Artificial neural networks (ANNs) power tons of different technologies we use in our personal lives and in professional settings. From photo apps on our mobile devices to advanced medical diagnosis tools in hospitals, ANNs use the human brain as inspiration for building smart systems that can handle complex tasks.
Modelled after the human brain, ANNs use a unique architecture to create a vast system that can instantly respond to a number of varied data inputs, making them fitting for many different industries such as manufacturing, healthcare, transportation, and many others.
In this post, we’ll take a closer look at the different layers that make up artificial neural networks, how they work in practice, different types of ANNs, and their applications with real-world technologies such as aiOla speech AI.
Artificial Neural Network Definition
An artificial neural network is a computing system that’s built to mimic the structure and function of human brains. Instead of neurons, ANNs rely on nodes, or units, which are organized in layers and process input and output data, identifying patterns to transmit information to subsequent layers.
Like a real brain, an artificial neural network algorithm learns from data through training, where a network weight is adjusted to reduce errors and improve predictions. Training is done through large datasets on tasks like image and speech recognition and predictive analytics.
Structure of Artificial Neural Networks
Taking inspiration from biological neurons, ANNs are structured around multiple layers that react to one another. The architecture involves interconnected artificial neurons that operate on three main layers. Here’s how it works:
- Nodes: Nodes act as a brain’s neuron and are the basic unit of an ANN. Each node processes information when it receives inputs and applies an activation function to transmit outputs to other neurons.
- Layers: The layers of a node are organized into three parts, each with a distinct role.
- Input layer: The first layer receives raw data that the network needs to analyze in order to understand
- Hidden layer: There’s usually at least one hidden layer, which contains nodes that process and transform the received data so that the network can capture complex patterns before passing it to the next layer
- Output layer: The output layer provides the final result or response of all data processed by previous layers
- Connections and Weights: Nodes are interconnected between their layers, and each connection has weights that represent connections between one another. Nodes with higher weights are more valuable since it means that they contribute more by having more connections and influence over other nodes.
How Do Artificial Neural Networks Work?
ANNs can learn to make predictions based on recognizing patterns and processing data through a series of interconnected nodes. This is done through a series of steps that depend on one another for the system to learn and advance.
Training Process
ANNs first need to be trained on diverse datasets to understand relationships between data points. This process involves feeding input data through the network and adjusting weights to reduce the difference between a network’s output and the target values. Training is done over multiple iterations where the network learns how to make predictions more accurately.
Activation Functions
Activation functions introduce non-linearity to the network, which is what enables it to learn and understand more complex patterns. When data passes through a node, an activation function determines if the node should be activated to pass data forward. This process allows the network to model intricate relationships between data points.
Backpropagation
Backpropagation is the method ANNs use to adjust their weights. After every time data is passed forward, the network calculates error probability and differences by comparing the output to the target value. This works backward by distributing the error and adjusting weights one layer at a time to minimize the error. Backpropagation also helps the network improve accuracy over time by repeating this cycle regularly.
Applications of Artificial Neural Networks
ANNs are having a significant impact in many fields by enabling machines to analyze complex patterns and make intelligent decisions independently. Different types of ANNs, as we’ll see below, can be applied to different scenarios, making it smoother for these networks to work with different data inputs and mediums, like images and speech.
Image Recognition
ANNs are widely applied to image recognition, where the networks can identify people, objects, or scenes in videos and photos. Convolutional neural networks (CNNs) are a type of ANN that is focused on image analysis due to their ability to capture relevant features like spatial patterns, edges, color, and depth. CNNs have a wide range of applications, from simple day-to-day facial recognition in apps like Google Photos to medical imaging that helps medical teams diagnose conditions.
Natural Language Processing (NLP)
ANNs are crucial to natural language processing technology as they enable machines to understand, interpret, and generate human-sound language. Models like GPT and BERT are powered by ANN architectures and are used for tools like chatbots, language translation, and summarization. For example, Google Translate uses these technologies to provide accurate language translations.
Speech Recognition
In speech recognition, ANNs turn spoken language into text. Recurrent neural networks (RNNs) and Long short-term memory (LSTM) networks are types of speech recognition models that can process sequential data like speech, making it easier for these networks to follow the flow of language and decipher meaning. Real-world applications of this type of speech technology include virtual assistants like Siri and Alexa both rely on ANNs to recognize voice commands.
Predictive Analytics
ANNs are used for predictive analytics to forecast outcomes based on historical data. They can analyze patterns and trends in different industries like stock predictions in finance, disease diagnosis in healthcare, and predictive maintenance for manufacturing. For instance, IBM’s Watson uses ANNs to predict equipment failure and optimize maintenance schedules.
Advantages and Limitations of ANNs
While ANNs have proven highly effective in multiple fields, they come with both advantages and limitations. Here’s a look at their key strengths and challenges.
Advantages
- Pattern recognition: Excel in identifying complex, non-linear patterns
- Adaptability: Accuracy improves over time by learning from new data
- Automation: Reduce the need for manual intervention by automating tasks like image classification and predictive analytics
- Versatility: Applicable across diverse areas, from healthcare to finance to NLP
Limitations
- Data dependency: Require large datasets for accuracy, which may not always be available
- Computationally intensive: Training demands significant time and computational power
- Transparency: Understanding how ANNs operate can be difficult, limiting transparency
- Risk of memorization: Without careful tuning, ANNs might memorize data rather than generalize, leading to poor performance on new inputs
Harness The Power of ANNs with aiOla Speech AI
aiOla is a speech AI technology that helps workers use speech to trigger actions, automations, and data collection. By relying on ANNs and RNNs to process speech data, aiOla makes it easier for companies to work more efficiently by automating manual tasks, improving predictive maintenance, and using accurate data to inform high-level decision-making.
With aiOla, companies across different fields can improve productivity and efficiency in different ways, such as:
- Reduce inspection time on machinery or fleets by completing inspections visually and through speech
- Decrease human error by relying on speech to accurately collect data
- Enhance real-time communication between diverse and global teams
- Work more cost-effectively by avoiding costly breakdowns and mistakes
- Help frontline workers complete workflows quicker just by speaking
By relying on artificial neural networks and machine learning and training these systems on vast speech datasets, aiOla is able to understand over 120 languages including different accents, dialects, and industry jargon. It can also operate in any acoustic environment while still delivering a superior level of accuracy.
Bringing the Power of ANNs Into Your Workflows
While the behind-the-scenes of artificial neural networks involves a lot of moving parts, what’s essential to understand is that these systems power technologies that make it simpler for us to complete tasks. Whether on our personal devices with apps like Siri or Alexa, or at work with advanced speech AI technologies like aiOla, ANNs are only growing smarter and more accurate by the day and helping companies grow, advance, and innovate.
Book a demo with one of our experts to understand how aiOla can help your company work more productively.
