How Deep Learning Algorithms Work
Many of the artificial intelligence (AI) products you use or interact with on a daily basis were developed using deep learning, a fascinating subdomain of machine learning (ML) that’s based in neural networks. Neural networks, also known as artificial neural networks (ANNs) have been around in some form since the 1970s, but thanks to the increase in computing power and data storage, they’re experiencing a new level of popularity.
Neural networks have several key benefits for data scientists and organizations building AI. Most importantly, they learn organically by themselves, which saves teams from investing huge amounts of time and money on training algorithms. With these advantages in mind, data scientists have increasingly relied on deep learning techniques to build innovative AI technology with speed and scalability.
Neural Networks Defined
An artificial neural network is a computer simulation that attempts to model the processes of the human brain in order to imitate the way in which it learns. Such systems essentially teach themselves by considering examples, generally without task-specific programming by humans, and then use a corrective feedback loop to improve their performance. This process, known as deep learning, is a type of machine learning that uses data representations, rather than task-specific algorithms, as in traditional software. Deep learning has been used in applications such as computer vision, speech recognition, natural language processing and audio recognition. The goal of these algorithms is to create more sophisticated software and machines that can perform human activities like seeing, listening, and thinking.
How Neural Network Technology Works
A neural network consists of node layers, an input layer, output layer, and some number of hidden layers. The nodes are connected to one another and each have a specific weight and bias (threshold). If a node’s output is higher than its threshold, the node activates and passes data to the next layer of the network. Most neural networks flow in one direction, so are considered feedforward.
A neural network with several layers is considered a deep neural network (also known as a deep learning algorithm). When we refer to deep learning, we are simply referring to a neural network several layers deep.
Neural networks require a lot of data to learn from. The neural network analyzes this data (known as training data) to identify patterns and relationships. The more data provided, the faster the neural network will infer rules about it and be able to make accurate predictions. If you provide a neural network with thousands of images of various flowers labeled with their corresponding names, an effective neural network will learn to identify characteristics unique to roses, lilies, etc.
Deep Learning Needs Structured Data
Finding data to use in deep learning isn’t the issue. We collectively generate about 2.5 quintillion bytes of data each day in the form of images, videos, emails, and more. But a great deal of this data is unstructured and unlabeled, so the wealth of intelligence that can be mined and used to make smart decisions is buried and largely unusable. To take advantage of neural networks and deep learning’s potential, it is essential to train algorithms with data that has been structured by skilled human annotators.
Use Cases of Neural Networks
Achievements in neural network technology have made astounding progress in recent years. In a 2012 Large Scale Visual Recognition Challenge, for example, they outperformed all other algorithms in an industry-standard image dataset by more than 10%. The New York Times recently reported that an AI program created by scientists at the Swiss AI Lab at the University of Lugano won a pattern recognition contest. The winning program successfully identified 99.46 percent of the images in a set of 50,000 signs, while the average for humans was 98.84 percent. Studies like these demonstrate that neural networks are now the most advanced algorithm that can be used to understand complex sensory data. Here are a few other exciting use cases of neural network technology
Tech companies have noticed these improvements and are seeking to capitalize on them. Google, for example, has announced a major initiative to develop artificial intelligence with new technology called AutoML. With this algorithm, neural networks use machine learning to build even more sophisticated neural networks in an iterative process. These neural networks are designed to teach themselves, mimicking the way the human brain teaches itself through a process called reinforcement learning. The goal is to create networks that are more powerful, efficient, and easy to use.
Dueling Neural Networks
Neural networks are also capable of improving each other. Forbes has reported on a technology breakthrough called Dueling Neural Networks. In this approach, neural networks actually compete with each other. The goal is to allow AI systems to go beyond mere learning and develop something akin to human imagination. Organizations such as Google Brain, Deep Mind, and Nvidia are working to develop systems that are capable of creating ultra-realistic, computer-generated images and sounds that are far more advanced than those we see today.
Neural Networking Chips
While much of the development of neural networks is happening in the cloud, the technology is starting to appear in hardware as well. Researchers at MIT have created a neural networking chip. The chip can utilize machine learning without having to relay the data to cloud-based applications. The prototype developed by MIT has been shown to increase the speed of machine-learning computations by up to 700 percent. As an added benefit, power consumption is reduced by 93 to 96 percent. An updated version of the chip with even greater computational capabilities is only a few years away.
What Appen Can Do For You
With over 20 years of experience working with global firms in various industries, Appen has a proven track record of solving a wide variety of data challenges. We have deep expertise in more than 235 languages and dialects, and access to a global crowd of over one million skilled contractors that help our clients collect and structure data to optimize their machine learning algorithms.
Contact us today to discuss your needs for structured data to create neural networks and deploy deep learning.