User:VjiaoBlack/sandbox

The following is a draft of my edits to the History of AI page for my CIV class.

Deep learning, big data and artificial general intelligence: 2000–present
In the first decades of the 21st century, access to large amounts of data (known as "big data"), faster computers and advanced machine learning techniques were successfully applied to many problems throughout the economy. By 2016, the market for AI related products, hardware and software reached more than 8 billion dollars and the New York Times reported that interest in AI had reached a "frenzy". The applications of big data began to reach into other fields as well, such as training models in ecology and for various applications in economics. Advances in deep learning (particularly deep convolutional neural networks and recurrent neural networks) drove progress and research in image and video processing, text analysis, and even speech recognition.

Deep learning
Deep learning is a branch of machine learning that models high level abstractions in data by using a deep graph with many processing layers. According to the Universal approximation theorem, deep-ness isn't necessary for a neural network to be able to approximate arbitrary continuous functions. Even so, there are many problems that are common to shallow networks (such as overfitting) that deep networks help avoid. As such, deep neural networks are able to realistically generate much more complex models as compared to their shallow counterparts.

However, deep learning has problems of its own. A common problem for recurrent neural networks is the vanishing gradient problem, which is where gradients passed between layers gradually shrink and literally disappear as they are rounded off to zero. There have been many methods developed to approach this problem.

State-of-the-art deep neural network architectures can sometimes even rival human accuracy in fields like computer vision, specifically on things like the MNIST database, and traffic sign recognition.

Artificial general intelligence
Artificial general intelligence (AGI) describes research that aims to create machines capable of general intelligent action. Although there are still no human-constructed AGI systems, one can certainly argue that we are making progress on that path.

Language processing engines powered by smart search engines can easily beat humans at answering general trivia questions (such as IBM Watson), and recent developments in deep learning have produced astounding results in competing with humans, in things like Go, Doom (which, being a FPS, has sparked some controversy), and even running data centers.