Summary:  The annual Burtch Works salary survey with data through April shows that opportunities and salaries are still excellent for both new and experienced data scientists.  They also offer some anecdotal observations about the impact of the first few months of COVID on our work and opportunities.

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Summary:  Transfer Learning (TL) may be the most important aid to adoption of deep learning in the last several years.  This new LEEP measure predicts the accuracy of the transfer and should make TL faster, cheaper, and better.

What is the single most important innovation in deep learning in the last several…

Summary:  Less than 9%?  What this study really shows and what we should take away from it.

Wow.  Less than 9%!  Can this be true?  Well according to a large scale survey study conducted by the US Census Bureau it’s actually a…

Summary:  Bias in modeling has long been a public concern that is now amplified and focused on the disparate treatment models may cause for African Americans.  Defining and correcting the bias presents difficult issues for data scientists that need to be carefully thought through before reaching conclusions.

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Summary:  Explaining data science to a non-data scientist isn’t as easy as it sounds.  You may know a lot about math, tools, techniques, data, and computer architecture but the question is how do you explain this briefly without getting buried in the detail.  You might try this approach.

We’ve all been…

Summary:  What is an AI Product Manager and how do you know when you need one.

The role of Product Manager (PM) can mean many things dependent on the specifics of the company, its markets, its channels, and the variety of its products.  It’s almost impossible to put a single label on the responsibilities of a…

Summary:  In a comprehensive study of 18 recently presented DNN advancements in top-N recommenders, only 7 presented sufficient data to allow reproduction.  Worse, of the 7 that could be reproduced none showed an actual improvement over simple linear and KNN techniques when those were properly optimized.

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Summary: As we have become ever more enamored with DNNs, and their accuracy and utility has been paced only by their complexity we will need to answer the question of whether we will ever really be able to explain what goes on inside.

As we have become ever more enamored with DNNs, and their accuracy and…

Summary:  Analytic Platforms are rapidly being augmented with features previously reserved for data scientists.  They are presented as easy to use but require substantial data literacy and advanced DS skills for the most complex.  Business users and analysts can pursue more complex problems on their own, but need good oversight.

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Summary:  Now that you have a little time for introspection, how about reviewing the performance of your chatbots.

Chances are this lock down period has given you a little more time for introspection and observation than you had before.  Let me suggest that this would be a good time to take a look at how your…

Summary:  COVID-19 and the changes it creates in the business environment for the next 12 to 24 months means our current AI strategies need to thoroughly reviewed and probably retargeted.

Whether you are a Zoom-connected CXX level living out your WFH isolation or one the ‘fortunates’ in essential industries…

Summary:  High stakes models like those that allocate scarce resources to competing hospitals are headline news.  New thinking contrasting model-based versus model-free learning are emerging to describe new conditions we must consider before building or evaluating those models.

At this point we’re all…

Summary: Whether trying to predict the life outcomes of disadvantaged kids or to model where ventilators will be most needed, a little humility is in order.  As this study shows, the best data and the broadest teams failed at critical predictions.  Getting the model wrong, or more importantly using it in the wrong way can hurt all of us.

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Summary:  An interesting documentary about the earliest days of AI/ML and my alternate take on how we should really be describing the development of our profession to the newly initiated.

What could be more natural in these days of the Corona Virus than binge watching great videos.  I was intrigued by a…

Summary: Since COVID-19 is occupying most of our thoughts these days, it seems appropriate to highlight where AI/ML is making a contribution to getting us out of our homes and back to work. 

Since COVID-19 is occupying most of our thoughts these days, it seems appropriate to highlight where AI/ML is making a…

Summary:  If you’re planning your AI/ML business strategy watch out for the confusion in categories and overly risky ratings given by some research and review sources.  Read the research, then consult with your own data scientists for a better evaluation of risk. It’s likely not as bad as you think. 

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Summary: The Gartner Magic Quadrant for Data Science and Machine Learning Platforms is just out the big news is how much more capable all the platforms have become.  Of course there are also some interesting winner and loser stories.

The Gartner Magic Quadrant for Data Science and Machine Learning Platforms is just out for 2020.  The really big news is how many excellent choices are now available.  In a remarkable move, the whole field of…

Summary:  Centaur AI is the best marriage of the machine’s ability to remember, analyze, and detect issues along with the human’s intuition to evaluate or take action on those results.  Instead of focusing on AI replacing humans, we should focus on AI in its role of augmenting humans.

In this angsty age where…

Summary:  Looking at the 12 hottest world-changing segments in the VC-funded world shows that AI will play a key role.  Here’s a little more detail.

From the inside of the data science profession looking out it’s easy to imagine that almost everything that is or will be important somehow depends on AI.  Maybe…

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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