scholarly journals Swarm robotics:design and implementation

2020 ◽  
Vol 10 (2) ◽  
pp. 2173
Author(s):  
Ashraf Abuelhaija ◽  
Ayham Jebrein ◽  
Tarik Baldawi
Keyword(s):  
Artificial Intelligence ◽  
Circuit Design ◽  
Single Unit ◽  
Simple Circuit ◽  
Consumer Markets ◽  
Ir Sensors ◽  
Simple Limit ◽  
The Brain ◽  
Swarming Behaviour

This project presents a swarming and herding behaviour using simple robots. The main goal is to demonstrate the applicability of artificial intelligence (AI) in simple robotics that can then be scaled to industrial and consumer markets to further the ability of automation. AI can be achieved in many different ways; this paper explores the possible platforms on which to build a simple AI robots from consumer grade microcontrollers. Emphasis on simplicity is the main focus of this paper. Cheap and 8 bit microcontrollers were used as the brain of each robot in a decentralized swarm environment were each robot is autonomous but still a part of the whole. These simple robots don’t communicate directly with each other. They will utilize simple IR sensors to sense each other and simple limit switches to sense other obstacles in their environment. Their main objective is to assemble at certain location after initial start from random locations, and after converging they would move as a single unit without collisions. Using readily available microcontrollers and simple circuit design, semiconsistent swarming behaviour was achieved. These robots don’t follow a set path but will react dynamically to different scenarios, guided by their simple AI algorithm.

Sources of the Scalp-Recorded Amplitude-Modulation Following Response

2002 ◽  
Vol 13 (04) ◽  
pp. 188-204 ◽  
Author(s):  
Shigeyuki Kuwada ◽  
Julia S. Anderson ◽  
Ranjan Batra ◽  
Douglas C. Fitzpatrick ◽  
Natacha Teissier ◽  
...  
Keyword(s):  
Animal Model ◽  
Amplitude Modulation ◽  
Single Unit ◽  
Modulation Frequency ◽  
Multiple Sources ◽  
High Modulation ◽  
Before And After ◽  
Single Unit Recordings ◽  
Composite Response ◽  
The Brain

The scalp-recorded amplitude-modulation following response (AMFR)” is gaining recognition as an objective audiometric tool, but little is known about the neural sources that underlie this potential. We hypothesized, based on our human studies and single-unit recordings in animals, that the scalp-recorded AMFR reflects the interaction of multiple sources. We tested this hypothesis using an animal model, the unanesthetized rabbit. We compared AMFRs recorded from the surface of the brain at different locations and before and after the administration of agents likely to enhance or suppress neural generators. We also recorded AMFRs locally at several stations along the auditory neuraxis. We conclude that the surface-recorded AMFR is indeed a composite response from multiple brain generators. Although the response at any modulation frequency can reflect the activity of more than one generator, the AMFRs to low and high modulation frequencies appear to reflect a strong contribution from cortical and subcortical sources, respectively.

Transfer of consciousness. Considering its possibility or fantasy from the religious and scientific perspectives

DIALOGO ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 189-200
Author(s):  
Tudor-Cosmin Ciocan ◽  
Any Docu Axelerad ◽  
Maria CIOCAN ◽  
Alina Zorina Stroe ◽  
Silviu Docu Axelerad ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Eternal Life ◽  
Human Consciousness ◽  
Religious Believer ◽  
Actual Knowledge ◽  
Holistic Understanding ◽  
The World ◽  
The Subject ◽  
Human Feature ◽  
The Brain

