The dream of creating certain forms of intelligence that mimic ourselves has long existed. While most of them appear in science fiction, over recent decades we have gradually been making progress in actually building intelligent machines that can perform certain tasks just like a human. This is an area called artificial intelligence. The beginning of AI can perhaps be traced back to Pamela McCorduck’s book, Machines Who Think, where she described AI as an ancient wish to forge the gods.
Deep learning is a branch of AI, with the aim specified as moving machine learning closer to its original goals: AI.
The path it pursues is an attempt to mimic the activity in layers of neurons in the neocortex, which is the wrinkly 80% of the brain where thinking occurs. In a human brain, there are around 100 billion neurons and 100 ~ 1000 trillion synapses.
It learns hierarchical structures and levels of representation and abstraction to understand the patterns of data that come from various source types, such as images, videos, sound, and text.
Higher level abstractions are defined as the composition of lower-level abstraction. It is called deep because it has more than one state of nonlinear feature transformation. One of the biggest advantages of deep learning is its ability to automatically learn feature representation at multiple levels of abstraction. This allows a system to learn complex functions mapped from the input space to the output space without many dependencies on human-crafted features. Also, it provides the potential for pre-training, which is learning the representation on a set of available datasets, then applying the learned representations to other domains. This may have some limitations, such as being able to acquire good enough quality data for learning. Also, deep learning performs well when learning from a large amount of unsupervised data in a greedy fashion.
The following figure shows a simplified Convolutional Neural Network (CNN):
The deep learning model, that is, the learned deep neural network often consists of multiple layers. Together they work hierarchically to build an improved feature space. The first layer learns the first order features, such as color and edges. The second layer learns higher-order features, such as corners. The third layer learns about small patches or texture. Layers often learn in an unsupervised mode and discover general features of the input space. Then the final layer features are fed into a supervised layer to complete the task, such as classification or regression.
Between layers, nodes are connected through weighted edges. Each node, which can be seen as a simulated neocortex, is associated with an activation function, where its inputs are from the lower layer nodes. Building such large, multi-layer arrays of neuron-like information flow is, however, a decade-old idea. From its creation to its recent successes, it has experienced both breakthroughs and setbacks.
With the newest improvements in mathematical formulas, increasingly powerful computers, and large-scale datasets, finally, spring is around the corner. Deep learning has become a pillar of today’s tech world and has been applied in a wide range of fields. In the next section, we will trace its history and discuss the ups and downs of its incredible journey.
