Sometimes, you need to tread ground that you have already walked.

There is more than one way to skin a cat and find stuff in a P2P network.

How do you find a needle in a haystack? Let's keep it simple. Why complicate things?

RSA was patented in 1983 and has since become a cornerstone of secure communications in the modern world. How does it work?

What does it take to communicate privately in peace? Lots of math and several decades of research.

Our modern digital world depends on it. Few other software is more implemented. It is SHA-256.

Sometimes, you want to go somewhere where everybody knows your name, but not who you are.

Sometimes, we have to find a way to work with the unreliable. How?

What can you do when different entities can agree? Quite a lot.

Theories do not pay the bills. In the real world, we need something we can use.

We live in an imperfect world. So let’s hope for the best and prepare for the worst.

It’s playful and vibrant. It’s TANGO... because bug triaging takes two.

Machine learning means that a computer can learn from experience without being specifically programmed. But, is the computer learning the right things?

Gears, servos, and circuits, oh my! So, many things go into a Transformer! So many things to examine! Oh...wait...that’s the wrong Transformer.

When you need to go faster, but RNNs cannot keep up. Whom do you call? Transformers, of course.

Let’s take a step back and examine a machine learning architecture that has become the go-to for image recognition and computer vision.

Memory is essential to NTMs. So, understanding NTMs’ memory addressing system is a must.

We have added attention and gates to our neural networks. Now, let’s revolutionize machine learning with memory.

LSTMs fix vanishing and exploding gradients, but LSTMs are not the only architectures that do that.

LSTMs are widely used in machine learning now. What are they? How do they work?

Backpropagation is so commonplace in neural network training that it is easy to take it for granted. But what is it?

Transforming a toy prototype into a market-ready AI product requires plenty of technology.

What does it take to bring Woobo to life? Lots and lots of elbow grease.

Applying the attention principle to more tasks meant that the attention mechanism had to be modified.

The attention mechanism has become widespread in neural NLP modeling, but where did it come from?