What are Neural Networks and Why Are They Important for AI?
Neural networks are vital in artificial intelligence. These can shape AI’s future and add flexibility. AI depends largely on neural networks. Neural networks are quite effective and offer various applications like speech recognition, analyzing patterns, healthcare, etc. A neural network is a breakthrough in artificial intelligence, allowing AI to gain more recognition. The brain structure inspires this concept and has been helping machines behave like humans.
Here, we will understand neural networks and their role in AI.
What is a Neural Network?
A neural network in Ai teaches the machine to process data inspired by the human brain. It is a machine learning process or deep learning that uses layered neurons like a human brain. It designs a system where the computers learn from their mistakes and improve their processes in the future. Artificial neural networks have the potential to solve several complicated problems with precision.
The Role of Neural Networks in AI
Neural networks help machines make smart decisions. These systems can do the following:
● Make Observations and Predict Data
Neural networks can analyze complex data and make observations. These can recognize different input sentences and understand their meaning. These artificial neutral networks can analyze huge amounts of data and make predictions. Therefore, the neural network finds applications in various predictive analytics like financial forecasting, supply chain optimization, marketing analytics, etc.
● Speech recognition
Neural networks can recognize speech irrespective of the speech patterns. The speech may have different pitches, language, and accents too. For example, Alexa and Siri work on a speech recognition model. Speech recognition helps the call center to automatically classify calls, and convert clinical conversations into documentation, subtitle videos, etc.
● Natural language processing
Natural language processing can process human-created text. Neural networks help machines to decode text and documents. NLP helps with automated virtual agents and chatbots. It also helps to automate data organization and classification. Natural language processing also finds application in business intelligence analysis of long forms, summarizing documents, writing new articles, etc. It can do the indexing of key phrases like social media comments.
● Facial Recognition
Neural networks can identify and classify objects into pictures. You can enter several pictures, allowing the system to perform precise facial recognition. Convolutional neural networks are a type of neural network that performs image classification and object detection.
● Stock Market Prediction
It is quite challenging to predict changes in the stock market. But neural networks can make real-time stock predictions. MLP, or multilayer perceptron, is a popular type of neural network that can make accurate predictions. MLP neurons predict future trends based on past stock performance assessments and annual returns.
● Personalized Recommendations
Neural networks in AI can track user activity. By evaluating the user behavior, the system suggests new products or services to a specific user. Some companies have started to use their social media posts in sales conversion. Neural networks find and suggest products relevant to the user’s social media activity. Multilayer Perceptron artificial neural network can gather social media users’ data and draw insights about their unique preferences.
● Healthcare
Neural networks are widely used in the healthcare industry as convolutional and recurrent neural networks. While the former can study and analyze data like X-rays, CT scans, ultrasounds, etc., the latter is used in voice recognition systems and tracks patient data.
The Future of Neural Networks
Who would have known that one day neural networks would handle complex tasks? Neural Networks have a promising future due to the significant improvement and advancements in algorithms, architectures, optimization techniques, etc.
In the future, we hope to see a hybrid architecture that uses many types of neural networks and deep learning models. This will create a better-performing and integrated computer program. Such hybrid architecture may leave the current AI limitations behind and improve overall performance.
Transfer learning, one of the popular deep learning trends, can train neural networks using minimal data. Neural is being used in transfer learning techniques, and we can expect more such research-based models.
Wrapping up
Neutral networks are machine learning algorithms that identify data patterns and make predictions. Its future is promising, and it will surpass its applications in computer vision, NLP, robotics, healthcare, etc. Neural networks are used in several other industries and businesses. For example, the manufacturing industry uses neural networks for optimizing the supply chain, predicting demand, etc. The banking industry uses the same to detect fraud, automate banking services, etc. Retail uses these networks for customer engagement, customer queries, etc., through chatbots.