Ancient beliefs such as astral projection, human possession, abduction and other similar are not only universal, taught by all religions, but also used as premises for core believes/expectations, such as after-life, eternal damnation, reincarnation, and many others. Transferring Consciousness to a Synthetic Body is also a feature of interest in our actual knowledge, both religious as for science. If immortality were an option, would you take it into consideration more seriously? Most people would probably dismiss the question since immortality isn’t a real deal to contract. But what if having eternal life was a possibility in today’s world? The possibility of the transfer of human consciousness to a synthetic body can soon become a reality, and it could help the world for the better. Thus, until recently, the subject was mostly proposed by religion(s) and saw as a spiritual [thus, not ‘materially real’ or ‘forthwith accomplishable’] proposal therefore not really fully engaged or trust if not a religious believer. Now, technology is evolving, and so are we. The world has come to a point where artificial intelligence is breaking the boundaries of our perception of human consciousness and intelligence. And with this so is our understanding about the ancient question ‘who are we?’ concerning consciousness and how this human feature sticks to our body or it can become an entity beyond the material flesh. Without being exhaustive with the theme's development [leaving enough room for further investigations], we would like to take it for a spin and see how and where the religious and neuroscience realms intersect with it for a global, perhaps holistic understanding. Developments in neurotechnology favor the brain to broaden its physical control further the restraints of the human body. Accordingly, it is achievable to both acquire and provide information from and to the brain and also to organize feedback processes in which a person's thoughts can influence the activity of a computer or reversely.

Something Old, Something New, Something Borrowed, Something Blue: Part 3 – An Elephant Never Forgets

2017 ◽  
Vol 26 (3) ◽  
pp. 433-437
Author(s):  
Mark Dougherty
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Natural Systems ◽  
European Court ◽  
New Knowledge ◽  
The Right ◽  
Contextual Environment ◽  
The Brain ◽  
Index Services ◽  
Incomplete Picture

AbstractForgetting is an oft-forgotten art. Many artificial intelligence (AI) systems deliver good performance when first implemented; however, as the contextual environment changes, they become out of date and their performance degrades. Learning new knowledge is part of the solution, but forgetting outdated facts and information is a vital part of the process of renewal. However, forgetting proves to be a surprisingly difficult concept to either understand or implement. Much of AI is based on analogies with natural systems, and although all of us have plenty of experiences with having forgotten something, as yet we have only an incomplete picture of how this process occurs in the brain. A recent judgment by the European Court concerns the “right to be forgotten” by web index services such as Google. This has made debate and research into the concept of forgetting very urgent. Given the rapid growth in requests for pages to be forgotten, it is clear that the process will have to be automated and that intelligent systems of forgetting are required in order to meet this challenge.

scholarly journals Artificial intelligence, dynamic efficiency and Economics

2021 ◽  
pp. 337-350
Author(s):  
Vincent Wolters
Keyword(s):  
Artificial Intelligence ◽  
Optimization Problems ◽  
Evolutionary Process ◽  
Human Action ◽  
Medical Doctor ◽  
Dynamic Efficiency ◽  
Demand And Supply ◽  
New Information ◽  
Back Ground ◽  
The Brain

In this work I will lend support to the theory of «dynamic efficien - cy», as outlined by Prof. Huerta de Soto in The Theory of Dynamic Efficiency (2010a). Whereas Huerta de Soto connects economics with ethics, I will take a different approach. Since I have a back-ground in Artificial Intelligence (A.I.), I will show that this and related fields have yielded insights that, when applied to the study of economics, may call for a different way of looking at the eco-nomy and its processes. At first glance, A.I. and economics do not seem to have a lot in common. The former is thought to attempt to build a human being; the latter is supposed to deal with depressions, growth, inflation, etc. That view is too simplistic; in fact there are strong similarities. First, economics is based on (inter-)acting individuals, i.e. on human action. A.I. tries to understand and simulate human (and animal) behavior. Second, economics deals with information pro-cessing, such as how the allocation of resources can best be orga-nized. A.I. also investigates information processing. This can be in specific systems, such as the brain, or the evolutionary process, or purely in an abstract form. Finally, A.I. tries to answer more philosophical questions like: what is intelligence? What is a mind? What is consciousness? Is there free will? These topics play a less prominent role in economics, but are sometimes touched upon, together with the related topic of the «entrepreneurial function». The paradigm that was dominant in the early days of A.I. is static in nature. Reaching a solution is done in different steps. First: gathering all necessary information. Second: processing this in - formation. Finally: the outcome of this process, a clear conclusion. Each step in the process is entirely separate. During information gathering no processing is done, and during processing, no new information is added. The conclusion reached is final and cannot change later on. Logical problems are what is mostly dealt with, finding ways in which a computer can perform deductions based on the information that is represented as logical statements. Other applications are optimization problems, and so-called «Expert Systems», developed to perform the work of a judge reaching a verdict, or a medical doctor making a diagnosis based on the symptoms of the patient. This paradigm is also called «top-down», because information flows to a central point where it is processed, or «symbolic processing», referring to deduction in formal logic.1 In economics there is a similar paradigm, and it is still the do-minant one. This is the part of economics that deals with opti - mization of resources: given costs and given prices, what is the allocation that will lead to the highest profit? Also belonging to this paradigm are the equilibrium models. Demand and supply curves are supposed to be knowable and unchangeable, and the price is a necessary outcome. The culmination is central planning that supposes all necessary information, such as demand and supply curves and available resources to be known. Based on this, the central planner determines prices.

