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.

In the Tradition

1996 ◽  
Author(s):  
Bruce I. Blum
Keyword(s):  
Artificial Intelligence ◽  
User Interface ◽  
Graphic User Interface ◽  
Geographic Locations ◽  
Half Century ◽  
New Information ◽  
Intelligent Behavior ◽  
Electronic Computers ◽  
The Brain

Fifty years ago there were no stored-program binary electronic computers. Indeed, in the mid 1940s computer was a job description; the computer was a person. Much has happened in the ensuing half-century. whereas the motto of the 1950s was “do not bend, spindle, or mutilate,” we now have become comfortable with GUI wIMP (i.e., Graphic User Interface; windows, Icons, Mouse, and Pointers). whereas computers once were maintained in isolation and viewed through large picture windows, they now are visible office accessories and invisible utilities. whereas the single computer once was a highly prized resource, modern networks now hide even the machines’ geographic locations. Naturally, some of our perceptions have adapted to reflect these changes; however, much of our understanding remains bound to the concepts that flourished during computing’s formative years. For example, we have moved beyond thinking of computers as a giant brain (Martin 1993), but we still hold firmly to our faith in computing’s scientific foundations. The purpose of this book is to look forward and speculate about the place of computing in the next fifty years. There are many aspects of computing that make it very different from all other technologies. The development of the microchip has made digital computing ubiquitous; we are largely unaware of the computers in our wrist watches, automobiles, cameras, and household appliances. The field of artificial intelligence (AI) sees the brain as an organ with some functions that can be modeled in a computer, thereby enabling computers to exhibit “intelligent” behavior. Thus, their research seeks to extend the role of computers through applications in which they perform autonomously or act as active assistants. (For some recent overviews of AI see waldrop 1987; Crevier 1993.) In the domain of information systems, Zuboff (1988) finds that computers can both automate (routinize) and informate, that is, produce new information that serves as “a voice that symbolically renders events, objects, and processes so that they become visible, knowable, and sharable in a new way” (p. 9).

scholarly journals Conceptual and Practical Issues in the Pharmacological Treatment of Brain Injury

1993 ◽  
Vol 4 (3) ◽  
pp. 227-237 ◽  
Author(s):  
Donald G. Stein ◽  
Marylou M. Glasier ◽  
Stuart W. Hoffman
Keyword(s):  
Central Nervous System ◽  
Brain Injury ◽  
Time Course ◽  
Chemical Activity ◽  
Behavioral Testing ◽  
Naturally Occurring ◽  
New Information ◽  
Definition Of ◽  
Injury And Repair ◽  
The Brain

It is only within the last ten years that research on treatment for central nervous system (CNS) recovery after injury has become more focused on the complexities involved in promoting recovery from brain injury when the CNS is viewed as an integrated and dynamic system. There have been major advances in research in recovery over the last decade, including new information on the mechanics and genetics of metabolism and chemical activity, the definition of excitotoxic effects and the discovery that the brain itself secretes complex proteins, peptides and hormones which are capable of directly stimulating the repair of damaged neurons or blocking some of the degenerative processes caused by the injury cascade. Many of these agents, plus other nontoxic naturally occurring substances, are being tested as treatment for brain injury. Further work is needed to determine appropriate combinations of treatments and optimum times of administration with respect to the time course of the CNS disorder. In order to understand the mechanisms that mediate traumatic brain injury and repair, there must be a merging of findings from neurochemical studies with data from intensive behavioral testing.

The ideological divide in public perceptions of self-driving cars

2020 ◽  
Vol 29 (4) ◽  
pp. 436-451
Author(s):  
Yilang Peng
Keyword(s):  
Artificial Intelligence ◽  
United States ◽  
Scientific Literacy ◽  
Autonomous Vehicles ◽  
The United States ◽  
Public Perceptions ◽  
New Information ◽  
Nationally Representative ◽  
Self Driving Cars ◽  
Social Conservatism

Applications in artificial intelligence such as self-driving cars may profoundly transform our society, yet emerging technologies are frequently faced with suspicion or even hostility. Meanwhile, public opinions about scientific issues are increasingly polarized along the ideological line. By analyzing a nationally representative panel in the United States, we reveal an emerging ideological divide in public reactions to self-driving cars. Compared with liberals and Democrats, conservatives and Republicans express more concern about autonomous vehicles and more support for restrictively regulating autonomous vehicles. This ideological gap is largely driven by social conservatism. Moreover, both familiarity with driverless vehicles and scientific literacy reduce respondents’ concerns over driverless vehicles and support for regulation policies. Still, the effects of familiarity and scientific literacy are weaker among social conservatives, indicating that people may assimilate new information in a biased manner that promotes their worldviews.

