Cognition as a Service: An Industry Perspective

By Spohrer, Jim; Banavar, Guruduth | AI Magazine, Winter 2015 | Go to article overview

Cognition as a Service: An Industry Perspective


Spohrer, Jim, Banavar, Guruduth, AI Magazine


From an industry perspective, the era of cognitive computing has dawned with the promise of human-centered cognitive prostheses, as just one of many benefits anticipated (Kelly and Hamm 2013). Gartner, the technology industry analyst firm, projects that by 2017, 10 percent of all computers will be learning (Plummer 2013). However, scholars familiar with the field of artificial intelligence and cognitive science are rightfully cautious, after witnessing a roller coaster of ups and downs over AI's relatively short 60-year history. Simply put, hard problems still remain.

So how can we know if this time is really different? After all, three of the key pillars of cognitive computing, namely machine learning (ML), natural language processing (NLP), and hypotheses generation with evidence-based explanation (EBE) capabilities, have existed for quite some time. The first author personally recalls programming hidden Markov model (HHM) learning algorithms for speech recognition in a startup company in the late 1970s, as well as statistical natural language processing at the same company (for example, Verbex, from which Dragon Systems sprang) in the early 1980s. In graduate school at Yale a few years later, along with fellow graduate students, the first author built a number of explanation systems, including a master's thesis that provided expert and novice explanations of economic arguments after ingesting articles from the Wall Street Journal, as well as a dissertation that provided explanations of bugs by simulating novice programmers (Spohrer 1992, Kahney 1993). Frankly, while there was significant progress made in those earlier times, the context today is dramatically different and the commercial and scientific opportunities are vastly greater.

The most obvious differences between now and then are well documented (Brynjolfsson and McAfee 2014). First, there is no doubt that processor speeds, software algorithms, as well as storage capacity for data sets, both training and test data, have advanced by orders of magnitude, while costs have plummeted. Second, most of us are walking around with smart phones that not only provide easy access to the sum of crowdsourced human knowledge, but also provide easy access to a rapidly growing number of cloudbased global service organizations ready to compete for a share of our attention and wealth. Nevertheless, societal challenges, including the rising cost of continuously reskilling the work force and effectively addressing unemployment due to skills gaps even among recent college graduates, loom large in the minds of policy makers.

In this article, we provide an industry perspective on the topics of cognitive computing, cognitive systems, cognition as a service, and human-centered smart service systems. In conclusion, we describe a number of remaining research and commercialization challenges, and a new set of university programs aimed at addressing those challenges.

Cognitive Computing

Figure 1 highlights some of the recent growth of industrial strength cognitive computing componentry, which can be found in the diverse offering from many companies; every month there are more announcements, from small university startups to large company business unit formations, projects, products, and acquisitions (Harris 2013, Spivak 2013, Ardire and Roe 2014, Austin and LeHong 2014).

Cognitive computing is a term coined by industry leaders to refer to a third era in computing beyond the tabulator era and the programming era (Kelly and Hamm 2013). A proposed shared definition of cognitive computing by Ardire and Roe (2014) is narrowly focused on natural language processing, machine learning, and big data:

Natural language processing of structured, unstructured, streaming-in big data, or smart data layers with machine learning for reasoning and learning to generate textual patterns and associations that enable humans to connect the dots faster and smarter for more informed decisions to drive better outcomes. …

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