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.

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.

scholarly journals Research on How Human Intelligence, Consciousness, and Cognitive Computing Affect the Development of Artificial Intelligence

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yanyan Dong ◽  
Jie Hou ◽  
Ning Zhang ◽  
Maocong Zhang
Keyword(s):  
Artificial Intelligence ◽  
Human Brain ◽  
Tacit Knowledge ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Human Intelligence ◽  
Cognitive Computing ◽  
Human Beings ◽  
Intelligent Machines ◽  
Value Rationality

Artificial intelligence (AI) is essentially the simulation of human intelligence. Today’s AI can only simulate, replace, extend, or expand part of human intelligence. In the future, the research and development of cutting-edge technologies such as brain-computer interface (BCI) together with the development of the human brain will eventually usher in a strong AI era, when AI can simulate and replace human’s imagination, emotion, intuition, potential, tacit knowledge, and other kinds of personalized intelligence. Breakthroughs in algorithms represented by cognitive computing promote the continuous penetration of AI into fields such as education, commerce, and medical treatment to build up AI service space. As to human concern, namely, who controls whom between humankind and intelligent machines, the answer is that AI can only become a service provider for human beings, demonstrating the value rationality of following ethics.

Applications of Machine Learning in Cyber Security Domain

2020 ◽  
pp. 64-82 ◽  
Author(s):  
Sailesh Suryanarayan Iyer ◽  
Sridaran Rajagopal
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cyber Security ◽  
Traditional Society ◽  
Future Trends ◽  
Human Beings ◽  
Online Transactions ◽  
Network Intrusion ◽  
The World ◽  
Applications Of Machine Learning

Knowledge revolution is transforming the globe from traditional society to a technology-driven society. Online transactions have compounded, exposing the world to a new demon called cybercrime. Human beings are being replaced by devices and robots, leading to artificial intelligence. Robotics, image processing, machine vision, and machine learning are changing the lifestyle of citizens. Machine learning contains algorithms which are capable of learning from historical occurrences. This chapter discusses the concept of machine learning, cyber security, cybercrime, and applications of machine learning in cyber security domain. Malware detection and network intrusion are a few areas where machine learning and deep learning can be applied. The authors have also elaborated on the research advancements and challenges in machine learning related to cyber security. The last section of this chapter lists the future trends and directions in machine learning and cyber security.

Artificial Intelligence, Robots and Bioethical Challenges

2021 ◽  
Vol 66 (Special Issue) ◽  
pp. 140-140
Author(s):  
Renzo Pegoraro ◽  
◽  
Keyword(s):  
Artificial Intelligence ◽  
Numerical Data ◽  
Important Variable ◽  
Prescribe Medication ◽  
Human Beings ◽  
Cognitive Mechanisms ◽  
Common Definition ◽  
Concrete Form ◽  
The World ◽  
Ibm Watson

"The arrival of digital research, where the object of research is transformed into numerical data, makes it possible to study the world and medicine, using new epistemological paradigms. What matters now is only the correlation between two quantities of data, with no concern for any consistent theory that explains such correlation. Today these correlations are used to predict with acceptable accuracy. What seems to be the outcome of this new revolution is the dominance of information, a conceptual labyrinth whose most common definition is based on an equally problematic category-data. The technological evolution of information and of the world seen as a series of data takes its concrete form in artificial intelligence (AI) and in robots. We are now able to construct machines that can make autonomous decisions and coexist with human beings. And in the context of healthcare it is possible to develop diagnostic approach, prescribe medication (see IBM Watson Program) or offer radiosurgery systems like Cyberknife. Contemporary society presents extremely delicate challenges where the most important variable is not intelligence but rather the little time available in which to make a decision. Here, cognitive mechanisms can have important applications. A series of anthropological and bioethical reflections can help to understand the challenges in the healthcare field: “Is it clear how this logic of hyper-individualization, governed by the use of artificial intelligence, will undermine the humanistic need for solidarity in this in deeds and mindset, in favor of private relationships between individuals and organizations?” (E. Sadin). "

scholarly journals A Generic Model of the World and Intelligence

10.29007/s6vh ◽  
2019 ◽  
Author(s):  
Harris Wang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Object Oriented ◽  
Generic Model ◽  
Human Beings ◽  
Primary Methods ◽  
The World ◽  
Object Hierarchies

The resurgence of interest in Artificial Intelligence and advances in several fronts of AI, machine learning with neural network in particular, have made us think again about the nature of intelligence, and the existence of a generic model that may be able to capture what human beings have in their mind about the world to empower them to present all kinds of intelligent behaviors. In this paper, we present Constrained Object Hierarchies (COHs) as such a generic model of the world and intelligence. COHs extend the well-known object-oriented paradigm by adding identity constraints, trigger constraints, goal constraints, and some primary methods that can be used by capable beings to accomplish various intelligence, such as deduction, induction, analogy, recognition, construction, learning and many others.In the paper we will first argue the need for such a generic model of the world and intelligence, and then present the generic model in detail, including its important constructs, the primary methods capable beings can use, as well as how different intelligent behaviors can be implemented and achieved with this generic model.

