scholarly journals Review on the Artificial Brain Technology: BlueBrain

2020 ◽  
Vol 1 (1) ◽  
pp. 1-11
Author(s):  
Madhulika Vinny ◽  
◽  
Pawan Singh ◽  
Keyword(s):  
Artificial Intelligence ◽  
Human Brain ◽  
Reverse Engineering ◽  
Human Mind ◽  
The State ◽  
Brain Disorders ◽  
Working Model ◽  
Artificial Brain ◽  
Special Software ◽  
Human Brains

Blue brain is a supercomputer programmed such that it can function as an artificial brain, which can also be called a virtual brain. IBM is developing this virtual brain which would be the world’s first such created machine. Its main aim is to create a machine in which the information of the actual brain can be uploaded. This would ensure that a person’s knowledge, personality, memories, and intelligence are preserved and safe. The Blue Brain project utilizes the technologies of reverse engineering and artificial intelligence at its core and is implemented through the use of supercomputers and nanobots. Special software like BBP-SDK are also specifically developed for the Blue Brain project. The Blue Brain project is centered towards finding viable solutions to brain-disorders, a working model close to the actual brain which would help in greater understanding of the human brain and the human mind and the state of consciousness, a step towards building an independently thinking machine, and finally collecting information of hundreds of years from the human brains and storing it in the form of a databases. The Blue Brain project mimics the human brain by acquiring the data from its surrounding through special software, interpreting through neural electrophysiology and morphology, and simulating them on computers. Thus, The Blue Brain project is a powerful tool for the study and analysis of the human brain and for the advancement of the human brain and society.

scholarly journals Long- and Short-Term Conductance Control of Artificial Polymer Wire Synapses

Polymers ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 312
Author(s):  
Naruki Hagiwara ◽  
Shoma Sekizaki ◽  
Yuji Kuwahara ◽  
Tetsuya Asai ◽  
Megumi Akai-Kasaya
Keyword(s):  
Artificial Intelligence ◽  
Human Brain ◽  
High Speed ◽  
Conductive Polymer ◽  
Long Term Potentiation ◽  
Short Term ◽  
Synaptic Functions ◽  
Term Potentiation ◽  
Artificial Brain ◽  
External Stimuli

Networks in the human brain are extremely complex and sophisticated. The abstract model of the human brain has been used in software development, specifically in artificial intelligence. Despite the remarkable outcomes achieved using artificial intelligence, the approach consumes a huge amount of computational resources. A possible solution to this issue is the development of processing circuits that physically resemble an artificial brain, which can offer low-energy loss and high-speed processing. This study demonstrated the synaptic functions of conductive polymer wires linking arbitrary electrodes in solution. By controlling the conductance of the wires, synaptic functions such as long-term potentiation and short-term plasticity were achieved, which are similar to the manner in which a synapse changes the strength of its connections. This novel organic artificial synapse can be used to construct information-processing circuits by wiring from scratch and learning efficiently in response to external stimuli.

scholarly journals Does the Human Brain Resort to AI's Deep Learning in Order to Solve Problems

2020 ◽  
pp. 73-86
Author(s):  
Prof. M S S El Namaki ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Problem Solving ◽  
Human Brain ◽  
Learning Technologies ◽  
Human Brains

Problem solving is a daily occurrence in business and, also, in human brains. Businesses resort to a variety of modes in order to find an answer to these problems.Human brains adopt, also, a variety of measures to solve their own brand of problems. Artificial Intelligence technologies seem to have been extending a helping hand to business in the search for problem solving mechanisms. Machine learning and deep learning are currently recognized as prime modes for business insight and problem solving. Does the human brain possess competencies and instruments that could compare to the deep learning technologies adopted by AI?

Artificial Intelligence

2020 ◽  
pp. 48-69
Author(s):  
Daeyeol Lee
Keyword(s):  
Artificial Intelligence ◽  
Human Brain ◽  
Building Blocks ◽  
Human Intelligence ◽  
Intelligent Robots ◽  
Artificial Intelligence Technology ◽  
Functional Units ◽  
Human Brains ◽  
Digital Computers

Compared to the human brain, current artificial intelligence technology is limited in that its goals are determined by human developers and users. Similarly, despite their superficial similarities, modern-day computers and human brains have many differences. Building blocks of human brain that are functionally equivalent to transistors, functional units of digital computers, have not been identified, and we do not know whether hardware and software are separable in the human brain. This chapter uses Mars rovers as a case study to illustrate the autonomy of intelligent robots, because machines dependent on human intelligence is not genuinely intelligent.

