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…Continue
Added by William Vorhies on August 3, 2020 at 1:00pm — No Comments
Summary: An update and observations about China’s plan to become the world leader in AI.
Whether you get your news from Facebook or from the Wall Street Journal you can’t help having heard that China is out to displace the US as the world leader in AI. Variously you may have heard that…Continue
Added by William Vorhies on July 28, 2020 at 8:32am — No Comments
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
This the second part of Reinforcement Learning (Q-learning). If you would like to understand the RL, Q-learning, and key terms please read Part 1.
In this part, we will implement a simple example of Q learning using the R programming language from scratch. It is expected from you to understand the basics of R programming and complete the reading of Part 1 of this article.
We are coding the algorithms using the R base package…Continue
Added by Nitin Agarwal on June 4, 2020 at 8:23pm — No Comments
Have you heard about AI learning to play computer games on their own and giving tough competitions to expert Human gamers?
A very popular example being Deepmind whose AlphaGo program defeated the South Korean Go world champion in 2016. Other than this there are other AI agents developed with the intent of playing Atari games like…Continue
Added by Nitin Agarwal on June 4, 2020 at 7:51pm — 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: 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
Added by Janardhanan PS on February 16, 2020 at 9:02pm — 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
Summary: Just how much should you trust your AI systems? Best practice points to constant review, strong governance, and the willingness to override results that seem illogical.
Added by William Vorhies on January 20, 2020 at 8:26am — No Comments