Getting started with cognitive computing
Confidence based on contextual clues
The system may return different responses for each of the queries with different confidence levels about the appropriate answer. In comparing answers, a cognitive computing system uses a confidence score based on the strength of the input signals. Algorithms approach problems from multiple perspectives and paths in order to converge on the “correct” (actually, the most likely) answer.
One algorithm might process a query by comparing it to crowd-sourced variations in the question to find the closest match. Another might arrive at a normalized query by performing part of speech analysis and word sense disambiguation. In each case, the quality of the match can be ascertained and given a relative score—one algorithm might have different confidence levels for responses, or multiple algorithms might propose candidates with each providing a confidence level. That use of a confidence score is an example of a probabilistic approach.
One of the important features of cognitive computing is a learning mechanism. Cognitive computing systems can learn both from automated process and from human-curated and human-assisted processes. Feedback loops for continual improvement can be based on input from user paths and behaviors: whether they click on results, execute another query or “pogo stick” out from the content once they have clicked, rephrase their question or ask for live help. Those metrics form the performance measures for manual curation and user experience adjustments as well as the inputs for automated machine learning and hybrid approaches.
Increasing the size of sampling data sets (providing more occurrences of variations in how a question is phrased, for example) can also provide a mechanism for the system to learn and improve over time. The association of an input (the query) with the output (response) and the user’s subsequent behavior (continuing to the next step or contacting customer service) will provide feedback to the scoring algorithm to adjust confidence the next time a similar query is received.
Extending established approaches
Cognitive computing builds on established approaches such as content curation, metrics-driven governance and sound information architecture. With the addition of natural language processing and learning algorithms and careful use of crowd-sourced elements, intelligent virtual assistants will become pervasive sooner than most people might think.
As additional signals are incorporated as inputs into algorithms, machine learning can begin to determine which signals improve outcomes. Perhaps features extracted from a user’s purchase history can add a signal to improve search result accuracy. A human analyst can add the data set and allow machine learning to detect relevant patterns. The pattern may be too subtle to notice or may emerge from a combination of dozens of elements. That correlation can go on behind the scenes and be used to impact future interactions.
Those aspects of cognitive computing learning mechanisms help intelligent virtual assistants improve through the ability to process information that is large in volume, complex in nature and heterogeneous.
Pattern matching, user profiles and cross-sell recommendations
The same matching algorithm that builds a feature profile of a user for help queries might also apply that feature profile to cross-sell recommendations. That information, when integrated into a comprehensive profile of the customer, might prompt an offer after the help query is resolved. The system begins to understand the customer’s goals and patterns and filters answers by context, considering the customer’s needs and interests. It finds patterns in the data that are both expected and unexpected, across many different kinds of use cases.
Virtual assistants built on cognitive computing technology will be used to make sophisticated recommendations based on higher level needs of users. Perhaps an investor wants to make a stock purchase but changes in the market causes him or her to reconsider. A virtual assistant can understand risk tolerance, investment objectives and market conditions, and then provide a range of alternatives based on human expertise embedded in the system but also correlated with real-time inputs.
Cognitive computing systems build a constantly evolving understanding of users and their goals and behaviors based on continually processing inputs. They learn, build their knowledgebase and develop mechanisms for understanding user queries. They continually fine-tune their outputs. They start with a clearly defined problem and content to solve that problem and then evolve ways of interpreting human interactions and providing responses to help people achieve their goals.