Machine Learning in Healthcare

2020 ◽  
pp. 92-109
Author(s):  
Stavros Pitoglou
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Statistical Theory ◽  
Human Brain ◽  
Daily Practice ◽  
Human Mind ◽  
Academic Community ◽  
Acceptable Solution ◽  
Active Involvement ◽  
Definition Of

Machine learning, closely related to artificial intelligence and standing at the intersection of computer science and mathematical statistical theory, comes in handy when the truth is hiding in a place that the human brain has no access to. Given any prediction or assessment problem, the more complicated this issue is, based on the difficulty of the human mind to understand the inherent causalities/patterns and apply conventional methods towards an acceptable solution, machine learning can find a fertile field of application. This chapter's purpose is to give a general non-technical definition of machine learning, provide a review of its latest implementations in the healthcare domain and add to the ongoing discussion on this subject. It suggests the active involvement of entities beyond the already active academic community in the quest for solutions that “exploit” existing datasets and can be applied in the daily practice, embedded inside the software processes that are already in use.

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 A thousand brains: toward biologically constrained AI

2021 ◽  
Vol 3 (8) ◽  
Author(s):  
Kjell Jørgen Hole ◽  
Subutai Ahmad
Keyword(s):  
Artificial Intelligence ◽  
Human Brain ◽  
Intelligent Systems ◽  
The State ◽  
Experimental Techniques ◽  
Bounded Domains ◽  
Fundamental Limitations ◽  
The Creation ◽  
Current Systems

AbstractThis paper reviews the state of artificial intelligence (AI) and the quest to create general AI with human-like cognitive capabilities. Although existing AI methods have produced powerful applications that outperform humans in specific bounded domains, these techniques have fundamental limitations that hinder the creation of general intelligent systems. In parallel, over the last few decades, an explosion of experimental techniques in neuroscience has significantly increased our understanding of the human brain. This review argues that improvements in current AI using mathematical or logical techniques are unlikely to lead to general AI. Instead, the AI community should incorporate neuroscience discoveries about the neocortex, the human brain’s center of intelligence. The article explains the limitations of current AI techniques. It then focuses on the biologically constrained Thousand Brains Theory describing the neocortex’s computational principles. Future AI systems can incorporate these principles to overcome the stated limitations of current systems. Finally, the article concludes that AI researchers and neuroscientists should work together on specified topics to achieve biologically constrained AI with human-like capabilities.

Management of Economics and State "Bottom"

2020 ◽  
Vol 17 (6) ◽  
pp. 76-91
Author(s):  
E. D. Solozhentsev
Keyword(s):  
Quality Of Life ◽  
Artificial Intelligence ◽  
Probabilistic Models ◽  
Human Life ◽  
Special Software ◽  
Human Quality ◽  
Relationship Of ◽  
The Relationship

The scientific problem of economics “Managing the quality of human life” is formulated on the basis of artificial intelligence, algebra of logic and logical-probabilistic calculus. Managing the quality of human life is represented by managing the processes of his treatment, training and decision making. Events in these processes and the corresponding logical variables relate to the behavior of a person, other persons and infrastructure. The processes of the quality of human life are modeled, analyzed and managed with the participation of the person himself. Scenarios and structural, logical and probabilistic models of managing the quality of human life are given. Special software for quality management is described. The relationship of human quality of life and the digital economy is examined. We consider the role of public opinion in the management of the “bottom” based on the synthesis of many studies on the management of the economics and the state. The bottom management is also feedback from the top management.

scholarly journals Artificial intelligence and machine learning in design of mechanical materials

2021 ◽  
Author(s):  
Kai Guo ◽  
Zhenze Yang ◽  
Chi-Hua Yu ◽  
Markus J. Buehler
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
State Of The Art ◽  
The State

This review revisits the state of the art of research efforts on the design of mechanical materials using machine learning.

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