This booklet covers four main steps of designing a machine learning system: Project setup Data pipeline Modeling: selecting, training, and debugging Serving: testing, deploying, and maintaining It comes with links to practical resources that explain each aspect in more details. It also suggests case studies written by machine learning engineers at major tech companies who have deployed machine learning systems to solve real-world problems.

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This is a short introduction on methods that use neural networks in an offensive manner (bug hunting, shellcode obfuscation, etc.) and how to exploit neural networks found in the wild (information extraction, malware injection, backdooring, etc.).

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Winner of Mozilla’s $50,000 award for art and advocacy exploring AI. Stealing Ur Feelings is an augmented reality experience that reveals how your favorite apps can use facial emotion recognition technology to make decisions about your life, promote inequalities, and even destabilize American democracy. Using the AI techniques described in corporate patents, Stealing Ur Feelings learns your deepest secrets just by analyzing your face. If you haven’t tried this yet, drop what you’re doing and give it a go. Top notch production.

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Colin Lecher reporting for The Verge: Last week, Gov. Gavin Newsom signed into law AB 730, which makes it a crime to distribute audio or video that gives a false, damaging impression of a politician’s words or actions. While the word “deepfake” doesn’t appear in the legislation, the bill clearly takes aim at doctored works. Lawmakers have raised concerns recently that distorted deepfake videos, like a slowed video of House Speaker Nancy Pelosi that appeared over the summer, could be used to influence elections in the future. This is the first (but likely not the last) piece of legislation aimed at fighting the potential impact of GANs Gone Wild. It’ll be interesting to watch this game play out. I think the only long-term, sustainable solution will emerge from the same arena where the problem began: technological advances.

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Folks have been talking about TensorFlow 2 for some time now (See Practical AI #42 for one excellent example), but now it’s finally here. The bulleted list: Easy model building with Keras and eager execution. Robust model deployment in production on any platform. Powerful experimentation for research. API simplification by reducing duplication and removing deprecated endpoints. This is a huge release. Check out the highlights list in the changelog to see for yourself.

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RAPIDS.ai, for the uninitiated, is a data science framework that lets you execute entirely on GPUs.

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Straight from the horse’s mouth: We’re partnering to develop a hardware and software platform within Microsoft Azure which will scale to AGI. We’ll jointly develop new Azure AI supercomputing technologies, and Microsoft will become our exclusive cloud provider—so we’ll be working hard together to further extend Microsoft Azure’s capabilities in large-scale AI systems. Sometimes it’s hard to see the value traded in large scale investments like these. What do both sides get? With this particular investment, however, it’s pretty obvious what Microsoft is getting (Azure++) and what OpenAI is getting (an expanded R&D budget). It’s also worth noting that this is specifically focused on Artificial General Intelligence, not merely advancing the current state of the art in Machine Learning.

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Sergio De Simone, reporting for InfoQ: In a recent paper, MIT researchers introduced Gen, a general-purpose probabilistic language based on Julia that aims to allow users to express models and create inference algorithms using high-level programming constructs.

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Kick Olivia’s tires (mixed metaphor alert!) by chatting with her online or start with the docs if you’d like to set up and host your own instance.

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The latest machine learning research from my friends at Fast Forward Labs. Shiou Lin Sam and Nisha Muktewar teach us what meta-learners are and how they learn.

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The report focuses on 5 questions about the internet. Is it safe? How open is it? Who is welcome? Who can succeed? Who controls it? The answer is complicated, and the report doesn’t make any particular conclusions so much as share a series of research & stories about each topic. Includes some fascinating looks at what’s going on in AI, inclusive design, open source, decentralization and more.

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On Practical AI #41, Adam Berenzweig gave a sweeping history of human-computer interaction (HCI) and a glimpse into what the future might hold.

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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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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, 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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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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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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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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Daniel and I talked about this AI painting on Practical AI #18 a few weeks ago. At the time the painting was expected to yield $7,000 - $10,000 US. Since then, Christie’s held an auction, and the Portrait of Edmond Belamy sold for an incredible $432,500 US.

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I love how this article is arguing for those interested in AI to get their hands dirty. It doesn’t always require expensive GPU machines and a math Ph.D. Just start trying to build something that’s interesting to you!

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The purpose of this project is to provide a shortcut to developers and researchers for finding useful resources about Deep Learning for Natural Language Processing. Organized by papers, datasets, courses, books, blogs, tutorials and more…

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Abhishek Singh isn’t deaf or mute, but that didn’t stop him from asking the question: If voice is the future of computing interfaces, what about those who cannot hear or speak? This thought led to a super cool project wherein a computer interprets sign language and speaks the results to a nearby Alexa device. Live demo here and code here.

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Floor Drees: Yesterday I attended a Craftwork Amsterdam workshop with the promise to ‘create a GitHub App’. With the help of ‘Hubbers’ Don, Bas and Anisha we built a bot that triages GitHub issues. It’s always fun to read how folks pull together different tools/services to scratch an itch (or just have some fun).

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I’m relatively familiar with Machine Learning at this point, but I had never heard of Reinforcement Learning until I watched this excellent talk by Kaleo Ha’o at ML4ALL. I knew it was going to be good as soon as he laid out this comparison: if Machine Learning is teaching computers by example, then Reinforcement Learning is teaching computers by experience. Fascinating stuff!

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As we gear up for the launch of Practical AI and more AI/ML/DS related news coverage, I wanted to bring to your attention to this 6-month apprenticeship (compensated) in AI research at OpenAI. We’re now accepting applications for the next cohort of OpenAI Fellows, a program which offers a compensated 6-month apprenticeship in AI research at OpenAI. We designed this program for people who want to be an AI researcher, but do not have a formal background in the field. Applications for Fellows starting in September are open now and will close on July 8th at 12AM PST. Apply here.

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