Import AI

Q: ‘Why did your self-driving car just go through a red light?’ A: ‘Because it was trained in Grand Theft Auto 5, officer.’ Some folk wisdom about AI research is that only a few companies have the wherewithal to build up the tremendous stores of information necessary to develop world-changing AI systems, like self-driving cars. This was true for a while but is now changing as researchers get better at teaching computers to use data from simulated environments. A new paper, called ‘Driving in the matrix: Can virtual worlds replace human-generated annotations for real world tasks’ (PDF), describes a way to generate training data for a self-driving car by pulling screenshots & depth information & object labels from popular videogame Grand Theft Auto 5. The researchers harvest data from the game to create a self-driving car vision model that performs comparably to ones made of real-world data. They’re even able to tap into GTAV’s complex weather systems to get driving data in a variety of conditions, like fog, rain, haze, snow, and so on. This research implies that the competitive walls around self-driving car development are somewhat lower than people realize. The same phenomenon is taking place in robotics with new papers from DeepMind (PDF) and OpenAI (PDF) outlining ways to train AI systems in simulators then transfer into the real world.

Demystifying t-SNE: t-SNE is a powerful tool for visualizing the sorts of high-dimensional datasets, but developing good intuitions about what it shows you is extremely difficult. That’s mostly because we’re trapped in three spatial dimensions and so trying to develop a mental model of a 500-dimensional data representation is hard, like an ant having to grok the concept of high-frequency trading. This thorough explainer may prove helpful.

300 lines of code can build an AI agent that learns use a deep deterministic policy gradient algorithm to drive a car.

The future is… a million machine-generated episodes of Cheers, Friends, and Happy Days: research from the University of Leeds shows how to build ‘virtual talking avatars of characters fully automatically from TV shows’ (PDF).The approach use a generative model to sample the style of speech and video appearance of a character from a TV show, letting you, say, re-animate Joey from Friends or Norm from Cheers and make them do and say unspeakable things. Once the rest of the AI community develops better language models this could be used to create endless, machine-generated television shows. It could also work with current AI techs, though the results would be a little unsatisfying. As RNN-generated Joey says: ‘Seriously give me a clown on the table that’s all!’ *cue theme tune*

Memory & cognition: DeepMind has published a new Nature paper on its system that pairs differentiable memory with neural networks. The approach lets computers teach themselves about interconnected concepts, like nodes in a transit system or a family tree, and then reason about them. The differentiable neural computer (PDF), is based on earlier work called the Neural Turing Machine. (Due to the immense lag imposed by traditional, paywalled publishing this paper is relatively old, having been submitted in January of this year. Since then we’ve seen new memory-based techniques from DeepMind, the University of Montreal, Facebook, and others.) Additional perspective on DeepMind’s tech from Facebook AI scientist Yuandong Tian here. Tian also reveals that Facebook will shortly publish a paper about its DOOMBOT.

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Transcontinental data vein: from the perspective of an alien the story of the internet is really the story of the world being connected by an ever-growing tangle of data-conducting cables, propagating information across an otherwise fractured planet. So they’d probably cheer at seeing Facebook, Google, Pacific Light Data Communication, and TE SubCom, team up to build a 12,800km-long 120Tbps cable between Hong Kong and Los Angeles. If that piques your interest then you might like this photo tour aboard an Alcatel-Lucent cable ship.

Machine learning is the new statistics because it helps you achieve a more sophisticated REALITY ANALYSIS LEVEL.

Better drug design with machine learning: One of the dreams of AI researchers is to invent software that can conduct scientific experiments (whether this is due to laziness or curiosity on their part is less clear.) A new paper called ‘Automatic chemical design using a data-driven continuous representation of molecules’ (PDF) goes down this path. The system uses similar technologies used by Google to read your email and offer AI-generated responses (Smart Reply),to let scientists explore molecules and search for new drugs. The software converts molecules into a form (a fixed-dimensional vector) that machine learning approaches can deal with, making the immense combinatorial space of chemistry navigable to ML software. Once you have a vector representation of a molecule you can start to fiddle with the dials that describe its characteristics and use this to explore nearby molecules you may not have previously studied (the same principle works for words in translation systems, where you can fiddle with the dials of the representation of ‘cat’ and navigate to nearby entities like dogs, mice, and so on). The scientists are able to use this approach to discover some molecules that have even better properties than known ones. This brings us closer to an era where machines can help us to discover new drugs and treatments. Fascinating paper worth multiple reads.

The future is here and it is made of advertising. : Earlier this summer Uber advertised its services in Mexico City via drones that hovered above traffic, holding signs that scolded drivers. The future is here and it’s made of advertising.

/// OpenAI bits&pieces ///

Self-organizing conferences are surprisingly viable: OpenAI held its first self-organizing conference on machine learning and things went well. But don’t take my word for it, read this post from Victoria Krakovna about some of the AI&Safety things we talked about. You can find minutes from some of the other sessions on the wiki.

Language matters: Language is inextricably tied to the environment the thinking entity grows up in, so researchers are starting to design worlds that will encourage baby AI minds to develop their own language systems and in doing so (hopefully) become smarter. Facebook has published software tools and papers in this area, and both DeepMind and the University of Oxford have made great strides in having multiple agents learn to communicate with one another to solve problems. OpenAI is conducting research in this area as well; Jon Gauthier and Igor Mordatch have proposed (PDF) a way you might want to do this.

Matrix robots: A new paper, Transfer from Simulation to Real World through Learning Deep Inverse Dynamics Model (PDF) outlines a technique to train a robot in a simulator then transfer some of those insights into a real world machine, and lists some future research directions. We’re a few years off from ‘I know kung-fu’, but we’ll get there eventually.