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Exploring the Use of LLMs and BERT for Language Tasks

Analytics Vidhya

Introduction In the rapidly evolving landscape of artificial intelligence, especially in NLP, large language models (LLMs) have swiftly transformed interactions with technology. Since the groundbreaking ‘Attention is all you need’ paper in 2017, the Transformer architecture, notably exemplified by ChatGPT, has become pivotal.

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Beginners’ Guide to Finetuning Large Language Models (LLMs)

Analytics Vidhya

The seismic impact of finetuning large language models has utterly transformed NLP, revolutionizing our technological interactions. Rewind to 2017, a pivotal moment marked by […] The post Beginners’ Guide to Finetuning Large Language Models (LLMs) appeared first on Analytics Vidhya.

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5 key areas for tech leaders to watch in 2020

O'Reilly on Data

Up until 2017, the ML+AI topic had been amongst the fastest growing topics on the platform. After several years of steady climbing—and after outstripping Java in 2017—Python-related interactions now comprise almost 10% of all usage. Not necessarily: Java-related searches increased by 5% between 2017 and 2018. Coincidence?

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Infor’s Velocity Summit Highlights Multiple Advances and Enhancements

David Menninger's Analyst Perspectives

Infor introduced its original AI and machine learning capabilities in 2017 in the form of Coleman, which uses its Infor AI/ML platform built on Amazon’s SageMaker to create predictive and prescriptive analytics. It also offered a chatbot that utilized Amazon Lex.

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Monetizing Analytics Features: Why Data Visualizations Will Never Be Enough

Think your customers will pay more for data visualizations in your application? Five years ago they may have. But today, dashboards and visualizations have become table stakes. Discover which features will differentiate your application and maximize the ROI of your embedded analytics. Brought to you by Logi Analytics.

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Notes on artificial intelligence, December 2017

DMBS2

Voice interaction is already revolutionary in certain niches (e.g. The most advanced organizations developing and using those rely on machine learning. I don’t see an important distinction between machine learning and “artificial intelligence” in this area. smartphones — Siri et al.).

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Fertility care provider Ovum Health gives patients information using chat and scheduling tools with IBM watsonx Assistant

IBM Big Data Hub

With over 67,000 registered FertilityAnswers iOS and Android users on Ovum Health’s free question-and-answer platform, the bot can interact with patients at scale while maintaining the care and empathy of a healthcare professional.