Once again, it seems that understanding certain Anglo-Saxon terms related to technology makes it difficult for jurists. But in a world and in a sector where the basis lies in the data we handle, it seemed important to me to see what is involved and if something is currently being done in Spain with respect to Online Assignment Help the so-called Machine Learning . This article does not intend to convert the one who reads it as an expert, but at least it does provide a minimum knowledge base about what it implies and what changes it can bring to a lawyer's work.

The Machine Learning is a branch of applied artificial intelligence ( so -called Weak AI) trying to make machines learn automatically. This model of machine learning is based on training algorithms so that patterns obtained from data analysis, predictions made perfecting models that help us generate ideas and make better decisions. Therefore, the more data available to learn and the richer and more complete the algorithm, the better it will work.

Learning algorithms are usually classified as:

• The supervised algorithms: they require that human beings provide both the input of data ( inputs ) and the desired data output ( outputs ) to go adjusting predictions accurately during training. Once the training is completed, the algorithm will apply what has been learned to the new data. These algorithms in turn are used to develop predictive models by techniques of:
  • Classification to predict discrete responses as if a mail should be classified as spam or not, whether a tumor is benign or not, speech recognition ...
  • Regression to predict continuous responses, for example, the forecast of changes in temperature or fluctuations in the demand for energy or the stock market.
• Unsupervised algorithms : they do not need to be trained with the desired result data. Instead, they use an approach called deep learning ( Deep Learning ) to review the data and draw conclusions. It is about creating an "artificial network of neurons" and instead of teaching the computer a huge list of rules to solve a problem, we give it a model so that it can evaluate examples and a small collection of instructions to modify the model when they occur. mistakes. For example, Facebook uses it for facial recognition of people differentiating it from images with objects ...