Deep learning algorithms have brought a revolution in computer vision applications by putting machines to work for a lot of tasks like image recognition, natural language processing, driving autonomous vehicles, predicting the eventuality of a certain occurrence in business, and so on. In this article, we will see how deep learning has become a massive force in the AI world.
Introduction of deep learning In the early years of AI, researchers created algorithms that were built on a set of rules. These rules gave AI the ability to perform a task, but only if they were programmed correctly. However, as technology evolved and computing power increased, it became possible for machines to carry out these tasks without being programmed. This is known as machine learning, and it is modeled upon the way humans learn — through experience.
Deep learning is a subset of machine learning that uses neural networks to mimic how brain cells work together to improve their understanding of something. These artificial neural networks are built on layers of interconnected machines with each layer providing an increasingly accurate representation of information as it passes between them. This is how a deep learning system learns, by constantly receiving input that builds a more accurate model of its intended output based on the layer immediately before it.
Deep learning systems aren’t limited to recognizing images and sounds. They can also be used for processing text, voice recognition, or even robot control depending on the number of layers they contain. With each additional layer, the neural network becomes capable of making increasingly complex decisions about the data it receives.
There are many examples where deep learning has shown clear advantages over traditional programming techniques including:
- Speech recognition: Google’s voice search application was predicted to have an 88% accuracy rate in 2014 but this has increased to 97% today — largely due to the use of deep learning.
- Image recognition: Researchers at Google have used deep learning algorithms that are trained on internet images, including millions of cat photos, to help robots recognize objects in their environment.
- Fraud protection: The giant French bank, Société Générale, is using machine learning techniques inspired by deep neural networks to try and automatically detect fraudulent transactions as they happen instead of after they have taken place.
On average it takes a human 45 minutes or more depending on the complexity of each transaction. Automating this process will allow much faster fraud protection and save the bank an estimated $1bn per year in losses from fraud alone.
Deep learning vs traditional programming Deep learning is not without its limitations. These systems are difficult to create and require large amounts of data to be effective, meaning they aren’t widely accessible yet. However, this will ultimately change as hardware capabilities continue to improve.
Artificial neural networks need examples of what each object looks like before they can determine if something similar appears in an image or sound recording — known as “supervised learning”. For example, deep learning algorithms that help robots recognize objects in their environment have already been trained using hundreds of thousands of images that humans have labeled with the exact coordinates of the objects within them.
This sort of training takes a long time and requires lots of computing power which makes it expensive for businesses to implement, but the cost savings should reveal themselves once these algorithms are built.
Machine learning that doesn’t need this level of human supervision is an area that is receiving increased attention from researchers and businesses. It’s known as unsupervised learning, and it allows the machine to look for patterns in data without any predetermined examples or labels.
What does deep learning mean for businesses?
Deep learning techniques enable machines to make complex decisions on previously unseen data — meaning they can be used to solve problems where there isn’t enough historical data available to train a traditional algorithm.
This could make it easier for new companies like Deep Instinct, which uses unsupervised deep learning algorithms to provide predictive cyber security solutions that stop malware before it has even been released into the wild, or Skytree, which uses unsupervised machine learning techniques to discover hidden insights within open-source data.
Deep learning has also been used to create AI systems that can generate natural languages, such as this piece of writing by Google’s new algorithm, and others that can produce original paintings in the style of famous artists.
It is clear from these examples that deep learning will impact businesses across many industry verticals and business functions. It may take a few years for large companies to widely utilize deep learning across their operations but we’re starting to see early adopters creating highly effective solutions using these techniques today.