Brain Computations

2020 ◽  
Author(s):  
Edmund T. Rolls
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Brain Function ◽  
Computational Science ◽  
Theoretical Physics ◽  
Medical Sciences ◽  
Computational Approaches ◽  
Potential Applications ◽  
The Subject ◽  
The Brain

The subject of this book is how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed. The book will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics.

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.

Recent Progress in Quantum Machine Learning

2021 ◽  
pp. 232-256
Author(s):  
Amandeep Singh Bhatia ◽  
Renata Wong
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Quantum Computing ◽  
Recent Progress ◽  
Machine Learning Algorithms ◽  
Learning Models ◽  
Exciting Field ◽  
Quantum Machine Learning ◽  
The Subject ◽  
Machine Learning Models

Quantum computing is a new exciting field which can be exploited to great speed and innovation in machine learning and artificial intelligence. Quantum machine learning at crossroads explores the interaction between quantum computing and machine learning, supplementing each other to create models and also to accelerate existing machine learning models predicting better and accurate classifications. The main purpose is to explore methods, concepts, theories, and algorithms that focus and utilize quantum computing features such as superposition and entanglement to enhance the abilities of machine learning computations enormously faster. It is a natural goal to study the present and future quantum technologies with machine learning that can enhance the existing classical algorithms. The objective of this chapter is to facilitate the reader to grasp the key components involved in the field to be able to understand the essentialities of the subject and thus can compare computations of quantum computing with its counterpart classical machine learning algorithms.

scholarly journals Biological Faithfulness is Unnecessary for Machine Learning

2019 ◽  
Vol 87 (2) ◽  
pp. 27-29
Author(s):  
Meagan Wiederman
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Feature Detection ◽  
Black Box ◽  
Compression Algorithms ◽  
Recent Success ◽  
Human Connectome Project ◽  
Definition Of ◽  
The Brain

Artificial intelligence (AI) is the ability of any device to take an input, like that of its environment, and work to achieve a desired output. Some advancements in AI have focused n replicating the human brain in machinery. This is being made possible by the human connectome project: an initiative to map all the connections between neurons within the brain. A full replication of the thinking brain would inherently create something that could be argued to be a thinking machine. However, it is more interesting to question whether a non-biologically faithful AI could be considered as a thinking machine. Under Turing’s definition of ‘thinking’, a machine which can be mistaken as human when responding in writing from a “black box,” where they can not be viewed, can be said to pass for thinking. Backpropagation is an error minimizing algorithm to program AI for feature detection with no biological counterpart which is prevalent in AI. The recent success of backpropagation demonstrates that biological faithfulness is not required for deep learning or ‘thought’ in a machine. Backpropagation has been used in medical imaging compression algorithms and in pharmacological modelling.

Fairness and Ethics in Artificial Intelligence-Based Medical Imagining

2022 ◽  
pp. 71-85
Author(s):  
Satvik Tripathi ◽  
Thomas Heinrich Musiolik
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Patient Care ◽  
Data Sharing ◽  
Ethical Issues ◽  
Medical Systems ◽  
Healthcare Facilities ◽  
The World ◽  
Potential Applications ◽  
Medical Imagining

Artificial intelligence has a huge array of current and potential applications in healthcare and medicine. Ethical issues arising due to algorithmic biases are one of the greatest challenges faced in the generalizability of AI models today. The authors address safety and regulatory barriers that impede data sharing in medicine as well as potential changes to existing techniques and frameworks that might allow ethical data sharing for machine learning. With these developments in view, they also present different algorithmic models that are being used to develop machine learning-based medical systems that will potentially evolve to be free of the sample, annotator, and temporal bias. These AI-based medical imaging models will then be completely implemented in healthcare facilities and institutions all around the world, even in the remotest areas, making diagnosis and patient care both cheaper and freely accessible.

Simulation of neural network for determination of technical state of internal state engine (ICE)

2020 ◽  
pp. 74-78
Author(s):  
K. Sovin
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Internal State ◽  
Optimal Solution ◽  
Technical State ◽  
Mathematical Functions ◽  
The Subject ◽  
Human Thinking

The article considers Artificial Intelligence (AI), which is science and technology, where the idea of modeling the processes of human thinking by using the capabilities of the computer is laid down. Machine learning, which can be imagined as a set of certain algorithms and methods that allow computers to be trained to obtain specific conclusions for the subject matter on the basis of available data. Neural networks are controlled by complex mathematical functions and solve problems of increased complexity using mathematical models built on analytical or other methods. Efficient neural network is reduced to creation of optimal solution as a result of analysis and conversion of incoming parameters of network for determination of technical state of ICE engine.

scholarly journals Obesity and Brain Function: The Brain–Body Crosstalk

Medicina ◽  
2020 ◽  
Vol 56 (10) ◽  
pp. 499
Author(s):  
Sophia X. Sui ◽  
Julie A. Pasco
Keyword(s):  
Brain Function ◽  
Empirical Studies ◽  
Measurement Techniques ◽  
Health Condition ◽  
Wide Range ◽  
Potential Risk Factors ◽  
The Central Nervous System ◽  
Research Gap ◽  
The Subject ◽  
The Brain

Dementia comprises a wide range of progressive and acquired neurocognitive disorders. Obesity, defined as excessive body fat tissue, is a common health issue world-wide and a risk factor for dementia. The adverse effects of obesity on the brain and the central nervous system have been the subject of considerable research. The aim of this review is to explore the available evidence in the field of body–brain crosstalk focusing on obesity and brain function, to identify the major research measurements and methodologies used in the field, to discuss the potential risk factors and biological mechanisms, and to identify the research gap as a precursor to systematic reviews and empirical studies in more focused topics related to the obesity–brain relationship. To conclude, obesity appears to be associated with reduced brain function. However, obesity is a complex health condition, while the human brain is the most complicated organ, so research in this area is difficult. Inconsistency in definitions and measurement techniques detract from the literature on brain–body relationships. Advanced techniques developed in recent years are capable of improving investigations of this relationship.

