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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 #39

Making the world a better place at the AI for Good Foundation

Longtime listeners know that we’re always advocating for ‘AI for good’, but this week we have taken it to a whole new level. We had the privilege of chatting with James Hodson, Director of the AI for Good Foundation, about ways they have used artificial intelligence to positively-impact the world - from food production to climate change. James inspired us to find our own ways to use AI for good, and we challenge our listeners to get out there and do some good!

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Hamel Husain towardsdatascience.com

How to automate tasks on GitHub with machine learning for fun and profit

This is an explainer on how to build a GitHub App that predicts and applies issue labels using Tensorflow and public datasets. Hamel Husain writes: In order to show you how to create your own apps, we will walk you through the process of creating a GitHub app that can automatically label issues. Note that all of the code for this app, including the model training steps are located in this GitHub repository. See also: Issue Label Bot

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Practical AI Practical AI #36

Growing up to become a world-class AI expert

While at the NVIDIA GPU Technology Conference 2019 in Silicon Valley, Chris enjoyed an inspiring conversation with Anima Anandkumar. Clearly a role model - not only for women - but for anyone in the world of AI, Anima relayed how her lifelong passion for mathematics and engineering started when she was only 3 years old in India, and ultimately led to her pioneering deep learning research at Amazon Web Services, CalTech, and NVIDIA.

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NVIDIA Developer Blog Icon NVIDIA Developer Blog

NVIDIA Jetson Nano - A $99 computer for embedded AI

Google, Intel, and others have recently been targeting AI at the edge with things like Coral and the Neural Compute Stick, but NVIDIA is taking things a step farther. They just announced the Jetson Nano, which is a $99 computer with 472 GFLOPS of compute performance, an integrated NVIDIA GPU, and a Raspberry Pi form factor. According to NVIDIA: The compute performance, compact footprint, and flexibility of Jetson Nano brings endless possibilities to developers for creating AI-powered devices and embedded systems. And it’s not only for inference (which is the main target of things like Intel’s NCS). The Jetson Nano can also handle AI model training: since Jetson Nano can run the full training frameworks like TensorFlow, PyTorch, and Caffe, it’s also able to re-train with transfer learning for those who may not have access to another dedicated training machine and are willing to wait longer for results. Check it out! You can pre-order now.

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The Allen Institute for AI Icon The Allen Institute for AI

China to overtake US in AI research

China has committed to becoming the world leader in AI by 2030, with goals to build a domestic artificial intelligence industry worth nearly $150 billion (according to this CNN article). Prompted by these efforts, the Semantic Scholar team at the Allen AI Institute analyzed over two million academic AI papers published through the end of 2018. This analysis revealed the following: Our analysis shows that China has already surpassed the US in published AI papers. If current trends continue, China is poised to overtake the US in the most-cited 50% of papers this year, in the most-cited 10% of papers next year, and in the 1% of most-cited papers by 2025. Citation counts are a lagging indicator of impact, so our results may understate the rising impact of AI research originating in China. They also emphasize that US actions are making it difficult to recruit and retain foreign students and scholars, and these difficulties are likely to exacerbate the trend towards Chinese supremacy in AI research.

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OpenAI Icon OpenAI

OpenAI creates a "capped-profit" to help build artificial general intelligence

OpenAI, one of the largest and most influential AI research entities, was originally a non-profit. However, they just announced that they are creating a “capped-profit” entity, OpenAI LP. This capped-profit entity will supposedly help them accomplish their mission of building artificial general intelligence (AGI): We want to increase our ability to raise capital while still serving our mission, and no pre-existing legal structure we know of strikes the right balance. Our solution is to create OpenAI LP as a hybrid of a for-profit and nonprofit—which we are calling a “capped-profit” company. The fundamental idea of OpenAI LP is that investors and employees can get a capped return if we succeed at our mission, which allows us to raise investment capital and attract employees with startup-like equity. But any returns beyond that amount—and if we are successful, we expect to generate orders of magnitude more value than we’d owe to people who invest in or work at OpenAI LP—are owned by the original OpenAI Nonprofit entity. To some this makes total sense. Others have criticized the move, because they say that it misrepresents money as the only barrier to AGI or implies that OpenAI will develop it in a vacuum. What do you think? Learn more about OpenAI’s mission from one of it’s founders in this episode of Practical AI.

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Python github.com

GIPHY's celebrity-detecting deep learning model 🕵️‍♀️

GIPHY is proud to release our custom machine learning model that is able to discern over 2,300 celebrity faces with 98% accuracy. The model was trained to identify the most popular celebs on GIPHY, and can identify and make predictions for multiple faces across a sequence of images, like GIFs and videos. Give it a try on the demo page or download the model yourself and follow along with the examples.

