Algorithmia Blog

Emergent // Future: Autopilot Saves Lives, Algorithms IRL, and Neural Nets in Python

Issue 38
This week we look at the
40% drop in car crashes using Tesla’s Autopilot, why Microsoft is investing into Montreal AI research, check out some algorithms IRL, and building neural networks in Python.

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Last week: we looked at training self-driving cars using Grand Theft Auto V and the CB Insights AI 100 landscape.

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Tesla Autopilot > Human-Driven Cars 🚗

The U.S. Department of Transportation closed it’s probe into Tesla’s Autopilot after data showed a 40% drop in car crashes when the feature is enabled.

Tesla was able to provide the impact of Autopilot on crash rates, since not all Tesla owners have enabled the self-driving software.

The data showed a 1.3% crash rate per million miles driven without Autopilot enabled vs a .8% crash rate with.

OH? Postmates and DoorDash are testing self-driving delivery bots in California and DC.

Montreal’s AI Moment 🚀

Microsoft is doubling the size of their AI research outpost in Montreal after acquiring the Canadian startup, Maluuba, earlier this month.

Microsoft will invest $7M Montreal-based AI research at the University of Montreal and McGill University over the next five years, in addition to doubling the Maluuba team over the next two years.

Google invested invested $3.4M in the Montreal Institute for Learning Algorithms in late 2016. Google also plans to open an AI research group in their Montreal offices.

Algorithms IRL 💻

Here’s three examples of algorithms in the wild:

Neural Networks in Python 🌐

Facebook’s AI team released a PyTorch, a Python implementation of the Torch machine learning framework.

The package enables GPU-accelerated deep neural networks in Python, giving developers the ability to arbitrarily change the way their neural nets behave without having to build the network from scratch.

Developers can also extend PyTorch with popular Python packages, like NumPy and SciPy.

What We’re Reading 📚

Things To Try At Home 🛠

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