can AI read you just by scanning you?

As a scary fact. yes. But the details can vary. Also, how safe AI can be depends on the person using it and the purpose it is being used for.

Convolutional Neural Networks

This week I will cover a deep-learning method known as Convolutional Neural Networks (CNNs)

With their ability to effectively analyze and extract features from images, CNNs have become a powerful tool for tasks such as image classification, object detection, and image segmentation. In this article, we will delve into the world of CNNs, exploring their architecture, applications, and advantages.

A convolutional neural network (CNN or ConvNet) is a network architecture for deep learning that learns directly from data.?Designed to process and analyze visual data. Unlike traditional feedforward neural networks, CNNs employ specific architectural components that enable them to capture spatial and hierarchical patterns from images. The key components of a CNN include convolutional layers, pooling layers, and fully connected layers.

CNNs are particularly useful for finding patterns in images to recognize objects, classes, and categories. They can also be quite effective for classifying audio, time series, and signal data.

a) Convolutional Layers: Convolutional layers form the backbone of a CNN. They apply a series of filters to the input image, extracting different features through a process known as convolution. Each filter detects specific patterns such as edges, textures, or shapes. The filters slide over the image, producing feature maps that highlight the presence of those patterns.

b) Pooling Layers: Pooling layers reduce the spatial dimensions of the feature maps produced by convolutional layers. They aggregate the information by taking the maximum or average value within small regions. Pooling helps to make the network more robust to variations in the input data and reduces the computational complexity of the network.

c) Fully Connected Layers: Fully connected layers are responsible for making predictions based on the extracted features. These layers connect every neuron from the previous layer to every neuron in the subsequent layer, allowing the network to learn complex relationships between the features and the target labels. The output of the fully connected layers is usually passed through an activation function to produce the final predictions.

Convolutional Neural Networks offer several advantages that make them particularly effective for visual data analysis:

a) Translation Invariance: CNNs exhibit translation invariance, meaning they can identify patterns regardless of their location in an image. This property makes CNNs robust to image variations and allows them to generalize well to unseen data.

b) Parameter Sharing: CNNs utilize parameter sharing, where the same filters are applied across different spatial locations of an image. This sharing of parameters reduces the number of learnable parameters, making CNNs computationally efficient and easier to train.

c) Hierarchical Feature Learning: Through multiple layers of convolutions, CNNs can learn hierarchical representations of features.

Below is a simple example of training a (CNN) to classify data.

How is CNN used in real-time

Medical Imaging:?CNNs can examine thousands of pathology reports to visually detect the presence or absence of cancer cells in images.

Audio Processing:?Keyword detection can be used in any device with a microphone to detect when a certain word or phrase is spoken (“Hey Siri!”). CNNs can accurately learn and detect the keyword while ignoring all other phrases regardless of the environment.

Object Detection:?Automated driving relies on CNNs to accurately detect the presence of a sign or other object and make decisions based on the output.

Synthetic Data Generation:?Using?Generative adversarial networks (GANs), new images can be produced for use in deep learning applications including face recognition and automated driving.

It is up to humans to decide the usefulness of such advanced methods. I hope this was helpful. To read more subscribe to the newsletter.