Human thinking processes are rich and variable, whether in search, problem solving, learning, perceiving and recognizing stimuli, or decision-making. But people are subject to limitations on the complexity of their computations and especially the capacity of their ‘working’ (short-term) memory. As consumers, they frequently need to struggle with large amounts of information on numerous brands, products or services with varying characteristics, available from a variety of retailers and e-tailers, stretching the consumers’ cognitive abilities and patience. Wait no longer, a new class of increasingly intelligent decision aids is being put forward to consumers by the evolving field of Cognitive Computing. Computer-based ‘smart agents’ will get smarter, yet most importantly, they would be more human-like in their thinking.
Cognitive computing is set to upgrade human decision-making, consumers’ in particular. Following IBM, a leader in this field, cognitive computing is built on methods of Artificial Intelligence (AI) yet intends to take this field a leap forward by making it “feel” less artificial and more similar to human cognition. That is, a human-computer interaction will feel more natural and fluent if the thinking processes of the computer resemble more closely those of its human users (e.g., manager, service representative, consumer). Dr. John E. Kelly, SVP at IBM Research, provides the following definition in his white paper introducing the topic (“Computer, Cognition, and the Future of Knowing”): “Cognitive computing refers to systems that learn at scale, reason with purpose and interact with humans. Rather than been explicitly programmed, they learn and reason from interactions with us and from their experiences with their environment.” The paper seeks to rebuke claims of any intention behind cognitive computing to replace human thinking and decisions. The motivation, as suggested by Kelly, is to augment human ability to understand and act upon the complex systems of our society.
Understanding natural language has been for a long time a human cognitive competence that computers could not imitate. However, comprehension of natural language, in text or speech, is now considered one of the important abilities of cognitive computing systems. Another important ability concerns the recognition of visual images and objects embedded in them (e.g., face recognition receives particular attention). Furthermore, cognitive computing systems are able to process and analyse unstructured data which constitutes 80% of the world’s data, according to IBM. They can extract contextual meaning so as to make sense of the unstructured data (verbal and visual). This is a marked difference between the new computers’ cognitive systems and traditional information systems.
The Cognitive Computing Forum, which organises conferences in this area, lists a dozen characteristics integral to those systems. In addition to (a) natural language processing; and (b) vision-based sensing and image recognition, they are likely to include machine learning, neural networks, algorithms that learn and adapt, semantic understanding, reasoning and decision automation, sophisticated pattern recognition, and more (note that there is an overlap between some of the methodologies on this list). They also need to exhibit common sense.
The power of cognitive computing is derived from its combination between cognitive processes attributed to the human brain (e.g., learning, reasoning) and the enhanced computation (complexity, speed) and memory capabilities of advanced computer technologies. In terms of intelligence, it is acknowledged that cognitive processes of the human brain are superior to computers inasmuch as could be achieved through conventional programming. Yet, the actual performance of human cognition (‘rationality’) is bounded by memory and computation limitations. Hence, we can employ cognitive computing systems that are capable of handling much larger amounts of information than humans can, while using cognitive (‘neural’) processes similar to humans’. Kelly posits in IBM’s paper: “The true potential of the Cognitive Era will be realized by combining the data analytics and statistical reasoning of machines with uniquely human qualities, such as self-directed goals, common sense and ethical values.” It is not sufficiently understood yet how cognitive processes physically occur in the human central nervous system. But, it is argued, there is growing knowledge and understanding of their operation or neural function to be sufficient for emulating at least some of them by computers. (This argument refers to the concept of different levels of analysis that may and should prevail simultaneously.)
The distinguished scholar Herbert A. Simon studied thinking processes from the perspective of information processing theory, which he championed. In the research he and his colleagues conducted, he traced and described in a formalised manner strategies and rules that people utilise to perform different cognitive tasks, especially solving problems (e.g., his comprehensive work with Allen Newell on Human Problem Solving, 1972). In his theory, any strategy or rule specified — from more elaborate optimizing algorithms to short-cut rules (heuristics) — is composed of elementary information processes (e.g., add, subtract, compare, substitute). On the other hand, strategies may be joined in higher-level compound information processes. Strategy specifications were subsequently translated into computer programmes for simulation and testing.
The main objective of Simon was to gain better understanding of human thinking and the cognitive processes involved therein. He proclaimed that computer thinking is programmed in order to simulate human thinking, as part of an investigation aimed at understanding the latter (1). Thus, Simon did not explicitly aim to overcome the limitations of the human brain but rather simulate how the brain may work-out around those limitations to perform various tasks. His approach, followed by other researchers, was based on recording how people perform given tasks, and testing for efficacy of the process models through computer simulations. This course of research is different from the goals of novel cognitive computing.