scholarly journals Brain-inspired model for decision-making in the selection of beneficial information patterns among signals received by an unpredictable information-development environment

2021 ◽  
Author(s):  
Alireza Asgari ◽  
yvan beauregard
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Health And Safety ◽  
Vital Role ◽  
Successful Implementation ◽  
Development Environment ◽  
Brain Functions ◽  
Operational Costs ◽  
Industrial Operations ◽  
The Brain

With its diversification in products and services, today’s marketplace makes competition wildly dynamic and unpredictable for industries. In such an environment, daily operational decision-making has a vital role in producing value for products and services while avoiding the risk of loss and hazard to human health and safety. However, it makes a large portion of operational costs for industries. The main reason is that decision-making belongs to the operational tasks dominated by humans. The less involvement of humans, as a less controllable entity, in industrial operation could also favorable for improving workplace health and safety. To this end, artificial intelligence is proposed as an alternative to doing human decision-making tasks. Still, some of the functional characteristics of the brain that allow humans to make decisions in unpredictable environments like the current industry, especially knowledge generalization, are challenging for artificial intelligence. To find an applicable solution, we study the principles that underlie the human brain functions in decision-making. The relative base functions are realized to develop a model in a simulated unpredictable environment for a decision-making system that could decide which information is beneficial to choose. The method executed to build our model's neuronal interactions is unique that aims to mimic some simple functions of the brain in decision-making. It has the potential to develop for systems acting in the higher abstraction levels and complexities in real-world environments. This system and our study will help to integrate more artificial intelligence in industrial operations and settings. The more successful implementation of artificial intelligence will be the steeper decreasing operational costs and risks.

scholarly journals Psychoneuroimmunoendocrinology and Immune Homeostasis: Gut-brain Axis, Obesity and Cognitive Function

2020 ◽  
Vol 6 (12) ◽  
pp. 124-154
Author(s):  
S. Bulgakova ◽  
N. Romanchuk ◽  
O. Pomazanova
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Gut Microbiota ◽  
Immune Responses ◽  
Cognitive Functions ◽  
Immune Homeostasis ◽  
Obesity And Diabetes ◽  
Human Immune ◽  
The Cross ◽  
The Brain

