Precision in language can be important; which is why some people chafe at terms like “intelligence” and “cognition” when applied to computer systems. Suggesting today’s artificial intelligence (AI) or cognitive computing systems are capable of human-like thought is a stretch. The term “cognitive computing” was adopted specifically to ensure people understood such systems were not self-aware (i.e., sentient). Cognition is defined as “the action or process of acquiring knowledge and understanding through thought, experience, and the senses.” Of course, that definition has to be modified slightly when applied to a “cognitive” machine. A cognitive system is a system that discovers knowledge, gains insights, and establishes relationships through analysis, machine learning, and sensing of data.
I define cognitive computing as a combination of semantic reasoning (i.e., the use of machine learning, natural language processing, and ontologies) and computational intelligence (i.e., advanced analytics and mathematics). There are, however, a number of approaches that fall under the cognitive computing rubric. Most of those approaches involve machine learning, natural language processing, and advanced analytics. My company’s entry in this field is called the Enterra Enterprise Cognitive System™ (Aila™) — a system that can Sense, Think, Act, and Learn®. Despite people’s best efforts to be precise in their use of language, it’s difficult when talking about the field of artificial intelligence. Steven Astorino (@astorino_steven), Vice President of Development for Analytics at IBM, laments, “If you work in the area of artificial intelligence and cognitive computing, you might use buzzwords and phrases that, to others, might be perceived as confusing jargon.” For example, Deloitte principals, Nitin Mittal (@nmittalanalytic), Peter Lowes, and Rajeev Ronanki (@RajeevRonanki), write, “Emerging machine intelligence capabilities and exploding data volumes could enable IT systems to make inferences and predictions, ushering in a new era of cognitive advances.” The term “machine intelligence” implies a capability that really doesn’t exist. Astorino notes, “Cognitive computing is the ability of computers to simulate the human behaviors of understanding and thought processing.” Simulating or mimicking thought processes does not make a machine intelligent in way we normally think about intelligence.
Cognitive Computing and Business
Don Schuerman (@donpega), Chief Technology Officer at Pegasystems, accepts that language can be confusing and suggests people concentrate on outcomes rather than definitions. He prefers a term coined by Forrester — pragmatic AI — to describe business-friendly technologies. He explains, “[Pragmatic AI] manages to cut through the AI hype to provide meaningful impacts for businesses in the here and now.” Mittal and his Deloitte colleagues suggest a confluence of three powerful forces is driving advances in cognitive systems. They are:
1. Exponential data growth. “The digital universe — comprising all data created and stored — is doubling in size every 12 months, and will likely grow more rapidly as new signals from the internet of things (IoT), dark analytics, and other sources proliferate. The more data these systems consume, the ‘smarter’ they become by discovering relationships, patterns, and potential implications.”
2. Faster distributed systems. “Networks that make data accessible to individual users have become exponentially more powerful — making possible advanced system designs such as those supporting multicore and parallel processing, and data storage techniques that support rapid retrieval and analysis of archived data. Distributed networks can now interface seamlessly with infrastructure, platforms, and applications residing in the cloud, and can digest and analyze ever-growing data volumes within it. They can also provide the power needed to analyze and actuate streamed data from IoT sensors and embedded intelligent devices.”
3. Smarter algorithms. “In recent years, increasingly powerful machine intelligence algorithms have advanced steadily toward simulating human thought processes.”
Deloitte analysts are correct about the impact cognitive technology is having in the business world. Ronald van Loon (@Ronald_vanLoon), a big data business consultant, explains, “Behind the cloud of hype that is surrounding the technology currently, there lies a potential for increased productivity, the ability to solve problems deemed too complex for the average human brains and better knowledge based transactions and interactions with consumers.” If you are wondering what kind of capabilities cognitive computing can provide businesses, Mittal and his colleagues suggest the following:
- Optimization, planning, and scheduling: “Optimization automates complex decisions and tradeoffs about limited resources. Similarly, planning and scheduling algorithms devise a sequence of actions to meet processing goals and observe constraints.”
- Machine learning: “Machine learning enables automatic discovery of patterns in data without following explicitly programmed instructions. Once identified, patterns can be used to make predictions.”
- Deep learning: “Interconnected modules run mathematical models continuously tuned based on processing a large number of inputs.”
- Probabilistic inference: “New AI capabilities use graph analytics and models to identify the conditional dependencies of random variables and infer likely outcomes.”
- Semantic computing: “Computer vision (the ability to analyze images), voice recognition, and various text analytics capabilities can uncover naturally expressed intention and the semantics of computational content. The information can then be used to support data categorization, mapping, and retrieval.” At Enterra Solutions®, we use ontologies to provide semantic context.
- Natural language engines: “These engines understand written text as humans do but can manipulate that text in sophisticated ways, such as automatically identifying all of the people and places mentioned in a document; recognizing the main topic of a document; or extracting and tabulating the terms and conditions in a stack of contracts.” Here again, ontologies can improve results.
- Robotic process automation: “Software robots, or ‘bots,’ can perform routine business processes by mimicking how people interact with software applications. Many enterprises are beginning to employ RPA in tandem with cognitive technologies to automate perceptual and judgment-based tasks once reserved for humans.”
Mittal and his colleagues assert the combination of these capabilities can provide businesses with cognitive insights, cognitive engagement, and cognitive automation. Jennifer Zaino (@) adds, “Cognitive Computing increasingly will be put to work in practical, real-world applications. The industries that are adopting it are not all operating at the same maturity levels; there remain some challenges to conquer. The wheels are very much in motion to make cognitive-driven Artificial Intelligence (AI) applications a key piece of enterprise toolsets.”
Timo Elliott (@timoelliott), an Innovation Evangelist for SAP, concludes it doesn’t matter how you define AI or what AI technology you use as long it benefits the bottom line. He writes, “Precise definitions should never get in the way of the primary goal of getting more value from your data. You have business needs, and there are now new technologies available that can help.” Schuerman adds, “AI is ready to add value to your business today, and the truth is that many organizations have been using it for years without even realizing it. So do yourself a favor and jump in.”
 Steven Astorino, “Navigating the AI and Cognitive Maze,” DZone, 26 December 2017.
 Nitin Mittal, Peter Lowes, and Rajeev Ronanki, “Machine Intelligence Mimics Cognition,” The Wall Street Journal, 5 June 2017.
 Don Schuerman, “Artificial Intelligence: Overhyped and Underappreciated,” CMS Wire, 20 June 2017.
 Ronald van Loon, “Cognitive computing: Moving From Hype to Deployment,” Data Science Central, 8 February 2018.
 Jennifer Zaino, “Cognitive Computing, Artificial Intelligence Apps Have Big Future in the Enterprise,” Dataversity, 17 September 2015.
 Timo Elliott, “What is Artificial Intelligence Called?!” Digital Business & Business Analytics, 19 June 2017.