Changelog

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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Ok, so I’m equally excited and concerned by this AI demo. James Vincent writes for The Verge: The most impressive demo at Google I/O was a phone call to book a haircut. This call wasn’t made by a human, but by the Google Assistant, which did an uncannily good job of asking the right questions, pausing in the right places, and even throwing in the odd “mmhmm” for realism. You have to hear this AI phone call for yourself! There’s a video of the demo embedded in this post.

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Elon Musk – SpaceX, Tesla, and co-creator of OpenAI – says this in a related video on YouTube: I am concerned about certain directions AI could take that would be not good for the future. I think it would be fair to say that not all AI future’s are benign. If we create some artificial super intelligence that supersedes us in every way by a lot, it’s very important that that be benign. Elon goes on to talk more specifically about his fears of AI and that if we have this incredible power, that it not be concentrated in the hands of a few. He doesn’t exactly say Google, but everyone knows that’s who he means. From OpenAI: We’ve co-authored a paper that forecasts how malicious actors could misuse AI technology, and potential ways we can prevent and mitigate these threats. This paper is the outcome of almost a year of sustained work with our colleagues at the Future of Humanity Institute, the Centre for the Study of Existential Risk, the Center for a New American Security, the Electronic Frontier Foundation, and others.

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Andrew Ng shared his plans for his newly created AI Fund — with investments including NEA, Sequoia, Greylock Partners, SoftBank Group, and others. Andrew Ng: I am excited to announce the formation of the AI Fund. We have raised $175 million, and will be sequentially initiating new businesses that use AI to improve human life. In the early days of electricity, much of the innovation centered around slightly different improvements in lighting. While this was an important foundation, the really transformative applications, in which electric power spurred massive redesigns in multiple industries, took longer to be grasped. AI is the new electricity, and is at a similar inflection point. Given Andrew Ng’s prominence and success in bringing AI to industry, and his partnership with some of the world’s premier technology investment firms, this announcement may well signal the next wave of capitalization for AI-oriented startups.

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