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. Software released under a Free Software license guarantees that users can use, study, share and improve it however they want, with anybody they want. Once one accesses the source code and the accompanying license(s), he or she can run the software. Indeed, most software runs on commodity hardware. However, this is not true for AI and deep learning, the branch of AI powering most of the recent successful AI technologies.

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. GitHub’s Copilot has been out of technical preview since June, and ChatGPT was released in November. Copilot is based on OpenAI Codex and acts as a source code generator (which raises several issues of its own). ChatGPT is a language model built for dialogue, where a user can chat with the AI, ask questions and have them answered. Both are trained with data from web scrapping, with source code for Copilot and webpages for ChatGPT. Those models work particularly well for their respective purposes, and can thus be used to generate seemingly convincing source code or prose.

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!

As Artificial Intelligence is solving increasingly hard problems, it’s becoming more and more complex. This complexity leads to an often overlooked issue: the lack of transparency. This is problematic, because by taking answers at face value from an uninterpretable model (a black box), we’re trading accuracy for transparency. This is bad for a couple of reasons:

Software being more and more used to get metrics and insights for critical areas of our societies such as our healthcare system, crime recidivism risk assessment, job application review or loan approval, the question of algorithms fairness is becoming more important than ever. As algorithms learn from human-generated data, they often magnify human bias in decision making, making them prone to judging something in an unfair way. For example, the Amazon CV review program was found to be unfair to women. Because the program learned from already reviewed resumes (with unbalanced genders), it learned to dislike resume of women.

With its recent gain in popularity, a lot of things have been called “Artificial Intelligence”. But what is it anyway? According to Wikipedia, it’s “intelligence demonstrated by machines”, but does such a thing exist? At time of writing, they are 4 main types of AI development algorithms.

• Expert systems defines a category of computer programs that are specifically designed to do a task using prior human knowledge. Software engineers work closely with a domain expert to build the program, that will act in a predicable way, like the domain expert would have done if he or she had the same processing power. For example, Deep Blue is an expert system designed by IBM to play chess that won against the former world champion Kasparov.

The drawback of the expert system is that it’s tough to maintain (because it requires an expert on the domain at hand) and it’s costly.