Xenobots – A remarkable combination of Artificial Intelligence based biological living robot

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0
Keyword(s):  
Artificial Intelligence ◽  
Human Brain ◽  
Design Method ◽  
Humanoid Robots ◽  
Live Cell ◽  
The World ◽  
Biological Aspects ◽  
Modern Era ◽  
Day By Day ◽  
The Ideal

Technology is improving day by day and every new face of it is engrossing, making applied science astonishment. Robotics and Artificial Intelligence have taken the world beyond automation. Automation was once considered as a challenge, but now the same technology has stunned the whole world, with the transformation of vision to the reality of live cell robots. In this modern era, evolutionary algorithms with Artificial Intelligence have made an impact on the automation and the creation of rare live-cell species by integrating biological aspects of frog cells. It would be thus useful in various domains to build technologies using self-renewing, and biocompatible materials of which the ideal candidates are living themselves. Thus, this paper presents a live cell robot named Xenobots, its design method, formation, applications, and transformation of live cell robots to humanoid robots that mimic the human brain.

Dialogue and Artificial Intelligence

2019 ◽  
Vol 9 (2) ◽  
pp. 294-315 ◽  
Author(s):  
Edda Weigand
Keyword(s):  
Artificial Intelligence ◽  
Human Brain ◽  
Game Model ◽  
Human Beings ◽  
Holistic Model ◽  
Language Competence ◽  
Learning Machines ◽  
The World ◽  
Modelling Competence

Abstract The article focuses on a few central issues of dialogic competence-in-performance which are still beyond the reach of models of Artificial Intelligence (AI). Learning machines have made an amazing step forward but still face barriers which cannot be crossed yet. Linguistics is still described at the level of Chomsky’s view of language competence. Modelling competence-in-performance requires a holistic model, such as the Mixed Game Model (Weigand 2010), which is capable of addressing the challenge of the ‘architecture of complexity’ (Simon 1962). The complex cannot be ‘the ontology of the world’ (Russell and Norwig 2016). There is no autonomous ontology, no hierarchy of concepts; it is always human beings who perceive the world. ‘Anything’, in the end, depends on the human brain.

scholarly journals Role of Artificial Intelligence in Distinct Sector: A Study

2021 ◽  
Vol 10 (1) ◽  
pp. 18-28
Author(s):  
Harsh Jindal ◽  
Devanshu Kumar ◽  
Ishika . ◽  
Santosh Kumar ◽  
Rakesh Kumar
Keyword(s):  
Artificial Intelligence ◽  
Medical Engineering ◽  
Emerging Technology ◽  
Storage Management ◽  
Application Domain ◽  
Internal Part ◽  
The World ◽  
Crop Diseases ◽  
Day By Day

The artificial intelligence (AI) plays a significant role in distinct fields to solve the complex problems. The digital technical field is increasing day by day in the world and it makes an internal part of our life. Hence, the knowledge of emerging technology is must for making our life easy. However, there are some major areas which are creating the problems to human such as agriculture field that comprises crop diseases, lack of storage management, pesticide control etc. These problems can be solved by artificial intelligence, IOT, machine learning and deep learning. Hence, the aim of this paper is to discuss the role of artificial intelligence to solve different issues of distinct sectors (medical, engineering, agriculture, business, defenses etc.) especially in medicine (COVID-19). Finally, future scope, challenges and application domain of artificial intelligence is also described.

scholarly journals The Technical Buzzword: Artificial Intelligence

2021 ◽  
Vol 1 (4) ◽  
pp. 1-3
Author(s):  
Banya Arabi Sahoo ◽  
Keyword(s):  
Artificial Intelligence ◽  
Human Brain ◽  
Real World ◽  
Human Being ◽  
Science And Engineering ◽  
The Real ◽  
Advantages And Disadvantages ◽  
Real World Problem ◽  
John Mccarthy ◽  
The World

AI is the incredibly exciting technique to the world. According to John McCarthy it is “The science and engineering of making intelligent machine, especially intelligent computers”. AI is the way of creating extraordinary powerful machine which is similar as human being. The AI is being accomplished by studying how human brain think, how they learn, decide, work, solving the real world problem and after that verify the outcomes and studying it. Primarily you can learn here what AI is and how it works, its types, its history, its agents, its applications, its advantages and disadvantages.

