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Retailers can tap into generative AI to enhance support for customers and employees

IBM Big Data Hub

With the rise of highly personalized online shopping, direct-to-consumer models, and delivery services, generative AI can help retailers further unlock a host of benefits that can improve customer care, talent transformation and the performance of their applications.

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Perplexing Impacts of AI on The Future Insurance Claims

Smart Data Collective

We previously talked about the benefits of data analytics in the insurance industry. One report found that big data vendors will generate over $2.4 Key benefits of AI include recognizing speech, identifying objects in an image, and analyzing natural or unstructured data forms. Spotting fraudulent cases.

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How an Infrastructure Switch Sped Up AI Model Results and Business Value for BEN Group

CIO Business Intelligence

If you trained a model on data from last month or last year, the data you feed it next month or next year is going to look wildly different.”. These new advertiser challenges caused the company to develop AI models to find creators and influencers who provide more authentic, natural and non-disruptive brand-to-customer interactions.

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Top Data Science Tools That Will Empower Your Data Exploration Processes

datapine

Exclusive Bonus Content: Download Our Free Data & Science Checklist! Geet our bite-sized free summary and start building your data skills! In the past, data scientists had to rely on powerful computers to manage large volumes of data. Our Top Data Science Tools. Let’s get started.

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An AI Data Platform for All Seasons

Rocket-Powered Data Science

I believe that the time, place, and season for artificial intelligence (AI) data platforms have arrived. To see this, look no further than Pure Storage , whose core mission is to “ empower innovators by simplifying how people consume and interact with data.”

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Ontotext’s Semantic Approach Towards LLM, Better Data and Content Management: An Interview with Doug Kimball and Atanas Kiryakov

Ontotext

This is the case with the so-called intelligent data processing (IDP), which uses a previous generation of machine learning. LLMs do most of this better and with lower cost of customization. We use other deep learning techniques for such tasks. That’s something that LLMs cannot do.

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Themes and Conferences per Pacoid, Episode 11

Domino Data Lab

Scale the problem to handle complex data structures. Part of the back-end processing needs deep learning (graph embedding) while other parts make use of reinforcement learning. Interactive Query Synthesis from Input-Output Examples ” – Chenglong Wang, Alvin Cheung, Rastislav Bodik (2017-05-14).

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