Deep learning technology has changed our lives dramatically. From autonomous vehicles made by Tesla to the speech recognition technology used by Google Translate, the deep neural networks of the deep learning model have made deep learning technology a part of our daily lives in recent years.
But what is deep learning technology?
Deep Learning Technology Defined
The deep learning model is an evolution of the machine learning model because deep learning is a subset of machine learning. Of course, to understand deep learning, you must first understand machine learning.
Machine learning is how artificial intelligence is done in the real world. In science fiction movies such as “I, Robot” and “2001: A Space Odyssey,” robots and computers can think for themselves. In the real world, we are not yet capable of creating that.
What we can do, however, is create algorithms that are capable of performing tasks. In fact, algorithms are the cornerstone of computers and programming.
We can improve the efficiency of algorithm performance and the complexity of their tasks through a training process and a learning process. When a set of algorithms is commanded to perform a new task, different algorithms will perform at varying degrees of success. This is the training process. The programmer then eliminates unsuccessful algorithms and creates new algorithms based on the successful ones. The programmer repeats the cycle, again and again, creating better and better algorithms. This process is called the learning process.
These cycles are called layers and there are many different layers: First layer, second layer, previous layer, input layer, output layer, hidden layer, final layer, etc. These layers, when joined, form an artificial neural network. This neural network is based on the biological model of the biological neural network formed by the neurons and nodes of the human brain. This is the essence of machine learning.
Before the advent of deep learning, however, machine learning was limited by supervised learning. For regular machine learning, the programmer has to personally oversee the training process and learning process. With deep learning, however, the programmer can create algorithms that are specially designed to oversee the training process and the learning process without human intervention. This unsupervised learning separates deep learning from machine learning.
Deep learning technology is the technology created by deep learning applications. This includes the object recognition and image recognition used by Tesla and other auto manufacturers to make autonomous vehicles as well as the image recognition and feature extraction used by Ring security systems or the speech recognition used by Google Translate.
In medicine and health care, deep learning technology has a host of applications with its ability to aggregate large amounts of data and big data needed to make an accurate medical diagnosis. Call centers have used chatbots to improve customer service and gamers have created AlphaGo, the first program capable of defeating a human go champion.
Finally, in the investment sphere, deep learning technology has been used to improve the science of prediction in regards to the market, which has lead to the creation of other sciences: data analytics, predictive analytics, business analytics, and data virtualization.
Deep Learning Technology Conclusion
Will robots conquer humans? This burning question has both enticed and terrified many science fiction aficionados. The optimization of artificial intelligence is a reality that could, conceivably, make human intelligence obsolete. The possibility of humans being replaced on the evolutionary hierarchy by robots in the near future, however, is probably best left to the science fiction writers.
What is a real possibility, however, is the Industrial Revolution 2.0, when artificial intelligence results in the automation of our entire workforce. When that happens, just as in the 19th-century Industrial Revolution, many humans will be left behind. Those who cultivate a deep understanding of deep learning technology will be the last ones to be left behind.