Integrating Information Visualization and Dimensionality Reduction: A pathway to Bridge the Gap between Natural and Artificial Intelligence

By importing some natural abilities from human thinking into the design of computerized decision support systems, a cross-cutting trend of intelligent systems has emerged, namely, the synergetic integration between natural and artificial intelligence. While natural intelligence provides creative, parallel, and holistic thinking, its artificial counterpart is logical, accurate, able to perform complex and extensive calculations, and tireless. In the light of such integration, two concepts are important: controllability and interpretability. The former is defined as the ability of computerized systems to receive feedback and follow users’ instructions, while the latter refers to human-machine communication. A suitable alternative to simultaneously involve these two concepts—and then bridging the gap between natural and artificial intelligence—is bringing together the fields of dimensionality reduction (DimRed) and information visualization (InfoVis).By importing some natural abilities from human thinking into the design of computerized decision support systems, a cross-cutting trend of intelligent systems has emerged, namely, the synergetic integration between natural and artificial intelligence. While natural intelligence provides creative, parallel, and holistic thinking, its artificial counterpart is logical, accurate, able to perform complex and extensive calculations, and tireless. In the light of such integration, two concepts are important: controllability and interpretability. The former is defined as the ability of computerized systems to receive feedback and follow users’ instructions, while the latter refers to human-machine communication. A suitable alternative to simultaneously involve these two concepts—and then bridging the gap between natural and artificial intelligence—is bringing together the fields of dimensionality reduction (DimRed) and information visualization (InfoVis)

Programming pedagogy in the age of accessible artificial intelligence

In recent years, new teaching opportunities have emerged as artificial intelligence has gained increasing attention in computational thinking education. However, to design effective pedagogy based on the present research landscape, the technology solution must be tailored to a learning environment through a collaboration between human-computer interaction and human-artificial intelligence interaction research. The thesis aims to enhance programming experiences and increase accessibility to programming resources for students in remote schools and post-secondary graduate settings using human-computer interaction and human-artificial intelligence interaction techniques. It addresses the limited computational thinking education resources and the potential of artificial intelligence-assisted coding in a self-learning method suitable for remote Northwestern First Nation communities in Canada. This thesis proposes methods to cater to students’ learning styles in two different learning environments using human-computer interaction for kindergarten to grade 12 students and human-artificial intelligence interaction for university students. Incorporating these research principles can help novice programmers overcome cognitive overload and poor user experience and achieve an optimal user experience. The thesis begins with bibliometric analysis and provides a holistic perspective of computational thinking and artificial intelligence trending strategies. It then presents an empirical study on human-computer interaction, investigating computational thinking in remote kindergarten to grade 12 schools with blended learning environments. It also presents another empirical study on human-artificial intelligence interaction to experiment with a self-learning style for artificial intelligence coding assistants for university students using massive open online courses. [...

Artificial Intelligence and Human Intelligence——On Human-Computer Competition from the Five-Level Theory of Cognitive Science

It is generally accepted that the human mind and cognition can be viewed at five levels; nerves, psychology, language, thinking and culture. Artificial intelligence(AI) simulates human intelligence at all five levels of human cognition, however, AI has yet to outperform human intelligence, although it is making progress. Presently artificial intelligence lags far behind human intelligence in higher-order cognition, namely, the cognitive levels of language, thinking and culture. In fact, artificial intelligence and human intelligence fall into very different intelligence categories. Machine learning is no more than a simulation of human cognitive ability and therefore should not be overestimated. There is no need for us to feel scared even panic about it. Put forward by John R. Searle, the"Chinese Room"argument, a famous AI model and standard, is not yet out of date. According to this argument, a digital computer will never acquire human intelligence. Given that, no artificial intelligence will outperform human intelligence in the foreseeable future

AAAI 2008 Workshop Reports

AAAI was pleased to present the AAAI-08 Workshop Program, held Sunday and Monday, July 13-14, in Chicago, Illinois, USA. The program included the following 15 workshops: Advancements in POMDP Solvers; AI Education Workshop Colloquium; Coordination, Organizations, Institutions, and Norms in Agent Systems, Enhanced Messaging; Human Implications of Human-Robot Interaction; Intelligent Techniques for Web Personalization and Recommender Systems; Metareasoning: Thinking about Thinking; Multidisciplinary Workshop on Advances in Preference Handling; Search in Artificial Intelligence and Robotics; Spatial and Temporal Reasoning; Trading Agent Design and Analysis; Transfer Learning for Complex Tasks; What Went Wrong and Why: Lessons from AI Research and Applications; and Wikipedia and Artificial Intelligence: An Evolving Synergy

