Between this, the GAN evaluation paper which happened to be really similar to a previously published paper by other authors, and DeepMind's PR machine while lacking in exhibiting the crucial details which make their Go models so good, I am definitely more and more disappointed in DeepMind ...

It is our great pleasure to announce the release of Open-unmix, a MIT-licensed python implementation for DNN-based music separation. In the recent years, deep learning-based systems could break a long-standing crystal ceiling, and finally allow high-quality music separation. However, until now, no open-source implementation was available that matches the performance of the best systems proposed more than four years ago. Not being able to reproduce state of the art performance makes it difficult to clearly identify the sources for discrepancies and rooms for improvement. In this context, we release Open-Unmix (UMX) as closing this gap by providing a reference implementation for DNN-based music separation.

We are pleased to share with you our meta-learning library, that started as a project at the PyTorch hackathon. Our goal is to support as many meta-learning algorithms as possible (be it few-shots, meta-descent, or meta-RL) and to enable researchers to develop better methods and easily compare against existing literature. Modular API: implement your own training loops with our low-level utilities. Let us know what you think and how we can help you in your research! PS: learn2learn was also accepted as a poster to the PyTorch Dev Conference, so you'll know all about it there!

I have a bachelor degree in CS and I've 5 years experience of working in multiple tech companies. I have mostly worked in machine learning and artificial intelligence including deep neural nets. Alongside, I am a huge music addict and a self learned amateur piano player and sometimes, I love to compose music out of an experiment. Now, I wish to pursue masters. But, I want to give myself a platform where my crave for technology and passion for music will find the perfect blend.

To create a dataset for detection I need to get a lot of deepfake images. I'm looking for some architectures for deepfake image generation so I can quickly create my dataset. Does anyone know about any research done in deepfakes for images? The alternative is to create normal deepfake videos and get frames for that but it takes several hours for one video and it's really not viable to create a large dataset.

It has nothing to do with any of the things you listed. Machine learning and pattern recognition basically come down to learning a model of the dataset and then predicting something based on that model. If the model is "biased" then it's because the dataset was "biased". I don't understand what you are getting at when you talk about the black/white/male/female stuff. Black/white/male/female are just arbitrary labels defined by you.

Entering college soon and since the past year and a half I've been studying ML pretty rigorously. So far I've loved every day of it and as of right now I like the idea of being able to develop my own machine learning algorithms once I graduate for a living. My advisor wants me to pick a degree to focus on but I have no idea which of the 4 I listed above will give me the amount of value for developing ML algorithms. I assume all 4 will pretty helpful and I originally planned on perusing either CS or Statistics, however, I've heard the math covered in Electrical Engineering is very applicable for ML and heard Jeremy Howard (creator of AI) say people with a classical Statistics background are actually the one's who have the hardest time of all his students picking up deep learning... i have no idea which one to choose really:\ If anyone here who's already graduated could give me some advice, I'd really appreciate it.

ORLANDO, Fla. - What do you think of when you hear the term Artificial Intelligence? More often than not, you might think of a movie with an evil robot intent on destroying the world. Hollywood films often present a frightening image of Artificial Intelligence (AI), but the reality is that we use AI in our daily lives without even knowing it. During its world premiere at Orlando Science Center this fall, Artificial Intelligence: Your Mind & The Machine will show visitors exactly what AI is, how it works, and what it might do in the future. From illusions that trick our brains to machines that can identify your emotions and translate languages, Artificial Intelligence: Your Mind & The Machine shows numerous ways that the human mind and thinking machines work.

"When you press the shutter button it takes one long exposure, and then in just one second the neural engine analyzes the fused combination of long and short images, picking the best among them, selecting all the pixels, and pixel by pixel, going through 24 million pixels to optimize for detail and low noise," Schiller said, describing a feature called "Deep Fusion" that will ship later this fall. It was the kind of technical digression that, in years past, might have been reserved for design chief Jony Ive's narration of a precision aluminum milling process to produce the iPhone's clean lines. But in this case, Schiller, the company's most enthusiastic photographer, was heaping his highest praise on custom silicon and artificial intelligence software. The technology industry's battleground for smartphone cameras has moved inside the phone, where sophisticated artificial intelligence software and special chips play a major role in how a phone's photos look. "Cameras and displays sell phones," said Julie Ask, vice president and principal analyst at Forrester.

We're excited to announce that Samsung has won an 2019 AI TechAward in the category: Bots/Chatbots. The 2019 AI Tech Awards celebrates technical innovation, adoption and reception in the AI, Machine Learning & Data Science industry and by the developer community. The 2019 AI TechAwards will be presented at the 2019 AI TechAwards Ceremony during AI DevWorld (Oct 8-10, 2019, San Jose Convention Center), the largest Artificial Intelligence, Machine Learning & Data Science conference with tracks covering NLP, Open Source AI, AI for the Enterprise, Deep AI, Neural Networks and more. The 2019 AI TechAwards received hundreds of nominations, and the Advisory Board to the AI TechAwards selected our product/technology based on three criteria: (1) attracting notable attention and awareness in the AI, Machine Learning & Data Science industry; (2) general regard and use by the developer & engineering community; and (3) being a leader in its sector for innovation. "Samsung is a great example of the newest AI & Machine Learning technologies now allowing developers & engineers to build upon the burgeoning AI industry. Today's cloud-based software and hardware increasingly runs on systems needing increased data and intelligence, and Samsung's win here at the 2019 AI TechAwards is evidence of their leading role in the growth of the AI ecosystem," said Jonathan Pasky, Executive Producer & Co-Founder of DevNetwork, producer of AI Dev World & the 2019 AI TechAwards.