This Neural Net Hallucinates Sheep

March 7, 2018

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If you’ve been on the internet today, you’ve probably interacted with a neural network. They’re a type of machine learning algorithm that’s used for everything from language translation to finance modeling. One of their specialties is image recognition.

Several companies—including Google, Microsoft, IBM, and Facebook—have their own algorithms for labeling photos. But image recognition algorithms can make really bizarre mistakes.

Source: nautil.us

Google-Landmarks: A New Dataset and Challenge for Landmark Recognition

A few examples of images from the Google-Landmarks dataset, including landmarks such as Big Ben, Sacre Coeur Basilica, the rock sculpture of Decebalus and the Megyeri Bridge, among others.

Learning by playing

Our new paper proposes a new learning paradigm called ‘Scheduled Auxiliary Control (SAC-X)’ which seeks to overcome the issue of exploration in control tasks. SAC-X is based on the idea that to learn complex tasks from scratch, an agent has to learn to explore and master a set of basic skills first. Just as a baby must develop coordination and balance before she crawls or walks—providing an agent with internal (auxiliary) goals corresponding to simple skills increases the chance it can understand and perform more complicated tasks.

Bonsai AI: Using Simulink for Deep Reinforcement Learning

Simulink provides a great training environment for DRL as it allows 3rd parties like Bonsai to integrate and control simulation models from the outside. This ability is one of the basic requirements for simulation platforms to be feasible for Deep Reinforcement Learning using Bonsai AI. More requirements can be found here.

This Neural Net Hallucinates Sheep

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Google-Landmarks: A New Dataset and Challenge for Landmark Recognition

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