Remove Experimentation Remove Insurance Remove Modeling Remove Statistics
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Interview with Dominic Sartorio, Senior Vice President for Products & Development, Protegrity

Corinium

Ahead of the Chief Data Analytics Officers & Influencers, Insurance event we caught up with Dominic Sartorio, Senior Vice President for Products & Development, Protegrity to discuss how the industry is evolving. Are you seeing any specific issues around the insurance industry at the moment that should concern CDAOs?

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What Are ChatGPT and Its Friends?

O'Reilly on Data

It’s important to understand that ChatGPT is not actually a language model. It’s a convenient user interface built around one specific language model, GPT-3.5, is one of a class of language models that are sometimes called “large language models” (LLMs)—though that term isn’t very helpful. with specialized training.

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Getting ready for artificial general intelligence with examples

IBM Big Data Hub

While these large language model (LLM) technologies might seem like it sometimes, it’s important to understand that they are not the thinking machines promised by science fiction. LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language.

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Reflections on the Data Science Platform Market

Domino Data Lab

These solutions help data analysts build models by automating tasks in data science, including training models, selecting algorithms, and creating features. These tools support a breadth of use cases including data science, data engineering, and model operations. Proprietary (often GUI-driven) data science platforms.

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Building Smarter Financial Services: The Role of Semantic Technologies, Knowledge Graphs and Generative AI

Ontotext

Nimit Mehta: I think that 2024 is going to be a buckle-down year, but, at the same time, we’ll see a rapid explosion of experimentation. For example, when I want to insure some property and want to find out if the CEO has been involved in crime. These are not statistical inferences. Maybe later. What is a customer? What is risk?

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How a Discovery Data Warehouse, the next evolution of augmented analytics, accelerates treatments and delivers medicines safely to patients in need

Cloudera

How can he make it easy to see statistics, and do calculations, on discovered commonalities, across structured and unstructured data? And most importantly, once shared security model across data sets and compute accessing the data. It would enable faster experimentation with easy, protected, and governed access to a variety of data.