Deep neural networks are at the heart of today’s artificial intelligence (AI). Inspired by the structure of the human brain, they have become synonymous with technological progress and innovation.
Read on to find out more.
Table of contents
What are deep neural networks?
Deep neural networks are advanced computational architectures that mimic the functioning of the human brain. A deep neural network uses a large number of processing layers, called ‘hidden layers’, to automatically learn patterns and abstract representations from the raw data, without the need to manually design features.
Each layer is ‘deep‘ precisely because it works in synergy with the others to identify and interpret complex data patterns. The more layers the network has, the more intelligent it is. Between each layer, numerous interconnected artificial neurons, thanks to deep learning algorithms (such as backpropagation), process the data and progressively adjust the weight of each connection to minimise errors.
Deep neural network types
There are different architectures of deep neural networks:
1. Convolutional neural networks
Convolutional neural networks excel in images and videos processing and are so called because they use convolutional layers to extract local features.
Specifically, they are designed to process data with a grid structure, such as images. They take their name from the mathematical operation of convolution, which is used to filter and transform input data.
They use convolutional layers to automatically extract features and hierarchies from images, which makes them particularly effective for computer vision tasks such as classification, object detection and segmentation.
2. Recurrent neural networks
Recurrent neural networks are ideal for processing sequential data such as text and audio due to their ability to store information over time.
Recurrent neural networks have ‘feedback connections’ that allow them to maintain an internal memory of past inputs and thus capture long-term contextual data, making them suitable for tasks such as machine translation, speech recognition and text generation.
3. Graph neural networks
Graph neural networks learn vector representations of nodes (the entities or objects in the context of the query), incorporating information about the structure of the graph and the attributes of the nodes themselves; this makes them applicable to a wide range of domains, such as social network analysis, computational chemistry, bioinformatics and recommendation systems.
4. Autoencoder neural networks
Autoencoders are a type of unsupervised neural network used for learning compressed representations of input data. They consist of an encoding, which maps the input data into a smaller latent representation, and a decoding, which reconstructs the original data from the latent representation.
By training the autoencoder to minimise the reconstruction error, they will be able to learn to capture the salient features and underlying structures of the data. Autoencoders find application in dimensionality reduction, denoising, feature learning and new data generation.
Each of the architectures described here can be combined with the others in various ways to create specialised neural networks.
Deep neural network applications
Deep neural networks, as is evident, are used in a wide range of AI applications, including:
- image and video recognition
- natural language processing;
- speech synthesis and recognition;
- autonomous driving;
- medical diagnosis;
- drug discovery;
- machine translation;
- complex games.
In each of the above fields, deep neural networks have reached and surpassed human performance, opening the way to new possibilities.
Future challenges and opportunities
In spite of the invention of neuromorphic CPUs and GPUs, which accelerate machine learning, effectively decreeing an epochal turning point in the evolution of artificial intelligence, the greatest difficulty lies in fully interpreting their decision-making process.
Researchers around the world are working side by side, every day, to solve the puzzle and develop networks that are safer, before being more powerful. Any drift and loss of control of AI could have catastrophic consequences.
The most significant challenges undoubtedly concern:
Impenetrability
As mentioned above, deep neural networks are often referred to as ‘black boxes‘ because they are ‘impenetrable‘ in the sense that it is not clear how they make decisions.
Robustness
Thousands of examples can be found on the net, specifically designed to deceive networks; some of which raise serious concerns about their ‘robustness‘.
Hardware
Training large amounts of data requires powerful hardware that is often not easy to find.
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