2017-12-technique-illuminates-artificial-intelligence-language.html

@machinelearnbot

Neural networks, which learn to perform computational tasks by analyzing huge sets of training data, have been responsible for the most impressive recent advances in artificial intelligence, including speech-recognition and automatic-translation systems. During training, however, a neural net continually adjusts its internal settings in ways that even its creators can't interpret. Much recent work in computer science has focused on clever techniques for determining just how neural nets do what they do. In several recent papers, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Qatar Computing Research Institute have used a recently developed interpretive technique, which had been applied in other areas, to analyze neural networks trained to do machine translation and speech recognition. They find empirical support for some common intuitions about how the networks probably work.


Reading a neural network's mind

MIT News

Neural networks, which learn to perform computational tasks by analyzing huge sets of training data, have been responsible for the most impressive recent advances in artificial intelligence, including speech-recognition and automatic-translation systems.


Artificial Neural Networks and Neural Networks Applications - XenonStack Blog

#artificialintelligence

Artificial Neural Networks are the computational models inspired by the human brain. Many of the recent advancements have been made in the field of Artificial Intelligence, including Voice Recognition, Image Recognition, Robotics using Artificial Neural Networks. These biological methods of computing are considered to be the next major advancement in the Computing Industry. The term'Neural' is derived from the human (animal) nervous system's basic functional unit'neuron' or nerve cells which are present in the brain and other parts of the human (animal) body. It receives signals from other neurons. It sums all the incoming signals to generate input.


Artificial Neural Networks and Neural Networks Applications - XenonStack

#artificialintelligence

Artificial Neural Networks are the computational models inspired by the human brain. Many of the recent advancements have been made in the field of Artificial Intelligence, including Voice Recognition, Image Recognition, Robotics using Artificial Neural Networks. These biological methods of computing are considered to be the next major advancement in the Computing Industry. The term'Neural' is derived from the human (animal) nervous system's basic functional unit'neuron' or nerve cells which are present in the brain and other parts of the human (animal) body. It receives signals from other neurons. It sums all the incoming signals to generate input.


An Introduction to Deep Learning

#artificialintelligence

A brief history of Machine learning • Most of the machine learning methods are based on supervised learning Input Feature Representation Learning Algorithm 10.