Studying Language Evolution With Reinforcement Learning and more from TWiML & AI

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    • Facebook and Google researchers build RL framework to study language evolution. Their research paper proposes a computational framework allowing agents to interact in a series of games and uses it to demonstrate that symmetric communication protocols (i.e. languages) emerge and evolve without any innate, explicit mechanisms built in the agent.
    • Google releases synthetic speech dataset to help researchers combat “deep fakes.” Google has published a dataset of synthetic speech containing thousands of phrases spoken by deep learning based text-to-speech models. By training models on both real and computer-generated speech, researchers can develop systems that learn to distinguish between the two.
    • Carbon Relay to optimize energy efficiency in data centers with AI. Carbon Relay launched a new data center energy management product using deep reinforcement learning and augmented intelligence to offer customers energy efficiency improvements. Google announced its success with a similar internal project last year.
    • AWS open sources Neo-AI project to accelerate ML on edge devices. Recall that the project extends TensorFlow, MXNet, PyTorch, ONNX, and XGBoost models to perform at up to twice the speed of the original model with no loss in accuracy on multiple hardware platforms. It’s based on the TVM and Treelite compilers developed at the University of Washington.
    • Microsoft and MIT work to detect ‘blind spots’ in self-driving cars.model developed by MIT and Microsoft researchers identifies instances where autonomous cars have learned actions from training examples that could cause real-world errors on the road.
    • Amazon facial-identification software used by police falls short on tests for accuracy and bias. AWS may be digging itself a deeper and deeper hole as it attempts to refute claims of bias for its facial-recognition software, Rekognition, marketed to local and federal law enforcement as a crime-fighting tool, struggles to pass basic tests of accuracy.
    • Spell expands cloud AI platform. The Spell platform uses Kubernetes to automatically scale models as necessary and provides metrics, monitoring, and logs for everything running in real time. Its latest edition adds team and collaboration features. The company also announced new funding; see below.


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