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AI and Blockchain Integration for Preserving Privacy

Unite.AI

NLP in particular has been a subfield that has been focussed heavily in the past few years that has resulted in the development of some top-notch LLMs like GPT and BERT. Large-scale data analysis methods that offer privacy protection by utilizing both blockchain and AI technology.

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Innovation in Synthetic Data Generation: Building Foundation Models for Specific Languages

Unite.AI

Synthetic data , artificially generated to mimic real data, plays a crucial role in various applications, including machine learning , data analysis , testing, and privacy protection. These models, trained on extensive text data from diverse sources, exhibit significant language generation and understanding capabilities.

NLP 167
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The Age of Health Informatics: Part 1

Heartbeat

Image from "Big Data Analytics Methods" by Peter Ghavami Here are some critical contributions of data scientists and machine learning engineers in health informatics: Data Analysis and Visualization: Data scientists and machine learning engineers are skilled in analyzing large, complex healthcare datasets.

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Large Language Models: A Complete Guide

Heartbeat

This technique is commonly used in neural network-based models such as BERT, where it helps to handle out-of-vocabulary words. Three examples of tokenization methods; image from FreeCodeCamp Tokenization is a fundamental step in data preparation for NLP tasks.

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Large Language Models in Pathology Diagnosis

John Snow Labs

The potential of LLMs, in the field of pathology goes beyond automating data analysis. These early efforts were restricted by scant data pools and a nascent comprehension of pathological lexicons. This capability opens up possibilities in pathology where accurate and timely diagnoses can greatly influence patient outcomes.