cognitive computing offers a brave new world where data isn’t just collected but harnessed for the greater good
Big Data, for all of its charged associations and lofty dimensions, is a resource we’ve yet to fully harness. This is, in large part, because we have only touched on the surface of a layered and encompassing ecosystem, whose very scope escapes us. Having adequate data gathered on nutrition levels in developing countries, for instance, is commendable but until we learn how to fully harness the knowledge and act on it, we are missing out on opportunities to do better. Understanding Big Data is a task deserving of a framework that extends beyond the scope of human ability.
The emergence of Big Data requires technology capable of interacting with and deriving clear actionable goals from layered data, just like humans do. By harnessing data-driven insights and bridging the gap between human intelligence and computer data, we can build a society that functions better. This intersection of human soft intelligence and hard-hitting data is where IBM Watson, the first cognitive computing platform, lives and thrives.
It is also the reason PSFK and IBM Watson have chosen to collaborate in The Good Data Contest―calling leading creative minds to ideate how IBM Watson’s capabilities can improve our lives, and to make us all better equipped to understand how cognitive computing can—and will—drive our future.
Here, we sit with Jonas Nwuke, who is the business development manager at the Watson Ecosystem, to understand how his team is helping third party companies apply Watson technology to real world problems.
PSFK: Can you tell us a little bit about what you do, and about IBM Watson?
JonasNwuke: I lead a team of business development managers in what we call the Watson Ecosystem.
Watson, the technology, is something that was first conceived back in the mid 2000s as a research project. Specifically, it’s something that we, at IBM, call a grand challenge. It is the latest in the IBM tradition of going after really, really tough problems. The previous grand challenge had been Deep Blue, or teaching a machine to play and master chess, which we were ultimately successful at.
In the mid 2000s, our researchers were trying to conceive of hard problems to tackle. What they came up with was Watson, which was a direct attempt at answering the question: Can you train a system to understand natural language and respond to various human questions accurately?
One of the highest levels of achievement is really understanding nuanced human language, specifically English. [And what better avenue for Watson to parse the intricacies of the English language than by participating in the television game show Jeopardy!, where it took on and beat human competitors Ken Jennings and Brad Rutter].
If you can get a machine to successfully compete on that show, then what you’ve proved is a novel dynamic in human and machine interaction. Something that’s more on the human side than the machine side. You’re no longer programming a machine; it’s really understanding the nuanced way that humans communicate with one another.
PSFK: And what bold undertakings has Watson moved on to since Jeopardy!?
JN: After that success, of course, the idea was: how do we commercialize this technology? We realized we’ve got a lot to offer to the world if we can find the right type of problem to solve.
The right type of problem is something that a trained professional may be good at solving, but not at scale. Therefore, we think about Watson as a platform for the democratization of expertise.
For instance, in any given organization, you take its experts and you have them train the Watson system. You can then take that trained system and share it with the non-experts in the organization; now, everybody can call upon an organization’s best practices when executing an overarching mission.
You think about healthcare, which by the way, after Jeopardy!, was one of the first places that we started our work. You think about the best doctor in any given hospital, on training the system based on that doctor sharing his or her particular knowledge, you can train first-year interns based on that doctor’s career experience.
With interns diagnosing and treating patients, they can not only rely on their own training but on the expertise found within Watson, and in this case, rely on the added training of all the experts found within the organization.
PSFK: Can you touch upon, in more detail, the efforts undertaken in the healthcare industry? Or on any other industry Watson is currently assisting in?
JN: The amount of information in the healthcare industry, especially in the United States, is overwhelming, to say the least. So, we can help doctors keep up with medical journals, clinical trials, things of that nature. Things that will help doctors be better at what they do.
After that, we realized there were other ways to bring Watson to market. We created our solutions group, and the idea here was to build Watson products. We took the Watson services, combined them with other IBM technology, and created what we called “advisors of one kind or another.”
The most mature iteration that you might have heard about is something called Watson Engagement Advisor. The base use case is customer service—let’s say I have a problem with my iPhone.
