Dexterity Network (Dex-Net) 2.0 Dataset for Deep Grasping — “deep grasping” sounds like the tagline for late-stage capitalism, but is actually applying deep learning to the robotics task of grabbing things. This Berkeley-released training data set consists of 6.7M sets of synthetic point clouds and depth information as a robot grasps an object, labeled with how successful the robot’s grasp was. It enables researchers to build their own models of how successful grasps will be, so their robots can make better decisions than Berkeley’s. Bless their grasping little paws.
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Diffblue Research — applying AI to software creation. It’s not just pop that will eat itself. Topics include: automated detection of relevant code features, making changes to code automatically, and designing techniques to find exploitable bugs.
Cryptographically Protected Database Search — we provide the following contributions: 1) An identification of the important primitive operations across database paradigms. We find there are a small number of base operations that can be used and combined to support a large number of database paradigms. 2) An evaluation of the current state of protected search systems in implementing these base operations. This evaluation describes the main approaches and tradeoffs for each base operation. Furthermore, it puts protected search in the context of unprotected search, identifying key gaps in functionality. 3) An analysis of attacks against protected search for different base queries. 4) A roadmap and tools for transforming a protected search system into a protected database, including an open source performance evaluation platform and initial user opinions of protected search. (via Adrian Colyer)