scholarly journals How Biological Concepts and Evolutionary Theories Are Inspiring Advances in Machine Intelligence

2021 ◽  
Author(s):  
Abigail R. Gutai ◽  
Thomas E. Gorochowski
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Machine Intelligence ◽  
Evolutionary Theories ◽  
Intelligent Machines ◽  
Innovative Solutions ◽  
Biological Concepts ◽  
Multi Level ◽  
Evolution By Natural Selection ◽  
The Brain

Since its advent in the mid-twentieth century, the field of artificial intelligence (AI) has been heavily influenced by biology. From the structure of the brain to evolution by natural selection, core biological concepts underpin many of the fundamental breakthroughs in modern AI. Here, focusing specifically on artificial neural networks (ANNs) that have become commonplace in machine learning, we show the numerous connections between theories based on coevolution, multi-level selection, modularity and competition and related developments in ANNs. Our aim is to illuminate the valuable but often overlooked inspiration biologists have provided AI research and to spark future contributions at this intersection of biology and computer science. Although recent advances in AI have been swift, many significant challenges remain requiring innovative solutions. Thankfully, biology in all its forms still has a lot to teach us, especially when trying to create truly intelligent machines.

The Quest for Intelligent Machines

2017 ◽  
Author(s):  
Arlindo Oliveira
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Decision Trees ◽  
Bayesian Learning ◽  
Turing Test ◽  
Intelligent Machines ◽  
The Brain ◽  
Existing Data

This chapter addresses the question of whether a computer can become intelligent and how to test for that possibility. It introduces the idea of the Turing test, a test developed to determine, in an unbiased way, whether a program running in a computer is, or is not, intelligent. The development of artificial intelligence led, in time, to many applications of computers that are not possible using “non-intelligent” programs. One important area in artificial intelligence is machine learning, the technology that makes possible that computers learn, from existing data, in ways similar to the ways humans learn. A number of approach to perform machine learning is addressed in this chapter, including neural networks, decision trees and Bayesian learning. The chapter concludes by arguing that the brain is, in reality, a very sophisticated statistical machine aimed at improving the chances of survival of its owner.

A Behavioral Economics Approach to Digitalization

2019 ◽  
pp. 107-122
Author(s):  
Dirk Beerbaum ◽  
Julia Margarete Puaschunder
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Social Media ◽  
Behavioral Economics ◽  
Field Research ◽  
Learning Systems ◽  
Business Reporting ◽  
Intelligent Machines ◽  
Private Investors ◽  
Coding Rules

Technological improvement in the age of information has increased the possibilities to control the innocent social media users or penalize private investors and reap the benefits of their existence in hidden persuasion and discrimination. This chapter takes as a case the transparency technology XBRL (eXtensible Business Reporting Language), which should make data more accessible as well as usable for private investors. Considering theoretical literature and field research, a representation issue for principles-based accounting taxonomies exists, which intelligent machines applying artificial intelligence (AI) nudge to facilitate decision usefulness. This chapter conceptualizes ethical questions arising from the taxonomy engineering based on machine learning systems and advocates for a democratization of information, education, and transparency about nudges and coding rules.

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.

Machine reengineering: robots and people working smarter together

2017 ◽  
Vol 45 (6) ◽  
pp. 50-54 ◽  
Author(s):  
Prashant Shukla ◽  
H. James Wilson ◽  
Allan Alter ◽  
David Lavieri
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Design Methodology ◽  
It Professionals ◽  
Content Type ◽  
Public Agency ◽  
Intelligent Machines ◽  
Intelligent Machine ◽  
Working Together ◽  
Practical Implications

Purpose The authors explore the potential of machine learning, computers employ that an algorithm to sort data, make decisions and then continuously assess and improve their functionality. They suggest that it be used to power a radical redesign of company processes that they call machine reengineering. Design/methodology/approach The authors interpret a survey of more than a thousand corporate public agency IT professionals on their use of artificial intelligence and machine learning. Findings Companies that embrace machine learning find that it adds value to the work product of their employees and provides companies with new capabilities. Practical implications Working together with an intelligent machine, workers become custodians of powerfully smart tools, tools that personalize work to maximize their most productive ways of working. Originality/value A guide to establishing a culture that empowers employees to thrive alongside intelligent machines.

scholarly journals MACHINE INTELLIGENCE. ESSAYS ON THE THEORY OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE

2019 ◽  
Author(s):  
Сергей Шумский ◽  
Sergey Shumskiy
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Scientific Revolution ◽  
Machine Intelligence ◽  
General Intelligence ◽  
New Science ◽  
Artificial General Intelligence ◽  
Science Of Mind ◽  
Artificial Brains

This book is about the nature of mind, both human and artificial, from the standpoint of the theory of machine learning. It addresses the problem of creating artificial general intelligence. The author shows how one can use the basic mechanisms of our brain to create artificial brains of future robots. How will this ever-stronger artificial intelligence fit into our lives? What awaits us in the next 10-15 years? How can someone who wants to take part in a new scientific revolution, participate in developing a new science of mind?

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.

On Abstract Intelligence

2012 ◽  
pp. 14-31
Author(s):  
Yingxu Wang
Keyword(s):  
Artificial Intelligence ◽  
Formal Model ◽  
Machine Intelligence ◽  
Human Intelligence ◽  
Engineering Applications ◽  
Measurement Framework ◽  
Wide Range ◽  
Intelligence Theories ◽  
The Brain ◽  
Intelligent Quotient

Abstract intelligence is a human enquiry of both natural and artificial intelligence at the reductive embodying levels of neural, cognitive, functional, and logical from the bottom up. This paper describes the taxonomy and nature of intelligence. It analyzes roles of information in the evolution of human intelligence, and the needs for logical abstraction in modeling the brain and natural intelligence. A formal model of intelligence is developed known as the Generic Abstract Intelligence Mode (GAIM), which provides a foundation to explain the mechanisms of advanced natural intelligence such as thinking, learning, and inferences. A measurement framework of intelligent capability of humans and systems is comparatively studied in the forms of intelligent quotient, intelligent equivalence, and intelligent metrics. On the basis of the GAIM model and the abstract intelligence theories, the compatibility of natural and machine intelligence is revealed in order to investigate into a wide range of paradigms of abstract intelligence such as natural, artificial, machinable intelligence, and their engineering applications.

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 Machine intelligence today: applications, methodology, and technology

2021 ◽  
Vol 44 (2) ◽  
pp. 104-114
Author(s):  
Bernhard G. Humm ◽  
Hermann Bense ◽  
Michael Fuchs ◽  
Benjamin Gernhardt ◽  
Matthias Hemmje ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Best Practices ◽  
Machine Intelligence ◽  
Strong Impact ◽  
Industrial Manufacturing ◽  
Knowledge Based ◽  
Current Trends ◽  
Culture Education

AbstractMachine intelligence, a.k.a. artificial intelligence (AI) is one of the most prominent and relevant technologies today. It is in everyday use in the form of AI applications and has a strong impact on society. This article presents selected results of the 2020 Dagstuhl workshop on applied machine intelligence. Selected AI applications in various domains, namely culture, education, and industrial manufacturing are presented. Current trends, best practices, and recommendations regarding AI methodology and technology are explained. The focus is on ontologies (knowledge-based AI) and machine learning.

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