I can chat, either directly with Watson, which has been set up on customer support as a first point-of-contact to handle all of those queries that maybe a person doesn’t need to get involved in. Or, I can have somebody go out on an issue, chat with a customer support agent who is using one, and is empowered to access it. I go to the long tail of the information, all of the information that the best customer service agent in the organization might have.
Again, you’ve got that paradigm of democratizing expertise. That was the second thing that we thought of when creating Watson as a product.
The third thing we did was set up an ecosystem. The Watson Ecosystem is a partner program, and its vision for Watson is that it become pervasive technology. That it becomes one of those technologies that you reply upon that maybe you don’t notice that you’re using it every day, but if it went away, you sure would.
To make that happen, we realized that we need to and want to work with partners, third parties, non-IBM organizations to help us build a thriving network powered by Watson applications.
Or, to provide content for others to build applications on top, or even honestly, to provide talent to those organizations that may have a Watson vision, but for whatever reason are unable to execute on that idea.
That’s what the ecosystem really represents, our partnership with the broader tech community.
PSFK: You mentioned the applications for tech support. Is there any particular way you envision Watson being ingrained in everyday life for people?
JN: I do want to start out by saying I am talking from the perspective of the ecosystem, and the ecosystem, the folks that we working with are third parties who are using Watson to solve the problems that they are in business to solve.
If I think about each one of the dimensions of live, work and play, on the live side, we’ve got partners in the medical space and the healthcare space.
We’ve got a partner called Welltok, and they created an application called CaféWell, a wellness concierge that makes sure you are eating the right things, getting the right kind of exercise, generally, just living better.
We also have a partner in the veterinary space called LifeLearn. They’re involved in veterinary medicine. Some of the work that we’ve done in medicine is help doctors diagnose and treat cancers of various kinds, but what you can do with LifeLearn is provide an application or tool to veterinarians that helps them diagnose and treat our four-legged friends.
On the work side, we’ve got a partner called Spark Cognition. Spark Cognition is helping security professionals better respond to a world of ever-evolving threats. Their service helps security technicians and security professionals better respond to a DDoS attack or something along that nature, or to better secure their servers.
Red Ant is helping sales professionals be better sales professionals.
If you think about retail, and you think about going shopping for clothes, every once in a while you’ll run into that sales associate who is maybe new on the job, and still learning, and doesn’t have all the knowledge at his or her fingertips just yet.
To help you successfully navigate the purchase that you’re looking to make. With RedAnt, they’re providing an application that these novice sales folks can use to help them to provide better customer support, better customer service.
PSFK: In terms of the Red Ant application, what exactly would Watson technology provide to the sales associate?
Jonas: If the sales staff is not entirely sure what’s on the floor, for example, or what the specifics of the description of a specific item are, or what it’s good for, or if there are any particular details about an item that would be valuable to know, the Red Ant application will help the sales associates address that. Whether it’s giving new associates more proficiency or informing a trained associate on new products the app is able to empower the staff with the expertise of the entire retail network at the point of customer interaction.
PSFK: What would you say are some of the limitations Watson is trying to sort out?
JN: Specifically in the Waston Ecosystem the benefit of what we have is that we’ve got a platform. But then also, the challenge of what we have is precisely that we’ve got a platform.
We see a lot of opportunity in a lot of places. But, I think the question for us is not where do we start but what are some of the broadly applicable use cases or organization patterns? How do we start to solidify those and how do we start to move toward more interesting, more creative use cases?
I think about it more as ‘what are the problems that Watson could be used to solve?’ as opposed to ‘this is where there’s a “limitation.”’
The other thing to keep in mind is Watson—maybe at one point in time—was an individual product, but not anymore. It’s a family of capabilities, and each one of those capabilities can be pointed at either a specific problem or a set of problems. When you start to take two, three, four, five, or 15 Watson services and put them together, string them together into a new application or a new solution that no one’s ever seen before.
When you think about where is Watson going, that’s the way we think about it, and that is what we are working toward.
Imagine if you could collect all the world’s data, understand it, and improve people’s lives by helping them live, work and play better. What would you create? IBM Watson and PSFK are collaborating to explore the future of cognitive computing and want you to dream big and share your best ideas around what could be possible.
Enter The Good Data Contest now.