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.
Added by William Vorhies on June 29, 2020 at 11:31am — No Comments
Summary: Objectively identifying hateful or abusive speech on social media platforms would allow those platforms to better control it. However to be objective and without bias that identification would have to be independent of the author especially where elected officials are involved.
Added by William Vorhies on June 8, 2020 at 2:25pm — No Comments
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.
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…Continue
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.
Added by William Vorhies on May 19, 2020 at 12:53pm — No Comments
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.
Added by William Vorhies on May 11, 2020 at 2:41pm — No Comments
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.
Added by William Vorhies on May 4, 2020 at 1:06pm — No Comments
Summary: Now that you have a little time for introspection, how about reviewing the performance of your chatbots.
Added by William Vorhies on April 28, 2020 at 11:12am — No Comments
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.
Added by William Vorhies on April 21, 2020 at 10:53am — No Comments
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.
Added by William Vorhies on April 13, 2020 at 2:01pm — No Comments
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.
Added by William Vorhies on April 6, 2020 at 2:56pm — No Comments
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.
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…Continue
Summary: Mid-career switching into data science is difficult. But if you already have substantial data base experience becoming a Data Engineer can be a great career by itself, or even the first stepping stone to becoming a Data Scientist.
Added by William Vorhies on March 16, 2020 at 11:26am — No Comments
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.Continue
Added by William Vorhies on March 2, 2020 at 12:42pm — No Comments
Summary: How to measure the degree and value of AI adoption among companies or even countries is hard. Here’s a beginning proposal on how to get started.
We talk a great deal about whether there are enough data scientists to go around, whether our advancements in AI techniques are better than others, if…Continue
Added by William Vorhies on February 25, 2020 at 10:07am — No Comments
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…Continue
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.
Added by William Vorhies on February 11, 2020 at 1:19pm — No Comments
Summary: Can all AI strategies be defined by a few common needs or are the different AI strategy models sufficiently unique that they need to be considered as separate approaches.
Added by William Vorhies on February 3, 2020 at 11:15am — No Comments
Summary: Workforce forecasting and scheduling applications are rapidly upgrading their use of AI. Techniques of time series forecasting ranging from the simple Holt Winters to the complex, DNNs and Multiple Temporal Aggregation are available on some but not all platforms. Increasingly, AI differentiates the usefulness of these apps.
Added by William Vorhies on January 28, 2020 at 2:15pm — No Comments