scholarly journals An End-to-End Deep Reinforcement Learning-Based Intelligent Agent Capable of Autonomous Exploration in Unknown Environments

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3575 ◽  
Author(s):  
Amir Ramezani Dooraki ◽  
Deok-Jin Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intelligent Agent ◽  
Social Robots ◽  
Fusion Method ◽  
Autonomous Exploration ◽  
Unknown Environments ◽  
Real Robot ◽  
Different Shapes ◽  
High Degree

In recent years, machine learning (and as a result artificial intelligence) has experienced considerable progress. As a result, robots in different shapes and with different purposes have found their ways into our everyday life. These robots, which have been developed with the goal of human companionship, are here to help us in our everyday and routine life. These robots are different to the previous family of robots that were used in factories and static environments. These new robots are social robots that need to be able to adapt to our environment by themselves and to learn from their own experiences. In this paper, we contribute to the creation of robots with a high degree of autonomy, which is a must for social robots. We try to create an algorithm capable of autonomous exploration in and adaptation to unknown environments and implement it in a simulated robot. We go further than a simulation and implement our algorithm in a real robot, in which our sensor fusion method is able to overcome real-world noise and perform robust exploration.

scholarly journals Unsupervised by any other name: Hidden layers of knowledge production in artificial intelligence on social media

2019 ◽  
Vol 6 (1) ◽  
pp. 205395171881956 ◽  
Author(s):  
Anja Bechmann ◽  
Geoffrey C Bowker
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Knowledge Production ◽  
Classification Theory ◽  
Human Knowledge ◽  
Learning Models ◽  
Work Process ◽  
Learning Development ◽  
High Degree ◽  
Machine Learning Models

Artificial Intelligence (AI) in the form of different machine learning models is applied to Big Data as a way to turn data into valuable knowledge. The rhetoric is that ensuing predictions work well—with a high degree of autonomy and automation. We argue that we need to analyze the process of applying machine learning in depth and highlight at what point human knowledge production takes place in seemingly autonomous work. This article reintroduces classification theory as an important framework for understanding such seemingly invisible knowledge production in the machine learning development and design processes. We suggest a framework for studying such classification closely tied to different steps in the work process and exemplify the framework on two experiments with machine learning applied to Facebook data from one of our labs. By doing so we demonstrate ways in which classification and potential discrimination take place in even seemingly unsupervised and autonomous models. Moving away from concepts of non-supervision and autonomy enable us to understand the underlying classificatory dispositifs in the work process and that this form of analysis constitutes a first step towards governance of artificial intelligence.

scholarly journals Artificial Intelligence for Smart Systems Critical Analysis of the Human Centered Approach

2021 ◽  
pp. 85-92
Author(s):  
Zoran Galic Hajnal
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Critical Analysis ◽  
Intelligent Agent ◽  
The Other ◽  
Specific Strategy ◽  
Smart Systems ◽  
Cultural Aspects ◽  
Modern Age ◽  
The Status

A program for Artificial Intelligence (AI) is knowledge as intelligent agent, which typically interacts with the ecosystem. This agent is capable of identifying the status of the ecosystem using the sensors before affecting the state via the actuators. We call the smart systems "agents” whenever they are able to make some decisions on their own with respect on particular goals. On the other hand, Machine Learning (ML) signifies a specific strategy meant to design smart systems whereby these systems can adapt to specific behaviors with respect to data. In the modern age, humans are rapidly collaborating with ML and AI systems. The AI that is human-based is a perspective of ML and AI, which algorithms have to be established with the awareness that they are a major segment of the massive system incorporating human. In this paper, we have presented a research that means that AI systems understand humans with respect to their socio-cultural aspects and that AI system assist humans comprehend them. We also present an argument of the challenges of social responsibility e.g. transparency, interpretability, accountability and fairness.

scholarly journals “Does cinema form the future of robotics?”: a survey on fictional robots in sci-fi movies

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
Ehsan Saffari ◽  
Seyed Ramezan Hosseini ◽  
Alireza Taheri ◽  
Ali Meghdari
Keyword(s):  
Artificial Intelligence ◽  
Science Fiction ◽  
Social Robots ◽  
List Type ◽  
Ethical Aspects ◽  
Social Design ◽  
The Future ◽  
Appearance Design ◽  
Normal Human ◽  
Real Robot

