To manage brands and social platforms in today’s online communities, companies must have a way to quickly and easily monitor shared content. Tuputech is introducing its cloud-based image recognition solution to the U.S. to help companies identify, tag and filter objectionable images—pornographic, violent or otherwise explicit or inappropriate.
Tuputech Introduces Image Recognition Solution to Identify Objectionable or Explicit Online Content through Its Deep Learning AI Platform
October 6, 2017 by
Podcast: Do It Yourself Deep Learning
December 31, 2016 by
In this AI Podcast, Bob Bond from Nvidia and Mike Senese from Make magazine discuss the Do It Yourself movement for Artificial Intelligence. “Deep learning isn’t just for research scientists anymore. Hobbyists can use consumer grade GPUs and open-source DNN software to tackle common household tasks from ant control to chasing away stray cats.”
Podcast: Where Deep Learning Is Going Next
December 8, 2016 by
In this Nvidia podcast, Bryan Catanzaro from Baidu describes how machines with Deep Learning capabilities are now better at recognizing objects in images than humans. “AI gets better and better until it kind of disappears into the background,” says Catanzaro — NVIDIA’s head of applied deep learning research — in conversation with host Michael Copeland on this week’s edition of the new AI Podcast. “Once you stop noticing that it’s there because it works so well — that’s when it’s really landed.”
FPGAs Speed Machine Learning at SC16 Intel Discovery Zone
December 5, 2016 by
In this video from SC16, Intel demonstrates how Altera FPGAs can accelerate Machine Learning applications with greater power efficiency. “The demo was put together using OpenCL design tools and then compiled to FPGA. From an end-user perspective, they tied it together using Intel MKL-DNN with CAFFE on top of that. This week, Intel announced the DLIA Deep Learning Inference Accelerator that brings the whole solution together in a box.”
Image Recognition with Coprocessors
December 17, 2015 by
“Applications in diverse industries such as the hospitality and retail industry, social networks and surveillance can benefit from real time image recognition. Parallelism at the system level can be divided into two main areas. First, at the database level and second at the image recognition level. The compute load per thread on the host system can just be calculated as the total number of images in the database divided by the number of threads. The image matching algorithms can then be parallelized on the coprocessor.”
