Berkeley researchers take on StarCraft II with modular RL system:…Self play + modular structure makes challenging game tractable…Researchers with the University of California at Berkeley have shown how to use self-play to have AI agents learn to play real-time strategy game StarCraft II. “We propose a flexible modular architecture that shares the decision responsibilities among multiple independent modules, including worker management, build order, tactics, micromanagement, and scouting”, the researchers write. “We adopt an iterative training approach that first trains one module while others follow very simple scripted behaviors, and then replace the scripted component of another module with a neural network policy, which continues to train while the previously trained modules remain fixed”.  Results: **The resulting system can comfortably beat the Easy and Medium in-game AI systems, but struggles against more difficult in-built bots; the best AI systems discussed in the paper use a combination of learned tactics and learned build orders to obtain win rates of around 30% when playing against the game’s in-built ‘Elite’ difficulty AI agents.  Transfer learning:** The researchers also try to test how general their various learned modules are by trying their agent out against competitors in different maps from the map on which it was trained. The agent’s performance drops a bit, but only by a few percentage points. “Though our agent’s win rates drop by 7.5% on average against Harder, it is still very competitive,” they write.  What is next: **“Many improvements are under research, including deeper neural networks, multi-army-group tactics, researching upgrades, and learned micromanagement policies. We believe that such improvements can eventually close the gap between our modular agent and professional human players”.Why it matters: Approaches like those outlined in this paper suggest that contemporary reinforcement learning techniques are tractable when applied against StarCraft II, and the somewhat complex modular system used by these researchers suggests that a simple system that obtained high performance would be an indication of algorithmic advancement.Read more:**Modular Architecture for StarCraft II with Deep Reinforcement Learning (Arxiv).

I originally published the Big Data Storymap on January 2013 as a way to creatively communicate the key factors to a successful Big Data initiative (see Figure 1).

Introduction These past few weeks I’ve been writing about a new package I created, MCHT. Those blog posts were basically tutorials demonstrating how to use the package. (Read the first in the series here.) I’m done for now explaining the technical details of the package. Now I’m going to use the package for purpose I initially had: exploring the distribution of time separating U.S. economic recessions.

Today, we are announcing the new IP Insights algorithm for Amazon SageMaker. IP Insights is an unsupervised learning algorithm for detecting anomalous behavior and usage patterns of IP addresses. In this blog post, we introduce the problem of identifying fraudulent behavior using IP addresses, describe the Amazon SageMaker IP Insights algorithm, demonstrate how you can use it in a real-world application, and share some of our results using it internally.

Recommender systems use algorithms to provide users with product or service recommendations. Recently, these systems have been using machine learning algorithms from the field of artificial intelligence. An increasing number of online companies are utilizing recommendation systems to increase user interaction and enrich shopping potential. Use cases of recommendation systems have been expanding rapidly. They across many aspects of eCommerce and online media, and we expect this trend to continue.

I’m a big Tom Wolfe fan.

Sponsored Post.By Brandon Dey, ODSC

Transfer Learning – Machine Learning’s Next Frontier

We are excited to announce the launch of the Amazon Kinesis Video Streams Inference Template (KIT) for Amazon SageMaker. This capability enables customers to attach Kinesis Video streams to Amazon SageMaker endpoints in minutes. This drives real-time inferences without having to use any other libraries or write custom software to integrate the services. The KIT comprises of the Kinesis Video Client Library software packaged as a Docker container and an AWS CloudFormation template that automates the deployment of all required AWS resources. Amazon Kinesis Video Streams makes it easy to securely stream audio, video, and related metadata from connected devices to AWS for analytics, machine learning (ML), playback, and other processing. Amazon SageMaker is the managed platform for developers and data scientists to build, train, and deploy ML models quickly and easily.