Visualizing the Computational Intelligence Field

Computational Intelligence Magazine, IEEE, 2006

In this paper, we visualize the structure and the evolution of the computational intelligence (CI) field. Based on our visualizations, we analyze the way in which the CI field is divided into several subfields. The visualizations provide insight into the characteristics of each subfield and into the relations between the subfields. By comparing two visualizations, one based on data from 2002 and one based on data from 2006, we examine how the CI field has evolved over the last years. A quantitative analysis of the data further identifies a ...

Challenges for computational intelligence, 2007

What is Computational Intelligence (CI) and what are its relations with Artificial Intelligence (AI)? A brief survey of the scope of CI journals and books with "computational intelligence" in their title shows that at present it is an umbrella for three core technologies (neural, fuzzy and evolutionary), their applications, and selected fashionable methods. At present CI has no comprehensive foundations and is more a bag of tricks than a solid branch of science. The change of focus from methods to challenging problems is advocated, with CI defined as a part of computer science devoted to solution of non-algoritmizable problems. In this view AI is a part of CI focused on problems related to higher cognitive functions, while the rest of the CI community works on problems related to perception and control, or lower cognitive functions. Grand challenges on both sides of this spectrum are addressed.

2014 IEEE 26th International Conference on Tools with Artificial Intelligence, 2014

Computational Intelligence (CI) embraces techniques designed to address complex real-world problems in which traditional approaches are ineffective or infeasible. Some of these techniques are being used to solve several complex problems, such as the team allocation, building products portfolios in a software product line and test case selection/prioritization. However, despite the usefulness of these applications, the development of solutions based in CI techniques is not a trivial activity, since it involves the implementation/adaptation of algorithms to specific context and problems. This work presents Athena, a visual tool developed aiming at offering a simple approach to develop CIbased software systems. In order to do this, we proposed a dragand-drop approach, which we called CI as a Service (CIaaS). Based on a preliminary study, we can state that Athena can help researchers to save time during the development of computational intelligence approaches.

Computational intelligence (CI) refers to recreating human-like intelligence in a computing machine. It consists of a set of computing systems with the ability to learn and deal with new situations such that the systems are perceived to have some attributes of intelligence. It is efficient in solving real-world problems which require reasoning and decision-making. It produces more robust, simpler, and tractable solutions than the traditional techniques. This paper provides a brief introduction to computational intelligence. Introduction Computational intelligence (CI) is the study of the design of intelligent systems. A system is regarded as " intelligent " only if it satisfies learning and decision-making requirements. It is familiar that the best-known manifestation of intelligence is human intelligence. The characteristic of "intelligence" is usually attributed to humans so that CI is a way of performing like human beings and using human-like reasoning, i.e. it uses inexact and fuzzy knowledge. Thus, the goal of CI is to recreate human-like intelligence in a human-made machine. The term " computational intelligence " was coined by John McCarthy in 1956. The ongoing worldwide computerization has created new opportunities for researchers. All branches of science and art have become computational: computational biology, computational physics, computational chemistry, computational ecology, computational linguistics, computational electromagnetics, computational finance, computational mechanics, computational social science, computational epistemology, computational intelligence, and so on. CI uses a combination of five main complementary techniques: (1)fuzzy logic which enables the computer to understand natural language, (2)

2020

This paper presents some preliminary results after having analyzed the metadata of 146,585 and the full text of 9,879 relevant AI-related papers. Other datasets collected from different sources were prepared for further analysis, too. The idea is to investigate the use of capabilities that denote intelligence in the AI scientific discourse with the goal to track the evolution of the narrative around intelligence and its manifestations in humans and machines over time. Intelligence capabilities and related properties that are used when defining intelligence were extracted from previous work in this area in the form of a catalog and spanning over different fields, AI included. Although still an elusive, difficult to define concept, analyzing and understanding the discourse around intelligence may shape both the way we use it and how intelligent artifacts are and will continue to be developed in the future.

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2007

In this paper, a bibliometric study of the computational intelligence field is presented. Bibliometric maps showing the associations between the main concepts in the field are provided for the periods 1996-2000 and 2001-2005. Both the current structure of the field and the evolution of the field over the last decade are analyzed. In addition, a number of emerging areas in the field are identified. It turns out that computational intelligence can best be seen as a field that is structured around four important types of problems, namely control problems, classification problems, regression problems, and optimization problems. Within the computational intelligence field, the neural networks and fuzzy systems subfields are fairly intertwined, whereas the evolutionary computation subfield has a relatively independent position.

2016 IEEE Symposium Series on Computational Intelligence (SSCI), 2016

Computational Intelligence is a dead-end attempt to recreate human-like intelligence in a computing machine. The goal is unattainable because the means chosen for its accomplishment are mutually inconsistent and contradictory: "Computational" implies data processing ability while "Intelligence" implies the ability to process information. In the research community, there is a lack of interest in data versus information divergence. The cause of this indifference is the Shannon's Information theory, which has dominated the scientific community since the early 1950s. However, today it is clear that Shannon's theory is applicable only to a specific case of data communication and is inapplicable to the majority of other occasions, where information about semantic properties of a message must be taken into account. The paper will try to explain the devastating results of overlooking some of these very important issueswhat is intelligence, what is semantic information, how they are interrelated and what happens when the relationship is disregarded.

2 The future is already here I N THE LAST decade, artificial intelligence (AI) has progressed from near-science fiction to common reality across a range of business applications. In intelligence analysis, AI is already being deployed to label imagery and sort through vast troves of data, helping humans see the signal in the noise. 1 But what the intelligence community (IC) is now doing with AI is only a glimpse of what is to come. These early applications point to a future in which smartly deployed AI will supercharge analysts' ability to extract value from information. The adoption of AI has been driven not only by increased computational power and new algorithms but also the explosion of data now available. By 2020, the World Economic Forum expects there to be 40 times more bytes of digital data than there are stars in the observable universe. 2 For intelligence analysts, that proliferation of data means surefire information overload. Human analysts simply cannot cope with that much data. They need help. Intelligence leaders know that AI can help cope with this data deluge but they may also wonder what impact AI will have on their work and workforce. According to surveys of private sector companies, there is a significant gap between the introduction of AI and understanding its impact.

Bhartiya Krishi Anusandhan Patrika, Volume 38 Issue 3: 203-209 (September 2023)

Dairy industry is a self-reliant India’s identity. The dairy sector in India has come a long way since the days of the White Revolution launched by late Prime Minister Shri Lal Bahadur Shastri in 1965. The National Dairy Development Board (NDDB) was established in 1965 to implement the Operation Flood program which aimed to make India self-sufficient in milk production. Today, India is the world’s largest milk producer, with milk production of about 221.06 million tonnes in 2021-2022. The dairy industry in India is not only an important source of income for millions of farmers and rural households, but it also plays a vital role in the country’s economy. It contributes to more than 4% of the country’s GDP and provides employment opportunities to millions of people, both directly and indirectly. The dairy sector in India also plays a critical role in ensuring food security for a large population and provides a source of animal protein for a large vegetarian population.