A neural network trained via slow-mo movies will artificially generate additional frames for your footage. Even 14 fps films can easily be boosted to 60 fps. A neural network specifically trained using our own dataset will provide you with an approximate artificial colorization of each frame. While not historically accurate, the colorization appears natural. Each frame can be upscaled using specifically-targeted data that perfectly aligns with your footage. Our neural network will "redraw" the missing data and increase the frame resolution 4x or more. Our team of engineers will be glad to help you resolve various digital tasks with our machine learning magic.
Efficiently navigating a superpressure balloon in the stratosphere1 requires the integration of a multitude of cues, such as wind speed and solar elevation, and the process is complicated by forecast errors and sparse wind measurements. Coupled with the need to make decisions in real time, these factors rule out the use of conventional control techniques2,3. Here we describe the use of reinforcement learning4,5 to create a high-performing flight controller. Our algorithm uses data augmentation6,7 and a self-correcting design to overcome the key technical challenge of reinforcement learning from imperfect data, which has proved to be a major obstacle to its application to physical systems8. We deployed our controller to station Loon superpressure balloons at multiple locations across the globe, including a 39-day controlled experiment over the Pacific Ocean.
There are many great articles about Artificial Intelligence (AI) and its benefits for business and society. However, many of these articles are too technical for the average reader. I love reading about AI, but I sometimes think to myself, 'Gee, I wish the author had explained this in simple English.' I will try and explain AI and its related technologies in simple terms, using real-life examples, as though I were talking to someone at a party. Your colleagues or your (close) friends may tolerate your endless and complex ramblings, but I guarantee you that people at parties are far less forgiving.
For the longest time, artificial intelligence (AI) was nothing more than exciting material for science fiction novels and movies. In 1982, Harrison Ford was hunting down replicants in'Blade Runners'; in 2001, Steven Spielberg wove an intricate tale of a relationship between human and machine in'A. Technologies that emulate or surpass human skills and characteristics are fascinating – not only on the big screen. For decades, scientists have dedicated time and money to the topic of artificial intelligence. The workshop'Dartmouth Summer Research Project on Artificial Intelligence', held in 1956 at Dartmouth College in New Hampshire, USA, is seen as the starting point of AI as academic subject.
This is a place to share machine learning research papers, journals, and articles that you're reading this week. If it relates to what you're researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you've read. Please try to provide some insight from your understanding and please don't post things which are present in wiki. Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links. Besides that, there are no rules, have fun.
If you're worried about facial recognition firms or stalkers mining your online photos, a new tool called Anonymizer could help you escape their clutches. The app was created by Generated Media, a startup that provides AI-generated pictures to customers ranging from video game developers creating new characters to journalists protecting the identities of sources. The company says it built Anonymizer as "a useful way to showcase the utility of synthetic media." The system was trained on tens of thousands of photos taken in the Generated Media studio. The pictures are fed to generative adversarial networks (GANs), which create new images by pitting two neural networks against each other: a generator that creates new samples and a discriminator that examines whether they look real. The process creates a feedback loop that eventually produces lifelike profile photos.