Music production for guitar has a lot of options on Linux. We will see how to install the required software, and how to use Guitarix together with Ardour either with the standalone version of Guitarix or with an embedded version inside Ardour.
SymPy is a lightweight symbolic mathematics library for Python under the 3-clause BSD license that can be used either as a library or in an interactive environment. It features symbolic expressions, solving equations, plotting and much more!
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.