Google's London based DeepMind has developed a training method to teach artificial intelligence how to play video games, on the Atari platform.

In order to train DeepMind the company elected to show the artificial intelligence platform YouTube videos of games being played, rather than go through the painstaking process of playing against the platform game after game. The aim was to try to strengthen a weakness with artificial intelligence: one of exploration. Most platforms are weak in attempting to find new places to go, and this is a step towards thinking creatively. The gaming platform used to help improve artificial intelligence was classic Atari video games. The main game used was Montezuma's Revenge, which is a 1984 platform game for Atari 8-bit computers.

The firm's latest Go-playing system not only defeated all previous versions of the software, it did it all by itself. "The most striking thing for me is we don't need any human data anymore," says Demis Hassabis, the CEO and co-founder of DeepMind. While the first version of AlphaGo needed to be trained on data from more than 100,000 human games, AlphaGo Zero can learn to play from a blank slate. Not only has DeepMind removed the need for the initial human data input, Zero is also able to learn faster than its predecessor. David Silver, the main programmer on DeepMind's Go project, says the original AlphaGo that defeated 18-time world champion Lee Sedol 4-1 required several months of training.

We've all seen the Terminator movies, and the apocalyptic nightmare that the self-aware AI system, Skynet, wrought upon humanity. And behaviour tests conducted on Google's DeepMind AI system make it clear just how careful we need to be when building the robots of the future. In tests in 2016, Google's DeepMind AI system demonstrated an ability to learn independently from its own memory, and beat the world's best Go players at their own game. Then it started figuring out how to seamlessly mimic a human voice. More recently in 2017, researchers tested its willingness to cooperate with others, and revealed that when DeepMind feels like it's about to lose, it opts for "highly aggressive" strategies to ensure that it comes out on top.

Google has been testing its latest machine learning system, called DeepMind, to explore the limits of game theory, using AI as a mirror to analyze how we respond to cooperative and competitive situations. The team found that in highly competitive environments, DeepMind will use "highly aggressive" tactics to either win the game or cause as much damage it can to its opponent. The premise for this game was collecting apples, and when apples became scarce, the AI would turn on each other and try to steal as many as possible. When testing the same principle in another game where the AI were taught to cooperate with one another, however, the AI behaved more socially, showing that the AI evaluated its environment to decide the optimal strategy for survival. Using this knowledge, Google can now construct environments and learning scenarios that help AI develop cooperative behaviors.

In zero-shot learning (ZSL), a classifier is trained to recognize visual classes without any image samples. Instead, it is given semantic information about the class, like a textual description or a set of attributes. Learning from attributes could benefit from explicitly modeling structure of the attribute space. Unfortunately, learning of general structure from empirical samples is hard with typical dataset sizes. Here we describe LAGO, a probabilistic model designed to capture natural soft and-or relations across groups of attributes. We show how this model can be learned end-to-end with a deep attribute-detection model. The soft group structure can be learned from data jointly as part of the model, and can also readily incorporate prior knowledge about groups if available. The soft and-or structure succeeds to capture meaningful and predictive structures, improving the accuracy of zero-shot learning on two of three benchmarks. Finally, LAGO reveals a unified formulation over two ZSL approaches: DAP (Lampert et al. 2009) and ESZSL (Romera-Paredes & Torr, 2015). Interestingly, taking only one singleton group for each attribute, introduces a new soft-relaxation of DAP, that outperforms DAP by ~40%.

This is a genuine question! If you look into the code, you'll find they are calling properties on the observation space variables that are passed into the learners that don't exist. I am trying to do policysearch with a dict based observationspace. Nothing suggests that wouldn't be possible. None, None) # None for shape and dtype, since it'll require special handling so ... rewriting the code to be a tuple now.

Demand for compute to train artificial intelligence models has shot up enormously over the past six years and is showing no signs of slowing down. Not for profit research firm OpenAI - which is sponsored by Peter Thiel, Elon Musk, Microsoft and Amazon Web Services, among others - published an analysis that showed the amount of compute used for the largest AI training runs has doubled every three-and-a-half months since 2012. This means compute amounts have grown by more than 300,000 times over the past six years, OpenAI said. In comparison, the well-known Moore's Law, which observed the number of transistors in an integrated circuit would double every year-and-a-half, would yield only a twelve-fold increase in performance over the same period. Part of the reason AI models still have enough compute is because of the use of massively parallel video cards or graphics processing units (GPUs) that can have thousands of cores per unit. Furthermore, over the past two years, optimisations such as huge batch sizes, architecture search and expert iteration using improved and specialised hardware such as Tensor processing units (TPUs) and fast data interconnects have increased past limits for algorithmic parallelism.

Despite the advancement of supervised image recognition algorithms, their de- pendence on the availability of labeled data and the rapid expansion of image categories raise the significant challenge of zero-shot learning. Zero-shot learn- ing (ZSL) aims to transfer knowledge from labeled classes into unlabeled classes to reduce human labeling effort. In this paper, we propose a novel self-training ensemble network model to address zero-shot image recognition. The ensemble network is built by learning multiple image classification functions with a shared feature extraction network but different label embedding representations, each of which facilitates information transfer to different subsets of unlabeled classes. A self-training framework is then deployed to iteratively label the most confident images in each unlabeled class with predicted pseudo-labels and update the ensem- ble network with the training data augmented by the pseudo-labels. The proposed model performs training on both labeled and unlabeled data. It can naturally bridge the domain shift problem in visual appearances and be extended to the generalized zero-shot learning scenario. We conduct experiments on multiple standard ZSL datasets and the empirical results demonstrate the efficacy of the proposed model.

The company's DeepMind artificial intelligence subsidiary has developed an AI that has learned how to navigate like a human being, the company announced in a blog post. Specifically, DeepMind's AI has developed a system of spacial awareness that mimics human's and other mammal's grid cells–specific cells in the brain that allow for vector-based navigation, which allow us to calculate the direction and a distance to a location even if we've never traveled that route before. What's most impressive about the AI's mimicking of mammalian grid cells is that the AI did it on its own–it wasn't programmed to mimic them.