There’s a lot of guides explaining how to protect your online privacy, but none of them tell why they exist in the first place. They exist because privacy is understated. We don’t value it enough. Here are the reasons.
Threats to privacy are not obvious Despite recent attempts to regulate online data processing (e.g the GDPR in the EU) as well as privacy breaches, it’s still not clear why all of that threatens privacy.
Despite what the bad media are saying, computers haven’t understood human language (yet). We need to turn sentences and words into a format that can be effectively manipulated by a Machine Learning or Deep Learning algorithm. This is called language modeling. Here I will explain several methods that can turn words into a meaningful representation.
Integer encoding This approach is the simplest. Once we have a list of the tokens composing the vocabulary, we associate each one with an integer.
This procedure has been tested on Fedora 29, on a HP laptop with this graphical card: NVIDIA Corporation GP107M GeForce GTX 1050 Mobile (rev a1)
The commands have to be run as the root user. This tutorial assumes the nvidia driver is already working.
Install pip dnf install python3-pip Install Cuda 10.0 Download the installer from the Nvidia website and run it. Make sure to install the Perl module Term::ReadLine::Gnu beforehand because the cuda installer relies on it.
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.
Stochastic Gradient Descent (SGD) is used in many Deep Learning models as an algorithm to optimize the parameters (the weights of each layer). Here is how it works:
At each step in the training process, the goal is to update the weights towards the optimal value. For this, SGD uses the equation:
As Deep learning is becoming more and more popular, there is an ongoing debate on whether it’s possible to create Deep Learning applications with a Free Software license. See for example this discussion on the debian-devel mailing list.
The argument we often see is that:
It’s impossible to study the inner workings of a Deep Learning software (for example, an image classifier or a text generator) or improve it, because one cannot understand how it’s going to make predictions only by looking at the weights of the Deep Learning model Training a Deep Learning model requires a specialized and expensive hardware that runs non-Free software But the first statement misses the point of Deep Learning programs.