Summary: Things are getting repetitious and that can be boring. Still, looking at lessons from the 90s it’s clear there are at least one or two decades of important economic advances that will based on our current AI/ML. Then some thoughts on where that next really huge breakthrough will come from that will return our initial excitement.
Added by William Vorhies on December 8, 2020 at 11:12am — No Comments
Summary: There is now sufficient experience among mid and large sized companies starting their AI journey to identify a single best practice for moving from AI experimentation to scale-up: the AI COE (Center of Excellence).
If you are a mid-sized business, government organization, or educational…Continue
Added by William Vorhies on December 2, 2020 at 3:18pm — No Comments
Summary: Some industries are a clear slam-dunk for AI/ML applications and some less so. The legal, regulatory, and compliance businesses (law firms, internal legal departments, and the contract review and regulatory compliance departments of heavily regulated industries) fall in this last category. This is a review of seven companies found by TopBots to be successful; pointing to opportunities others can follow.
Added by William Vorhies on November 4, 2020 at 10:00am — No Comments
Added by William Vorhies on October 24, 2020 at 11:30am — No Comments
Summary: The annual Burtch Works salary survey with data through April shows that opportunities and salaries are still excellent for both new and experienced data scientists. They also offer some anecdotal observations about the impact of the first few months of COVID on our work and opportunities.Continue
Added by William Vorhies on September 1, 2020 at 8:00am — No Comments
Summary: Transfer Learning (TL) may be the most important aid to adoption of deep learning in the last several years. This new LEEP measure predicts the accuracy of the transfer and should make TL faster, cheaper, and better.
Added by William Vorhies on August 21, 2020 at 10:26am — No Comments
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: 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: 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