Abstract--Accurate and automated detection of anomalous samples in a natural image dataset can be accomplished with a probabilistic model for end-to-end modeling of images. Such images have heterogeneous complexity, however, and a probabilistic model overlooks simply shaped objects with small anomalies. This is because the probabilistic model assigns undesirably lower likelihoods to complexly shaped objects that are nevertheless consistent with set standards. To overcome this difficulty, we propose an unregularized score for deep generative models (DGMs), which are generative models leveraging deep neural networks. We found that the regularization terms of the DGMs considerably influence the anomaly score depending on the complexity of the samples. By removing these terms, we obtain an unregularized score, which we evaluated on a toy dataset and real-world manufacturing datasets. Empirical results demonstrate that the unregularized score is robust to the inherent complexity of samples and can be used to better detect anomalies. Image-based anomaly detection has recently attracted considerable attention in the field of machine learning. This technique can be used to detect pedestrians behaving abnormally from surveillance video in order to prevent accidents [1], [2], or to detect lesions in medical images to provide early diagnosis [3]. In manufacturing plants, moreover, image-based anomaly detection can reject products not coincident with set standards.

This robotic hand practiced rotating a block for 100 years inside a 50 hour simulation! Is this the next revolutionary step for neural networks? A.I. Is Monitoring You Right Now and Here's How It's Using Your Data - https://youtu.be/KpybityrXfs Read More: OpenAI: Learning Dexterity https://blog.openai.com/learning-dext... "Our system, called Dactyl, is trained entirely in simulation and transfers its knowledge to reality, adapting to real-world physics using techniques we've been working on for the past year. Dactyl learns from scratch using the same general-purpose reinforcement learning algorithm and code as OpenAI Five. Our resultsshow that it's possible to train agents in simulation and have them solve real-world tasks, without physically-accurate modeling of the world."

Generative adversarial networks have been successfully applied to inpainting in natural images. However, the current state-of-the-art models have not yet been widely adopted in the medical imaging domain. In this paper, we investigate the performance of three recently published deep learning based inpainting models: context encoders, semantic image inpainting, and the contextual attention model, applied to chest x-rays, as the chest exam is the most commonly performed radiological procedure. We train these generative models on 1.2M 128 $\times$ 128 patches from 60K healthy x-rays, and learn to predict the center 64 $\times$ 64 region in each patch. We test the models on both the healthy and abnormal radiographs. We evaluate the results by visual inspection and comparing the PSNR scores. The outputs of the models are in most cases highly realistic. We show that the methods have potential to enhance and detect abnormalities. In addition, we perform a 2AFC observer study and show that an experienced human observer performs poorly in detecting inpainted regions, particularly those generated by the contextual attention model.

Last week, humanity struck back against the machines -- sort of. Actually, we beat them at a video game. In a best-of-three match, two teams of pro gamers overcame a squad of AI bots that were created by the Elon Musk-founded research lab OpenAI. The competitors were playing Dota 2, a phenomenally popular and complex battle arena game. But the match was also something of a litmus test for artificial intelligence: the latest high-profile measure of our ambition to create machines that can out-think us. In the human-AI scorecard, artificial intelligence has racked up some big wins recently.

AI may have beaten the world's best Go player, but Dota 2 pros have shown that in their game, humans are still top of the food chain -- for now, at least. Last week, Dota 2 players from around the world clashed at the biggest tournament of the year, The International, with team OG taking the title and over $11 million in prize money. Arguably more important, though, was the contest of man versus machine(-learning) in a best-of-three exhibition series. OpenAI, the research group co-founded by Elon Musk, took its team of five bots to The International to square up against professional players in their toughest test yet. Earlier in August, OpenAI wiped the floor with a squad of Dota 2 casters and ex-pro players in a warm-up match.

Last August at the Dota 2 International tournament in Seattle, OpenAI introduced an AI bot that upset the world's top 1v1 human player. The San Francisco-based AI research institute is now at the International 2018 in Vancouver, where their team of state-of-the-art bots is battling professional human teams in a highly anticipated best-of-three 5v5 Dota 2 showdown. Alas, the humans drew first blood: In a match that would make John Connor proud, Brazilian pro team "paiN" dispatched the "OpenAI Five" Bots yesterday in 52 minutes. OpenAI Five had more kills and a slight economy edge in the midgame, but did not push their advantage and kept losing their towers. The smart bots also made some dumb moves, such as warding in the wrong positions, bad item choice, and fewer gankings (leaving your lane to kill an enemy Hero in another lane).

OpenAI's bots were knocked out of The International – the Dota 2 computer game's annual Olympics – on Thursday after they lost to human pros 2-0 in a best-of-three contest. Dota 2 is a hugely popular online battle strategy game (think Command and Conquer meets Tolkien) played by several hundred thousand people worldwide. The aim is to storm a map, take over bases by destroying towers, and take the ultimate prize: the enemy's "Ancient." Teams are made up of five players known as heroes. There is a pool of more than a hundred different hero types all with their own strengths and weaknesses.

Artificial intelligence has swept the board with humans in games like chess and Go, but taking on e-sports might be a step too far -- for now. At The International tournament last night in Vancouver, a team of human pro gamers defeated a team of AI bots at the battle arena game Dota 2. The victory for team human was decisive but by no means inevitable, with the AI players putting up a valiant fight. And with two more games to play this week, machine might yet triumph over humanity. The bots were the creation of OpenAI, a non-profit research lab founded by tech luminaries such as SpaceX CEO Elon Musk. The lab's main goal is to develop artificial intelligence that "benefits all of humanity," but teaching bots to play Dota has been an important research task for some time now.

One way to measure progress in artificial intelligence is to chart victories by algorithms over champions of increasingly challenging games--checkers, chess, and, in 2016, Go. On Wednesday, five bots sought to extend AI's mastery to e-sports, in the fantasy battle game Dota 2. They failed, as a team of pro gamers from Brazil called paiN defended humanity's honor--for now. A crowd of thousands in Vancouver's hockey arena watched the bots battle paiN over 52 tense minutes packed with spells and firebolts. The human-machine contest was a side event to The International, a Dota 2 tournament that boasts the biggest purse in e-sports, at $25 million. The five bots that lost Wednesday were created by OpenAI, a research institute cofounded by Tesla CEO Elon Musk to work towards human-level artificial intelligence, and make the technology safe.

The International In the past hour, OpenAI's artificially intelligent bots lost their first match against professional players at smash-hit computer game Dota 2 at The International – the video game's annual championship tournament. It's the first bout in a best-of-three competition between human professional players versus OpenAI's code, the other two rounds will take place over the next two days, each day a different human team. Thousands of hardcore Dota fans lit up by glowing bracelets sat down at the Rogers Arena in Vancouver, Canada, to watch pros battle against a machine running OpenAI's software in this first round. The humans – dubbed Team paiN – were five players from Brazil, while OpenAI Five is made up five long-short-term memory neural-network-based agents. Dota 2 is a popular battle strategy game played online.