We may identify multiple levels in research on cognition: an information processing level (‘mental’), a neural-functional level, and a neurophysiological level (i.e., how elements of thought emerge and take form in the brain). Moreover, researchers aim to obtain a comprehensive picture of brain structures and areas responsible for sensory, cognitive, emotional and motor phenomena, and how they inter-relate. Progress is made by incorporating methods and approaches of the neurosciences side-by-side with those of cognitive psychology and experimental psychology to establish coherent and valid links between those levels.
Simon created explicit programmes of the steps required to solve particular types of problems, though he aimed at developing also more generalised programmes that would be able to handle broader categories of problems (e.g., the General Problem Solver embodying the Means-End heuristic) and other cognitive tasks (e.g., pattern detection, rule induction) that may also be applied in problem solving. Yet, cognitive computing seeks to reach beyond explicit programming and construct guidelines for far more generalised processes that can learn and adapt to data, and handle broader families of tasks and contexts. If necessary, computers would generate their own instructions or rules for performing a task. In problem solving, computers are taught not merely how to solve a problem but how to look for a solution.
While cognitive computing can employ greater memory and computation resources than naturally available to humans, it is not truly attempted to create a fully rational system. The computer cognitive system should retain some properties of bounded rationality if only to maintain resemblance to the original human cognitive system. First, forming and selecting heuristics is an integral property of human intelligence. Second, cognitive computing systems try to exhibit common sense, which may not be entirely rational (i.e., based on good instincts and experience), and introduce effects of emotions and ethical or moral values that may alter or interfere with rational cognitive processes. Third, cognitive computing systems are allowed to err:
- As Kelly explains in IBM’s paper, cognitive systems are probabilistic, meaning that they have the power to adapt and interpret the complexity and unpredictability of unstructured data, yet they do not “know” the answer and therefore may make mistakes in assigning the correct meaning to data and queries (e.g., IBM’s Watson misjudged a clue in the quiz game Jeopardy against two human contestants — nonetheless “he” won the competition). To reflect this characteristic, “the cognitive system assigns a confidence level to each potential insight or answer”.
Applications of cognitive computing are gradually growing in number (e.g., experimental projects with the cooperation and support of IBM on Watson). They may not be targeted directly for use by consumers at this stage, but consumers are seen as the end-beneficiaries. The users could first be professionals and service agents who help consumers in different areas. For example, applied systems in development and trial would:
- help medical doctors in identifying (cancer) diagnoses and advising their patients on treatment options (it is projected that such a system will “take part” in doctor-patient consultations);
- perform sophisticated analyses of financial markets and their instruments in real-time to guide financial advisers with investment recommendations to their clients;
- assist account managers or service representatives to locate and extract relevant information from a company’s knowledge base to advise a customer in a short time (CRM/customer support).
The health-advisory platform WellCafé by Welltok provides an example of application aimed at consumers: The platform guides consumers on healthy behaviours recommended for them whereby the new assistant Concierge lets them converse in natural language to get help on resources and programmes personally relevant to them as well as various health-related topics (e.g., dining options). (2)
Consider domains such as cars, tourism (vacation resorts), or real-estate (second-hand apartments and houses). Consumers may encounter tremendous information in these domains on numerous options and many attributes to consider (for cars there may also be technical detail more difficult to digest). A cognitive system has to help the consumer in studying the market environment (e.g., organising the information from sources such as company websites and professional and peer reviews [social media], detecting patterns in structured and unstructured data, screening and sorting) and learning vis-à-vis the consumer’s preferences and habits in order to prioritize and construct personally fitting recommendations. Additionally, it is noteworthy that in any of these domains visual information (e.g., photographs) could be most relevant and valuable to consumers in their decision process — visual appeal of car models, mountain or seaside holiday resorts, and apartments cannot be discarded. Cognitive computing assistants may raise very high consumer expectations.
Cognitive computing aims to mimic human cognitive processes that would be performed by intelligent computers with enhanced resources on behalf of humans. The application of capabilities of such a system would facilitate consumers or the professionals and agents that help them with decisions and other tasks — saving them time and effort (sometimes frustration), providing them well-organised information with customised recommendations for action that users would feel they have reached themselves. Time and experience will tell how comfortably people interact and engage with the human-like intelligent assistants and how productive they indeed find them, using the cognitive assistant as the most natural thing to do.
Ron Ventura, Ph.D. (Marketing)
1. “Thinking by Computers”, Herbert A. Simon, 1966/2008, reprinted in Economics, Bounded Rationality and the Cognitive Revolution, Massimo Egidi and Robin Marris (eds.)[pp. 55-75], Edward Elgar.
2. The examples given above are described in IBM’s white paper by Kelly and in: “Cognitive Computing: Real-World Applications for an Emerging Technology”, Judit Lamont (Ph.D.), 1 Sept. 2015, KMWorld.com