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Recently, Google's CEO, Sundar Pichai, likened the impact of artificial intelligence to the discovery of fire. In the same way our distant ancestors' world was transformed by an invention that literally turned night into day, the foundations of how our society operates is about to undergo a similar shift. But will artificial intelligence really live up to the hype? That all depends on you and what you decide to do with it. Fire was meaningless until creative cavemen figured out exactly what to do with it.

To be honest, some applications of artificial intelligence can be mind-boggling and in fact, the technology is luring businesses into taking unimaginable ventures. For the longest time, Alibaba has maintained the position of being China's leading e-commerce firm.

I am looking for a series of videos that I can use to learn Pytorch. I found out that there are some presentations, but any recommended videos to go through Deep Learning tutorials with Pytorch would be appreciated.

Hoy traemos a este espacio el nuevo número de iJET International Journal of Emerging Technologies in Learning el último de 2017 Vol 12, No 12 (2017) Table of Contents Papers Application of Digital Music Technology in Music Pedagogy Peiwei Zhang, Xin Sui Music Solfeggio Learning Platform Construction and Application Qiao Zhou, Baihui Yan The Effects of the CALL Model on College English Reading Teaching Dan Zhang, Xiaoying Wang The Construction of Intelligent English Teaching Model Based on Artificial Intelligence Design and Implementation of English Reading Examination System Based on WEB Platform Lan Guo, Zhiyu Zhao, Lu Bai, Jing Lv, Xin Zhao On Spoken English Phoneme Evaluation Method Based on Sphinx-4 Computer System Computer Multimedia Assisted English Vocabulary Teaching Courseware Multi-Interactive Teaching Model of College English in Computer Information Technology Environment Design Flow of English Learning System Based on Item Response Theory Yuemei Liu, Xuetao Zhao Application of Kinect Technology in Blind Aerobics Learning Short Papers Discovery and Recommendation of First-Hand Learning Resources Based on Public Opinion Cluster Analysis Haiyun Li, Xuebo Zhang, Junhui Wang Evaluation of Sports Visualization Based on Wearable Devices Application of Data Mining in Library-Based Personalized Learning A Personalized Recommender System Based on Library Database Music Learning Based on Computer Software Baihui Yan, Qiao Zhou International Journal of Emerging Technologies in Learning.

There has been a discussion recently about using RL to land a SpaceX booster. Coincidentally I've been working on exactly this in OpenAI. It was as much fun as it was frustrating at times. It's trained with a PPO implementation from Unity that I've changed to work with OpenAI (GitHub). The official OpenAI implementation is convoluted and impossible to work with in my opinion. This particular agent took 200'000 tries over the course of 12 hours and 20 million frames (with a frame skip value of 5, so 100 million total frames).

It's a widely known (if little discussed) fact that the adult film industry often serves as a pioneer for video technology. It was the industry's early backing of VHS that helped to bury Betamax and its embrace of Blu-ray that helped shovel dirt over rival HD-DVD. So we should all take notice of a series of reports in Motherboard about the emergence of a new type of adult video technology--only this format doesn't have the imprimatur of established adult filmmakers or tech companies behind it. Instead, it's the brainchild of coders working with machine learning tools to swap the faces of adult actresses with those of established Hollywood celebrities. These so-called "deep fakes" are growing in popularity, Motherboard notes.

I don't see why you're so eager to bash this that hard. Most GAN papers work on images 128x128 which is about the sample size in 1s audio, and even with the most clever tricks so far like LAPGAN or PGGAN the best is about 1024x1024 images. This is the very first published GAN model that is successfully trained with 1-D convolutions without skip connections - which means that it can generate audio samples with completely unsupervised fashion directly from latent samples. Can you imagine the new possibilities on generative audio modeling stemming from this, like people did on images during last couple years? Also, people created videos from frames obtained from CycleGAN and they didn't linearly scale everything like you like to do so much.

Well, some people try to apply algebraic topology (and even algebraic geometry) to ML, so abstract algebra, a prerequisite for AT and AG, is useful in that sense. However, I'd rather read many good recent papers in deep learning to apply them for your research instead of studying AA and AT, as I see that's likely to result in more substantial results. Some recent AT application to ML includes On Characterizing the Capacity of Neural Networks using Algebraic Topology .

As wise speakers go, Apple's HomePod is late to the birthday party. The company has made a addiction out of no longer letting the competition pressure it into dashing to market with an unfinished product. The iPod, iPhone, and Apple Watch are just just a few examples of Apple's tried-and-true approach of now not being first. With the $ 350 HomePod, Apple positions it as a superior speaker first, with the delivered bonus of Siri. And, from that point of view, HomePod is a clear winner.