Introduction to Cindicator (CND) Part 1

Types of Intelligence. Human Intelligence and Cognitive computing.

 

Cindicator is a decentralized ecosystem that combines human and machine intelligence. The creators of Cindicator call the combination of human and machine intelligence hybrid intelligence. Hybrid intelligence allows the Cindicator ecosystem to make better decision in the circumstances of growing complexity and uncertainty, which benefits both individual members of the ecosystem and the community as a whole.

Human intelligence is the intellectual power of people, which combines complex cognition including capabilities to study, learn, create concepts and apply reasoning and logic, and use of communication to explain the process and results of cognition.

Currently, machine intelligence is a buzzword that different people use to describe different things, which is why this article will describe different types of intelligence, and explain what they are and what they are not.

Machine intelligence is the ability of machines to perform tasks that are typically performed by humans and require human intelligence. Machine intelligence consists of four areas. They are machine learning, artificial intelligence, deep learning and cognitive computing. While some people often use these four types of machine intelligence interchangeably, they are actually very different.

 

Cognitive computing

The objective of cognitive computing is to replicate human thought process using sensors and algorithms so that machines can make decisions like humans and behave like humans. In cognitive computing, machines typically have sensors that allow them to “see,” “touch,” “smell,” “hear,” and “feel.” Typically, computers can process visual information due to the presence of image sensors and audio information with microphones. Speech-to-text and text-to-speech functionality allows them to communicate with humans. This is what devices such as Google Home, Amazon Alexa, Apple’s Siri and Microsoft’s Cartana use to get information from people and to deliver information to people. These machines are not cognitive systems in the true sense because they can’t process any request and answer any question. They are pre-programmed to respond to given questions and can’t answer any question. For example, when you ask a home assistant to tell you the weather forecast, the assistant connects to a weather service and then uses text-to-speech recognition to tell you the answer. This is a pre-programmed set of actions that does not require any true cognitive capabilities. However, with time the functionality of home assistants will be increasing and they will be able to answer more and more questions, and, at a certain point, create unique answers based on their cognition abilities.

There is nothing new in attempts to add cameras, microphones and sensors to machines. What is new is the addition of software that allows the machines to make decisions based on the information they get from the sensors. This addition is what is allowing machines to function and behave in the way humans do.

 

An example of cognitive computing

IBM has participated in the TED Talk series and has explained what it envisions IBM Watson, a computer system by IBM that is able to answer questions based on the information it receives, will be able to do in the future. The developers at IBM aim to create a computer that will be able to analyze knowledge from a variety of sources in the medical field, including patient history, patient examinations and tests data, articles from journals, and best practices in the field to provide recommendations that doctors could then use to make better decisions about treatment options.

In other words, the goal of IBM Watson will be not to replace humans but to expand the capabilities of humans by analyzing and processing vast amounts of data that a person would not be usually able to retain and process, and to provide a summary and suggested next steps.

A similar process could benefit many fields where problem-solving and coming up with answers require analysis of large quantities of data that comes as a result of sensory and cognitive perception of the world, including the legal field, the financial industry, and education.

Cognitive computing could be also beneficial in marketing in business, where organizations need to analyze the behavior of customers, including shopping habits, choices of travel destinations, and more. For example, Hilton Hotels has recently introduced its first robotic concierge that can answer questions guests may have about the hotel they are staying at and the attractions nearby.