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
June 12, 2020
Description: Majority of AI approaches are based on the construct of training against historical data and then inferencing new data. While this is a sound and proven approach, a lot of IoT assets coming online don’t have historical data and we don’t necessarily have the time to wait.
Added by Jane Howell on June 12, 2020 at 2:30pm — 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
How to maintain Model Effectiveness after deployed
When we ready to deploy a good predictive model which given a good accuracy score on training and testing dataset, there is one more problem to solve. How long will this model effectively solve the problem with the same high accuracy and what is the strategy to maintain the model accuracy. We also need to…Continue
Added by Ramesh on May 17, 2020 at 11:32am — 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: 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
Filters are the key thing in Computer Vision(Processing image data). You would have probably used different kinds of filters like the blur filter, vintage filters, etc in photo editing apps. Ever thought about how those filters work? . How do they give the…Continue
Added by Sameer Nigam on February 25, 2020 at 11:00pm — 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
In this modern age, self-driving cars, voice-based assistants, social media feeds, and more are the tools fuelled by the technological marvel of the 21st century called machine learning.
If you wish to learn about the importance of web personalization and how machine learning impacts the Drupal development, then this article is for you.…
Added by Ryan Williamson on February 13, 2020 at 8:25pm — No Comments
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
Machine learning (ML) is a hot topic nowadays. Everyone speaks about the new programming paradigm, models are implemented in very different domains, more and more startups are relying mainly on ML.
At the same time, machine…Continue