2017 Blog Experimentation Metadata 
Sisense
JULY 24, 2019
In my previous blog , I wrote about Natural Language Query (NLQ, or search analytics for some), as one of the major topics that we, the AI group in Sisense, are working on. In this blog, I would like to expand on NLQ and discuss how this AI technology can be leveraged in our domain. Machine Intent vs. User Intent.
Ontotext
MAY 18, 2020
Ever since Hippocrates founded his school of medicine in ancient Greece some 2,500 years ago, writes Hannah Fry in her book Hello World: Being Human in the Age of Algorithms , what has been fundamental to healthcare (as she calls it “the fight to keep us healthy”) was observation, experimentation and the analysis of data. Certainly not!
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O'Reilly on Data
JULY 25, 2023
So far I’ve read a gazillion blog posts about people’s experiences with these AI coding assistance tools. I also installed the latest VS Code (Visual Studio Code) with GitHub Copilot and the experimental Copilot Chat plugins, but I ended up not using them much. This choice also inspired me to call my project Swift Papers.
O'Reilly on Data
MARCH 23, 2023
All of these models are based on a technology called Transformers , which was invented by Google Research and Google Brain in 2017. But Transformers have some other important advantages: Transformers don’t require training data to be labeled; that is, you don’t need metadata that specifies what each sentence in the training data means.
Cloudera
APRIL 9, 2019
of application workloads were still on-premises in enterprise data centers; by the end of 2017, less than half (47.2%) were on-premises. For example, if you want to optimize for agility and experimentation, you probably will be better off doing so with an ephemeral public cloud infrastructure. appeared first on Cloudera Blog.
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