“CNNS are like the Swiss Army knife of deep learning. They can be applied to a wide range of problems, from image classification to natural language processing.”- Andrew Ng, computer scientist, and AI expert.
Artificial neural networks are a type of convolutional neural network commonly used in image and video recognition applications. CNNs are automatically designed and adaptive to learn spatial hierarchies of features from input data.
Several layers are comprised of CNNs, such as convolutional layers, pooling layers, and fully connected layers. The convolutional layers apply a set of filters to the input image, detecting and highlighting different features such as edges and corners.
The pooling layers then downsample the output of the convolutional layers, reducing the spatial dimensions of the data and helping to prevent overfitting. The fully connected layers are performing a classification or regression task on the output of the pooling layers.
The important role played by CNNs is their effective use for image recognition tasks, as they can automatically learn the features relevant enough for classification problems without requiring manual feature engineering.
The CNNs perform well in object detection, image segmentation, and facial recognition.
Some notable companies included in the CNNs are:
- Google is one of the most active companies in the artificial intelligence field, with significant contributions to the development of CNNs. Applications developed using CNNs include Google Photos, object detection in Google Lens, and face detection in Google’s Nest cameras.
- Facebook is also a major player in the AI field, having developed several applications such as image recognition in Facebook’s News Feed and object detection in Facebook’s Marketplace.
- Microsoft is also actively involved in AI development with several applications, such as image recognition in Microsoft’s Cognitive Services and object detection in Microsoft’s Azure platform.
- NVIDIA is a technology company specializing in graphic processing units (GPUs) and a major supplier of hardware used for CNN training.
- Amazon is also involved in the AI field through the Amazon Web Services (AWS) platform, with several services offered for machine learning, including CNNs. AWS provides several pre-built models for image and video recognition, such as Amazon Recognition.
Some of the powerful predictions and potential future developments for CNNs are:
- Improve accuracy: More training data is available in deep learning techniques to improve the CNNs accuracy in the coming years. It leads to significant advancement in areas such as medical imaging, autonomous vehicles, and robotics.
- Faster processing: CNNs are computationally expensive and require an immense amount of processing power. The recent advancements in hardware, such as specialized GPUs and ASICs, can also be seen in the training and inference of CNNs.
- More efficient architectures: Researchers are actively working on an efficient CNN architecture to achieve similar performance with few parameters and lower computational requirements.
- Multi-modal learning: CNNs are typically used for image and video recognition tasks, but there is an increasing interest in using them for multi-modal learning such as audio, text, and sensor data.
- Explainable AI: As CNNs become more complex and accurate, there is a growing need for them to be explainable and transparent.
The above-mentioned explanation explains the need for researchers to interpret and visualize the internal workings of CNNs to develop more trustworthy AI systems.