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European Biopharmaceutical Review

Beyond Big Data

We’ve all seen and experienced the benefits that Big Data have brought to machine learning. Computers have become good at tasks like understanding human language and interpreting images – self-driving cars are arguably one of the most impressive examples.

Big Data mean that you have millions, if not billions, of observations and for each observation you have a manageable amount of data. For instance, when data scientists analyse Twitter data they have hundreds of millions of tweets; however, each tweet is less than 280 characters. In a nutshell, Big Data are when you want to learn something simple and you have an abundance of examples.

Most machine learning techniques work more efficiently when Big Data are provided. The availability of Big Data can sometimes eclipse the algorithms used. You might know the Modified National Institute of Standards and Technology data – they’re a large number of images (around 50,000) of hand-written digits (zero to nine) and tons of machine learning tutorials use this data set. Even the most basic machine learning models can identify 97% of the digits correctly. With complicated deep nets, you get up to 99.7% accuracy – showing how the most sophisticated artificial intelligence (AI) algorithm provides a whopping 2.7% increase.

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Joern Klinger is a developmental psychologist, computational linguist, geneticist, and entrepreneur. During his graduate studies in Oxford and Texas, Joern investigated human cognition in order to optimise AI. Insights from hands-on research in an indigenous community, neural-networks, and agent models led to an outside-the-box machine learning understanding, which he’s applying at his start-up biotx.ai.
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