scholarly journals Object Sensing and its Identification & Motion Sensing

2019 ◽  
Vol 8 (9S3) ◽  
pp. 545-549
Keyword(s):  
Object Recognition ◽  
Human Brain ◽  
20Th Century ◽  
Human Beings ◽  
Motion Sensing ◽  
The World

In Today’s world, we usually see and deal with many visuals like, every time you look at something or somewhere, there is a visual at your sight but, "what are all these visuals?" In simple terms, these are nothing but images at that particular point of time. Now let’s see how these are differentiated, and as far as we are concerned with visuals, we usually deal with them in the form of images. Each visual or a sight at that point of time can be considered as an image or a set of images. But here a question arises that “how do we really recognize an object or a thing and how do we actually differentiate between them and the answer obvious that’s the human brain which is one among the exiting wonders of the world. As we all know a human brain does many exceptional functions which are not possible without its existence, And some of which cannot be completely replaced by any other artificial means, the fact is that there are some of its functions which actually seem so complex for humans to understand and therefore even in this very 20th century we are still unable to replace humans in some functions, as our science is still limited in some of its fields. But we all know that, more the number of robots that can replace a human in doing the tasks the better the output we get, there is a great need for human replacement in today world as time, resources come to a minimum when we start replacing human beings. Here in this paper we are dealing with object recognition as in today life this has become a challenge in science

scholarly journals Research and development of artificial intelligence in China

2016 ◽  
Vol 3 (4) ◽  
pp. 538-541 ◽  
Author(s):  
Jane Qiu
Keyword(s):  
Artificial Intelligence ◽  
Neurological Diseases ◽  
Machine Perception ◽  
Learning Machines ◽  
Chinese Academy Of Sciences ◽  
The World ◽  
Peking University ◽  
Set Up ◽  
World Champion ◽  
Academy Of Sciences

Abstract This year saw several milestones in the development of artificial intelligence. In March, AlphaGo, a computer algorithm developed by Google's London-based company, DeepMind, beat the world champion Lee Sedol at Go, an ancient Chinese board game. In October, the same company unveiled in the journal Nature its latest technique that allows a machine to solve tasks that require logic and reasoning, such as finding its way around the London Underground using a map it has never seen before. Such progress in recent years has provided significant impetus to developing cutting-edge learning machines around the world, including China. In 2015, the Chinese Academy of Sciences (CAS) set up the Centre for Excellence in Brain Science and Intelligence Technology—a consortium of laboratories from more than 20 CAS institutes and universities. Early this year, China rolled out the China Brain Project, a fifteen-year programme that will focus on brain mapping, neurological diseases and brain-inspired artificial intelligence. In a forum chaired by National Science Review's Executive Associative Editor, Mu-ming Poo, who also leads the CAS centre for excellence and the China Brain Project, several researchers discussed China's latest initiatives and progress in artificial intelligence, where the future lies and what the main challenges are. Yunji Chen Institute of Computing Technology, Chinese Academy of Sciences, Beijing Tieniu Tan Institute of Automation, Deputy President of Chinese Academy of Sciences, Beijing Yi Zeng Institute of Automation, Chinese Academy of Sciences, Beijing Hongbin Zha Director of Key Lab of Machine Perception (MOE), Peking University, Beijing Mu-ming Poo (Chair) Director of Institute of Neuroscience, Chinese Academy of Sciences, Shanghai

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.

scholarly journals Instead of People Using Technology, Technology Is Using People

2021 ◽  
Vol 13 (1) ◽  
pp. 42-45
Author(s):  
Douglas Rushkoff
Keyword(s):  
Artificial Intelligence ◽  
New Technologies ◽  
Human Life ◽  
Team Sport ◽  
Human Beings ◽  
The World ◽  
The Future

Abstract The progress of artificial intelligence and new technologies triggers hot debates about the future of human life. While fans of the singularity say that artificial intelligence will become smarter than human beings and should take over the world, for others, such a vision is a sheer nightmare. Douglas Rushkoff is clearly part of the second group and takes a passionate pro-human stance. He explains why giving too much way to technologies is a mistake and why humans deserve a place in the digital future. Already today, technologies have a much stronger impact on our lives than most of us would believe. For him, being human is a team sport, and he asks for a more conscious use of technologies while keeping rapport with other people. To safeguard the humanness in a tech world, he advises to carefully select the values we embed in our algorithms. Rather than serving perpetual growth, technologies ought to help people reconnect with each other and their physical surroundings.

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.

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