All is open source, though not "published" yet so excuse me if the repository is a bit hard to navigate / unclear. I've been building a Unity-esque 2D engine with pygame, which should be easy to plug in with OpenAI's gym. Goal here isn't to build the most optimal environments per se, but a way to implement games that are human and AI playable. Hopefully I'll get to release a slightly more convenient setup soon, with each (sub)project separated into their own repo:)

There has been a discussion recently about using RL to land a SpaceX booster. Coincidentally I've been working on exactly this in OpenAI. It was as much fun as it was frustrating at times. It's trained with a PPO implementation from Unity that I've changed to work with OpenAI (GitHub). The official OpenAI implementation is convoluted and impossible to work with in my opinion. This particular agent took 200'000 tries over the course of 12 hours and 20 million frames (with a frame skip value of 5, so 100 million total frames).

We've designed a method that encourages AIs to teach each other with examples that also make sense to humans. Our approach automatically selects the most informative examples to teach a concept -- for instance, the best images to describe the concept of dogs -- and experimentally we found our approach to be effective at teaching both AIs and humans. Some of the most transformative applications of powerful AI will come from computers and humans collaborating, but getting them to speak a common language is hard. Think about trying to guess the shape of a rectangle when you're only shown a collection of random points inside that rectangle: it's much faster to figure out the correct dimensions of the rectangle when you're given points at the corners of the rectangle instead. Our machine teaching approach works as a cooperative game played between two agents, with one functioning as a student and the other as a teacher.

Artificial intelligence (AI) is already besting human intelligence in a number ways. Google and DeepMind's AlphaGo Zero is arguably the greatest Go player in the world, and it learned the game by teaching itself. DeepMind researchers claim they never even reached the limit of the AI's potential, meaning it could be capable of even more impressive tasks. AlphaGoZero is just one of many AIs under development across the globe, and as the industry continues to grow, these systems are going to get smarter and smarter. According to futurist Ian Pearson, humanity's only option if it wants to maintain pace is to merge with AI.

Your Gmail inbox is about to get weird. As part of an update to its Accelerated Mobile Pages project, Google will serve up content from the internet inside e-mails to provide always-up-to-date information. Backstory: The AMP project was designed to make web pages load faster, so you could click through from search results to content almost instantly. Now Google wants to do some ... interesting things with the technology. E-mail plus plus: Developers are going to be playing around with AMP widgets for Gmail messages.

A non-profit AI research company, OpenAI, basically, is now, to its list is releasing a new batch of seven unsolved problems which have come up in the course of their research at OpenAI. Very similar to their original Requests for Research which resulted in the upbringing of several papers, the company expects these problems for new people to enter the field to be a fun and a meaningful way to do the same, as well as to hone the skills for practitioners. Not to forget that is also is a great way to get a job at OpenAI that aims at enacting and discovering the path to safe general artificial intelligence. Also, If one is not sure where to begin, they also have some solved starter problems. Environment: Start with two snakes, and scale from there and then with multiple snakes have a reasonably large field; snakes grow when eating randomly-appearing fruit; a snake dies when colliding with another snake, itself, or the wall; and the game ends when all snakes die.

The problem is, the bar has been raised far too high. Like the Babel fish/Google Pixel Buds example, consumer AI has advanced so far, it has overtaken human capabilities many times over. Microsoft DeepCoder is now autonomously writing code, Google AutoML is AI writing AI and, DeepMind can accurately read lips better than any human being. Even previous projects are being dwarfed by new innovations. 'AlphaGo Zero didn't learn how to play Go from humans' Frankel explains, 'they just gave it the rule-set and it played against itself.

Mustafa Suleyman, who cofounded Google's deep-learning subsidiary, wants the artificial-intelligence community to focus on ethics in 2018. His argument: Writing in Wired UK, Suleyman explains that machine learning has the potential to improve or worsen inequalities in the world. To make sure it ends up being a net positive, he says, research into AI ethics needs to be prioritized. What's been done: This isn't a new concern for Suleyman. DeepMind established its own ethics and society research team earlier this year to work on these sorts of issues.

Instance-level retrieval has become an essential paradigm to index and retrieves images from large-scale databases. Conventional instance search requires at least an example of the query image to retrieve images that contain the same object instance. Existing semantic retrieval can only search semantically-related images, such as those sharing the same category or a set of tags, not the exact instances. Meanwhile, the unrealistic assumption is that all categories or tags are known beforehand. Training models for these semantic concepts highly rely on instance-level attributes or human captions which are expensive to acquire. Given the above challenges, this paper studies the Zero-shot Retrieval problem that aims for instance-level image search using only a few dominant attributes. The contributions are: 1) we utilise automatic word embedding to infer class-level attributes to circumvent expensive human labelling; 2) the inferred class-attributes can be extended into discriminative instance attributes through our proposed Latent Instance Attributes Discovery (LIAD) algorithm; 3) our method is not restricted to complete attribute signatures, query of dominant attributes can also be dealt with. On two benchmarks, CUB and SUN, extensive experiments demonstrate that our method can achieve promising performance for the problem. Moreover, our approach can also benefit conventional ZSL tasks.

Zero-shot learning aims to classify unseen image categories by learning a visual-semantic embedding space. In most cases, the traditional methods adopt a separated two-step pipeline that extracts image features are utilized to learn the embedding space. It leads to the lack of specific structural semantic information of image features for zero-shot learning task. In this paper, we propose an end-to-end trainable Deep Semantic Structural Constraints model to address this issue. The proposed model contains the Image Feature Structure constraint and the Semantic Embedding Structure constraint, which aim to learn structure-preserving image features and endue the learned embedding space with stronger generalization ability respectively. With the assistance of semantic structural information, the model gains more auxiliary clues for zero-shot learning. The state-of-the-art performance certifies the effectiveness of our proposed method.