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Practical AI Practical AI #34

The White House Executive Order on AI

The White House recently published an “Executive Order on Maintaining American Leadership in Artificial Intelligence.” In this fully connected episode, we discuss the executive order in general and criticism from the AI community. We also draw some comparisons between this US executive order and other national strategies for leadership in AI.

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Victor Zhou victorzhou.com

Machine learning for beginners

Victor Zhou writing on machine learning for beginners with this introduction to neural networks. …neural networks aren’t that complicated! The term “neural network” gets used as a buzzword a lot, but in reality they’re often much simpler than people imagine. This post is intended for complete beginners and assumes ZERO prior knowledge of machine learning. We’ll understand how neural networks work while implementing one from scratch in Python.

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Practical AI Practical AI #33

Staving off disaster through AI safety research

While covering Applied Machine Learning Days in Switzerland, Chris met El Mahdi El Mhamdi by chance, and was fascinated with his work doing AI safety research at EPFL. El Mahdi agreed to come on the show to share his research into the vulnerabilities in machine learning that bad actors can take advantage of. We cover everything from poisoned data sets and hacked machines to AI-generated propaganda and fake news, so grab your James Bond 007 kit from Q Branch, and join us for this important conversation on the dark side of artificial intelligence.

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Practical AI Practical AI #31

AI for social good at Intel

While at Applied Machine Learning Days in Lausanne, Switzerland, Chris had an inspiring conversation with Anna Bethke, Head of AI for Social Good at Intel. Anna reveals how she started the AI for Social Good program at Intel, and goes on to share the positive impact this program has had - from stopping animal poachers, to helping the National Center for Missing & Exploited Children. Through this AI for Social Good program, Intel clearly demonstrates how a for-profit business can effectively use AI to make the world a better place for us all.

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AI (Artificial Intelligence) towardsdatascience.com

A response to OpenAI's new dangerous text generator

Those of you following AI related things on Twitter have probably been overwhelmed with commentary about OpenAI’s new GPT-2 language model, which is “Too Dangerous to Make Public” (according to Wired’s interpretation of OpenAI’s statements). Is this discussion frustrating or confusing for you? Well, Ryan Lowe from McGill University has published a nice response article. He discusses the model and results in general, but also gives some perspective on the ethical implication and where the AI community should go from here. According to Lowe: “The machine learning community really, really needs to start talking openly about our standards for ethical research release”

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Practical AI Practical AI #30

GirlsCoding.org empowers young women to embrace computer science

Chris sat down with Marta Martinez-Cámara and Miranda Kreković to learn how GirlsCoding.org is inspiring 9–16-year-old girls to learn about computer science. The site is successfully empowering young women to recognize computer science as a valid career choice through hands-on workshops, role models, and by smashing prevalent gender stereotypes. This is an episode that you’ll want to listen to with your daughter!

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Practical AI Practical AI #29

How Microsoft is using AI to help the Earth

Chris caught up with Jennifer Marsman, Principal Engineer on the AI for Earth team at Microsoft, right before her speech at Applied Machine Learning Days 2019 in Lausanne, Switzerland. She relayed how the team came into being, what they do, and some of the good deeds they have done for Mother Earth. They are giving away $50 million (US) in grants over five years! It was another excellent example of AI for good!

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Practical AI Practical AI #28

New year’s resolution: dive into deep learning!

Fully Connected – a series where Chris and Daniel keep you up to date with everything that’s happening in the AI community. If you’re anything like us, your New Year’s resolutions probably included an AI section, so this week we explore some of the learning resources available for artificial intelligence and deep learning. Where you go with it depends upon what you want to achieve, so we discuss academic versus industry career paths, and try to set you on the Practical AI path that will help you level up.

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The Changelog The Changelog #330

source{d} turns code into actionable insights

Adam caught up with Francesc Campoy at KubeCon + CloudNativeCon 2018 in Seattle, WA to talk about the work he’s doing at Source{d} to apply Machine Learning to source code, and turn that codebase into actionable insights. It’s a movement they’re driving called Machine Learning on Code. They talked through their open source products, how they work, what types of insights can be gained, and they also talked through the code analysis Francesc did on the Kubernetes code base. This is as close as you get to the bleeding edge and we’re very interested to see where this goes.

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Practical AI Practical AI #26

2018 in review and bold predictions for 2019

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 look back at 2018 - from the GDPR and the Cambridge Analytica scandal, to advances in natural language processing and new open source tools. Then we offer our predications for what we expect in the year ahead, touching on just about everything in the world of AI.

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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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