Machine-driven earth exploration: Artificial intelligence in oil and gas

2021 ◽  
Vol 40 (4) ◽  
pp. 298-301
Author(s):  
Tariq Alkhalifah ◽  
Ali Almomin ◽  
Ali Naamani
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Fault Detection ◽  
Oil And Gas ◽  
Image Resolution ◽  
The Earth ◽  
Recent Advances ◽  
Potential Applications ◽  
Boundary Mapping

Artificial intelligence (AI), specifically machine learning (ML), has emerged as a powerful tool to address many of the challenges we face as we try to illuminate the earth and make the proper prediction of its content. From fault detection, to salt boundary mapping, to image resolution enhancements, the quest to teach our computing devices how to perform these tasks accurately, as well as quantify the accuracy, has become a feasible and sought-after objective. Recent advances in ML algorithms and availability of the modules to apply such algorithms enabled geoscientists to focus on potential applications of such tools. As a result, we held the virtual workshop, Artificially Intelligent Earth Exploration Workshop: Teaching the Machine How to Characterize the Subsurface, 23–26 November 2020.

Artificial intelligence, the brain, and our society

2019 ◽  
pp. 233-242
Author(s):  
Thomas P. Trappenberg
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Human Intelligence ◽  
Learning Technology ◽  
Learning Models ◽  
The Impact ◽  
The Brain ◽  
Machine Learning Models

The concluding chapter is a brief venture into a more general discussion of machine learning, how it relates to artificial intelligence (AI), and the recent impact of this on society. It starts by discussing the relations of machine learning models in relation to the brain and human intelligence. The discussion then moves to the relation between machine learning and AI. While they are now often equated, it is useful to highlight some possible sources of misconceptions. It closes with some brief thought on the impact of machine learning technology our society.

scholarly journals Key challenges for delivering clinical impact with artificial intelligence

BMC Medicine ◽  
2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Christopher J. Kelly ◽  
Alan Karthikesalingam ◽  
Mustafa Suleyman ◽  
Greg Corrado ◽  
Dominic King
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Clinical Practice ◽  
Clinical Evaluation ◽  
Performance Metrics ◽  
Main Body ◽  
Negative Consequences ◽  
Technical Accuracy ◽  
Potential Applications ◽  
Randomised Controlled

Abstract Background Artificial intelligence (AI) research in healthcare is accelerating rapidly, with potential applications being demonstrated across various domains of medicine. However, there are currently limited examples of such techniques being successfully deployed into clinical practice. This article explores the main challenges and limitations of AI in healthcare, and considers the steps required to translate these potentially transformative technologies from research to clinical practice. Main body Key challenges for the translation of AI systems in healthcare include those intrinsic to the science of machine learning, logistical difficulties in implementation, and consideration of the barriers to adoption as well as of the necessary sociocultural or pathway changes. Robust peer-reviewed clinical evaluation as part of randomised controlled trials should be viewed as the gold standard for evidence generation, but conducting these in practice may not always be appropriate or feasible. Performance metrics should aim to capture real clinical applicability and be understandable to intended users. Regulation that balances the pace of innovation with the potential for harm, alongside thoughtful post-market surveillance, is required to ensure that patients are not exposed to dangerous interventions nor deprived of access to beneficial innovations. Mechanisms to enable direct comparisons of AI systems must be developed, including the use of independent, local and representative test sets. Developers of AI algorithms must be vigilant to potential dangers, including dataset shift, accidental fitting of confounders, unintended discriminatory bias, the challenges of generalisation to new populations, and the unintended negative consequences of new algorithms on health outcomes. Conclusion The safe and timely translation of AI research into clinically validated and appropriately regulated systems that can benefit everyone is challenging. Robust clinical evaluation, using metrics that are intuitive to clinicians and ideally go beyond measures of technical accuracy to include quality of care and patient outcomes, is essential. Further work is required (1) to identify themes of algorithmic bias and unfairness while developing mitigations to address these, (2) to reduce brittleness and improve generalisability, and (3) to develop methods for improved interpretability of machine learning predictions. If these goals can be achieved, the benefits for patients are likely to be transformational.

scholarly journals A Review of Current Applications of Artificial Intelligence and Machine Learning in Medical Science

2021 ◽  
pp. 316-325
Author(s):  
Jayant Kumar A Rathod ◽  
Naveen Bhavani ◽  
Prenita Prinsal Saldanha ◽  
Preethi M Rao ◽  
Prasad Patil
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Medical Science ◽  
Clinical Decision ◽  
Medical Sciences ◽  
Data Set ◽  
Time Problem ◽  
The Impact

Artificial Intelligence and Machine Learning are two fields that are causing substantial development in every field specifically in the field of medical sciences; for the stupendous potential that it can provide to assist the clinicians, researchers, in clinical decision making, automate time consuming procedures, medical imaging, and more. Most implementations of AI/ML rely on static data set, and this where the big data steps in. That is, these models are developed and trained on a data set that is already recorded and have been diligently reviewed for accuracy; leading to a precise decision-making process. Experts foresee that AI/ML based overarching care system will develop high-quality patient care and innovative research, aiding advanced decision support tools. In this paper we shall realize what are the current devices that are build and are being used for real time problem solving, also discuss the impact of Software as a Medical Device (SAMD) in future of medical sciences. [2,3,11]

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