Posts with the tag AI:

How the Integrated Gradients method works?

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.

Artificial Intelligence safety: embracing checklists

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?

ROC and Precision-Recall curves - How do they compare?

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?

What does a transformer?

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!

AI transparency: how the Local Interpretable Model-agnostic Explanation Framework works?

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:

Algorithms fairness

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.

Is Artificial Intelligence only a bunch of "if" statements?

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.