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.

scholarly journals Anatomical Study of Intrahemispheric Association Fibers in the Brains of Capuchin Monkeys (Sapajussp.)

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Kellen Christina Malheiros Borges ◽  
Hisao Nishijo ◽  
Tales Alexandre Aversi-Ferreira ◽  
Jussara Rocha Ferreira ◽  
Leonardo Ferreira Caixeta
Keyword(s):  
Nonhuman Primates ◽  
Evolutionary Process ◽  
Anatomical Study ◽  
Lateral View ◽  
Superior Longitudinal Fasciculus ◽  
Cognitive Behaviors ◽  
Behavioral Studies ◽  
Inferior Longitudinal Fasciculus ◽  
Association Fibers ◽  
The Brain

Previous studies suggest that the complexity of fiber connections in the brain plays a key role in the evolutionary process of the primate brain and behaviors. The patterns of brain fiber systems have been studied in detail in many nonhuman primates, but not inSapajussp. Behavioral studies indicated thatSapajussp. (bearded capuchins) show highly cognitive behaviors such as tool use comparable to those in other nonhuman primates. To compare the brain fiber systems in capuchins with those in other nonhuman primates and humans, the intrahemispheric fibers systems in 24 cerebral hemispheres ofSapajuswere dissected by a freezing-thawing procedure. Dissection of the hemispheres in lateral view indicated short arcuate fibers, uncinate fasciculus, and inferior longitudinal fasciculus, while that in a medial view indicated short arcuate fibers, the cingulum united with the superior longitudinal fasciculus, and inferior longitudinal fasciculus. The results showed that the fiber systems inSapajusare comparable to those in rhesus and humans, except for a lack of independent superior longitudinal fasciculus and cingulum inSapajus.

scholarly journals THE VISUAL SYSTEM: A "CHICKEN AND EGG" PROBLEM SOLVED

2009 ◽  
Vol 05 (01) ◽  
pp. 115-121
Author(s):  
ANDREW R. PARKER ◽  
H. JOHN CAULFIELD
Keyword(s):  
Visual System ◽  
Neural Plasticity ◽  
Neural Circuit ◽  
Process Data ◽  
System A ◽  
New Information ◽  
The Brain

"What comes first: the chicken or the egg?" Eyes and vision were a great concern for Darwin. Recently, religious fundamentalists have started to attack evolution on the grounds that this is a chicken and egg problem. How could eyes improve without the brain module to use the new information that eye provides? But how could the brain evolve a neural circuit to process data not available to it until a new eye capability emerges? We argue that neural plasticity in the brain allows it to make use of essentially any useful information the eye can produce. And it does so easily within the animal's lifetime. Richard Gregory suggested something like this 40 years ago. Our work resolves a problem with his otherwise-insightful work.

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 Restricted Boltzmann Machine-Assisted Estimation of Distribution Algorithm for Complex Problems

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Lin Bao ◽  
Xiaoyan Sun ◽  
Yang Chen ◽  
Guangyi Man ◽  
Hui Shao
Keyword(s):  
Optimization Problems ◽  
Probability Model ◽  
Computational Cost ◽  
Evolutionary Process ◽  
Estimation Of Distribution Algorithm ◽  
Restricted Boltzmann Machine ◽  
Model Management ◽  
Boltzmann Machine ◽  
Complex Problems ◽  
Estimation Of Distribution

A novel algorithm, called restricted Boltzmann machine-assisted estimation of distribution algorithm, is proposed for solving computationally expensive optimization problems with discrete variables. First, the individuals are evaluated using expensive fitness functions of the complex problems, and some dominant solutions are selected to construct the surrogate model. The restricted Boltzmann machine (RBM) is built and trained with the dominant solutions to implicitly extract the distributed representative information of the decision variables in the promising subset. The visible layer’s probability of the RBM is designed as the sampling probability model of the estimation of distribution algorithm (EDA) and is updated dynamically along with the update of the dominant subsets. Second, according to the energy function of the RBM, a fitness surrogate is developed to approximate the expensive individual fitness evaluations and participates in the evolutionary process to reduce the computational cost. Finally, model management is developed to train and update the RBM model with newly dominant solutions. A comparison of the proposed algorithm with several state-of-the-art surrogate-assisted evolutionary algorithms demonstrates that the proposed algorithm effectively and efficiently solves complex optimization problems with smaller computational cost.

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.

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