Building a Cognitive Business

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:

  1. Converse/interact
  2. Explore
  3. Analyze
  4. Personalize
  5. Diagnose/recommend

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.

Big Data and Cognitive Computing: The Next Industrial Revolution?

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

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

Emu: Context and design (oh! and also nice technology)

Breakthroughs in technology are sometimes less about the underlying technology than they are a leap in understanding how people need to use technology.  The iPod and its ecosystem, for instance, create a synergy between a handy gadget and the music and content people want to carry around and listen to.  By understanding that people want to download, listen to, and share music easily without having to shift from one application to another, the iPod and its successor devices changed our use of content and upended whole industries.  Bo Begole’s book,  “Ubiquitous Computing for Business”, emphasizes that designing an application with the task—the context—as a starting point trumps starting with a technology if you want people to adopt that software.  Business applications have lagged behind consumer applications in ease of use, but sooner or later, what we learn in the consumer space infuses new business interaction designs.

Today’s launch of Emu is another consumer breakthrough that will have broad implications in the business arena as well.  Consider the awkwardness of organizing an evening with friends.  Email or texting to find out if they are available.  Having everyone check one or more calendars.  Merging the answers.  Then agreeing on time and place.  Then seeing if restaurants are available and if that time will fit with the movie schedule.  A familiar and time consuming process.  What emu does is technically difficult but, on the surface, simple and obvious.  It lets you stay in SMS as you arrange a date, time and location.  It checks calendars, available times for favorite restaurants, movie times.  Then it makes reservations, and even shows where you all are on a map as you start to converge on the location.  

Yes, I know the founders of emu, but beyond that, I am taken with this application because it fits squarely into the trend of simple, usable applications that save time and hide technical complexity.  Check it out at