Recommender systems are one of the most useful technologies for B2C companies. Learn more about recommender systems on this episode!

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The coronavirus pandemic has shocked the markets, and society on a global level. What changes is it going to bring to the economy and how will this affect AI companies? What opportunities are going to arise? Learn more in this episode!

Read more about this topic at https://thedatascientist.com/covid-19-ai-industry/

Why is it so difficult to forecast significant events like the current COVID-19 pandemic? In this episode we discuss the reasons that make forecasting a difficult problem, and under what conditions we can have good forecasts.

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Is AI overhyped? As a data scientist, I have implemented many real world systems that had a significant impact. However, AI is often oversold, and we hear all kinds of crazy stories, which make people believe that terminator style robots are just around the corner. 

What is really the case? We explore more on this episode.

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Does history repeat itself? How would World War II have turned out if different strategic decisions had been taken? How would civilisation have unfolded if prominent figures hadn't been born?

Historians like to speculate about these kind of questions. But what if there was a data-driven way to study these hypotheses? On this episode we discuss how data science can help historians answer these type of questions and understand the factors that move history.

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There are many people (including prominent figures like Elon Musk, or the late Stephen Hawking) claiming that AI is a threat to humanity. But is it really? In this episode, we dig deeper into this subject and uncover the truth.

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With the world facing an unprecedented crisis, how can AI be used in order to help fight against the pandemic of COVID-19?

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The future of consumer businesses, like retail, lies with advanced personalisation. Context-aware recommender systems will play a major role in this revolution, which will allow shops to suggest the right product at the right time, increasing revenue dramatically.  In this episode we discuss more about this amazing technology.

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Causal inference is one of the most exciting topics in machine learning. Some prominent researchers, like Judea Pearl believe that we will never be able to create true AI until we crack this. In this podcast we discuss more.

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There are many different processes to create data product. The drivetrain approach is one such approach discussed on O'Reilly's blog. In this episode, I elaborate on how this approach can help a business use data science, and I compare it to other data science processes, such as the Predict-and-Optimise framework.

Make sure to visit The Data Scientist for more content around data science, AI, and blockchain.