Machine Learning 

ML is a subfield of artificial intelligence, and it corresponds to all algorithms that learn from experience. These models work by feeding large quantities of data in to it, where the model analyses such data finding correlations between it, in order to perform operations in newly unseen data. 
There is two types of ML: supervised learning which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data. 
Supervised ML has two techniques classification and regression. Classification models predict a discrete response so 0 and 1, an example of this would be you’re phone’s face detection. It can also be used to put data in categories. In the other hand regression algorithms predict continuous responses, for example predicting stock prices in the future.

AI is a widely problematic term, as it is vastly debated in what its definition is. To claim that this technology is intelligent, is in itself a complex philosophical question, as we as humans don’t truly understand what our intelligence is and isn’t. AI is more a term established by the tech giant multi-corporations, that use the term as a marketing technique or a technique to deviate the true uses of such technology. Dan McQuillan a computing academic and lecturer of Goldsmiths said:  ‘There is no intelligence in artificial intelligence, nor does it learn, even though its technical name is machine learning, it is simply mathematical minimization.’(Pasquinelli and Joler, 2020). I truly support McQuillan’s position in the subject and I seek to see ML as a new useful technology instead of an autonomous being. 

Because I did the year out in creative computing, I’ve learned some of the basic stuff about ML. I find the subject extremely interesting and I really want to incorporate it in my project. No matter the final form of this project I want to incorporate ML into it, as one of my goals this year is to build my own model and research  the field. At the same time I belive we live in exciting times where this technology is still fresh to experiment upon, it’s use in audio could be considered as the new tape, leading to manipulate audio in an avant-garde manner. As mentioned in the paragraph above AI is a very problematic subject, as I will be using ML for this project I have the responsability to use it ethically, but more specifily use it in a way that I can demonstrate that it’s a new technology and not independet being. 

Currently I’m learnig linear regression using Google brain foundational course. More info in ML here.