Abstract Robotics and Artificial Intelligence (AI) have always been among the most popular topics in science fiction (sci-fi) movies. This paper endeavors to review popular movies containing Fictional Robots (FR) to extract the most common characteristics and interesting design ideas of robots portrayed in science fiction. To this end, 134 sci-fi films, including 108 unique FRs, were investigated regarding the robots’ different design aspects (e.g., appearance design, interactive design and artificial intelligence, and ethical and social design). Also, in each section of this paper, some characteristics of FRs are compared with real social robots. Since some researches point to the significant role of the cinema in forming the community’s expectations, it is very important to consider these characteristics and differences in choosing the future pathway of robotics. As some examples of findings, we have found that unlike the non-metallic skins/covers of real social robots, most FRs are still covered by highly detailed metal components. Moreover, the FR ability of interactions are generally (more than 90%) shown to be similar or even more advanced than normal Human–Human interactions, and this milestone was achieved by ignoring the AI challenges of real HRI. On the other hand, the ethical aspects of movies do inspire us to consider the potential ethical aspects of real robot design. All in all, according to popularity of movies, studying FR could be a step toward more appropriate development of robotics and AI entities to be accepted by general users in the real world. Highlights: We reviewed 134 sci-fi movies containing 108 unique fictional robots regarding different design aspects. Fictional Robot (FR) is an artificial entity acting as a result of a fictional technology and playing a role in a movie. Investigating fictional robots can shed light on the development of real robotics and AI entities.

scholarly journals Performance of Machine Learning and other Artificial Intelligence paradigms in Cybersecurity

2020 ◽  
Vol 13 (1) ◽  
pp. 1-21
Author(s):  
Gabriel Kabanda
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Support Vector ◽  
Mixed Method Research ◽  
Group Discussions ◽  
A Value ◽  
Tree Algorithms ◽  
Artificial Neural ◽  
Robotic Vehicle ◽  
High Degree

Cybersecurity systems are required at the application, network, host, and data levels. The research is purposed to evaluate Artificial Intelligence paradigms for use in network detection and prevention systems. This is purposed to develop a Cybersecurity system that uses artificial intelligence paradigms and can handle a high degree of complexity. The Pragmatism paradigm is elaborately associated with the Mixed Method Research (MMR), and is the research philosophy used in this research. Pragmatism recognizes the full rationale of the congruence between knowledge and action. The Pragmatic paradigm advocates a relational epistemology, a non-singular reality ontology, a mixed methods methodology, and a value-laden axiology. A qualitative approach where Focus Group discussions were held was used. The Artificial Intelligence paradigms evaluated include machine learning methods, autonomous robotic vehicle, artificial neural networks, and fuzzy logic. A discussion was held on the performance of Support Vector Machines, Artificial Neural Network, K-Nearest Neighbour, Naive-Bayes and Decision Tree Algorithms.

scholarly journals Optimization of Feature Set for Sentiment Analysis using Artificial Butterfly Algorithm and Ensemble Machine Learning

2020 ◽  
Vol 6 (11) ◽  
pp. 21-27
Author(s):  
Jyoti Hanvat ◽  
Sumit Sharma
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Ensemble Learning ◽  
Sentiment Classification ◽  
Ensemble Machine Learning ◽  
Learning Techniques ◽  
Essential Components ◽  
Classification Tasks ◽  
High Degree

The current decade has witnessed the remarkable developments in the field of artificial intelligence, and the revolution of deep learning has transformed the whole artificial intelligence industry. Eventually, deep learning techniques have become essential components of any model in today’s computational world. Nevertheless, ensemble learning techniques promise a high degree of automation with generalized rule extraction for both text and sentiment classification tasks. This paper aims designed and implemented optimized feature matrix using ensemble learning used for sentiment classification and its applications.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

Development of innovative hygienic technologies for preservation of the workers’ reproductive health

2020 ◽  
pp. 792-792
Author(s):  
M. A. Fesenko ◽  
G. V. Golovaneva ◽  
A. V. Miskevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Health ◽  
Reproductive System ◽  
Preventive Measures ◽  
Learning Algorithms ◽  
Reproductive Function ◽  
Machine Learning Algorithms ◽  
New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

INTERDISCIPLINARY LINKS, INTEGRATIVE APPROACH,HEURISTIC METHODS AND ARTIFICIAL INTELLIGENCE IN THE MANAGEMENT OF INNOVATION - PART 1

2018 ◽  
Vol 28 (5) ◽  
pp. 1527-1532
Author(s):  
Hristo Patev
Keyword(s):  
Artificial Intelligence ◽  
Production Systems ◽  
Innovation Management ◽  
Integrative Approach ◽  
Heuristic Methods ◽  
Organizational Renewal ◽  
Innovation Activities ◽  
Management Of Innovation ◽  
Inventive Activity ◽  
High Degree

In this first work, out of the total of twenty-four, are considered: Integrative approach, interdisciplinary relations and transnational language in the technical and economic fundament of engineering and management, for the purpose of competitive innovation and successful business. Approaches to develop the innovation with a high degree of complexity. Interactive heuristic methods and algorithms for inventive activity, for inspiring and developing new industrial products and services for households and production systems. Implementing an effective business vocabulary for organizational renewal. Introduction of gaming and "art" methods in innovation management. Intensifying innovation activities through an attempt to introduce artificial intelligence into teamwork, with simultaneous implementation of an engineering and non-engineering approach.

Sign in / Sign up

Export Citation Format

Share Document