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Practical AI Practical AI #35

Social AI with Hugging Face

Clément Delangue, the co-founder and CEO of Hugging Face, joined us to discuss fun, social, and conversational AI. Clem explained why social AI is important, what products they are building (social AIs who learn to chit-chat, talk sassy and trades selfies with you), and how this intersects with the latest research in AI for natural language. He also shared his vision for how AI for natural language with develop over the next few years.

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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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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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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 #32

OpenAI's new "dangerous" GPT-2 language model

This week we discuss GPT-2, a new transformer-based language model from OpenAI that has everyone talking. It’s capable of generating incredibly realistic text, and the AI community has lots of concerns about potential malicious applications. We help you understand GPT-2 and we discuss ethical concerns, responsible release of AI research, and resources that we have found useful in learning about language models.

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Casey Newton The Verge

The secret lives of Facebook moderators in America

Eventually Artificial Intelligence will take over the human powered content moderation jobs for Facebook. Until then, this small population of humans employed by Cognizant (on behalf of Facebook) in Phoenix, Arizona accept the job of subjecting themselves to the worst of humankind to provide “a better Facebook experience.” Casey Newton writes for The Verge: The video depicts a man being murdered. Someone is stabbing him, dozens of times, while he screams and begs for his life. Chloe’s job is to tell the room whether this post should be removed. She knows that section 13 of the Facebook community standards prohibits videos that depict the murder of one or more people. When Chloe explains this to the class, she hears her voice shaking. Returning to her seat, Chloe feels an overpowering urge to sob. Another trainee has gone up to review the next post, but Chloe cannot concentrate. She leaves the room, and begins to cry so hard that she has trouble breathing. No one tries to comfort her. This is the job she was hired to do…

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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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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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Practical AI Practical AI #24

So you have an AI model, now what?

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 all things inference, which involves utilizing an already trained AI model and integrating it into the software stack. First, we focus on some new hardware from Amazon for inference and NVIDIA’s open sourcing of TensorRT for GPU-optimized inference. Then we talk about performing inference at the edge and in the browser with things like the recently announced ONNX JS.

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NVIDIA Developer Blog Icon NVIDIA Developer Blog

NVIDIA's PhysX project goes open source and beyond gaming

PhysX is NVIDIA’s hardware-accelerated physics simulation engine that’s now released as open source to move it beyond its most common use case in the gaming world, to give access to the embedded and scientific fields — think AI, robotics, computer vision, and self-driving cars. PhysX SDK has gone open source, starting today with version 3.4! It is available under the simple 3-Clause BSD license. With access to the source code, developers can debug, customize and extend the PhysX SDK as they see fit.

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Practical AI Practical AI #23

Pachyderm's Kubernetes-based infrastructure for AI

Joe Doliner (JD) joined the show to talk about productionizing ML/AI with Pachyderm, an open source data science platform built on Kubernetes (k8s). We talked through the origins of Pachyderm, challenges associated with creating infrastructure for machine learning, and data and model versioning/provenance. He also walked us through a process for going from a Jupyter notebook to a production data pipeline.

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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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