How do you explain the concepts and intuitions behind CNN, RNN, and ANN models to non-technical audiences?

Imagine your brain as a network of learning machines. All three (ANN, CNN, RNN) ANN: The basic model. It's like having interconnected experts, each analyzing a piece of the problem. They share information and come to a conclusion, like figuring out if an image is a cat or dog. CNN: Great at seeing patterns! Imagine looking at small tiles of an image, identifying edges and shapes in each. The CNN puts the pieces together to understand the whole picture, useful for tasks like recognizing faces. RNN: Deals with sequences, like remembering things. Imagine reading a sentence. An RNN reads each word, remembers the previous ones, and understands the sentence as a whole. This is useful for machine translation or speech recognition.

In my experience in the health sciences, I have seen the term ANN used quite often to refer to any type of Neural Network in general litterature not focused specifically on AI such as academic review papers, industry white papers, conference reports. I always understood it to be a way to distinguish with biological neural networks instead of a specific type of neural network. Often the cited network was a CNN.

An interesting notion here is that the earlier layers look for low-level features, such as simple edges, whereas the later layers combine those simple features to more complex ones, which could resemble a human eye or a nose when dealing with face detection.

Just like human memory, as the RNN learns new words, it gradually forgets what it learned previously. This is where the need for "attention", i.e., attending/remembering what is essential and forgetting the unnecessary details, comes in. Attention is one of the core principles behind the working of LSTMs. But more on that later.

magine you're reading a story. You start from the beginning and go through it bit by bit, right? You understand each part based on what came before it. That's kind of how a Recurrent Neural Network (RNN) works. It's a type of artificial intelligence that's great for handling sequences, like sentences or time series data. It processes information step by step, and each step considers what it's seen before. This helps RNNs to understand and generate sequences of data, making them really useful for tasks like language translation, speech recognition, and predicting future values in a time series.

Choose ANNs over CNN when you have diverse data types or when the spatial relationships within the data are not a primary factor in achieving the task.

In the case of ANN, CNN and RNN, the choice of model will be mostly driven by the type of input data. It will be CNNs for any task that processes an image or similarly (spatially) structured data. It will be RNNs if we have sequential data like time series or words in a sentence. It will be ANN, if we cannot exploit any such structure in the data. Only in the second instance will we be looking at performance and resources, namely when we decide e.g. which exact CNN-based model to use.

When deciding between ANN, CNN, and RNN models, the selection is typically guided by the nature of the input data. For tasks involving images or data with spatial structure, CNNs are usually preferred. RNNs are suitable for sequential data such as time series or textual information like sentences. ANN models are considered when there's no inherent structure to exploit in the data. Performance and resource considerations, particularly in selecting specific CNN-based models, become important only in cases where sequential data processing is required.

The type of neural network to use depends on the type of data for your problem. If you're doing image classification CNN would be ideal. If you're doing NLP an project, e.g. language translation then go for RNN.