The domain of deep learning is undergoing a gradual change. This is because of its application to new fields that were earlier uninfluenced by it. As new fields and domains are coming under the ambit of deep learning, it becomes necessary to train professionals in deep learning and related technology.
At this point of time, we are relatively short of the manpower required for catering to the growing field of deep learning. Although professionals from machine learning background often look to switch over domains, they fail at the deep learning interview stage itself. This is because the skill set for deep learning is different and requires a holistic knowledge of artificial neural networks. When it comes to the application prospects, deep learning is the underlying technology behind human computer interaction.
It is important to understand deep learning from the point of view of human computer interaction.
Human computer interaction
Human computer interaction refers to the way in which humans are able to communicate with machines. It is about bridging the dialectical barriers between man and the machine. Deep learning technology is at the core of human computer interaction. Deep learning helps to form a cognitive base so that machines comprehend the language of humans. Deep learning also helps in the formation of a knowledge base that can be mined when machines interact with humans. Human computer interaction involves a uniform semantic code through which communication between humans and machines becomes effective and expressive.
Deep learning technology
Deep learning technology is powered by artificial neural networks that operate with the help of numerous functions and algorithms. Artificial neural networks consist of nodes that operate on data sets with the help of sigmoid functions. Various nodes are connected to each other to form a complex network that has multidimensional applications.
For instance, text classification and sentiment analysis is possible with the help of deep learning technology. This technology also helps in audio classification as well as image classification including feature extraction. Different types of chatbots as well as machine translation systems operate with the help of deep learning technology. The search engine recommendation systems also use deep learning algorithms.
The need of deep learning in Human computer interaction
Human computer interaction becomes possible with the help of different supervised and unsupervised algorithms of deep learning. The three most important deep learning algorithms that are used in human computer interaction include artificial neural networks, recurrent neural networks and convolutional neural networks. The unsupervised algorithms that are used in human computer interaction include auto encoders and generative adversarial networks.
Various operations are performed with the help of different layers which are further classified into three types. The first layer is called the input layer that is fed with an independent value of the variable. The second layer is called the hidden layer that subjects the model to different types of functions and even helps in extraction of features that are required. The third layer is called the output layer and the desired result is exhibited through this layer.
Let us now understand the interlinkage between human computer interaction and deep learning with the help of a case study.
A case study of humanoids
Human computer interaction can be understood with the help of a case study taking into consideration the interaction of humanoids with homo sapiens. Let us assume that a particular set of instructions is passed by humans to the humanoids. In this case, the first important task that a humanoid needs to perform is to convert the human language into machine language so that it can be comprehended easily. This language is also stored in the knowledge base so that future instruction can be taken out of this repository. The knowledge base passes the instruction to the cognitive components of the humanoid. These instructions that are sourced out of the knowledge base are converted into human language. In this way, the cognitive component acts as a semantic bridge between knowledge base and cognitive component.
The way ahead
There is no doubt in the fact that deep learning technology has widened the frontiers of human computer interaction. In future as well, we may expect the blurring of semantic barriers between man and the machine.