- Researchers from NVIDIA recently published a paper detailing their new methodology for generative adversarial networks (GANs) that generated photorealistic pictures of fake celebrities.
- Rather than train a single neural network to recognize pictures, researchers train two competing networks.
- “The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses,” explained the researchers in their paper Progressive Growing of GANs for Improved Quality, Stability and Variation.
- Since the publicly available CelebFaces Attributes (CelebA) training dataset varied in resolution and visual quality — and not sufficient enough for high output resolution — the researchers generated a higher-quality version of the dataset consisting of 30,000 images at 1024 x 1024 resolution.
- Generating convincing realistic images with GANs are within reach and the researchers plan to use TensorFlow and multi-GPUs for the next part of the work.
Researchers from NVIDIA recently published a paper detailing their new methodology for generative adversarial networks (GANs) that generated photorealistic pictures of fake celebrities.
Continue reading “Generating Photorealistic Images of Fake Celebrities with Artificial Intelligence – NVIDIA Developer News Center”
- The previous version of AlphaGo beat the human world champion in 2016.
- The new AlphaGo Zero beat the previous version by 100 games to 0, and learned Go completely on its own.
- Here is the Nature paper explaining technical details (also PDF version: Mastering the Game of Go without Human Knowledge One of the main reasons for success was the use of a novel form of Reinforcement learning in which AlphaGo learned by playing itself.The system starts with a neural net that…
- It plays millions of games against itself and tuned the neural network to predict next move and the eventual winner of the games.The updated neural network was merged with the Monte Carlo Tree Search algorithm to create a new and stronger version of AlphaGo Zero, and the process resumed.
- In each iteration, the performance improved by a small amount, but because it can play millions of games a day, AlphaGo Zero surpassed thousands of years of human knowledge of Go in just 3 days., from DeepMind post This is a hugely significant advance for AI and Machine Learning research.Here…
The previous version of AlphaGo beat the human world champion in 2016. The new AlphaGo Zero beat the previous version by 100 games to 0, and learned Go completely on its own. We examine what this means for AI.
Continue reading “AlphaGo Zero: The Most Significant Research Advance in AI”
- A new study suggests that as many as four million human workers could be replaced by robots over the next decade.
- Robots could replace human workers in up to four million jobs in Britain over the next decade, according to research conducted by UK market research firm YouGov on behalf of the Royal Academy of the Arts.
- Chiefly, businesses have to make sure that the millions of workers who are replaced by robots and other automated systems aren’t left behind.
- Many robots are simply better equipped to perform menial tasks than humans are.
- Robots can raise overall productivity by doing the dirty, difficult, or otherwise unpleasant jobs that human workers would rather avoid.
A new study suggests that robots will replace as many as four million British workers in the next decade. Can we find new roles for these people to fill?
Continue reading “In Ten Years, Robots Could Replace More Than 4 Million Workers”
- A team of researchers at Rutgers University in New Jersey and Facebook’s AI Lab in California are using AI to create a new system for generating art.
- With this in mind, GAN works to create new styles that evoke a profound human response, perhaps understanding the idea Anthony Bourdain shared in an interview with Crave: “To me, in a perfect work, art causes the people who look at it to run out in the street and get into fistfights over whether its art or not.”
- The second set of human works featured a selection of 25 paintings exhibited at Art Basel 2016.
- “Being shown in Art Basel 2016 is an indication that these are art works at the frontiers of human creativity in paintings, at least as judged by the art experts and the art market,” the researchers determined.
- The experiment continued by testing perception and arousal, asking if participants could determine if the works of art were intentional, if they could see the visual structure of the work, if they felt the works communicated with them, and if they felt inspired and elevated by their interactions with the art.
A new AI program creates new styles of art that some prefer to that of humans.
Continue reading “Artificial Intelligence Laboratory Invents New Styles of Art”
- At the end of Google I/O, the company unveiled a new program to give researchers access to the company’s most advanced machine learning technologies for free.
- The TensorFlow Research Cloud program, as it will be called, will be application based and open to anyone conducting research, rather than just members of academia.
- If accepted, researchers will get access to a cluster of 1,000 Cloud TPUs for training and inference.
- If that level of openness isn’t your cup of tea, Google is also planning to launch a Cloud TPU Alpha program for internal, private sector, work.
- The application for the program isn’t open yet, but Google is directing interested parties to fill out a form indicating interest.
At the end of Google I/O, the company unveiled a new program to give researchers access to the company’s most advanced machine learning technologies for..
Continue reading “Google is giving a cluster of 1,000 Cloud TPUs to researchers for free”
- Our goal is to ensure that the most promising researchers in the world have access to enough compute power to imagine, implement, and publish the next wave of ML breakthroughs.
- We’re setting up a program to accept applications for access to the TensorFlow Research Cloud and will evaluate applications on a rolling basis.
- The program will be highly selective since demand for ML compute is overwhelming, but we specifically encourage individuals with a wide range of backgrounds, affiliations, and interests to apply.
- The program will start small and scale up.
Researchers need enormous computational resources to train the machine learning models that have delivered
recent advances in medical imaging, speech recognition, game playing, and many other domains. The TensorFlow
Research Cloud is a cluster of 1,000 Cloud TPUs that provides the machine learning research community with
a total of 180 petaflops of raw compute power — at no charge — to support the next wave of breakthroughs.
Continue reading “Accelerating open machine learning research with Cloud TPUs”
- At the moment, artificial intelligence lives in the cloud, but Google — and other big tech companies — want it work directly on your devices, too.
- At Google I/O today, the search giant announced a new initiative to help its AI make this leap down to earth: a mobile-optimized version of its machine learning framework named TensorFlowLite.
- The newly announced version, TensorFlowLite, will build on this, helping users slim down their machine learning algorithms to work on-device.
- The company also announced that an API for making machine learning work better with phone chips would be coming sometime in the future — a clear sign that Google thinks your next phone will have an AI-optimized chip in it.
- TensorFlowLite should help Google (and the wider AI research community) bring even more interesting functions like this to our most-used and most-important devices.
At the moment, artificial intelligence lives in the cloud, but Google — and other big tech companies — want it work directly on your devices, too. At Google I/O today, the search giant announced a…
Continue reading “Google’s new machine learning framework is going to put more AI on your phone”