Normally, you don’t need to be a fortune teller to predict Oscar winners, but the Academy has sprung a few surprises in recent years.

Recently, a team of data scientists tested whether their machine learning model could outsmart the bookmakers — with mixed results.

The boffins behind the BigML machine learning platform made their predictions with a Deepnets model, an optimised implementation of the Deep Neural Networks supervised learning technique.

Its biggest miss was in the hotly-contested best picture category. The model correctly rejected the bookie’s favourite, 1917, to pick an outsider. But it ultimately went for the wrong one, plumping for Once Upon a Time in Hollywood ahead of surprise winner Parasite.

One of the great things about the current wave of AI innovation is the large number of open source tools, technologies, and frameworks.

From TensorFlow to Python, Kafka to PyTorch, there’s an explosion in diversity of data science and big data tool sets.

However, when it comes to putting these tools together and building real-world AI applications, regular companies suffer from a serious technology gap compared to technology firms.

Here’s an interesting peice from Datanami on how to make AI work in the enterpise.

Many of the latest open source AI technologies are not known for being easy to work with, and typically require highly skilled data scientists to use. This puts a cap the applicability of the AI tech, and limits its use to companies that have the budget to hire experienced data scientists.