Deep learning have made dramatic improvements over the last decades. Part of this is attributed to improved methods that allowed training wider and deeper neural networks. This can also be attributed to better hardware, as well as the development of techniques to use this hardware efficiently. All of this leads to neural networks that grow exponentially in size. But is continuing down this path the best avenue for success?

For artificial intelligence (AI) transparency and to better shape upcoming policies, we need to better understand the AI’s output. In particular, one may want to understand the role attributed to each input. This is hard, because in neural networks input variables don’t have a single weight that could serve as a proxy for determining their importance with regard to the output. Therefore, one have to consider all the neural network’s weights, which may be all interconnected. Here is how Integrated Gradients does this.

Unfortunately, human errors are bound to happen. Checklists allows one to verify that all the required actions are correctly done, and in the correct order. The military has it, the health care sector has it, professional diving has it, the aviation and space industries have it, software engineering has it. Why not artificial intelligence practitioners?

The accuracy of a model is often criticized for not being informative enough to understand its performance trade offs. One has to turn to more powerful tools instead. Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves are standard metrics used to measure the accuracy of binary classification models and find an appropriate decision threshold. But how do they relate to each other?

Transformers are giant robots coming from Cybertron. There are two Transformer tribes: the Autobots and the Decepticons. They have been fighting each other over the Allspark, a mythical artifact capable of building worlds and mechanical beings. Well, there is also another kind of Transformers, but those are not about warfare. However they are pretty good at language understanding. Let’s see how!

The situation where an unskilled user can enjoy a well-working computer does only last so long.1 Either the user becomes good at maintaining the computer, or it will stop working correctly. That’s because computers are not reliable. If not used carefully, at some point, they will behave unexpectedly or stop working. Therefore, one will have to get their hands dirty and most likely learn something along the way. Some operating systems are more prone to gathering cruft, though.