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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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Machine Learning fastcompany.com

Pentagram designed the prettiest computer chip you’ve ever seen

These IPUs (Intelligence Processing Units — a term new to me) with visual design by Pentagram for Graphcore are really pretty. Also, I think the tech may be cool but it’s a bit over my head so maybe you can tell me? Here is their brief spiel: Our IPU systems are designed to lower the cost of accelerating AI applications in cloud and enterprise datacenters to increase the performance of both training and inference by up to 100x compared to the fastest systems today.

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Practical AI Practical AI #22

BERT: one NLP model to rule them all

Fully Connected – a series where Chris and Daniel keep you up to date with everything that’s happening in the AI community. This week we discuss BERT, a new method of pre-training language representations from Google for natural language processing (NLP) tasks. Then we tackle Facebook’s Horizon, the first open source reinforcement learning platform for large-scale products and services. We also address synthetic data, and suggest a few learning resources.

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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.

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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?

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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.

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Go blog.sourced.tech

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.

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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.

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Machine Learning haacked.com

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.

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