Understanding what makes a software Free (as in freedom) has been going on since the beginning of the Free Software movement in the 80’s (at least). This led to the Free Software licenses, which help users to control the technology they use. However, considering the peculiarities of Artificial Intelligence (AI) software, one may wonder whether those licenses account for those. Free Software licenses were designed so that users control technology, and facilitate their collaboration.

As deep learning models get better at representing human language, telling whether a text was written by a human being or a deep learning model becomes harder and harder. And because language models reproduce text found online (often without attribution); the risk of considering their output as if they were written by a human changes the reading experience for the reader. The last year has been incredible for natural (and programming) language processing.

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?