Coinbot: Intelligent Robotic Coin Bag Manipulation Using Artificial Brain

Author(s):  
Aleksei Gonnochenko ◽  
Aleksandr Semochkin ◽  
Dmitry Egorov ◽  
Dmitrii Statovoy ◽  
Seyedhassan Zabihifar ◽  
...  
Keyword(s):  
Polymers ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 312
Author(s):  
Naruki Hagiwara ◽  
Shoma Sekizaki ◽  
Yuji Kuwahara ◽  
Tetsuya Asai ◽  
Megumi Akai-Kasaya

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.


2021 ◽  
Vol 26 (jai2021.26(1)) ◽  
pp. 95-101
Author(s):  
Pisarenko V ◽  
◽  
Pisarenko J ◽  
Gulchak O ◽  
Chobotok T ◽  
...  

The practical experience of solving scientific tasks using artificial intelligence technologies is presented. The authors offered their understanding of the term "artificial intelligence". Describes the development of the dept. №265 of Mathematical Problems of Applied Informatics V.M. Glushkov Institute of Cybernetics of the NAS of Ukraine in the creation of technical systems with elements of AI mainly to work in extreme environments. The purpose of the authors is to provide useful information to develop a strategy for the development of AI in the Ukraine. Some of these studies: monitoring the territory and management of land use technologies using remote sensing technologies from aircraft, spacecraft, unmanned aerial vehicles; monitoring the technical equipment of the underwater environment (technical means of searching for a sunken object of the submarine type for emergency operations are being developed); mine safety control (risk research during mining, creating robotic systems with elements of artificial intelligence for studying the conditions of work in the mine, warning accidents and emergency rescue work). The next direction is the diagnosis and treatment of addictive patients using the principles of therapeutic methods BiofeedBack. Attention is paid to the development of robotic technical systems with AI for servicing cosmic long missions. For this, theoretical studies have been conducted on the creation of a live brain mathematical model for its use in the development of the "artificial brain" of robots. The authors gave a list of tasks that can solve AI in programs for long-term space flights, technologies and systems that should develop in the first place to implement these tasks


Author(s):  
Hugo de Garis ◽  
Chen Xiaoxi ◽  
Ben Goertzel

This chapter describes a 4 year research project (2008-2011) to build China’s first artificial brain. It takes an “evolutionary engineering” approach, by evolving 10,000s of neural net modules, (or “agents” in the sense of Minsky’s “Society of Mind” [Minsky 1988, 2007]), and connecting them to make artificial brains. These modules are evolved rapidly in seconds on a “Tesla” PC Supercomputer, and connected according to the artificial brain designs of human “BAs” (Brain Architects). The artificial brain will eventually contain thousands of pattern recognizer modules, and hundreds of decision modules that when suitably combined will control the hundreds of behaviors of a walking, talking robot.


2020 ◽  
pp. 1564-1619
Author(s):  
Jeremy Horne

In the last half century, we have gone from storing data on 5¼ inch floppy diskettes to the cloud and now use fog computing. But one should ask why so much data is being collected. Part of the answer is simple in light of scientific projects, but why is there so much data on us? Then, we ask about its “interface” through fog computing. Such questions prompt this article on the philosophy of big data and fog computing. After some background on definitions, origins and contemporary applications, the main discussion begins with thinking about modern data collection, management, and applications from a complexity standpoint. Big data is turned into knowledge, but knowledge is extrapolated from the past and used to manage the future. Yet it is questionable whether humans have the capacity to manage contemporary technological and social complexity evidenced by our world in crisis and possibly on the brink of extinction. Such calls for a new way of studying societies from a scientific point of view. We are at the center of the observation from which big data emerge and are manipulated, the overall human project being not only to create an artificial brain with an attendant mind, but a society that might be able to survive what “natural” humans cannot.


Kybernetes ◽  
2019 ◽  
Vol 49 (8) ◽  
pp. 2073-2090 ◽  
Author(s):  
Andrei Cretu

Purpose W. Ross Ashby’s elementary non-trivial machine, known in the cybernetic literature as the “Ashby Box,” has been described as the prototypical example of a black box system. As far as it can be ascertained from Ashby’s journal, the intended purpose of this device may have been to exemplify the environment where an “artificial brain” may operate. This paper describes the construction of an elementary observer/controller for the class of systems exemplified by the Ashby Box – variable structure black box systems with parallel input. Design/methodology/approach Starting from a formalization of the second-order assumptions implicit in the design of the Ashby Box, the observer/controller system is synthesized from the ground up, in a strictly system-theoretic setting, without recourse to disciplinary metaphors or current theories of learning and cognition, based mainly on guidance from Heinz von Foerster’s theory of self-organizing systems and W. Ross Ashby’s own insights into adaptive systems. Findings Achieving and maintaining control of the Ashby Box requires a non-trivial observer system able to use the results of its interactions with the non-trivial machine to autonomously construct, deconstruct and reconstruct its own function. The algorithm and the dynamical model of the Ashby Box observer developed in this paper define the basic specifications of a general purpose, unsupervised learning architecture able to accomplish this task. Originality/value The problem exemplified by the Ashby Box is fundamental and goes to the roots of cybernetic theory; second-order cybernetics offers an adequate foundation for the mathematical modeling of this problem.


2020 ◽  
Vol 10 (14) ◽  
pp. 4915 ◽  
Author(s):  
Sanjiban Sekhar Roy ◽  
Nishant Rodrigues ◽  
Y-h. Taguchi

Brain tumor classification is a challenging task in the field of medical image processing. Technology has now enabled medical doctors to have additional aid for diagnosis. We aim to classify brain tumors using MRI images, which were collected from anonymous patients and artificial brain simulators. In this article, we carry out a comparative study between Simple Artificial Neural Networks with dropout, Basic Convolutional Neural Networks (CNN), and Dilated Convolutional Neural Networks. The experimental results shed light on the high classification performance (accuracy 97%) of Dilated CNN. On the other hand, Dilated CNN suffers from the gridding phenomenon. An incremental, even number dilation rate takes advantage of the reduced computational overhead and also overcomes the adverse effects of gridding. Comparative analysis between different combinations of dilation rates for the different convolution layers, help validate the results. The computational overhead in terms of efficiency for training the model to reach an acceptable threshold accuracy of 90% is another parameter to compare the model performance.


2015 ◽  
Vol 151 ◽  
pp. 55-61
Author(s):  
Jérôme Leboeuf-Pasquier

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