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.
In recent years, several studies have provided insight on the functioning of the brain which consists of neurons and form networks via interconnection among them by synapses. Neural networks are formed by interconnected systems of neurons, and are of two types, namely, the Artificial Neural Network (ANNs) and Biological Neural Network (interconnected nerve cells). The ANNs are computationally influenced by human neurons and are used in modelling neural systems. The reasoning foundations of ANNs have been useful in anomaly detection, in areas of medicine such as instant physician, electronic noses, pattern recognition, and modelling biological systems. Advancing research in artificial intelligence using the architecture of the human brain seeks to model systems by studying the brain rather than looking to technology for brain models. This study explores the concept of ANNs as a simulator of the biological neuron, and its area of applications. It also explores why brain-like intelligence is needed and how it differs from computational framework by comparing neural networks to contemporary computers and their modern day implementation.
Yes. First, artificial neural networks as whole were inspired--as their name suggests--by the emerging biology of neurons being developed in the mid-20th century. Artificial neurons were designed to mimic the basic characteristics of how neurons take in and transform information. Second, the main features and computations done by convolutional networks were directly inspired by some of the early findings about the visual system. In 1962 Hubel and Wiesel discovered that neurons in primary visual cortex respond to specific, simple features in the visual environment (particularly, oriented edges). Furthermore, they noticed two different kinds of cells: simple cells--which responded most strongly to their preferred orientation only at a very particular spatial location--and complex cells--which had more spatial invariance in their response.
The field of machine learning has focused, primarily, on discretized sub-problems (i.e. vision, speech, natural language) of intelligence. While neuroscience tends to be observation heavy, providing few guiding theories. It is unlikely that artificial intelligence will emerge through only one of these disciplines. Instead, it is likely to be some amalgamation of their algorithmic and observational findings. As a result, there are a number of problems that should be addressed in order to select the beneficial aspects of both fields. In this article, we propose leading questions to guide the future of artificial intelligence research. There are clear computational principles on which the brain operates. The problem is finding these computational needles in a haystack of biological complexity. Biology has clear constraints but by not using it as a guide we are constraining ourselves.
This primer explores the exciting subject of intelligence. Intelligence is a fundamental component of all living things, as well as Artificial Intelligence(AI). Artificial Intelligence has the potential to affect all of our lives and a new era for modern humans. This paper is an attempt to explore the ideas associated with intelligence, and by doing so understand the implications, constraints, and potentially the capabilities of future Artificial Intelligence. As an exploration, we journey into different parts of intelligence that appear essential. We hope that people find this useful in determining where Artificial Intelligence may be headed. Also, during the exploration, we hope to create new thought-provoking questions. Intelligence is not a single weighable quantity but a subject that spans Biology, Physics, Philosophy, Cognitive Science, Neuroscience, Psychology, and Computer Science. Historian Yuval Noah Harari pointed out that engineers and scientists in the future will have to broaden their understandings to include disciplines such as Psychology, Philosophy, and Ethics. Fiction writers have long portrayed engineers and scientists as deficient in these areas. Today, modern society, the emergence of Artificial Intelligence, and legal requirements all act as forcing functions to push these broader subjects into the foreground. We start with an introduction to intelligence and move quickly onto more profound thoughts and ideas. We call this a Life, the Universe and Everything primer, after the famous science fiction book by Douglas Adams. Forty-two may very well be the right answer, but what are the questions?