Market Dynamics

Adopting Cognitive Computing: A Status Report

Cognitive computing is emerging as a significant part of the next generation of computing. Because it is early days in this new generation of computing, there is still no widespread understanding of what it is and how it differs from some of its relatives: AI, internet of things, machine learning, conversational systems, bots, or NLP. We see that in both the US and in Europe that companies are very interested, but are mostly still at the experimentation and proof of concept stage. We will be tracking some of these projects as they develop their cognitive applications and roll them out more broadly. There is no question, though, that interest is high, and that the ability to augment and assist users, as well as to move from static to dynamic systems has great appeal.

I recently had the opportunity to attend a focus group, sponsored by SAS Institute, on cognitive computing adoption outside the US. Attendees came from Denmark, Japan, Finland, Serbia, Netherlands, Sweden, Switzerland, India and Ireland. They represented financial services, telecom, consumer product manufacturers, government agencies, and airline companies. Here are some gleanings from their wide-ranging discussion.

How are you using or how do you expect to use cognitive computing?

  • Automatically revise and evolve rules to expedite adaptation
  • Uncover and improve best business practices and processes
  • Detect patterns of behavior. Detect abnormalities. Identify risks

Augment human agents who can’t handle the current workload by automating the more predictable aspects of the job.

Why move to cognitive computing?

  • Handle large amounts of data with many more variables. Especially textual data.
  • Reduce need for adding manpower. People just don’t scale.
  • Stay ahead of competitors
  • Uncover surprises. (This was a side benefit to demonstration project that was originally designed to augment the human workforce)
  • Curiosity to see what benefits might derive from cognitive computing that we can’t get now
  • Get rid of silos
  • Automate predictable or repeatable work
  • Augment human work by developing digital assistants

Examples of uses:

  • Speech-to-speech product sales. Their innovation lab is experimenting with this. App will be personalized. Will use machine learning to replace hundreds of business rules and 20 predictive models. Machine learning will allow the models to evolve and will help to revise rules faster.
  • Will discover and extract patterns of best business practices to establish KPI’s worldwide from hundreds of business managers. Need to understand what practices work and why.
  • Expedite transactional processing. Moving from rules based process to teaching a system how to assess to minimize delays.
  • They are tracking and analyzing invoices using rules and econometric models. Their goal is to teach a system to automate model development and modification for tracking and analyzing invoices. Extending beyond rules and econometric models they want to add sentiment from incoming, non-English communications.
  • Recognize patterns of behavior to find anomalies and predict risk to more thoroughly assess people and goods.
  • Automate responses to customers, but on a more individual level. Part of a project to analyze customer opinions—a big data project.

In all cases, augmenting existing applications and seeking net new benefit from the use of cognitive computing systems was consistent among all participants. Completely new product development or drastic changes to business processes from cognitive computing application wasn’t seen to provide the palatable business benefit needed to embrace adoption. In all cases, however, changes and improvements to existing business practices were expected.

Challenges

This group of early adopters was proceeding with caution. They had the bruises from past new technology experiments and don’t believe the hype around AI today. In each case, it was apparent that they had support from high-level management, and that they were starting with a proof of concept, or several. We have heard this from other buyers. Several are working with more than one vendor, trying to compare dissimilar products with little in the way of best practices to guide them.

Some of the concerns that emerged were first that these systems are often a black box; that it was not clear why they were getting the recommendations that were delivered. Because business systems are traditionally databased, this ambiguity appears to be unacceptable to them for some uses today. The buyers felt that they needed the evidence behind the results. Probabilistic systems, including search engines, have long struggled with this problem. Although we know that information systems of all sorts deliver only what you ask for and not what you should have asked for, nevertheless, they are seen as precise and complete. Managing expectations is a challenge for vendors and for IT managers.

Other group members were concerned about the need for a lot of computing power. Several mentioned the challenge of developing non-English applications because most of the research has been in English-based systems. Perhaps most intriguing in terms of issues, though, were the predicted “What-if” questions: will we lose the institutional memory that originally trained the system? If so and if the system breaks down, will we be able to fix it? Centralized systems are always a problem, they said. They must be up and running 24/7. They must be reliable. That’s a challenge for any system.

Finally, they pointed to interaction design as a great unknown, especially for non-IT, non-analyst business users who need access to data stores but won’t understand the system design behind the interface. Right now there are experiments, but no accepted best practices.

It is apparent that SAS is seizing this trend toward cognitive computing. The announcement of SAS Viya ™ at this conference, along with a variety of tools for both their loyal developer and analyst base and a wider business user audience positions them nicely as both a partner with other cognitive and IOT platforms and as a potential competitor.

We will continue to track cognitive use cases and report on them. The field is evolving rapidly. Focus groups like this, and like the Cognitive Computing Consortium’s soon-to-arrive discussion forum will enable experimenters to teach each other, perhaps mitigating mistakes that might otherwise be widespread.

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.

2016: the tipping point for 3rd platform, says IDC

IDC’s Third Platform –the next computing generation—rests on cloud computing, big data and analytics, social business, and mobility. Together, these form a foundation to provide scalable anywhere-anytime-any device computing. As these trends become ubiquitous, they enable and accelerate the Internet of Things (IOT), cognitive systems, robotics, 3D printing, virtual reality, self-driving cars, or better security.

At the same time, this brave new world wreaks havoc on the old one of PC’s, client-server software and legacy apps. I would also add another disruptive ingredient to the mix—open source software, which is no longer for hobbyists and is now embedded in most new applications. IDC predicts that 2016 is the year in which spending on third platform IT will exceed that for the second platform, with a CAGR of 12.7% for 2015-2020. At the same time, they predict that second platform investment will be down 5%.

Their recent surveys show that, in terms of maturity, today most companies are in the digital exploration or early platform development phase, with 14% having no interest in digital transformation, and only 8% already using digital transformation to disrupt competitors or markets. That will change by 2020 as 50% of businesses will be using this platform to disrupt and transform.

Other predictions:

  • Business owners, not IT will control more of the IT budget
  • Health services and financial services are two of the top industries to invest, reaping the rewards of faster, cheaper, and more comprehensive uses of their data.
  • Other top applications now in the works include marketing and sales, retail, security, education, media and entertainment.
  • Technology will be embedded in most applications and devices.
  • Start-ups are rife, and the shakeup has not yet begun
  • Cognitive computing and AI is a requirement for developer teams—by 2018, more than 50% of developer teams will be using AI for continuous sensing and collective learning (cognitive applications and IOT).

Where does existing IT infrastructure play in this game? In our scramble as analysts to pin down trends, we often neglect the fact that existing systems and applications a still valuable. They may well be good enough for a given task or process, or they may continue to churn on, feeding into newer layers of technology stacks when appropriate. Unlike newer versions, the kinks have been worked out. The challenge for business and IT managers will be to distinguish between the promise of the new and the security of the old: when to invest, when to explore, and when to stand back and watch. Good question!

Click here or more information on IDC’s take on the 3rd Platform

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 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 sue@synthexis.com.