When IBM’s Watson burst upon the scene in 2011, little did we know that it would kick off a new category of computing. Since that time, IBM has drawn most of its major divisions into the cognitive fold. That’s no surprise: cognitive computing is the ultimate Venn diagram, drawing on hundreds of technologies, from AI to Zookeeper, in order to create systems that “interact, understand, reason, and learn.” It was apparent at the Watson Analyst Day on May 23rd that IBM’s message has been refined, and that it has begun to gel. Just as we in the Cognitive Computing Consortium have moved from a vague understanding that we had something fundamentally new, so too has IBM’s understanding of what cognitive computing is, and what it’s good for become much more solid.
Realizing that the complexity of cognitive solutions can be a barrier to entry, IBM Watson has begun to offer “App Starter Kits” around clusters of technologies that are pre-integrated, like conversation agents, business intelligence, or audio analysis. But markets require more than a single vendor, and we have already seen the rise of new vendors that are not part of the Watson Partner constellation. Being able to mix and match platforms, apps, and technologies will require new standards for not just formats but also storage and terminology if all types of data are to be exchanged easily. Making Watson’s cloud-based cognitive services like sentiment extraction, NLP, predictive analytics, or speech-to-text on both Bluemix and Twilio is a good step in this direction. So are the emerging sets of tools to guide adopters through data selection and modeling, analytics selection, visualization choices, and interaction design.
Two years ago, IBM launched its Watson Division. It now has 550 partners in 45 countries, thousands of developers, and programs in conjunction with 240 universities. It continues to add new languages and services. This is the beginning of a market, but we believe that this phenomenon is bigger than a single technology market. Rather, IT will evolve from the current deterministic computing era to one that is more nuanced. We already see elements of cognitive computing creeping into new versions of older applications—more intelligent interactions, better, more contextual recommendations, In this new world, we will add probabilistic approaches, AI, predictive analytics, learning systems, etc., but we will also retain what works from the old. That calls for a much deeper understanding of which technologies solve what problems the most effectively. What kinds of problems demand a cognitive computing approach? The processes that IBM delineated as possible elements of a cognitive solution are:
They also emphasized the importance of data—curated, annotated data that is normalized in some way using ontologies for both categorization and reasoning. This should come as no surprise to those of us from the online industry, who know that there is no substitute for the blood, sweat and tears that go into building a credible, usable collection of information. The question today is how to do this at scale, and at least semi-automatically, using NLP, categorizers, clustering engines, and learning systems, training sets, and whatever other tools we can throw at this barrier to sense making.
By far, the biggest advances in cognitive applications have been made in healthcare. With good reason. Medicine has a long history of information curation. Advances in ontology building, controlled vocabularies (normalization) and categorization date back to the 1950’s. PubMed and its predecessors had already built multilingual online collections of medical publications, clinical data, toxicology, and treatment guidelines as early as the 1980’s. These resources predate IBM Watson health and have enabled it to address health information problems with an existing well-curated knowledge base. Healthcare requires extreme accuracy, big data analytics, advanced patient-doctor-machine natural interaction, and a probabilistic approach to solving a medical problem. Because the amount of possibly relevant information is staggering, and the outcome is a matter of life and death, the reasons for investment in cognitive systems are obvious for healthcare insurers and providers alike. There are also, of course, billions of healthcare dollars at stake. Customer engagement, retail sales, mergers and acquisitions, investment banking, security and intelligence are not far behind in their promise, but they lack that initial bootstrapping of existing knowledge bases.
In summary, cognitive computing is moving from dream to reality. New tools and more packaged applications have reduced the complexity and the time to deploy. Early adopters are still at the experimentation stage, but from IBM and other vendors and services firms, we see gradual adoption with associated ROI, a virtuous loop that attracts yet more buying interest.
We can all use a good personal assistant, one that keeps our health in mind, not just our appointments. This assistant needs to understand who we are today: our current state of mind, our location and our preferences. Recommendations on how to keep fit in July won’t work in January if you are snowed in with the flu. Instead, we need a sympathetic advisor who urges chicken soup instead of cookies, and suggests a hot shower, a nap and perhaps some gentle stretches for the aches.
