For instance, in order to recognize objects, ML can be used as a classifier. First, a training procedure is built by a sample data so that ML algorithms are tuned with the right parameters. Training is established with known data and its associated labels. Known data is first processed for feature extraction and then fed into the ML algorithm. The ML algorithm will find the pattern and classify it. This is called supervised learning. For new images fed into the model, the machine will classify accordingly. There is also unsupervised learning (clustering), where learning seeks patterns in the data that have no labels and reinforcement learning (robot planning), where learning is with trial and error (6). Introduction of deep learning came with Ukrainian mathematician Alexey Grigorevich in 1965, by stacking several perceptrons on top of one anotherresembling todays deep-learning architectures inspired from Neural Networks (NN) of the human brain.

There were a few notable successes before AI went into hibernation in the 1970s, which continued for a decade. By 1980s, Geoffrey Hinton et. al. introduced backpropagation, a mechanism to self-optimize Artificial NN without human intervention. His success was adjusting the weighs of the NN nodes across multiple layers. Deep learning is state of art machine learning! Deep Learning (DL) is a subset of machine learning and designed to mimic the network of neurons in a brain. Artificial Neural Networking (ANN) is an architecture where there is a hidden layer between input and output and the deep neural network (aka deep learning) utilizes more than one hidden layer, where the layers are stacked on top of each other. A layer is composed of nodes. Each node is connected to nodes in the previous and next layers as in brain. Each input is multiplied by a weight in each neuron. Multiplied outputs are input for the next layer. The

In 1989, Yann LeCun introduced the Convolutional Neural Network (CNN) for image recognition. In 1992, Bernhard E. Boser, Isabelle M. Guyon, and Vladimir N. Vapnik introduced Support Vector Machines (SVM), so that classifying the sentiment and understanding of human speech became easy for natural language processing. By 1991, Sepp Hochreiter introduced a Recurrent Neural Network (RNN) to be useful in sequencing tasks. And in 1997, Hochreiter and Jurgen Schmidhuber introduced Long ShortTerm Memory (LSTM) for advanced speech to text translation. In 2006, Hinton pre-trained the network with Deep-belief Network before employing backpropagation and coined the phrase deep learning.

Figure 1. Schematic represantation of neural network 1. 2. final layer is the output layer and provides the probability of each class for the classification task. These multiple hidden layers help optimization escape from bad local optimums. ANN is inspired by the human brain with the training of more than one hidden layer, introduced in 2006 by Geoffrey Hinton for unsupervised training (6, 7). Before this, technically, adjusting the weights by backpropagation was an issue (8). ness and society at large. The industrial era let machines do the physical work; the information era enabled machines to do the computation and storage, now the AI era will let machines make the decisions (13). Today, we are also seeing that some AIs are already passing the Turing Test (14)!