Post-Turing Methodology: Breaking the Wall on the Way to Artificial General Intelligence

This article offers comprehensive criticism of the Turing test and develops quality criteria for new artificial general intelligence (AGI) assessment tests. It is shown that the prerequisites A. Turing drew upon when reducing personality and human consciousness to “suitable branches of thought” re-flected the engineering level of his time. In fact, the Turing “imitation game” employed only symbolic communication and ignored the physical world. This paper suggests that by restricting thinking ability to symbolic systems alone Turing unknowingly constructed “the wall” that excludes any possi-bility of transition from a complex observable phenomenon to an abstract image or concept. It is, therefore, sensible to factor in new requirements for AI (artificial intelligence) maturity assessment when approaching the Tu-ring test. Such AI must support all forms of communication with a human being, and it should be able to comprehend abstract images and specify con-cepts as well as participate in social practices

Energetics of the brain and AI

Does the energy requirements for the human brain give energy constraints that give reason to doubt the feasibility of artificial intelligence? This report will review some relevant estimates of brain bioenergetics and analyze some of the methods of estimating brain emulation energy requirements. Turning to AI, there are reasons to believe the energy requirements for de novo AI to have little correlation with brain (emulation) energy requirements since cost could depend merely of the cost of processing higher-level representations rather than billions of neural firings. Unless one thinks the human way of thinking is the most optimal or most easily implementable way of achieving software intelligence, we should expect de novo AI to make use of different, potentially very compressed and fast, processes

OPTIMASI NAIVE BAYES BERBASIS PSO UNTUK ANALISA SENTIMEN PERKEMBANGAN ARTIFICIAL INTELLIGENCE DI TWITTER

At present the development of Artificial Intelligence technology is progressing rapidly. There are many new artificial intelligence technologies available in various fields. Artificial Intelligence is an artificial intelligence program that can study data, perform processes of thinking and acting like humans. The presence of Artificial Intelligence technology has many positive impacts, especially in increasing work effectiveness and efficiency. However, AI is also a threat to human resources because slowly human work is being replaced by Artificial Intelligence. Various opinions about the development of Artificial Intelligence are widely discussed on social media such as Twitter. Sentiment analysis is a computational study to automatically categorize opinions into positive or negative categories. In this study, the Naive Bayes algorithm was used to analyze sentiment or public opinion regarding the development of Artificial Intelligence for Twitter users. The data collection method used is crawling data on Twitter. The results of the sentiment classification test for the development of Artificial Intelligence using Naive Bayes yield an accuracy value of 86.42%. Meanwhile, the results of the sentiment classification test using Naive Bayes based on Particle Swarm Optimization (PSO) increased with an accuracy value of 87.55%. Based on the results of this study, the use of PSO as an optimization technique for the Naive Bayes algorithm is proven to be the best algorithm model in sentiment analysis for the development of Artificial Intelligence for English text

Analysis of New Advances in the Application of Artificial Intelligence to Education

Artificial intelligence is an important innovation in the rapid development of modern Internet. In the 21st century, human beings have been continuously researching and exploring Internet information technology. All kinds of application forms of Internet informatization begin to appear in our life. The rapid change of technology brings a high upgrade rate of internet products. This marks the technological innovation of some traditional concepts and thinking methods. The development mode of artificial intelligence plus education is an important innovation after the deep development of artificial intelligence technology and the achievement of cross-industry application practice. Robots will be the brains of the future education process. This paper aims to clarify the development trend of the application of artificial intelligence in modern education by analyzing the innovation progress of the combination of artificial intelligence technology and contemporary education. This is of great significance for better use of the advantages of artificial intelligence to build a future-oriented high-tech education system

New advances in the application of AI to education system

Artificial intelligence is an important innovation in the rapid development of modern Internet. In the 21st century, human beings have been continuously researching and exploring Internet information technology. All kinds of application forms of Internet informatization begin to appear in our life. The rapid change of technology brings a high upgrade rate of internet products. This marks the technological innovation of some traditional concepts and thinking methods. The development mode of artificial intelligence plus education is an important innovation after the deep development of artificial intelligence technology and the achievement of cross-industry application practice. Robots will be the brains of the future education process. This paper aims to clarify the development trend of the application of artificial intelligence in modern education by analyzing the innovation progress of the combination of artificial intelligence technology and contemporary education. This is of great significance for better use of the advantages of artificial intelligence to build a future-oriented high-tech education system. (DIPF/Orig.