Author Archives: David Calloway

About David Calloway

Hi! I'm David Calloway, the author of this blog on deep learning and artificial intelligence. I first started working with neural networks in the mid-80's, before the "dark winter" of neural networking technologies. I graduated from the U.S. Air Force Academy in 1979 with B.S. degrees in Physics and Electrical Engineering. In 1982, I received an MS degree in Electrical Engineering from Purdue University where I worked on early attempts at speech recognition. In 2005, I obtained another M.S. degree, this time in Biology from the University of Central Florida. My interest in neural networks and deep learning was rekindled recently, when I got involved in a project at Nova Technologies where I am using deep learning and TensorFlow to recognize and classify objects from satellite imagery.

DeepMind AI Masters Stratego

Stratego is a popular board game that involves strategy and deception. Players take turns moving their pieces, with the goal of capturing their opponent’s flag or trapping their opponent’s pieces. It is a challenging game that requires players to think … Continue reading

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Deep Reinforcement Learning for AI Chip Design

A team from Google Brain recently published a paper (on arXiv) describing the use of a Deep Reinforcement Learning algorithm to design chips customized for AI applications.  In other words, they used an AI to build AI chips.  The problem … Continue reading

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TensorFlow or PyTorch: Which Framework to Choose?

I was recently asked by Udacity to be a beta tester (and, subsequently, a mentor and project reviewer) for one of their newest course offerings:  the Deep Reinforcement Learning NanoDegree (DRLND) program.  This is a very interesting course with some … Continue reading

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Nvidia Announces Apex: A New PyTorch Extension

Nvidia recently announced a new, open-source PyTorch extension that helps users improve the performance of deep learning training on Nvidia’s Volta GPUs.  The key improvement that APEX brings to deep learning is that it enables engineers to use mixed precision … Continue reading

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IBM Debater

IBM just demonstrated a deep-learning system that is a follow-on of sorts to the Watson Jeopardy demonstration from several years ago.  For this IBM Debater project, Watson was trained to intelligently debate on approximately 100 different topics.  In this particular … Continue reading

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The h Index & the Top 15 Deep Learning Conferences and Journals

The Google Scholar resource ranks the top journals and conferences using a fully automated h-index score.  The h-index is named after Jorge Hirsch, a physicist at the University of California, San Diego (UCSD), who proposed the index to determine theoretical … Continue reading

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MIT’s RoadTracer Uses Deep Learning to Generate Road Networks from Satellite Imagery

The CSAIL group at the Massachusetts Institute of Technology (MIT) have improved the state-of-the art in inferring road networks from satellite imagery.  This is a time-consuming, tedious, and error-prone process that has traditionally relied on human inputs.  Open Street Map … Continue reading

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Deep Reinforcement Learning for Navigation

A recent Nature article (https://go.nature.com/2GdzegP) and accompanying blog post by the paper’s authors (https://deepmind.com/blog/grid-cells/) describes how the Andrea Banino et. al. from Deep Mind developed an artificial neural network to investigate how mammals use neural grid cells to perform vector-based navigation.  … Continue reading

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Google Duplex – I’m Pretty Amazed!

One of the things that we often see in Sci-Fi movies, but rarely experience in real life, is the ability to have a natural conversation with a computer. Rapid advances in AI and deep learning in recent years have brought … Continue reading

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Nvidia Quadro GV100 – A Deep Learning Supercomputer on a Card

At this year’s GPU Technology Conference (Mar 28-30, 2018), Nvidia announced a new GPU specifically designed for deep learning.  The Quadro GV100, based on Nvidia’s latest Volta architecture, sports 5120 CUDA cores, 640 tensor cores, and 32GB of VRAM – … Continue reading

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