We return to the question of terminology that we started this post with. Our feeling is that the term "artificial intelligence" has been used in so many ways that it is now confusing. People use AI to refer to all three approaches described above, plus others, and therefore has become almost meaningless. The term "machine learning" is a more narrowly defined term for machines that learn from data, including simple neural models such as ANNs and Deep Learning. We use the term "machine intelligence" to refer to machines that learn but are aligned with the Biological Neural Network approach. Although there still is much work ahead of us, we believe the Biological Neural Network approach is the fastest and most direct path to truly intelligent machines. This blog entry was modified on Thu Mar 24 2016 to clarify the timing of neural network research.
Thanks to the technological advancements in the field of data analytics, the global market for neutral network software is witnessing an exponential rise in its size and revenue. Since neutral network software is highly effective in reducing the cost and operational time in a number of enterprises, its usage in business application, such as such as fraud detection and risk assessment, is increased by leaps and bounds. The market is majorly driven by the remarkable rise in the demand for data archiving tools, used for organizing a massive amount of unorganized data created by various end users. Additionally, the high adoption rate of digital technologies and the increasing demand for predicting solutions are likely to boost this market in the near future. However, the slow digitization rate across emerging markets, dearth of technical expertise, and various other operational challenges may hinder the market' growth over the forthcoming years.
This is the 3rd part in my Data Science and Machine Learning series on Deep Learning in Python. At this point, you already know a lot about neural networks and deep learning, including not just the basics like backpropagation, but how to improve it using modern techniques like momentum and adaptive learning rates. You've already written deep neural networks in Theano and TensorFlow, and you know how to run code using the GPU. This course is all about how to use deep learning for computer vision using convolutional neural networks. These are the state of the art when it comes to image classification and they beat vanilla deep networks at tasks like MNIST.