There are many myths regarding Artificial Intelligence (AI) and Machine Learning (ML). As with other new technologies some are untrue and some are not. The myths that are untrue often create noise impairing us from making the right decisions about this new technology. In this blog post we will try to myth bust some of the most common myths within AI.
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When we help customers use their data by utilizing machine learning we sometimes get asked “why is re-training important?”. This is natural as in software development when the code works you should let it be, if it aint broke don’t fix it?
In this blog post we are talking about something that in machine learning is called data leakage. Please, do not misunderstand it as the leakage of data to the public.
Data leakage in machine learning is when using a feature for predicting the output, that at the time of prediction cannot be available. In many cases, the feature holds information about the value we are trying to predict.
“Reduce energy consumption in your household, and you will live longer”.
This could be a headline in your favorite tabloid. People have a tendency to see a correlation between two values, and determines immediately that there is causality. But would you really live longer without energy in your household?
Artificial Intelligence. The word itself says something about what it does. It tries to create an intelligence, artificially. But how? What exactly is it? Can it improve your everyday life? Can it improve businesses? What is Machine Learning in this picture?
It is now close to a year since our first blog post Why we started a machine learning consultancy which is still getting a lot of interest. So we thought it was time to do a part two, telling the story of where we are now and how much has changed.
In previous blog post about Credit Card Fraud we used an artificial neural network for predicting whether a given transaction is fraudulent, with great results. However, sometimes in problems like these, where we naturally have an imbalanced distribution in our dataset, we might not always have collected data for the minority of the outcomes, in this case, the fraudulent transactions.
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