Summary:  The annual Burtch Works salary survey tells us a lot about which industries are using the most data scientists and the difference between higher and lower skilled data scientists.  Salary increases show us whether demand is increasing, and finally we take a shot at determining which skills are most in demand.

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Summary:  Before starting to develop an AI strategy, make sure your team understands the limits of what is reasonable today, as well as incremental improvements that might be overlooked.  Focus should be on your LOB leaders who understand the business.  Make sure they are also able to recognize AI opportunities.

I…

Summary: Deep Learning, based on deep neural nets is launching a thousand ventures but leaving tens of thousands behind.  Transfer Learning (TL), a method of reusing previously trained deep neural nets promises to make these applications available to everyone, even those with very little labeled data.

Deep Learning, based on…

Summary:  GDPR carries many new data and privacy requirements including a “right to explanation”.  On the surface this appears to be similar to US rules for regulated industries.  We examine why this is actually a penalty and not a benefit for the individual and offer some insight into the actual wording of the GDPR regulation which also offers some relief.

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Summary:  Some observations about new major trends and directions in data science drawn from the Strata Data conference in San Jose last week.

I’m just back from my annual field trip to the Strata Data conference in San Jose last week.  Strata is on tour with four more stops before repeating next year.  The New York show is…

Summary:  Here are our 6 predictions for data science, machine learning, and AI for 2018.  Some are fast track and potentially disruptive, some take the hype off over blown claims and set realistic expectations for the coming year.

It’s that time of year again when we do a look back in order to offer a look forward.  What…

Summary:  As a profession we do a pretty poor job of agreeing on good naming conventions for really important parts of our professional lives.  “Machine Learning” is just the most recent case in point.  It’s had a perfectly good definition for a very long time, but now the deep learning folks are trying to hijack the term.  Come on folks.  Let’s make up our minds.

As a profession we do a pretty poor job of agreeing on good naming conventions…

Summary:  The addition of AI capabilities to our personal devices, applications, and even self-driving cars has caused us to take a much deeper look at what we call ‘User Experience’ (Ux).  A more analytical framework identified as Cognitive Ergonomics is becoming an important field for data scientists to understand and implement.

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Summary:  In just the last 10 months based only on facial characteristics deep learning has been used to predict who is a criminal and who is gay.  These are rigorous, peer reviewed studies published in academic journals.  How should this knowledge be used and how will the public react?

Were you as freaked out as I was when earlier this…

Summary:  Quantum computing is now a commercial reality.  Here’s the story of the companies that are currently using it in operations and how this will soon disrupt artificial intelligence and deep learning.

Like a magician distracting us with one hand while pulling a fast one with the other Quantum computing has crossed over from…

Summary:  We are swept up by the rapid advances in AI and deep learning, and tend to laugh off AI’s failures as good fodder for YouTube videos.  But those failures are starting to add up.  It’s time to take a hard look at the weaknesses in AI and where that’s leading us.

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Summary:  Some observations about new major trends and directions in data science drawn from the Strata+Hadoop conference in San Jose last week.

I’m fresh off my annual field trip to the Strata+Hadoop conference in San Jose last week.  This is always exciting, enervating, and exhausting but it remains the single best place to pick up on…

Summary:  As deep learning expands those capabilities are finding their way into the not-for-profit community in the service of conserving the earth’s wildlife and forests.

The for-profit world may be driving AI but it’s a solution to many problems in the not-for-profit world as well.  We were particularly impressed by the use of…

Summary:  The data science press is so dominated by articles on AI and Deep Learning that it has led some folks to wonder whether Deep Learning has made traditional machine learning irrelevant.  Here we explore both sides of that argument.

On Quora the other day I saw a question from an aspiring data scientist that asked – since all the…

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…

Summary:  What are the earliest seeds of artificial intelligence?  To whom do we owe thanks for starting us down this path?  Many modern researchers to be sure, but the earliest is Leonardo Torres of Spain, in about 1914.

As data scientists it’s very cool to be at the forefront in this age of techno-optimism.  Since the awakening of the digital age calculated by economic researchers to have begun about 1994, a wave of increased productivity has…

Summary:  Got a good AUC on your hold out data?  Think that proves that it’s safe to put the model into production.  This article shows you some of the pitfalls in this new era of black box Deep Learning Neural Nets and a method for identifying potentially devastating errors.

                        Image…

Summary:  Which of these terms means the same thing:  AI, Deep Learning, Machine Learning?  Are you sure?  While there’s overlap none of these is a complete subset of the others and none completely explains the others.

Take this quiz.

Which of the following are substantially the same things?

A. AI

B. Deep Learning

C.  Machine Learning

(Select your answer)

1.  A and B

2.  B and C

3.  A and…

Summary:  The most important developments in Deep Learning and AI in the last year may not be technical at all, but rather a major change in business model.  In the space of about six months all the majors have made their Deep Learning IP open source, hoping to gain on the competition from the power of the broader developer base and wide adoption.

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