Goto

Collaborating Authors

The relationship between Biological and Artificial Intelligence

#artificialintelligence

Intelligence can be defined as a predominantly human ability to accomplish tasks that are generally hard for computers and animals. Artificial Intelligence [AI] is a field attempting to accomplish such tasks with computers. AI is becoming increasingly widespread, as are claims of its relationship with Biological Intelligence. Often these claims are made to imply higher chances of a given technology succeeding, working on the assumption that AI systems which mimic the mechanisms of Biological Intelligence should be more successful. In this article I will discuss the similarities and differences between AI and the extent of our knowledge about the mechanisms of intelligence in biology, especially within humans. I will also explore the validity of the assumption that biomimicry in AI systems aids their advancement, and I will argue that existing similarity to biological systems in the way Artificial Neural Networks [ANNs] tackle tasks is due to design decisions, rather than inherent similarity of underlying mechanisms. This article is aimed at people who understand the basics of AI (especially ANNs), and would like to be better able to evaluate the often wild claims about the value of biomimicry in AI. Symbolic AI was the prevailing approach to AI until the early 90's. It is reliant on human programmers coding complex rules to enable machines to complete complex tasks. Continuing failure of this approach to solve many tasks crucial to intelligence provides a good contrast with Machine Learning -- an alternative approach to AI which is essential to the current advent of artificially intelligent machines. In 1994 the reigning chess champion Garry Kasparov was beaten by Deep Blue.


Interested In Deep Learning?

#artificialintelligence

This section introduces the integral components of deep learning and how they operate to emulate learning commonly found amongst humans. The brain is responsible for all human cognitive functions; in short, the brain is responsible for your ability to learn, acquire knowledge, retain information and recall knowledge. One of the fundamental building blocks of the learning systems within the brain is the biological neuron. A neuron is a cell responsible for transmitting signals to other neurons and, as a consequence, other parts of the body. Researchers, in some way, have replicated the functionality of the biological neuron into a mathematically representative model called the perceptron.


Synapse

#artificialintelligence

A synapse is the connection between nodes, or neurons, in an artificial neural network (ANN). Similar to biological brains, the connection is controlled by the strength or amplitude of a connection between both nodes, also called the synaptic weight. Multiple synapses can connect the same neurons, with each synapse having a different level of influence (trigger) on whether that neuron is "fired" and activates the next neuron. In ANNs, each neuron is defined through its input and its activation function, and its outputs. A synapse is often referred to as a node in the machine learning terminology.


In Search of a Universal Theory of Intelligence

#artificialintelligence

Intelligence in this "clean room" environment can be defined with respect to accumulated systematic methods of reasoning. So a logic proof system can be defined to be more efficient at solving a mathematical task than a comparable human. The more civilization transitions into a virtualized world, the more likely humans will find synthetic minds that are'more intelligent'. Classical definitions of computation tend to favor sequential processes. A consequence is that more natural parallel processes such as evolution tend to be ignored.


The relationship between Biological and Artificial Intelligence

arXiv.org Artificial Intelligence

Intelligence can be defined as a predominantly human ability to accomplish tasks that are generally hard for computers and animals. Artificial Intelligence [AI] is a field attempting to accomplish such tasks with computers. AI is becoming increasingly widespread, as are claims of its relationship with Biological Intelligence. Often these claims are made to imply higher chances of a given technology succeeding, working on the assumption that AI systems which mimic the mechanisms of Biological Intelligence should be more successful. In this article I will discuss the similarities and differences between AI and the extent of our knowledge about the mechanisms of intelligence in biology, especially within humans. I will also explore the validity of the assumption that biomimicry in AI systems aids their advancement, and I will argue that existing similarity to biological systems in the way Artificial Neural Networks [ANNs] tackle tasks is due to design decisions, rather than inherent similarity of underlying mechanisms. This article is aimed at people who understand the basics of AI (especially ANNs), and would like to be better able to evaluate the often wild claims about the value of biomimicry in AI. Acknowledgements I thank Kate Wilkinson for extensive editing and proofreading of this article. Dr Kristjan Kalm's critical review of this article was essential for its scientific integrity; however, not all views expressed in this article reflect Kristjan's own views. Finally, I would like to thank illumr Ltd, especially its founder and CEO Jason Lee, for sponsoring this work. Symbolic AI was the prevailing approach to AI until the early 90's.