Deep Learning in Computational Neuroscience

2020 ◽  
pp. 43-63
Author(s):  
Sanjay Saxena ◽  
Sudip Paul ◽  
Adhesh Garg ◽  
Angana Saikia ◽  
Amitava Datta
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Human Brain ◽  
Convolutional Neural Network ◽  
Computational Neuroscience ◽  
Deep Model ◽  
The World ◽  
Current Scenario

Computational neuroscience is inspired by the mechanism of the human brain. Neural networks have reformed machine learning and artificial intelligence. Deep learning is a type of machine learning that teaches computers to do what comes naturally to individuals: acquire by example. It is inspired by biological brains and became the essential class of models in the field of machine learning. Deep learning involves several layers of computation. In the current scenario, researchers and scientists around the world are focusing on the implementation of different deep models and architectures. This chapter consists the information about major architectures of deep network. That will give the information about convolutional neural network, recurrent neural network, multilayer perceptron, and many more. Further, it discusses CNN (convolutional neural network) and its different pretrained models due to its major requirements in visual imaginary. This chapter also deliberates about the similarity of deep model and architectures with the human brain.

The future of democracy: the end of democracy as we know it

Kybernetes ◽  
2019 ◽  
Vol 48 (10) ◽  
pp. 2237-2265
Author(s):  
Miguel Goede
Keyword(s):  
Artificial Intelligence ◽  
Representative Democracy ◽  
Content Type ◽  
Universal Form ◽  
Form Of Government ◽  
The World ◽  
The Future ◽  
Global Government ◽  
The Ideal

Purpose The purpose of this article is to explore the future of democracy, given the transition the countries of the world are experiencing. Methodology The paper draws on literature concerning democracy, ICT and artificial intelligence. A framework for understanding the working of democracy is developed. This framework or model is tested in 20 countries, and conclusions are presented. Findings Globally, there is a shift taking place away from representative democracy toward less democratic forms of government. Originality Most studies are implicitly dogmatic in assuming that representative democracy is a superior form of government. The influences of corporations, media and the elite are moving representative democracy away from the ideal of democracy. Conclusions The future of democracy is uncertain. It is not likely that representative democracy will become the universal form of government. Global government is possible, but it is not likely to be a representative democracy.

scholarly journals A Survey on Explainability: Why Should We Believe the Accuracy of A Model?

2020 ◽  
Author(s):  
Prarthana Dutta ◽  
Naresh Babu Muppalaneni ◽  
Ripon Patgiri
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
New Technologies ◽  
Research Community ◽  
Learning Technologies ◽  
The World ◽  
Current Scenario ◽  
Day By Day ◽  
Modern Technologies

The world has been evolving with new technologies and advances day-by-day. With the advent of various learning technologies in every field, the research community is able to provide solution in every aspect of life with the applications of Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, etc. However, with such high achievements, it is found to lag behind the ability to provide explanation against its prediction. The current situation is such that these modern technologies are able to predict and decide upon various cases more accurately and speedily than a human, but failed to provide an answer when the question of why to trust its prediction is put forward. In order to attain a deeper understanding into this rising trend, we explore a very recent and talked-about novel contribution which provides rich insight on a prediction being made -- ``Explainability.'' The main premise of this survey is to provide an overview for researches explored in the domain and obtain an idea of the current scenario along with the advancements published to-date in this field. This survey is intended to provide a comprehensive background of the broad spectrum of Explainability.

scholarly journals The Technical Buzzword: Artificial Intelligence

2021 ◽  
pp. 1-3
Author(s):  
Banya Arabi Sahoo ◽  
Keyword(s):  
Artificial Intelligence ◽  
Human Brain ◽  
Real World ◽  
Human Being ◽  
Science And Engineering ◽  
The Real ◽  
Advantages And Disadvantages ◽  
Real World Problem ◽  
John Mccarthy ◽  
The World

AI is the incredibly exciting technique to the world. According to John McCarthy it is “The science and engineering of making intelligent machine, especially intelligent computers”. AI is the way of creating extraordinary powerful machine which is similar as human being. The AI is being accomplished by studying how human brain think, how they learn, decide, work, solving the real world problem and after that verify the outcomes and studying it. Primarily you can learn here what AI is and how it works, its types, its history, its agents, its applications, its advantages and disadvantages.

