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data for supporting the hypothesis may not always structured. In real-world applications, the data is mostly unstructured. Figure 1.11 shows an upright pattern of hypothesis generation and scoring. Understanding and traversing through the unstructured information requires a new computing technology which is called cognitive computing. The intellectual frameworks can create different hypotheses dependent on the condition of information in the corpus at a given time. When all the hypotheses are generated then they can be assessed and scored. In the below fig of, IBM’s Watson derives the responses questions and score each response. Here 100 autonomous hypothesis might be produced for a question after parsing the question and extracting the features of the question. Each generated hypothesis might be scored using the pieces of evidence.
Figure 1.11 Hypotheses generation IBM Watson.
1.11.1 Hypothesis Generation
The hypothesis must be generalized and should map for the unseen cases also. The experiments are developed to test the general unseen case. There are two key ways a hypothesis might be produced in cognitive computing systems. The first is because of an express inquiry from the user, for example, “What may cause my fever and diarrhea?” The system generates all the possible explanations, like flu, COVID where we can see these symptoms. Sometimes the given data is not sufficient and might require some additional input and based on that the system refines the explanations. It might perceive that there are such a large number of answers to be valuable and solicitation more data from the client to refine the arrangement of likely causes.
This way to deal with hypothesis generation is applied where the objective of the model is to recognize the relations between the causes and its effects ex. Medical conditions and diseases. Normally, this kind of psychological framework will be prepared with a broad arrangement of inquiry/ answer sets. The model is trained using the available question and answer sets and generates candidate hypotheses.
The second sort of hypothesis generations doesn’t rely upon a client inquiring. Rather, the system continually searches for atypical information patterns that may demonstrate threats or openings. In this method, hypotheses are generated by identifying a new pattern. For example to detect unauthorized bank transactions the system generated those fraudulent transaction patterns, which became the hypothesis space. Then the cognitive computing model has to find the evidence to support or reject the hypothesis. The hypothesis space is mostly based on assumptions.
The two kinds of hypothesis generation methods produce at least one theory given an occasion, however in the primary case, the event is a client question, and in the second it is driven by similar pattern data.
1.11.2 Hypotheses Score
The next step is to evaluate or score these hypotheses based on the evidence in the corpus, and then update the corpus and report the findings to the user or another external system. Now, you have perceived how hypotheses are generated and next comes scoring the hypothesis scoring. In the scoring process, the hypothesis is compared with the available data and check whether there is evidence or not. Scoring or assessing a hypothesis is a procedure of applying measurable strategies to the hypothesis evidence sets to dole out a certain level to the theory and find the confidence level to each hypothesis. This confidence level weight might be updated based on the available training data. The threshold score is used to eliminate the unnecessary hypothesis. On the off chance that none of the hypothesis scores over the threshold the system may need more input which may lead to updating the candidate hypothesis. This information may be represented in a matrix format and several tools are available to manipulate these matrices. The scoring process is continued until the machine learns the concept.
1.12 Developing a Cognitive Computing Application
Cognitive computing is evolving at a good pace and in the next decade, a large number of applications can be built using this technology.
The organizations of different sectors are in the premature stages in developing the cognitive applications; its applications are from healthcare to production industries to governments, making a decision using the huge variety and volumes of data. There are some issues to be noted in the process of building the application [11].
1 A good decision can be taken if large volumes of data can be analyzed
2 There will be a change in decisions dynamically with the frequently varying data, obtaining data from the latest sources and also from the other forms of data
3 There should be a transfer of knowledge by the domain experts to the junior trainees through the training and mentoring process.
4 In the process of decision making a large amount of data is analyzed, several options and solutions to a problem are obtained.
To develop the cognitive application the first step is to define the objective, which requires understanding the types of problems the application can solve. It also has to consider the different types of users using the application. The most important thing is that it also has to take care of the types of issues the user is interested in and also what they are looking for and need to know. The next step is to define the domain, it is important because we need to identify and also assess the different data sources that match to build the application. Defining the domain helps to identify the subject experts.
In training the cognitive application the domain helps in identifying the subject experts that will be useful in training the cognitive application. Table 1.2 gives the examples of the Cognitive application domains.
Characterizing Questions and Exploring Insights
The cognitive applications that are developed in the early stages for customer engagement can be divided into two types:
1 Discovery and Exploration
2 Using sophisticated question and response mechanisms to
3 respond to inquiries as part of continuous exchange with the client.
The cognitive framework can build a relationship between questions, answers, and information to enable the client to better grasp the topic at a more profound level. The inquiries clients will pose can be set in two general classifications (Table 1.3):
1 Question–Response pairs: The responses to the inquiries can be unearthed in an information resource. There might be clashing responses inside the information resources, and the framework will break down the choices to furnish various reactions with related certainty levels.
2 Anticipatory analytics: The client takes part in an exchange with the cognitive application. The client may pose a few inquiries however not all the inquiries. The subjective application will utilize prescient models to envision the client’s next inquiry or arrangement of inquiries.
Table 1.2 Examples of cognitive application domains [11].
| S. no. | Domain | Data requirements to be selected | Subject experts |
|---|---|---|---|
| 1 | Medical |
Electronic medical health records, International |