We all experience video streaming issues, probably on a daily basis. But now that people aren’t just streaming for entertainment, but are relying on it in many cases to perform their jobs, it ups the ante to address some of these issues and provide an improved experience. Deep…

Summary:  In the literal blink of an eye, image-based AI has gone from high cost, high risk projects to quick and reasonably reliable.  C-level execs looking for AI techniques to exploit need to revisit their assumptions and move these up the list.  Here’s what’s changed.

For data scientists these are miraculous times.  We tend to think of miracles as something that occurs instantaneously but in our world that’s not quite so.  Still the rate…

Summary: There are some interesting use cases where combining CNNs and RNN/LSTMs seems to make sense and a number of researchers pursuing this.  However, the latest trends in CNNs may make this obsolete.

There are things that just don’t seem to go together.  Take oil and water for instance.  Both valuable, but try putting…

This blog  explores a typical image identification task using a convolutional ("Deep Learning") neural network. For this purpose we will use a simple JavaCNN packageby D.Persson, and make our example small and concise using the Python scripting language. This example can also be rewritten in Java, Groovy, JRuby or any scripting language supported by the Java virtual machine.



This example will use images in the grayscale format (PGM). The name "PGM" is an acronym derived from…

In a previous blog post we have seen how to build Convolutional Neural Networks (CNN) in Tensorflow, by building various CNN architectures (like LeNet5, AlexNet, VGGNet-16) from scratch and training them on the MNIST, CIFAR-10 and Oxflower17 datasets.

It starts to get interesting when you start thinking about the practical applications of CNN and other Deep Learning methods.  If you have been following the latest technical developments you probably know that CNN’s are…

Summary:  There’s a three way technology race to bring faster, easier, cheaper, and smarter AI.  High Performance Computing is available today but so are new commercial versions of actual Quantum computers and Neuromorphic Spiking Neural Nets.  These two new entrants are going to revolutionize AI and deep learning starting now.

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Summary:  We are approaching a time when we need to be concerned that our AI robots may indeed harm us.  The rapid increase in the conversation about what ethics should apply to AI is appropriate but needs to be focused on the real threats, not just the wild imaginings of the popular press.  Here are some data points to help you in thinking about this, what our concerns should be today, and what our concerns should be in the future.

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Time-series data arise in many fields including finance, signal processing, speech recognition and medicine. A standard approach to time-series problems usually requires manual engineering of features which can then be fed into a machine learning algorithm. Engineering of features generally requires some  domain knowledge of the discipline where the data has originated from. For example, if one is dealing with signals (i.e. classification of EEG signals), then possible features would involve…

In the past year I have also worked with Deep Learning techniques, and I would like to share with you how to make and train a Convolutional Neural Network from scratch, using tensorflow. Later on we can use this knowledge as a building block to make interesting Deep Learning applications.

The contents of this blog-post is as follows:

1. Tensorflow basics:
   • 1.1 Constants and Variables
   • 1.2 Tensorflow Graphs and Sessions
   • 1.3 Placeholders and…

Summary:  Just where are we in the Age of AI, where are we going, and what happens when we get there?

When things are changing fast, sometimes it’s necessary to take a step back and see where you are.  It’s very easy to get caught up in the excitement over the details.  The individual data science technologies that underlie AI are all moving forward on different paths at different speeds, but all of those speeds are fast.  So before you change careers or…

Summary:  Whether you are a startup person or data science-minded executive in a larger organization what logic can you apply to spot the most compelling opportunities for AI in your organization.

In 2014 Kevin Kelly, founder of Wired magazine and prolific futurist famously said, “The business plans of the next 10,000 startups are…

Summary:  For those of you traditional data scientist who are interested in AI but still haven’t given it a deep dive, here’s a high level overview of the data science technologies that combine into what the popular press calls artificial intelligence (AI).

We and others have written quite a bit about the various types of data science…

Summary:  In this last article in our series on recommenders we look to the future to see how the rapidly emerging capabilities of Deep Learning can be used to enhance recommender performance. 

In our first article, “Understanding…

Summary:  Convolutional Neural Nets are getting all the press but it’s Recurrent Neural Nets that are the real workhorse of this generation of AI.

 We’ve paid a lot of attention lately to Convolutional Neural Nets…

Summary:  What comes next after Deep Learning?  How do we get to Artificial General Intelligence?  Adversarial Machine Learning is an emerging space that points to that direction and shows that AGI is closer than we think.

Deep Learning, Convolutional Neural Nets (CNNs) have given us dramatic improvements in image, speech, and text recognition over the last two years.  They suffer from the flaw however that…

Summary:  Are there large, sustainable career opportunities in AI and if so where?  Do they lie in the current technologies of Deep Learning and Reinforcement Learning or should you focus your career on the next wave of AI?

If you’re a data scientist thinking about expanding your career options into AI you’ve got a forest and…