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Generative vs Predictive AI: Key Differences & Real-World Applications

Topbots

Generated with Bing and edited with Photoshop Predictive AI has been driving companies’ ROI for decades through advanced recommendation algorithms, risk assessment models, and fraud detection tools. On the other hand, generative models like diffusion models can create new images that are not present in the training data (e.g.,

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Image Captioning: Bridging Computer Vision and Natural Language Processing

Heartbeat

This technology has broad applications, including aiding individuals with visual impairments, improving image search algorithms, and integrating optical recognition with advanced language generation to enhance human-machine interactions. Various algorithms are employed in image captioning, including: 1.

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Cross-Modal Retrieval: Image-to-Text and Text-to-Image Search

Heartbeat

Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are often employed to extract meaningful representations from images and text, respectively. Images are visual data, while text is linguistic data.

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Beyond Text: Multi-Modal Learning with Large Language Models

Heartbeat

They owe their success to many factors, including substantial computational resources, vast training data, and sophisticated architectures. One of the standout achievements in this domain is the development of models like GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers).

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Large Language Models in Pathology Diagnosis

John Snow Labs

These early efforts were restricted by scant data pools and a nascent comprehension of pathological lexicons. in 2017 highlighted this by demonstrating a deep learning algorithm’s ability to classify skin cancer with accuracy comparable to that of human dermatologists, based on an extensive dataset of 129,450 clinical images.