Study Guide for Cognitive Information Processing Approaches to Cognitive Blockchain Architecture

Study Guide for Cognitive Information Processing Approaches to Cognitive Blockchain Architecture

Glossary

Term Definitions Cognitive Application A cloud-based application that interprets big data and learns from user engagement and data interactions. Dark Data Ignored information, environmental signals, and insights that can help organizations and individuals enhance their intelligence and provide actionable insights through the implementation of cognitive applications. Blockchain-Associated Cognitive Insight A cognitive insight generated at least in part by using blockchain data, or provided in the form of blockchain transactions. Blockchain Data Any data associated with a given blockchain, whether related to its overall data structure or its individual elements, the individual data elements it may contain, or its associated metadata. Smart Contract An executable computer code configured to generate downstream process instructions. One-Step Assurance Operation An operation that ensures the execution of a blockchain-associated cognitive insight through a single interaction. Compliance Requirement A requirement that complies with a policy, standard, regulation, or law. Public Blockchain A blockchain that has been implemented as a permissionless blockchain, meaning that anyone can read or write to it. Private Blockchain A blockchain whose participants are known and have read and write permissions granted by an authority that governs the use of the blockchain. Universal Knowledge Repository A collection of knowledge elements that can be used in various embodiments to generate one or more cognitive insights. Cognitive Persona A prototypical user model that represents a set of common attributes associated with a hypothetical group of users. Cognitive Profile An instance of a cognitive persona that references personal data associated with a specific user. Cognitive Learning Operation A cognitive learning technique used to generate a cognitive learning result. Cognitive Learning Style A generic learning method implemented by CILS that is used to perform a cognitive learning operation. Cognitive Learning Category A data type or data source used to perform a cognitive learning operation. Cognitive Learning Technique A cognitive learning technique that uses a cognitive learning style in combination with a cognitive learning category to perform a cognitive learning operation. Cognitive Session Graph A cognitive graph whose nodes are associated with cognitive sessions. Cognitive Session User, user group, topic, issue, question, intent, goal, task, assignment, process, situation, requirement, condition, responsibility, location, time period, block in a blockchain, blockchain transaction associated with a blockchain block, or any combination of the above. Short Answer Questions

What is dark data and how does it relate to cognitive applications? (2-3 sentences)

How do cognitive blockchain insights differ from traditional cognitive insights? (2-3 sentences)

What role do smart contracts play in blockchain-based cognitive systems? (2-3 sentences)

Describe the one-step guarantee operation and its significance in blockchain transactions. (2-3 sentences)

What are the main differences between public and private blockchains? (2-3 sentences)

What role does a general knowledge base play in cognitive information processing? (2-3 sentences)

Explain the concept of cognitive personas and their use in generating personalized insights. (2-3 sentences)

How do cognitive profiles relate to cognitive personas? (2-3 sentences)

What is the purpose of cognitive session graphs? (2-3 sentences)

Provide a real-world example of how feedback data can be used in cognitive learning operations to improve cognitive systems. (2-3 sentences)

Short answer questions

Dark data refers to data that is ignored and difficult to process and analyze, such as log files, social media posts, and sensor data. Cognitive applications use artificial intelligence and machine learning techniques to analyze dark data to discover hidden patterns, trends, and insights that help organizations make smarter decisions.

Cognitive blockchain insights are cognitive insights generated using blockchain technology, which means they are secure, transparent, and tamper-proof. This makes cognitive blockchain insights more reliable and trustworthy than traditional cognitive insights that may be susceptible to manipulation or bias.

Smart contracts are self-executing contracts stored on the blockchain that automatically execute when predefined conditions are met. In blockchain-based cognitive systems, smart contracts can be used to automatically execute certain actions, such as sharing data or making payments based on analytical results.

One-step guaranteed operations refer to blockchain transactions in which all necessary steps are completed with just one user action, such as providing a digital signature or making a payment. This simplifies the user experience and ensures that transactions are executed in a secure and efficient manner.

Public blockchains are open, decentralized networks that anyone can join and participate in transaction validation, while private blockchains are permissioned networks controlled by specific entities. Private blockchains offer increased security and privacy, but are less transparent than public blockchains.

A general knowledge base is a centralized repository of structured and unstructured data from a variety of sources, including text, images, videos, and sensor data. Cognitive information processing systems leverage this data to learn, reason, and generate insights.

A cognitive persona is a prototypical user model that represents a specific group of users, such as users with similar interests, behaviors, and preferences. By analyzing user data, cognitive systems can create personas and leverage them to provide personalized recommendations and insights.

A cognitive profile is an instance of a cognitive persona that represents the characteristics, preferences, and behaviors of a specific user. Cognitive systems use cognitive profiles to tailor the user experience and provide insights that are more relevant to individual users.

A cognitive session graph is used to represent a series of interactions between a user and a cognitive system. It captures user queries, system responses, and related contextual information to provide a more accurate and personalized user experience.

Online retailers can use feedback data to improve their recommendation systems. For example, if a user gives a negative review after purchasing an item, the system can learn from this feedback and avoid recommending the item to other users with similar profiles in the future.

Paper Title

Explore the transformative role of cognitive applications in unlocking the potential of dark data and driving innovation across industries.

Analyze the advantages and challenges of integrating blockchain technology into cognitive information processing systems to enhance data security, transparency, and trust.

Evaluate the effectiveness of different cognitive learning styles and categories in a cognitive learning framework and discuss their relative strengths and weaknesses.

Critically examine the ethical implications of cognitive personas and profiles in providing personalized user experiences and driving targeted advertising.

Envision the future of cognitive information processing, focusing on emerging trends, potential challenges, and impacts on society, such as advances in artificial intelligence and machine learning.