Last month, Filestack sponsored an AI meetup wherein I presented a brief introduction to reinforcement learning and evolutionary strategies. Beforehand, I had promised code examples showing how to beat Atari games using PyTorch. In reality, I did not have time for that kind of side project and so I found some other examples of training agents to play Flappy Bird using Keras, which were entertaining but not complete enough for me to recommend as a springboard for further exploration. Luckily, I recently found some time to develop the promised training scripts. Therefore, I would like to provide an in-depth look of how we can use the PyTorch-ES suite for training reinforcement agents in a variety of environments, including Atari games and OpenAI Gym simulations. In deep reinforcement learning that uses the Q-learning algorithm, which has become very popular, training an intelligent agent includes distinct phases for "observation" and "learning".

Deep generative modelling for robust human body analysis is an emerging problem with many interesting applications, since it enables analysis-by-synthesis and unsupervised learning. However, the latent space learned by such models is typically not human-interpretable, resulting in less flexible models. In this work, we adopt a structured semi-supervised variational auto-encoder approach and present a deep generative model for human body analysis where the pose and appearance are disentangled in the latent space, allowing for pose estimation. Such a disentanglement allows independent manipulation of pose and appearance and hence enables applications such as pose-transfer without being explicitly trained for such a task. In addition, the ability to train in a semi-supervised setting relaxes the need for labelled data. We demonstrate the merits of our generative model on the Human3.6M

When Elon Musk co-founded OpenAI its goal was to determine how AI technologies could best serve humanity. According to a new company charter, its mission going forward will be developing "highly autonomous systems that outperform humans at most economically valuable work." It wants to make machines smarter than people. It's called artificial general intelligence (AGI) and, depending on who you ask, it's either the Holy Grail or Pandora's Box when it comes to machine learning. Despite the fact that Musk recently distanced himself from the company -- stating Tesla's development of AI presented a conflict of interests for him – it still has his sense of ambition.

Retro video games have been a useful platform for machine learning research for years, and the systems created have been creeping through the classics, mastering them as they go. Sonic the Hedgehog may be the next to fall: OpenAI has announced a competition to apply machine learning to the classic Sega game. It's not vastly different from what's been attempted before, things like playing Super Mario Bros or Space Invaders, or even the likes of Doom. But the rules are a bit different here. A very basic summary of how AIs learn to play something like Mario is this: an algorithm is set up with some basic capabilities like recognizing objects on screen and monitoring the in-game score.

In this contest, participants try to create the best agent for playing custom levels of the Sonic games -- without having access to those levels during development. See our blog post for more details. This process is illustrated in the schematic below. We believe that the next step for reinforcement learning is to leverage past experience to quickly learn new environments. Current algorithms are very prone to memorization and can't adapt well to new situations.

Facebook abandoned an experiment after two artificially intelligent programs 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. But they were not told to use comprehensible English, allowing them to create their own "shorthand", according to researchers.

True artificial intelligence is on its way, and we aren't ready for it. Just as our forefathers had trouble visualizing everything from the modern car to the birth of the computer, it's difficult for most people to imagine how much truly intelligent technology could change our lives as soon as the next decade -- and how much we stand to lose if AI goes out of our control. Fortunately, there's a league of individuals working to ensure that the birth of artificial intelligence isn't the death of humanity. From Max Tegmark's Future of Life Institute to the Harvard Kennedy School of Government's Future Society, the world's most renowned experts are joining forces to tackle one of the most disruptive technological advancements (and greatest threats) humanity will ever face. Perhaps the most famous organization to be born from this existential threat is OpenAI.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

There is new code to play around with for those interested in machine learning and space, a model that predicts hilarious search trends for sex site YouPorn, and another funny story about an ostensibly intelligent medical chatbot in New Zealand. Hunting exoplanets with ML – The machine learning code that a Google engineer and an astrophysicist used to detect exoplanets has been published online. Christopher Shallue, a senior software engineer at Google, and Andrew Vanderburg, a postdoctoral fellow studying astrophysics at the University of Texas, USA, discovered another planet lurking in the Kepler-90 system. It was a special find. Not only was it spotted using a convolutional neural network, but it meant that the Solar System was no longer the biggest planetary system found so far.

There is new code to play around with for those interested in machine learning and space, a model that predicts hilarious search trends for sex site YouPorn, and another funny story about an ostensibly intelligent medical chatbot in New Zealand. Hunting exoplanets with ML – The machine learning code that a Google engineer and an astrophysicist used to detect exoplanets has been published online. Christopher Shallue, a senior software engineer at Google, and Andrew Vanderburg, a postdoctoral fellow studying astrophysics at the University of Texas, USA, discovered another planet lurking in the Kepler-90 system. It was a special find. Not only was it spotted using a convolutional neural network, but it meant that the Solar System was no longer the biggest planetary system found so far.