Submitted by: Big Data Expo

Big Data Expo Special - Data Science Pioneers

In this episode we talk to two main characters in the documentary 'Data Pioneers': Kia Eisinga from TomTom and Bernardo Nunes from Growth Tribe. In this documentary real data scientists talk about their job in the real context; they reveal the actual challenges they face during their work and of course their expectations of future challenges.

You can find the trailer at datascience.movie. Let’s get into the main characters of the documentary. Eisinga is a senior data scientist at TomTom. “We are a small start up within the company and help every department that wants to work with data science.” Nunes is Head of Science at Growth Tribe, which is a training company: “What we basically do is training professionals that are native in analytics to become data-savvy. At the same time, we train business professionals in understanding analytics.” 

Why watch?

Eisinga: “There is a big hype for big data, data science, AI etc. I see a lot of businesses that want to do something with these possibilities. But sometimes those terms are misused. This documentary can help provide a narrative that is really helpful. It also shows the possibilities and constraints at the moment. That’s why I think it’s valuable to watch.”

Nunes: “For the first time we started to combine three factors that we haven’t had put together. The algorithm itself is not new. But nowadays we have computing power that is much cheaper than three years ago, and we also have unstructured data to train the algorithms with. When we combine these three things – and this is what the documentary’s audience can learn from this movie – the catalysts become more accessible.”

AI scary?

The documentary shows some ‘dark opinions’ about AI overruling people. The data scientists, however, do not agree with that. Nunes: “What the documentary shows is that AI is narrow, which means that AI can outperform a human, but only on a very specific task. That takes away the myth that AI will overrule humans. Nowadays it is not the case.”

Eisinga: “I agree with Bernardo. However, I do think that the threat comes from how people use AI. Things like introducing biases in algorithms, or privacy matters when working with data. Or using machine learning in a way that is bad for the world. So, I think there are still some threats, but not the way the news represents it.” 

To find out how the two data scientists demystify those issues and what they think the opportunities are with data science, please listen to the podcast! 

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