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William Vorhies's Blog (282)

Causality – The Next Most Important Thing in AI/ML

Summary:  Finally there are tools that let us transcend ‘correlation is not causation’ and identify true causal factors and their relative strengths in our models.  This is what prescriptive analytics was meant to be.

 

Just when I thought we’d figured it all out, something comes along to make…

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Added by William Vorhies on April 22, 2019 at 8:47am — No Comments

AI/ML Lessons for Creating a Platform Strategy – Part 2

Summary:  McKinsey says platform companies will represent 30% of global business revenue by next year (2020).  In Part 1 of this article we started to lay out some important lessons learned and examples for you to follow.  Here are the rest.

 

McKinsey says platform companies will represent 30% of global…

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Added by William Vorhies on April 15, 2019 at 7:43am — No Comments

AI/ML Lessons for Creating a Platform Strategy – Part 1

Summary:  McKinsey says platform companies will represent 30% of global business revenue by next year (2020).  Here are some lessons and examples to help mature companies evaluate where they can create AI/ML-enabled platforms to remain competitive.  This is a long topic so this will be Part 1 of 2.

 

As data…

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Added by William Vorhies on April 8, 2019 at 9:29am — No Comments

A Radical AI Strategy - Platformication

Summary:  A new business model strategy based around intermediary platforms powered by AI/ML is promising the most direct path to fastest growth, profitability, and competitive success.  Adopting this new approach requires a deep change in mindset and is quite different from just adopting AI/ML to optimize your current operations.

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Added by William Vorhies on April 1, 2019 at 9:29am — No Comments

Now that We’ve Got AI What do We do with It?

Summary:  Whether you’re a data scientist building an implementation case to present to executives or a non-data scientist leader trying to figure this out there’s a need for a much broader framework of strategic thinking around how to capture the value of AI/ML.

 

There are many articles written from a tools…

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Added by William Vorhies on March 25, 2019 at 8:30am — No Comments

Is Robotic Process Automation (RPA) Really AI?

Summary:  Based on a McKinsey study we reported that 47% of companies had at least one AI/ML implementation in place.  Looking back at the data and the dominance of RPA as the most widely reported instance makes us think that the number is probably significantly lower.

 

We’ve been trying to get a handle on…

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Added by William Vorhies on March 18, 2019 at 9:05am — 3 Comments

The Coming Revolution in Recurrent Neural Nets (RNNs)

Summary: Recurrent Neural Nets (RNNs) are at the core of the most common AI applications in use today but we are rapidly recognizing broad time series problem types where they don’t fit well.  Several alternatives are already in use and one that’s just been introduced, ODE net is a radical departure from our way of thinking about the solution.

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Added by William Vorhies on March 11, 2019 at 7:43am — No Comments

Faster Better Cheaper Image Recognition

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…

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Added by William Vorhies on March 4, 2019 at 9:41am — No Comments

Where and How is AI Actually Being Adopted

Summary:  Adoption of AI/ML by larger companies has more than doubled since last year according to these survey results from McKinsey and Stanford’s Human-Centered AI Institute.  This new data gives us a much better idea of which global regions and which industries are adopting which AI/ML techniques.

 

We know…

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Added by William Vorhies on February 25, 2019 at 9:22am — No Comments

From Optimization to Prescriptive Analytics

Summary:  True prescriptive analytics requires the use of real optimization techniques that very few applications actually use.  Here’s a refresher on optimization with examples of where and how they’re best used.

 

Predictive analytics and optimization have gone hand in hand since the very beginning.  But in…

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Added by William Vorhies on February 18, 2019 at 10:01am — 1 Comment

Capturing the Value of ML/AI – the Challenge of Offensive versus Defensive Data Strategies

Summary:  A major pain point is standing in the way of many companies’ ability to maximize the value of their ML/AI initiatives.  The competing goals of data flexibility versus single version of the truth can only be solved with an effective data governance strategy.

