What Is an Artificial Neural Network (ANN)?
The textbook meaning of an artificial neural network (ANN) is a deep learning model made up of neurons that emulate the structure of the human brain. These neurons are designed to mimic the way nerve cells in the mind work while enabling the model to process and learn from a dataset independently.
Many AI researchers opt to use neural networks as they enable a model to progressively learn from its mistakes and solve complex problems with a high level of accuracy. This is particularly useful when it comes to data science, and making predictions or decisions.
Key Takeaways
- An artificial neural network is a deep learning model made up of thousands of artificial neurons.
- Neural networks can help to process large datasets and develop highly accurate decisions or predictions.
- Notable examples of neural network-powered tools include ChatGPT and Google Search.
- Artificial networks enable use cases like natural language processing, image classification, and speech recognition.
- These networks can be seen across multiple industries, including automotive, healthcare, finance, and logistics.
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How Does an Artificial Neural Network Work?
Artificial neural network is an artificial intelligence (AI) model built with a network of multiple neurons that connect together and share information. Each connection is assigned a weight or numerical value determining the strength and type of influence one neuron has over another.
The neural network itself has three main layers: an input layer, a hidden layer, and an output layer.
It’s worth noting that artificial neural networks are designed to process large datasets and extract insights that would be too complex for humans to perform independently.
Artificial Neural Networks Architecture
As mentioned above, an artificial neural network has three main layers.
These can be summarised as follows:
- Input layer: Input nodes process input data before passing it onto the hidden layer.
- Hidden layer – Receives input from the input layer and processes it before passing it on to the next layer (there can be multiple hidden layers).
- Output layer – The output layer outputs the result of the network’s processing activities. This layer can consist of a single output node for binary classification problems or multiple output nodes if the output is more complex.
Types of Artificial Neural Networks
There are many different types of artificial neural networks:
Artificial Neural Network Applications
There are many different types of consumer applications that use artificial neural networks and machine learning (ML) to function. One of the most famous examples is ChatGPT, which processes natural language text and voice inputs, to return a contextually relevant output.
If you ask ChatGPT who invented the Hubble telescope, the model will process your input, refer to its training data, and provide an accurate answer. Another similar example is Google Search.
We also see neural networks contributing to applications in a range of industries, including:
Artificial Neural Networks Uses
The use case for artificial neural networks is wide-ranging. Some of the most common use cases for the technology include:
- Natural language processing (NLP) – Processing and responding to natural language input with relevant outputs.
- Image classification – Processing and placing images into a particular category based on appearance.
- Speech recognition – Recognizing and responding to human speech from voice clips and providing a relevant response.
- Facial recognition – Processing image and video inputs and being able to recognize an individual’s likeness.
- Computer vision – Enabling computers and robots to reliably recognize images and objects in an environment.
- Autonomous driving – Helping to build self-driving cars capable of navigating the road, taking into account lane changes, other drivers, and signage.
- Anomaly detection – Detecting anomalous entries in a dataset that could signal a particular trend (widely used to detect data breaches and fraud).
- Stock market prediction – Predicting fluctuations in the stock market to guide investment decisions.
- Cybersecurity – Automatically blocking cyberattacks based on particular activities, such as lots of failed login attempts.
Artificial Neural Networks Pros & Cons
There are a number of pros and cons that come from using artificial neural networks:
Pros
- Learn and model complex non-linear relationships
- Process large data sets
- Deliver accurate predictions and decisions
- Improve continuously via ongoing learning from a dataset
Cons
- Difficult to understand or explain model decisions
- Control bias less effectively
- Take time to train on large datasets
- Incur high computational expenses during training and inference tasks
Future of Artificial Neural Networks
The future of artificial neural networks looks bright. Not only are we going to see AI vendors like OpenAI and Anthropic make more complex neural networks to enhance the performance of chatbots like ChatGPT and Claude, but we’re also going to see neural networks used as a foundational technology for building autonomous robots like the Tesla Optimus and Figure 02, and autonomous vehicles like the Robotaxi.
While it’s difficult to tell the future, we’re likely to see neural networks become even more complex as AI engineers seek to offer users more and more reliable predictions and functionality. In practice, this will mean that popular chatbots’ natural language understanding and speech recognition capabilities will continue to improve.
The Bottom Line
Now you know the definition of an artificial neural network, it’s worth noting that they are an imperfect attempt to replicate human cognition. While they can’t create sentient machines, they can replicate complex thought processes and decision making that result in powerful end user applications and tools.