Mind and Machine

Mind Shift ◽  
2021 ◽  
pp. 396-410
Author(s):  
John Parrington
Keyword(s):  
Artificial Intelligence ◽  
Human Brain ◽  
Genomic Analysis ◽  
Human Mind ◽  
Machine Intelligence ◽  
Human Consciousness ◽  
Human Beings ◽  
New Techniques ◽  
The World ◽  
Future Technologies

This chapter explores how future technologies might impact on human consciousness. It begins by discussing how new techniques are continuing to add to the understanding of the human mind. There are many exciting technologies available now to the neuroscientist, such as genomic analysis, optogenetics, gene editing, and brain organoids. To what extent could such technologies be used to investigate the model of human consciousness outlined in this book? The chapter then considers whether artificial intelligence might come to rival that of human beings, and possible interfaces between human and machine intelligence. Our growing ability to develop functioning robots raises the question of whether an artificial human brain might be used to control such a robot, creating in effect a cyborg. However, the creation of such an entity could make a big difference in terms of an artificial brain’s sense of identity in the world, as well as its rights.

A Systematic Review on Artificial Intelligence/Deep Learning Applications and Challenges to battle against COVID-19 Pandemic

2021 ◽  
pp. 90-99
Author(s):  
Manoj Agrawal ◽  
Shweta Agrawal
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Deep Learning ◽  
Vaccine Development ◽  
State Of The Art ◽  
Design Strategies ◽  
Look Ahead ◽  
The World ◽  
Fast Spreading ◽  
Day By Day

The eruption of COVID-19 Corona Virus, namely SARS-CoV-2, has created a disastrous condition throughout the world. The cumulative incidence of COVID-19 is increasing rapidly day by day all over the world. Technologies like Artificial Intelligence (AI), Internet of Things (IoT), Big Data and Deep Learning can support healthcare system to fight and look ahead against fast spreading of new disease COVID-19. These technologies can significantly improve treatment consistency and decision making by developing useful algorithms. These technologies can be deployed very effectively to track the disease, to predict growth of the epidemic, design strategies and policy to manage its spread and drug and vaccine development. Motivated by recent advances and applications of artificial intelligence (AI) and big data in various areas, this study aims at emphasizing their importance in responding to the COVID-19 outbreak and preventing the severe effects of the COVID-19 pandemic. This study first presents an overview of AI and big data along with their applications in fighting against COVID-19 and then an attempt is made to standardize ongoing AI and deep learning activities in this area. Finally, this study highlighted challenges and issues associated with State-of-the-Art solutions to effectively control the COVID-19 situation.

From PUFF to integrated concurrent engineering: A personal evolution

2007 ◽  
Vol 21 (1) ◽  
pp. 19-22
Author(s):  
JOHN KUNZ
Keyword(s):  
Artificial Intelligence ◽  
Expert System ◽  
Multiagent Systems ◽  
Concurrent Engineering ◽  
Design Method ◽  
The Past ◽  
The World ◽  
Engineering Models ◽  
Early Puff

Artificial intelligence (AI) emerged from the 1956 Dartmouth Conference. Twenty-one years later, my colleagues and I started daily operational use of what we think became the first application of AI to be used in practice: the PUFF pulmonary function system. We later described the design and initial performance of that system (Aikins et al., 1983; Snow et al., 1998). Today, easily recognizable descendants of that first “expert system” run on commercial products found in medical offices around the world (http://www.medgraphics.com/datasheet_pconsult.html), as do many other AI applications. My research now focuses on integrated concurrent engineering (ICE), a computer and AI-enabled multiparticipant engineering design method that is extremely rapid and effective (Garcia et al., 2004). This brief note compares the early PUFF, the current ICE work, and the modern AI view of neurobiological systems. This comparison shows the dramatic and surprising changes in AI methods in the past few decades and suggests research opportunities for the future. The comparison identifies the continuing crucial role of symbolic representation and reasoning and the dramatic generalization of the context in which those classical AI methods work. It suggests surprising parallels between animal neuroprocesses and the multihuman and multicomputer agent collaborative ICE environment. Finally, it identifies some of the findings and lessons of the intervening years, fundamentally the move to model-based multidiscipline, multimethod, multiagent systems in which AI methods are tightly integrated with theoretically founded engineering models and analytical methods implemented as multiagent human and computer systems that include databases, numeric algorithms, graphics, human–computer interaction, and networking.

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