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    The last sentence is particularly poignant given that this quote predates and predicts behaviour demonstrated in the Cambridge Analytica scandal. Press dating site then goes on to say: It is this ability Datijg manipulate views, and profit from increasingly relevant advertisements, that explains why Facebook spends so much money on the best Machine Learning engineers to llondon their newsfeed. These Dating training london ttaining us at a root level, and other corporations are able to get secondary access to our data on a lohdon scale.

    This data is not just being used analytically, it is being used algorithmically and generatively not only to take, but also to put back in to the digital sphere. A particularly recent example of this are headlines of mass Twitter accounts used to propagate propaganda from the Russian troll factories. Today it is possible to create convincing fake data which is very often generated using neural networks. Consuming this data at regular intervals may become much more normalised than it is today, and this is a worrying notion given the scope of what has already been happening globally, exemplified by the Cambridge Analytica scandal.

    Prior to this project, and on a smaller scale, I created a Facebook chatbot. It only interacted with around two hundred people to date, but I was astonished by how willingly those people parted with sensitive and identifying information about themselves including; birthdays; full names; email addresses; education histories and even signatures! The ease with which data can be harvested, stored and shared, raises a crucial question that is imperative to the progression of this project. Naturally, releasing, sharing or selling this information would be immoral, but is it okay to release a neural network that models merely the probability distribution of the data?

    It is for this reason I have no ethical qualms about this project, however prevent any premature mob-lynching, I will not actually be releasing the model in question, just some data sampled from the model. As part of my degree, I am exploring artificial intelligence within an artistic context, and I wanted to train some generative systems to examine a strand of this narrative of data harvesting, privacy, on a social networking application. What can one guy do with a laptop over a few weeks? Artists And Programmers Exploring Privacy Some fantastic artists are already exploring the role of privacy in data and studying things like dating profiles.

    Artist Kyle McDonald also has a history of creating projects built around privacy and data. After recording his face while using his computer over a period of time, he was surprised to note a lack of facial emotion. What of other people? To question this, he decided to test a wider audience.

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    McDonald was subsequently raided by the American Secret Service after he installed a script that pushed faces taken in an Apple store on the Mac Photobooth application to a private website. He talks about this and other projects here. He closes the above video with the comment: Artists are always going to be necessary for pushing boundaries and helping us understand where things break. Contrarily, plenty of programmers and artists are looking at the Veronica ballestrini dating of data and Dating training london from a preventative stance.

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    I subsequently harvested 40, Tinder profiles. All you need Dating app bay area a few Python modules and some fake Gmail profiles. A decent VPN might help too! I sent my scripts to various geolocations around London and collected the tinder profiles from these areas. Generating Dating Biographies After downloading the profiles, the first thing to do was to create a class that could store the sequences of numerical tokens that the neural network learns on. The idea is that the downloaded biographies are stored in a list of strings and are converted into larger list of fixed-length sequences of tokens.

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    I organised the neural network into a class that yields the two principal methods in deep learning; train and inference. The model can also be saved and loaded. Basic usage of the neural network with the dataset class above would look something like this: The model is defined as below. I defined the neural network in the build model method of the class. It is merely a multi-layer recurrent neural network, that is arranged in a many-to-one manner, so that it takes a sequence of embedding vectors and outputs a prediction vector that is the length of the number of unique characters in the dataset. The recurrent cell is a LSTM, which is widely famed for its ability to prevent vanishing gradients, amongst other benefits, and there are many great resources where you can read more about that.

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