Elon Musk's OpenAI is paying big money for the world's best AI researchers. There's been a lot of speculation in the last couple of years about how much money technology firms are paying the world's top artificial intelligence (AI) experts but concrete numbers have been hard to come by. That changed this week when Cade Metz, a journalist for The New York Times, revealed that he had stumbled upon a tax filing from OpenAI -- an AI research lab set up by Tesla CEO Elon Musk -- that included staff salaries and bonuses. The numbers are high, especially when you consider the fact that Open AI is a non-profit organisation. The company, which says it is working to ensure AI benefits all of humanity, was founded in San Francisco in 2015.

We're releasing an experimental metalearning approach called Evolved Policy Gradients, a method that evolves the loss function of learning agents, which can enable fast training on novel tasks. Agents trained with EPG can succeed at basic tasks at test time that were outside their training regime, like learning to navigate to an object on a different side of the room from where it was placed during training. EPG trains agents to have a prior notion of what constitutes making progress on a novel task. Rather than encoding prior knowledge through a learned policy network, EPG encodes it as a learned loss function[1]. Agents are then able to use this loss function, defined as a temporal-convolutional neural network, to learn quickly on a novel task. We've shown that EPG can generalize to out of distribution test time tasks, exhibiting behavior qualitatively different from other popular metalearning algorithms.

One of the poorest kept secrets in Silicon Valley has been the huge salaries and bonuses that experts in artificial intelligence can command. Now, a little-noticed tax filing by a research lab called OpenAI has made some of those eye-popping figures public. OpenAI paid its top researcher, Ilya Sutskever, more than $1.9m (£1.35m) in 2016. It paid another leading researcher, Ian Goodfellow, more than $800,000 (£570,000) – even though he was not hired until March of that year. Both were recruited from Google.

One of the poorest-kept secrets in Silicon Valley has been the huge salaries and bonuses that experts in artificial intelligence can command. Now, a little-noticed tax filing by a research lab called OpenAI has made some of those eye-popping figures public. OpenAI paid its top researcher, Ilya Sutskever, more than $1.9 million in 2016. It paid another leading researcher, Ian Goodfellow, more than $800,000 -- even though he was not hired until March of that year. Both were recruited from Google.

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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.