At DeepMind, we've built a unique culture and work environment where long-term ambitious research can flourish. Our interdisciplinary Science team applies the best of our artificial intelligence research to challenges within the natural sciences. The Science team has already made a number of high profile breakthroughs, and we have all the ingredients in place to make further significant progress over the coming years. To succeed in this role you will need to be passionate about advancing science using machine learning and other computational techniques. You'll join a spectrum of domain experts, ML research scientists, and engineers exploring a diverse set of important scientific problems in biology, physics, mathematics, and other areas.
Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks. Machine learning is closely related to computational statistics, which focuses on making predictions using computers.
The more I delve in data science, the more convinced I am that companies and data science practitioners must have a clear view on how to cut through the machine learning and AI hype, to implement an effective data science strategy that drives business value. This article hopes to establish a framework to conceptualize and implement effective data science projects. Showing that you as a data scientist can derive meaningful insights which improve revenue and profits will result in yourself being more valuable to the company. With highly optimized and easily implementable machine learning and deep learning libraries, any data monkey can build sophisticated AI algorithms with just a few lines of code. However, a sophisticated model does not equate to an effective model.
One of the most common questions I get asked is, "Should I learn R or Python?". My general response is: it's up to you! Both are popular open source data platforms with active, growing communities; both are are highly sought after by employers, and both have a rich set of capabilities for working with data. It really depends most on your interests and the kind of employer you want to work for. If your interests lean more towards traditional statistical analysis and inference as used within industries like manufacturing, finance, and the life sciences, I'd lean towards R. If you're more interested in machine learning and artificial intelligence applications, I'd lean towards Python.
When most people encounter artificial intelligence today, it's recommending a new song or movie. AI technology is increasingly used to open up new horizons for scientists and researchers. At the University of Chicago, researchers are using it for everything from scanning the skies for supernovae to finding new drugs from millions of potential combinations and developing a deeper understanding of the complex phenomena underlying the Earth's climate. Today's AI commonly works by starting from massive data sets, from which it figures out its own strategies to solve a problem or make a prediction--rather than rely on humans explicitly programming it how to reach a conclusion. The results are an array of innovative applications.