NVIDIA Developer News

It's a widely-used and widely-studied method, so the popular implementations are pretty optimized and the toolkits extensive. Unless you have some fundamental insight/optimization specific to your problem, I don't think it's actually worth it to roll your own. Easier to just start with something open sourced and build a small module on top. Nvidia is rolling FEM into Physx[1] - exciting stuff! GPU-accelerated FEM is going to be a game changer if they didn't cut too many corners in the pursuit of speed. I wonder if it can be coupled with FleX... [1]: https://news.developer.nvidia.com/announcing-nvidia-physx-sdk-5-0/

Here are a few more who have tried it or use deep learning for SAR: https://www.planckaero.com/maritimedrone/ https://flytbase.com/flytstories/search-and-rescue-drone/ https://news.developer.nvidia.com/autonomous-search-and-rescue-drones-… Data for training such models for practical use are difficult to get. Often, one has to train a model on a different but related dataset that contains your object of interest and ensure that your model generalizes well. These models are highly sensitive to the data that they are trained on, so it is best if you can gather data which is closer to the actual environment (in which you want to use the model).

Surely, that is a solved problem now that Google has "advance[d] the state of the art in natural language technologies and buil[t] systems that learn to understand and use language in context"? [1] Or, I guess, one can just buy more NVIDIA GPUs, and simply solve the problem using Torch, following their online guide on "understanding Natural Language with Deep Neural Networks Using Torch" [2]. In general, I hear that many language understanding tasks are finally conclusively conquered, as shown by OpenAI with a combination of supervised and unsupervised learning that has led to an ImageNet-class increase in the state-of-the art results in language understanding datasets [3]. Why, these days, we can even build an "AI System [that] Understands Music Like Humans Do"! (NVIDIA, again; [4]) The inhabitants of Scunthorpe and all the Dicks, Pennys and Dikshits of the world can finally rejoice! Machine learning is on the job! ______________ [1] https://ai.google/research/teams/language/ [2] https://devblogs.nvidia.com/understanding-natural-language-deep-neural… [3] https://blog.openai.com/language-unsupervised/ [4] https://news.developer.nvidia.com/ai-system-understands-music-like-hum…

Title is misleading. Nvidia have created a network which can slow down (i.e. interpolate) videos by a factor of four. Actual link: https://news.developer.nvidia.com/transforming-standard-video-into-slo… Most importantly, the footage shot in the video was at 2500 fps native [1] - at 300k FPS you have to use a significantly reduced image size. FPS is bottlenecked by the time it takes to read out the sensor, which is linear in the number of rows in the image. So if you read out half the sensor, you can double your frame rate. You can exploit this to get thousands of frames per second on a cheap machine vision camera. The camera framerate is largely irrelevant. While you can slow down footage by four times (neat), that doesn't mean you can get 300k fps footage out of a compact camera. The great benefit of this technique is really that you can convincingly fake super-slow motion without having to sacrifice sensor resolution. I can easily see this as a cool iOS or Android feature. Maybe if they'd actually used footage that was at the limit of camera technology this would be more impressive (I don't see a technical reason why not?). Why not combine it with super resolution too? Interesting security implications, as cameras with frame rates higher than (I believe) 1M fps are ITAR controlled. [1] https://www.youtube.com/watch?v=5mZovjRlkWs

> ML/AI has no conception of Time: False. Time can be included as an input. Previous system state can also be communicated forward in various ways (ex the LSTM architecture). Conceptual context: I'll agree, since that seems like it would require general abstract reasoning ability (ie strong AI). Similarly, robust treatment in the general case of physical continuity and cause and effect both seem like special cases of conceptual context to me. That being said, bear in mind that we already have examples of not so robust treatment for special cases of physical continuity as well as cause and effect. (Example: https://news.developer.nvidia.com/transforming-standard-video-into-slo… Who knows how close (or far) we might be from a general solution?

Link in case anyone wonders what GPT-3 is: https://news.developer.nvidia.com/openai-presents-gpt-3-a-175-billion-…

The video here talks about the difference between the two approaches. There's a comparison at 2:50. "I think the computers are winning, because in the upper right you can kind of see that back wall, there's a little strip of green. It's kind of blurred out a little bit too much with the human version, but the neural network has picked up on that and kept the vertical stripes there." https://news.developer.nvidia.com/ray-tracing-essentials-part-7-denois…

Associated blog post: https://news.developer.nvidia.com/rendering-millions-of-dynamics-light… Video demonstration: https://www.youtube.com/watch?v=HiSexy6eoy8

I think we already have the tools to do this, but there might be issues with fidelity. First: Use AI to generate the text description of an image. http://homepages.inf.ed.ac.uk/keller/publications/jair16.pdf https://www.captionbot.ai/ Second: Use AI to generate an image from the text description. https://news.developer.nvidia.com/ai-model-can-generate-images-from-na…

Pretty sure this is already a thing: https://news.developer.nvidia.com/logitechs-new-webcam-includes-dynami…

Maybe. This seems one approach: https://news.developer.nvidia.com/transforming-standard-video-into-slo… Direct link to video: https://www.youtube.com/watch?v=MjViy6kyiqs

I don't see nets? https://news.developer.nvidia.com/wp-content/uploads/2019/04/Speedgate… It's really different from rugby.

See also: * https://www.reddit.com/r/interestingasfuck/comments/b2ro7k/nvidias_new… This official announcement [0] from last year (and HN discussion [1]) about AI image reconstruction seems related, though this new sketch-to-photorealistic demo seems next-level by comparison. [0] https://news.developer.nvidia.com/new-ai-imaging-technique-reconstruct… [1] https://news.ycombinator.com/item?id=16903821

This system developped by Japanese researchers precedes this and seems to have better results: https://news.developer.nvidia.com/automatic-object-removal-and-realist… MIT Media Lab has much better PR, of course.

It shouldn't be any harder to create unique digital-only actors https://news.developer.nvidia.com/generating-photorealistic-fake-celeb… than to pirate likenesses.

Which is only supported on AMD cards. Given that NVidia has their own API (RTX) , which will be part of Gameworks [0], is collaborating with Microsoft, and is currently requesting papers for Ray Tracing Gems[1], I don't see them bothering with Vulkan support. [0] - https://blogs.nvidia.com/blog/2018/03/19/whats-difference-between-ray-… [1] - https://news.developer.nvidia.com/call-for-papers-real-time-ray-tracin…

See the article at NVIDIA developer blog: https://news.developer.nvidia.com/artificial-intelligence-software-eas…

In my line of work we say: "It ain't cancer until the pathologist says so." Basically it is a joke amongst the pathologists that no matter what MD does the diagnosis, s/he will always send a biopt to the pathologist and ask if it is cancer. Well, in simple terms of course. I guess the correct moment to say you have been diagnosed with cancer is when the pathologist tells the physician it's cancer. Beautiful thing is neural networks / deep learning can really outperform the pathologist now that we have introduced digital pathology. Sure they will for some time give the final verdict but before too long AI will do the actual diagnose with better accuracy than a human possibly could in a fraction of the time. Reports started coming in the last year. E.g. [0][1][2]. Last one is directly mentioning years. 0) https://www.extremetech.com/extreme/233746-ai-beats-doctors-at-visual-… 1) https://news.developer.nvidia.com/diagnose-heart-disease-with-ai/ 2) http://bigthink.com/paul-ratner/heres-when-machines-will-take-your-job…