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Machine Learning

Machine Learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
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Practical AI Practical AI #21

UBER and Intel’s Machine Learning platforms

We recently met up with Cormac Brick (Intel) and Mike Del Balso (Uber) at O’Reilly AI in SF. As the director of machine intelligence in Intel’s Movidius group, Cormac is an expert in porting deep learning models to all sorts of embedded devices (cameras, robots, drones, etc.). He helped us understand some of the techniques for developing portable networks to maximize performance on different compute architectures. In our discussion with Mike, we talked about the ins and outs of Michelangelo, Uber’s machine learning platform, which he manages. He also described why it was necessary for Uber to build out a machine learning platform and some of the new features they are exploring.


Facebook Engineering Blog Icon Facebook Engineering Blog

Facebook has a tool that learns to fix bugs automatically?!

This week on the Facebook code blog they shared details about a new tool called Getafix that automatically finds fixes for bugs and offers them to engineers to approve. 😎 Modern production codebases are extremely complex and are updated constantly. To create a system that can automatically find fixes for bugs — without help from engineers — we built Getafix to learn from engineers’ previous changes to the codebase. It finds hidden patterns and uses them to identify the most likely remediations for new bugs. Getafix has been deployed to production at Facebook, where it now contributes to the stability of apps that billions of people use. The goal of Getafix is to let computers take care of the routine work, albeit under the watchful eye of a human, who must decide when a bug requires a complex, nonroutine remediation. Whether or not this tool will be open sourced or shared at large remains to be seen. How cool would it be to have something like this deployed to your codebase to find and suggest fixes to your bugs?


Practical AI Practical AI #15

Artificial intelligence at NVIDIA

NVIDIA Chief Scientist Bill Dally joins Daniel Whitenack and Chris Benson for an in-depth conversation about ‘everything AI’ at NVIDIA. As the leader of NVIDIA Research, Bill schools us on GPUs, and then goes on to address everything from AI-enabled robots and self-driving vehicles, to new AI research innovations in algorithm development and model architectures. This episode is so packed with information, you may want to listen to it multiple times.



Detecting licenses in code with Go and ML

Why not just query GitHub’s API to get the licenses? we were not satisfied with its detection quality: many projects which actually contain the license file in a non-standard format are missed, and some are misclassified. What they came up with is go-license-detector, which detects 99% of licenses in a test dataset (compared to GitHub’s 75%) in a fraction of the time. And the winner is… MIT.


Smashing Magazine Icon Smashing Magazine

Making a mobile app with facial recognition features

This article isn’t a how-to, per se. It’s more like a research report written after attempting to build such an app for the first time. There’s nothing wrong with that, though, and this write-up is super useful if you’re about to tackle a similar problem space. Open source libraries are tried, facial recognition services are evaluated, and their takeaways are solid, if not a bit disappointing. As you can see, the really simple idea of using facial recognition functionality was not that simple to implement. The entire piece is worth a read.


Machine Learning

Analyzing GitHub issue comment sentiment with Azure

If you’ve been looking to dabble in some AI and serverless, Phil Haack shared his process to create a SentimentBot for GitHub issues with Azure Functions. Perhaps the combination of machine learning and human judgement could make the problem more tractable. I decided to play around with Azure Functions because they have specific support for GitHub Webhooks. GitHub Webhooks and Azure Functions go together like Bitters and Bourbon. If you want to skip the code and just test it out, head to this issue.


Machine Learning

What's the difference between data science, machine learning, and AI?

We’ve needed this post for a very long time. Thank you David Robinson. When I introduce myself as a data scientist, I often get questions like “What’s the difference between that and machine learning?” or “Does that mean you work on artificial intelligence?” But that overlap, tho. The fields do have a great deal of overlap, and there’s enough hype around each of them that the choice can feel like a matter of marketing. But they’re not interchangeable. Most professionals in these fields have an intuitive understanding of how particular work could be classified as data science, machine learning, or artificial intelligence, even if it’s difficult to put into words. Here’s the break down…

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