Artificial intelligence is developing fast, and although many people think of it as one type of technology, there are actually many different types and subsets of AI.
Deep learning, for example, is an AI technique that analyzes data in a way that simulates the human brain. It’s a subset of machine learning, designed with layers of algorithms to create something called an artificial neural network. These networks are able to make accurate decisions without supervision from humans and continue to learn over time.
A great example of deep learning in action comes from Google DeepMind with its AlphaGo system.

Google DeepMind developed its own deep learning system, AlphaGo, with one goal in mind – to design an AI that could defeat the ancient Chinese boardgame Go.
Go is a difficult game to play for the average human, requiring a strategic mind, a creative approach, and of course, skill. AlphaGo combined deep neural networks with advanced search algorithms, including a ‘policy network’ that selected moves and a ‘value network’ that predicted game outcomes.
AlphaGo was first trained through amateur games and reinforcement learning by playing against itself. Many of AlphaGo’s moves were innovative, displaying a creativity and strategic depth that rivalled seasoned players.
Given the right level of resources, deep learning models can achieve an extremely high level of accuracy. They’re often used for tasks like image and speech recognition because of their ability to learn complex patterns in large datasets. They’re also scalable to a business’s needs and can be used across multiple domains. For example, deep learning models that are trained for image recognition can be adapted for quality control purposes in manufacturing.
But, just like any technology, deep learning models often require quite a lot of computational power and good-quality data to work properly. So, it’s worth considering this before splashing out on any technology.

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