The billionaire says his artificial intelligence research startup OpenAI has developed the first bot to beat a professional esports gamer. Musk used the news to send out a warning about AI safety, saying the industry should be regulated and is more of a threat than North Korea.

"If you're not concerned about AI safety, you should be. Vastly more risk than North Korea," Musk tweeted after his $1 billion startup, OpenAI, made a surprise appearance at a $24 million video game tournament Friday night, beating the world's best players in the video game, "Dota 2." Musk claimed OpenAI's bot was the first to beat the world's best players in competitive eSports, but quickly warned that increasingly powerful artificial intelligence like OpenAI's bot -- which learned by playing a "thousand lifetimes" of matches against itself -- would eventually need to be reined in for our own safety. "Nobody likes being regulated, but everything (cars, planes, food, drugs, etc) that's a danger to the public is regulated. AI should be too," Musk said in another tweet on Friday night. Musk has previously expressed a healthy mistrust of artificial intelligence.

An artificial intelligence has beaten one of the world's top Dota 2 players in single combat today.

Tonight during Valve's yearly Dota 2 tournament, a surprise segment introduced what could be the best new player in the world -- a bot from Elon Musk-backed startup OpenAI. Engineers from the nonprofit say the bot learned enough to beat Dota 2 pros in just two weeks of real-time learning, though in that training period they say it amassed "lifetimes" of experience, likely using a neural network judging by the company's prior efforts. Musk is hailing the achievement as the first time artificial intelligence has been able to beat pros in competitive e-sports. OpenAI first ever to defeat world's best players in competitive eSports. Vastly more complex than traditional board games like chess & Go.

Ok, I know a bit about dota (been playing it for 8 years now). I will try my best to put this into perspective. What: It beat players that many considered to be the absolute best at dota. The environment: 2 players move along a lane with the goal of destroying the other's defensive structure or killing the player 2 times for victory. Every 30 seconds weak npc minions enter the lane attack each other and players.

Parameter noise helps algorithms more efficiently explore the range of actions available to solve an environment. After 216 episodes of training DDPG without parameter noise will frequently develop inefficient running behaviors, whereas policies trained with parameter noise often develop a high-scoring gallop. Parameter noise lets us teach agents tasks much more rapidly than with other approaches. After learning for 20 episodes on the HalfCheetah Gym environment (shown above), the policy achieves a score of around 3,000, whereas a policy trained with traditional action noise only achieves around 1,500. Parameter noise adds adaptive noise to the parameters of the neural network policy, rather than to its action space. Traditional RL uses action space noise to change the likelihoods associated with each action the agent might take from one moment to the next.

We have discussed several times about the impact of Artificial Intelligence (AI) on threat landscape, from a defensive perspective new instruments will allow the early detections of malicious patterns associated with threats, from the offensive point of view machine learning tools can be exploited to create custom malware that defeats current anti-virus software. At the recent DEF CON hacking conference, Hyrum Anderson, technical director of data science at security shop Endgame, demonstrated how to abuse a machine learning system to create malicious code that can avoid detections of security solutions. Anderson adapted the Elon Musk's OpenAI framework to create malware, the principle is quite simple because the system he created just makes a few changes to legitimate-looking code and convert them into malicious code. A few modifications can deceive AV engines, the system created by the experts was named OpenAI Gym. "All machine learning models have blind spots," he said.

Facebook has shut down two artificial intelligences that appeared to be chatting to each other in a strange language only they understood. The two chatbots came to create their own changes to English that made it easier for them to work – but which remained mysterious to the humans that supposedly look after them. The bizarre discussions came as Facebook challenged its chatbots to try and negotiate with each other over a trade, attempting to swap hats, balls and books, each of which were given a certain value. But they quickly broke down as the robots appeared to chant at each other in a language that they each understood but which appears mostly incomprehensible to humans. The robots had been instructed to work out how to negotiate between themselves, and improve their bartering as they went along.

You might think, based on the type of research they're doing, that the OpenAI office would be full of gadgets, full of wonder, full of weird experiments. There are no Faraday cages. Well, okay, there is a robot. And it's tucked away in a side room. It's surrounded by cobbled-together protective material so that it doesn't smash into itself if it starts flailing about due to a programming error.

Deep generative models have achieved impressive success in recent years. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as powerful frameworks for deep generative model learning, have largely been considered as two distinct paradigms and received extensive independent study respectively. This paper establishes formal connections between deep generative modeling approaches through a new formulation of GANs and VAEs. We show that GANs and VAEs are essentially minimizing KL divergences of respective posterior and inference distributions with opposite directions, extending the two learning phases of classic wake-sleep algorithm, respectively. The unified view provides a powerful tool to analyze a diverse set of existing model variants, and enables to exchange ideas across research lines in a principled way. For example, we transfer the importance weighting method in VAE literatures for improved GAN learning, and enhance VAEs with an adversarial mechanism for leveraging generated samples. Quantitative experiments show generality and effectiveness of the imported extensions.