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.

Recent news from the OpenAI people is all about a bonus trio. They are releasing new Gym environments--a set of simulated robotics environments based on real robot platforms--including a Shadow hand and a Fetch research robot, said IEEE Spectrum. In addition to that toolkit, they are releasing an open source version of Hindsight Experience Replay (HER). As its name suggests, it helps robots learn from hindsight, for goals-based robotic tasks. Last but not least, they released a set of requests for robotics research.

Graphs are fundamental data structures which concisely capture the relational structure in many important real-world domains, such as knowledge graphs, physical and social interactions, language, and chemistry. Here we introduce a powerful new approach for learning generative models over graphs, which can capture both their structure and attributes. Our approach uses graph neural networks to express probabilistic dependencies among a graph's nodes and edges, and can, in principle, learn distributions over any arbitrary graph. In a series of experiments our results show that once trained, our models can generate good quality samples of both synthetic graphs as well as real molecular graphs, both unconditionally and conditioned on data. Compared to baselines that do not use graph-structured representations, our models often perform far better. We also explore key challenges of learning generative models of graphs, such as how to handle symmetries and ordering of elements during the graph generation process, and offer possible solutions. Our work is the first and most general approach for learning generative models over arbitrary graphs, and opens new directions for moving away from restrictions of vector- and sequence-like knowledge representations, toward more expressive and flexible relational data structures.

In recent months, researchers at OpenAI have been focusing on developing artificial intelligence (AI) that learns better. Their machine learning algorithms are now capable of training themselves, so to speak, thanks to the reinforcement learning methods of their OpenAI Baselines. Now, a new algorithm lets their AI learn from its own mistakes, almost as human beings do. The development comes from a new open-source algorithm called Hindsight Experience Replay (HER), which OpenAI researchers released earlier this week. As its name suggests, HER helps an AI agent "look back" in hindsight, so to speak, as it completes a task.

The Shadow Robot Company, that manufactures robotic hands for grasping and manipulation for real world challenges from fruit picking to bomb disposal, is supplying its Shadow Dexterous Hands to OpenAI, a non-profit company focusing on the path to safe artificial intelligence. The research is claimed to have created eight newly released environments, four of which using the Shadow Hand robot to solve realistic manipulation tasks. The Shadow Hand is tactile enough to rotate a block and a solid egg and flexible enough to move a pen between its fingers. Each task has a'goal', such as achieving the desired orientation of a block in the Shadow hand block manipulation task. Along with releasing these new robotics environments, OpenAI is releasing code for Hindsight Experience Replay, a reinforcement learning algorithm that can teach and improve robotic technology to learn from failure.

It asks to use an LSTM and predict the parity of bit sequences of length 50. Naive attempts didn't work (large hidden state, different rnn cells, different optimization algos, etc). It was a fun challenge. I like that it's very easy to state but requires a bit of insight to figure out. Do you know of any others like that?