This is a great article and full of humour much like reading...

This is a great article and full of humour much like reading those early advertisements from the 19th century about the way to do your washing so quaint by comparison with modern day washing machine and dryer combo's.

I have extracted some entertaining paragraphs which come under the heading explaining how what Matlab is offering is State of the Art:

"What Makes Deep Learning State-of-the-Art?

Deep learning is a subtype of machine learning. With machine learning, you manually extract the relevant features of an image. With deep learning, you feed the raw images directly into a deep neural network that learns the features automatically.

Deep learning often requires hundreds of thousands or millions of images for the best results. It’s also computationally intensive and requires a high-performance GPU.

In the previous example, we used the network straight out of the box. We didn’t modify it in any way because AlexNet was trained on images similar to the ones we wanted to classify.

To use AlexNet for objects not trained in the original network, we can retrain it through transfer learning. Transfer learning is an approach that applies knowledge of one type of problem to a different but related problem. In this case, we simply trim off the last 3 layers of the network and retrain them with our own images.

If transfer learning doesn’t suit your application, you may need to train your own network from scratch. This method produces the most accurate results, but it generally requires hundreds of thousands of labelled images and considerable computational resources

Training a deep learning model can take hours, days, or weeks, depending on the size of the data and the amount of processing power you have available. Selecting a computational resource is a critical consideration when you set up your workflow.

Currently, there are three computation options: CPU-based, GPUbased, and cloud-based. CPU-based computation is the simplest and most readily available option. The example described in the previous section works on a CPU, but we recommend using CPU-based computation only for simple examples using a pretrained network. Using a GPU reduces network training time from days to hours. You can use a GPU in MATLAB without doing any additional programming.

We recommend an NVidia® 3.0 compute-capable GPU. Multiple GPUs can speed up processing even more. Cloud-based GPU computation means that you don’t have to buy and set up the hardware yourself. The MATLAB code you write for using a local GPU can be extended to use cloud resources with just a few settings changes."

Meanwhile NASA and AKIDA have loaded the rocket and left for Mars.

My opinion only but read the whole article it is really funny but I don't think they meant it to be it just turned out that way like a really badly made movie that achieves cult status.