Several of my friends have challenged me to get involved in the AI ethics discussion. I certainly do not have any special ethics training. But then again, maybe I do. I’ve been going to church most Sundays (and not just on Christmas Eve) since I was a kid and have been taught a multitude of “ethics” lessons from the Bible. So, respectively, let me take my best shot at sharing my thoughts about the critical importance of the AI Ethics topic.

What is AI…

Summary:  Reinforcement Learning (RL) is going to be critical to achieving our AI/ML technology goals but it has several barriers to overcome.  While reliability and a reduction in training data may be achievable within a year, the nature of RL as a ‘black box’ solution will bring scrutiny for its lack of transparency.

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Here is another resource I use for teaching my students at AI for Edge computing course. I like this resource because I like the cookbook style of learning to code. The resource is based on the book Machine…

Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week. To subscribe, follow this link.  …

“When I think back on all the crap I’ve learned in high school, It’s a wonder I can think at all” - Simon and Garfunkel, Kodachrome

Growing up in the ’60s in California, we were subjected to a lot of educational experiments. We had beanbag chairs in class. We squinted into uncomfortable metal and…

As a data scientist in an organization you frequently find yourself in a couple of situations:

In a recent blog I stated that “Crossing the AI Chasm" is primarily an organizational and cultural challenge, not a technology challenge. That “Crossing the AI Chasm” not only requires organizational buy-in, but more importantly, necessitates creating a culture of adoption and continuous learning fueled at the front-lines of customer and/or operational engagement (see Figure 1).…

Business intelligence is an umbrella term covering a variety of ways to collect, store and analyze business operations and activities data. This one picture shows you the different processes and methods that make up the business intelligence sphere.

References…

Through exposure to the news and social media, you are probably familiar with the fact that machine learning has become one of the most exciting technologies of our time. While it may seem that machine learning has become the buzzword of our age, it is certainly not just hype. This exciting field opens up the way to new possibilities and has become indispensable to our daily lives.

Being exposed to practical code examples and working through example applications

of machine…

The picture below is from the article Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey, posted here.

Authors: Giang Nguyen, Stefan Dlugolinsky, Martin Bobák, Viet…

In a recent blog, I described the American Psychiatric Association’s Diagnostic and Statistical Manual of Mental Disorders as an ontological reference.  I explained how this type of resource can be used for the codification of distributable events in order to build up a database.  In an organization, although the context surrounding the idea of a “disorder” is different;…

You can always learn a lot from the papers presented at NeurIPS

There is some good analysis already on the web.

From Chip Huygen – neurips 2019 analysis and from David…

Predictive analytics, prescriptive analytics and the fairly recent offshoot-- discovery analytics-- can all support business decision making. Although the three tools are very similar in that they build off the same analytic foundation, the types of decisions they support are quite different. This one picture explains the differences between the three methods.…

In the abundance of…

This article was written by Bob Hayes.

Data science requires the effective application of skills in a variety of machine learning areas and techniques. A recent survey by Kaggle, however, revealed that a limited number of data professionals possess competency in advanced machine learning skills. About half of data professionals said they were competent in…

Summary:  For all the hype around winning game play and self-driving cars, traditional Reinforcement Learning (RL) has yet to deliver as a reliable tool for ML applications.  Here we explore the main drawbacks as well as an innovative approach to RL that dramatically reduces the training compute requirement and time to train.

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Introduction

The utilization of biometrics as a tool for securing and verifying identity has been standard practice among government and law enforcement agencies for quite some time now. Some of the common uses of biometrics include the collection and storage of fingerprints to track government- and state-issued permits and licenses (in the cases of healthcare professionals, passports, drivers’ licenses, etc.), the prevention of…

Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week. To subscribe, follow this link.  …

AI and the future of work is a major topic of discussion

Most views on this subject are negative

More broadly, the negative impact of technology has been highlighted by Nicholas Carr (the shallows) ,…