Biometric systems such as fingerprint readers or facial recognition may be
appealing, but they have drawbacks worth mentioning. Their attack surfaces are
larger than commonly believed, they make it hard to enforce good security rules
and are not guaranteed to work all the time. These flaws should be considered
when assessing the value of biometric authentication.
If you want to distance yourself from the craziness of the world around and
happen to be a computer geek, only a few things are more satisfying than
blasting hordes of demons on Doom. Here is how to install
GZDoom - an OpenGL port of Doom released under
the GPLv3 license - and a mod
called Brutal Doom on Fedora 31.
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:
A Recurrent Neural Network (RNN) often uses ordered sequences as inputs. Real-world sequences have different lengths, especially in Natural Language Processing (NLP) because all words don’t have the same number of characters and all sentences don’t have the same number of words. In PyTorch, the inputs of a neural network are often managed by a DataLoader. A DataLoader groups the input in batches. This is better for training a neural network because it’s faster and more efficient than sending the inputs one by one to the neural network.
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.
Wanting to brush up my PyTorch skills, I’ve started to follow this tutorial. It explains how to create a deep learning model able to predict the origin of a name. At the end of the tutorial, there’s an invitation to try to improve the model. Which I did. Note that the point of the tutorial is not to create the most performant model but rather to demonstrate and explain PyTorch’s capabilities.