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Killing machines - why data science needs software engineering

Jupyter notebooks (and other notebooks-based system) are a very popular and handy tool in data science. With a notebook, you can quickly explore different alternatives, getting immediate feedback, producing plots, combining code and text, achieving a form of literate programming, and allowing a hassle-free sharing of your results – a key enabler for reproducibility, essential feature of any empirical discipline.

However notebooks are not enough.

Why? Let's get know to an story about artificial intelligence and the singularity.

Elon Musk and Mark Zuckerberg have recently engaged in a public debate about the perils of artificial intelligence (AI). On one side of the debate, Elon Musk defends that AI may give birth to future killing machines that will exploit humanity as slaves. The moment when it will happen is known as the singularity. From that moment on, machines may autonomously decide to kill us, or to enslave us, or to do us anything they please.

What Elon Musk fails to notice is that we have alredy created machines that kill humans autonomously: the Therac 25, a radiotherapy machine that killed 4 people and severely injured 2 more.

How did we as humankind design such a horrible machine? What kind of hyper-sophisticated super intelligence did we create for the Therac 25?

It was much easier than that. We just were sloppy. We did not follow (now common) best practices for software development.

Whether we like it or not, we as data scientists write code on a daily basis. If you don't want the humankind to be enslaved by machines, don't forget, notebooks are great but not enough. You need software engineering too.

Want to know more? Find the story and links with more details in the slides I presented at Databeers Madrid on June 29:

(grab a copy of the slides)

Written on Aug 29 2017 | Tags: #datascience, #notebooks, #software engineering
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