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Article

Multi-threading Line-of-Sight Calculations to Improve Sensory System Performance in Game AI

In this article, Alex Champandard describes how to accelerate Multi-threading Line-of-Sight calculations to improve AI sensory system performance through the concept of a centralized sensory system using a mini-game prototype AI Sandbox.
Authored by admin Last updated on 01/24/2018 - 15:35
Article

The Secrets of Parallel Pathfinding on Modern Computer Hardware

One of the first things that game AI developers parallelize is pathfinding as it is an expensive operation. The most common approach is to fire off the pathfinder in a separate thread. This article examines a multi-threaded pathfinding implementation.
Authored by Last updated on 12/31/2018 - 15:00
Blog post

Apache Spark* Innovation: Driving a Stronger Community Standard

This blog post was jointly written by Jiangang Duan, Jie Huang and Weihua Jiang (Intel), Alex Gutow (Cloudera), and Dale Kim (MapR)

 

Authored by Last updated on 03/11/2019 - 13:17
Article

Using the Intel® SSSE3 Instruction Set to Accelerate DNN Algorithm in Local Speech Recognition

The main algorithm of speech recognition has changed to DNN (Deep Neural Network). Without internet, the speech recognition service in your mobile devices nearly useless, very few times it can listen to what you said and work.With support for the SSSE3 instruction set on Intel’s CPU, we could easy run a DNN based speech recognition application without the internet. Adding direct SSSE3 support...
Authored by Last updated on 03/26/2019 - 16:08
Article

Building a Personality-Driven Poker AI for Lords of New York*

Writing artificial intelligence (AI) might be the best job in games. It’s creative, challenging, and blurs the line between game design and programming. AI is used for a variety of tasks ranging from the mechanical (such as auto-attacking enemies) and bot AI, to flocking group intelligence, even to deep-thinking military generals. Games that emphasize story and character-based immersion such as...
Authored by Coppock, Michael J (Intel) Last updated on 05/30/2018 - 07:00
Article

Caffe* Training on Multi-node Distributed-memory Systems Based on Intel® Xeon® Processor E5 Family

Caffe is a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) and one of the most popular community frameworks for image recognition. Caffe is often used as a benchmark together with AlexNet*, a neural network topology for image recognition, and ImageNet*, a database of labeled images.
Authored by Gennady F. (Blackbelt) Last updated on 03/21/2019 - 12:28
Article

IDF'15 Webcast: Data Analytics and Machine Learning

This Technology Insight will demonstrate how to optimize data analytics and machine learning workloads for Intel® Architecture based data center platforms. Speaker: Pradeep Dubey Intel Fellow, Intel Labs Director, Parallel Computing Lab, Intel Corporation
Authored by Mike P. (Intel) Last updated on 03/21/2019 - 12:00
Article

Caffe* Scoring Optimization for Intel® Xeon® Processor E5 Series

    In continued efforts to optimize Deep Learning workloads on Intel® architecture, our engineers explore various paths leading to the maximum performance.

Authored by Gennady F. (Blackbelt) Last updated on 03/21/2019 - 12:28
Article

Performance Comparison of OpenBLAS* and Intel® Math Kernel Library in R

Today, scientific and business industries collect large amounts of data, analyze them, and make decisions based on the outcome of the analysis. This paper compares the performance of Basic Linear Algebra Subprograms (BLAS), libraries OpenBLAS, and the Intel® Math Kernel Library (Intel® MKL).
Authored by Nguyen, Khang T (Intel) Last updated on 03/21/2019 - 12:08
Video

Design and Implementation of AI in Games

Over the course of the last few decades, the gaming industry has seen great strides.

Authored by John M. (Intel) Last updated on 01/24/2018 - 15:35