 

There are many reasons why companies…

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Added by William Vorhies on February 11, 2019 at 10:15am — No Comments

Advanced Analytic Platforms – Incumbents Fall – Challengers Rise

Summary: The Gartner Magic Quadrant for Data Science and Machine Learning Platforms is just out and once again there are big changes in the leaderboard.  Some major incumbents have fallen and some new challengers have emerged.

 

The Gartner Magic Quadrant for Data Science and Machine Learning Platforms is just out and once again there are big changes in the leaderboard.  Say what you will about our profession but as a platform developer you…

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Added by William Vorhies on February 4, 2019 at 9:30am — 2 Comments

One Shot Learning and Other Strategies for Reducing Training Data

Summary: Not enough labeled training data is a huge barrier to getting at the equally large benefits that could be had from deep learning applications.  Here are five strategies for getting around the data problem including the latest in One Shot Learning.

 

For at least the last two years we’ve been in an…

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Added by William Vorhies on January 28, 2019 at 9:56am — 1 Comment

Doctors are from Venus, Data Scientists from Mars – or Why AI/ML is Moving so Slowly in Healthcare

Summary: The world of healthcare may look like the most fertile field for AI/ML apps but in practice it’s fraught with barriers.  These range from cultural differences, to the failure of developers to really understand the environment they are trying to enhance, to regulatory and logical Catch 22s that work against adoption.  Part 3 of 3.

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Added by William Vorhies on January 21, 2019 at 7:59am — 2 Comments

The AI/ML Opportunity Landscape in Healthcare. Do It Right or It Will be More of a Mine Field.

Summary:  Despite hundreds of projects and thousands of data scientists devoted to bringing AI/ML to healthcare, adoption remains low and slow.  A good portion of this problem is our own fault for failing to see the processes being disrupted through the eyes of the physician users.  Here we lay out the healthcare opportunity landscape but for data scientists following classical disruption strategies, it may be more of a minefield.  Part 2 of…

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Added by William Vorhies on January 14, 2019 at 8:00am — No Comments

Seeing the AI/ML Future in Healthcare Through the Eyes of Physicians. It Doesn’t Look Quite the Way Data Scientists See It.

Summary: If you want to understand the promise of AI/ML in healthcare you need to see it through the eyes of physicians, the ultimate users.  Turns out these folks aren’t the rapid adopters you’d think they’d be and the problem is largely with the way data scientists have tried to implement.  Part 1 of 3.

 

It’s…

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Added by William Vorhies on January 7, 2019 at 8:00am — 4 Comments

5 Predictions about Data Science, Machine Learning, and AI for 2019

Summary:  Here are our 5 predictions for data science, machine learning, and AI for 2019.  We also take a look back at last year’s predictions to see how we did.

 

It’s that time of year again when we do a look back in order to offer a look forward.  What trends will speed up, what things will actually happen,…

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Added by William Vorhies on December 17, 2018 at 8:50am — No Comments

Who Do We Blame When an AI Finally Kills Somebody

Summary:  We’re rapidly approaching the point where AI will be so pervasive that it’s inevitable that someone will be injured or killed.  If you thought this was covered by simple product defect warranties it’s not at all that clear.  Here’s what we need to start thinking about.

 

So far the press…

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Added by William Vorhies on December 10, 2018 at 10:07am — 1 Comment

Can AI Make B2B Sales Better?

Summary: Sales is supposed to be an area that is more immune to replacement by AI than many others because of the high level of impromptu and improvisational human contact required.  That remains true.  But AI is showing that it can be a valuable augment to B2B sales and some early adopters are scoring big gains.

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Added by William Vorhies on December 4, 2018 at 9:59am — No Comments

The Two (Conflicting) Definitions of AI

Summary:  There are two definitions currently in use for AI, the popular definition and the data science definition and they conflict in fundamental ways.  If you’re going to explain or recommend AI to a non-data scientist, it’s important to understand the difference.

 

For a profession as concerned with accuracy as we are, we do a really poor job at naming things, or at least being consistent in the naming.  “Big Data” – totally misleading…

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Added by William Vorhies on November 27, 2018 at 8:23am — No Comments

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