Machine learning refers to a type of Artificial Intelligence (AI) that allows computers to learn beyond their initial static programming. These new programs are developed to analyze patterns in past data sets in order to adapt. More advanced computer programs are even capable of altering their code in response to prior exposure to an unfamiliar set of inputs, which opens a whole set of possibilities for the future of AI.
Some of the recent applications of machine learning include Google’s self-driving car and the algorithm behind the success of its web search function, companies providing online recommendation offers based on user’s’ browsing history, and fraud detection.
Deep learning is a branch of machine learning that focuses on the neural network model inspired by our understanding of the biology of the human brain. The human brain contains billions of neurons that are capable of sending signals and connecting to each other within a certain physical distance. Programmers incorporated that structure by creating artificial neural networks that have discrete layers, connections, and directions in which the data propagates.
How Does Deep Learning Work?
Deep learning enables computer programs to process a lot of input data simultaneously, and use it to make decisions based on the data. Each node in the neural network could be a logical gate or a construction of logical gates that extracts numerical data, asks a series of binary questions, and/or arranges and classifies input data and feeds it through channels in the subsequent layers.
The capacity of deep learning far surpasses that of old AI models and it can be trained to produce accurate and intelligent outputs. Consider the developing brain of a young child that is learning how to read. Receptors in the child’s eye breakdown the image of each letter the child is seeing and breaks it down into electrochemical signals sent through channels of neurons. Perhaps at first, the child will struggle to differentiate an upper-case I from a lowercase l , but after encountering this mistake again and again and analyzing the difference in the usage of I and l, at some point the network of neurons becomes trained enough that it gets the answer right practically all the time.
Some of the Present-Day Applications of Deep Learning
Deep learning is being used to enhance the motor control in robotics, allowing for sensors and motors to coordinate an appropriate reaction to stimuli from the environment. This is the technology behind self-driving cars and autonomous robotics used in spaceflight, household maintenance, and potentially for military purposes. So far, deep learning machines have the most success and accuracy in analyzing bulk inputs from data scraping, which is used by online marketers to promote specific products based on user’s search history.
Security programs also use the neural network model to detect anomalies in databases. If you try logging in your email abroad, or using an IP provided by virtual private network, there is a chance that your email service provider will take extra measures of authentication to verify your identity. The algorithm behind this function is based on historical patterns that the computer program is able to recognize through its deep learning. The same logic applies to the security monitoring suspicious credit card activities and similar possible instances of fraud.
The Future of Deep Learning
With enough breakthroughs in deep learning, computer programs may match or even surpass human intelligence. Self-programming artificial intelligence, as well as programs that can create unique programs may sound like mere science fiction, but AI is already integral in modern life: in fact, you may have read a new story on the internet written entirely by a machine. Right now, we make take chat robots like Siri for granted but there may come a time when artificial intelligence can keep up with our own.
Conclusion
Deep learning uses a layered structure of neural networks to emulate the learning process that many living creatures use to survive and function. Much like our own memory and analysis, it can learn from past patterns and adapt, developing more appropriate responses to stimuli. We have yet to determine the limits of this study, but for now, it’s important to be aware of the technology behind so many functions that make our lives easier.