This post may seem a far cry from our normal focus on cognitive computing, but in fact, it showcases one of the major leaps forward that cognitive computing will promote: true individualized recommendations that are presented within the framework of who you are, where you are, how you’re feeling, and what you like to do. Over the last two years, healthcare in particular has moved into the world of big data in order to provide individualized recommendations that are backed up with sound evidence. From cancer diagnoses to congestive heart failure, vast amounts of data have been mined to uncover new treatments or prevent hospital readmissions.
Cognitive computing is also moving into disease prevention. Welltok® rather than focusing on disease and diagnoses, has developed a Health Optimization Platform™, CaféWell®, to help healthcare plans, providers and employers keep consumers healthy and reward healthy behavior. The platform is a well-integrated combination of curated health and nutrition information and social and gaming technologies that drive consumer engagement.
To deliver more individualized health programs, Welltok partnered with IBM Watson in 2014 to add cognitive computing capabilities, thereby a personalized experience for consumers. The CaféWell® Concierge application powered by IBM Watson learns constantly from its users, so that it evolves to offer better, more appropriate suggestions as each individual uses the system. Jeff Cohen, Welltok’s co-founder and lead for their IBM Watson project, tells us that their goal is to make their existing platform more intelligent about each member’s health conditions and context. CaféWell strives to answer the question, “What can I do today to optimize my health?” for each of its members.
To accomplish this goal, Welltok starts with good information on health, exercise and nutrition—from healthcare systems and well-respected structured and unstructured data sources. It factors in individual information about health status, available benefits, demographics, interests and goals. The IBM Watson technology parses and processes this information to find facts, patterns and relationships across sources, using a proprietary Welltok approach. Welltok also adds its taxonomy of healthcare concepts and relationships. Then it creates question-answer pairs to train the system. These query-answer pairs are a key ingredient to help Watson enrich implicit queries. Welltok also provides navigation so that users don’t get lost as they seek answers. Free-flowing dialog between the user and the system is one of the earmarks of a cognitive application, but users need hints and choices in order to avoid frustration. Welltok provides these, constantly updating and retraining the system as it learns to predict pathways through the information. The information is filtered for each member’s health plan coverage and individual profile. Cognitive computing also incorporates temporal and spatial facets, so that the recommendations are suitable for the user’s time and place. This all eliminates information dead ends because it prevents inapplicable information from being displayed.
In addition to relevance, members are given incentives to participate and they are rewarded as they pass certain milestones. More importantly, the system learns their preferences and what motivates them to be healthy. For example, if you are only interested in exercising in groups, that’s what will be recommended, but if you prefer walks in the woods, you’ll instead get tips, perhaps, on places to walk or find mileage and terrain for common routes.
The Welltok use of cognitive computing has all the earmarks of a cognitive system. It’s dynamic and it learns. It parses both information sources and the user’s situation deeply, and matches the individual to the information and the recommendations. It is interactive, and it devours data—the more, the better.
One of the most fertile areas of development for cognitive applications is in this area of intelligent personal advisors. Suggestions for actions that are tailored to who you are make it more likely that you will try them. Now, where did I put the chicken soup?
Big Data and Cognitive Computing: The Next Industrial Revolution? updates the trends we covered in The Answer Machine, published by Morgan & Claypool last year. This webcast on Jan. 30, 2014 was given to the Cornell Entrepreneur Network, but was open to all. You can listen to the recording at https://cornell.webex.com/cornell/lsr.php?RCID=616468230cc9b30a45ddd07d778325e2.
In updating the book, we found that the nascent trends we discussed in 2012 have quickly exploded. Applications that aggregate information and integrate technologies are becoming common. Task-centered design is almost a requirement. The market, driven by the buzz around big data, and bombarded by information has started to demand what vendors foresaw: there’s immense value in putting together the pieces from disparate sources, and we need help in doing this. IBM’s Watson may have been the first to define cognitive computing, but we see others positioning themselves in this marketplace as the interest grows. We’ll be covering some of these new companies in the months ahead.
During the past year, as we work with vendors and technology buyers, we have found that one of the most difficult concepts to get across is probabilistic computing. Where does it fit in the current IT landscape? Does it replace traditional BI? We expect to explore this topic also in the coming months. Please contact me directly if you’d like to discuss it in depth, or to schedule a briefing for your company. I can be reached at email@example.com.