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From Jan 22nd thru 12th Feb. Referred by a friend? Sign up here. Want to share? Give it a tweet. 

Your guide to AI

February 12 · Issue #7 · View online
Monthly analysis of AI technology, geopolitics, research, and startups.

From Jan 22nd thru 12th Feb. Referred by a friend? Sign up here. Want to share? Give it a tweet

Technology news, trends and opinions
Research, development and resources
  • Asynchronous Methods for Deep Reinforcement Learning, Google DeepMind and University of Montreal. Stabilising deep reinforcement learning often requires lots of memory and computation because it uses experience replay: the idea of storing and sampling from an agent’s historical experience conducting a task. Here, the DeepMind team show that asynchronously running multiple agents in parallel on multiple instances of the environment is instead more effective and less computationally intensive. Check out this video for a demo application of an agent navigating a 3D video game environment. 
  • A model explanation systemNorthrop Grumman Corporation. Here, the author presents a general framework for explaining black box models, specifically linear classifiers. 
  • No more Autobahn! Scenic Route Generation Using Googles Street ViewUniversity of Bremen and Hasselt University. In a similar vein to Daniele Quercia’s Happy Maps project (watch his talk at our 2015 AI Summit), the authors present a vision-based system to generate scenic driving routes using Google Street View images. 
  • Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks, University of Oxford. The authors present an end-to-end RNN-based learning method for taking in a stream of raw sensor data (e.g. a scene where a robot sees lots of moving objects) and finding the positions of these objects even if they’re occluded from view and where the ground truth isn’t available. Video explanation here.
  • Brain4Cars: Car That Knows Before You Do via Sensory-Fusion Deep Learning Architecture, Cornell University, Stanford University and Brain of Things Inc. This work explores the problem of anticipating a car driver’s next actions to safeguard said driver from eventual dangers that might result based on their current environment (e.g. turning and hitting an unseen bicycle). Using RNNs with LSTMs that act on videos, vehicle dynamics, GPS, and street maps, the system can anticipate manoeuvres 3.5 seconds before they occur in real time.
Venture capital financings and exits
$93m raised by 22 companies and $250m in exit value created, including the following notable transactions:

Anything else catch your eye? Just hit reply! I’m actively looking for entrepreneurs building companies that build/use AI to rethink the way we live and work. 
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