The new competencies of psychoneuroimmunoendocrinology and psychoneuroimmunology play a strategic role in interdisciplinary science and interdisciplinary planning and decision-making. The introduction of multi-vector neurotechnologies of artificial intelligence and the principles of digital health care will contribute to the development of modern neuroscience and neuromarketing. The availability of innovative technologies, such as next-generation sequencing and correlated bioinformatics tools, allows deeper investigation of the cross-network relationships between the microbiota and human immune responses. Immune homeostasis is the balance between immunological tolerance and inflammatory immune responses — a key feature in the outcome of health or disease. A healthy microbiota is the qualitative and quantitative ratio of diverse microbes of individual organs and systems, maintaining the biochemical, metabolic and immune equilibrium of the macroorganism necessary to preserve human health. Functional foods, healthy biomicrobiota, healthy lifestyle and controlled protective environmental effects, artificial intelligence and electromagnetic information load/overload are responsible for the work of the human immune system and its ability to respond to pandemic attacks in a timely manner. Obesity continues to be one of the main problems of modern health care due to its high prevalence and polymorbidity. In addition to cardiometabolic diseases, lesions of the musculoskeletal system, obese individuals show impaired cognitive functions, have a high risk of developing depression and anxiety. The gut microbiota mediates between environmental influences (food, lifestyle) and the physiology of the host, and its change may partially explain the cross-link between the above pathologies. It is known that Western eating patterns are the main cause of the obesity epidemic, which also contributes to dysbiotic drift of the gut microbiota, which in turn contributes to the development of complications associated with obesity. Experimental studies in animal models and, to a lesser extent in humans, show that microbiota is associated with obesity and may contribute to the endocrine, neurochemical and development of systemic inflammation underlying obesity itself and related diseases. Nevertheless, a number of questions remain at present. Modeling the microbiota-gut-brain axis, provides the brain with information from the gut not only through the nervous system but also through a continuous stream of microbial, endocrine, metabolic and immune messages. The communication network provides important keys to understanding how obesity and diabetes can affect the brain by provoking neuropsychiatric diseases. The literature review is devoted to the analysis of data on the relationship of the gut-brain axis, obesity and cognitive functions, immune homeostasis and new competencies: psychoneuroimmunology and psychoneuroimmunoendocrinology.

scholarly journals How the Brain might use Division

2020 ◽  
Vol 8 ◽  
pp. 126-137
Author(s):  
Kieran Greer
Keyword(s):  
Artificial Intelligence ◽  
Binary System ◽  
Problem Size ◽  
Symbol Manipulation ◽  
Mathematical Functions ◽  
Binary Operations ◽  
Neural Architecture ◽  
Large Numbers ◽  
The Brain

One of the most fundamental questions in Biology or Artificial Intelligence is how the human brainperforms mathematical functions. How does a neural architecture that may organise itself mostly throughstatistics, know what to do? One possibility is to extract the problem to something more abstract. This becomesclear when thinking about how the brain handles large numbers, for example to the power of something, whensimply summing to an answer is not feasible. In this paper, the author suggests that the maths question can beanswered more easily if the problem is changed into one of symbol manipulation and not just number counting.If symbols can be compared and manipulated, maybe without understanding completely what they are, then themathematical operations become relative and some of them might even be rote learned. The proposed systemmay also be suggested as an alternative to the traditional computer binary system. Any of the actual maths stillbreaks down into binary operations, while a more symbolic level above that can manipulate the numbers andreduce the problem size, thus making the binary operations simpler. An interesting result of looking at this is thepossibility of a new fractal equation resulting from division, that can be used as a measure of good fit and wouldhelp the brain decide how to solve something through self-replacement and a comparison with this good fit.

The Engineer

Rebel Genius ◽  
2016 ◽  
Author(s):  
Tara H. Abraham
Keyword(s):  
Artificial Intelligence ◽  
Reticular Formation ◽  
Research Laboratory ◽  
Biological Systems ◽  
Theoretical Modelling ◽  
Research Culture ◽  
Cognitive Sciences ◽  
New Research ◽  
Scientific Identity ◽  
The Brain

This chapter examines the ways that McCulloch’s new research culture at MIT’s Research Laboratory of Electronics shaped the evolution of his scientific identity into that of an engineer. This was an open, fluid, multidisciplinary culture that allowed McCulloch to shift his focus more squarely onto understanding the brain from the perspective of theoretical modelling, and to promote the cybernetic vision to diverse audiences. McCulloch’s practices, performed with a new set of student-collaborators, involved modeling the neurophysiology of perception, understanding reliability in biological systems, and pursuing knowledge of the reticular formation of the brain. The chapter provides a nuanced account of the relations between McCulloch’s work and the emerging fields of artificial intelligence and the cognitive sciences. It also highlights McCulloch’s identities as sage-collaborator and polymath, two roles that in part were the result of his students’ observations and in part products of his own self-fashioning.

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