When AI and cybersecurity meet, ultra-interesting use cases emerge. For example, training an AI on the source code of thousands of phishing sites to create an extension that automatically gives you a risk score, to determine if you are visiting a
RESEARCH
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Orange trains an AI to predict network coverage in seconds
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Les équipes d’Orange ont construit une base de données à partir des données cartographiques , des antennes et de la couverture renvoyées par chacun de nos téléphones pour entraîner une IA qui va prédire la couverture réseau en quelques secondes pour 1 km carré ! La simulation de… pic.twitter.com/WZp1G94J0M
— Defend Intelligence (Anis Ayari) (@DFintelligence) 28 novembre 2023Orange teams built a database from cartographic data, antennas, and coverage returned by each of our phones to train an AI that will predict network coverage in seconds for 1 square kilometer! The simulation of
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Creation of a satellite image database for diseased trees
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Thus, one can build a database by labeling satellite images of diseased trees on agricultural land to enable a large-scale automatic detection and prevention solution to help small farmers.
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Orange develops AI detecting sick trees via multispectral satellites
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En collaboration avec des chercheurs et des agronomes en IA à Sfax en Tunisie, Orange a développé un système qui analyse des images satellites multispectrales pour entraîner une IA qui détecte les arbres malades. Pourquoi multispectrale ? Parce que un arbre malade ne va pas… pic.twitter.com/hsmts8UqX6
— Defend Intelligence (Anis Ayari) (@DFintelligence) 28 novembre 2023In collaboration with researchers and AI agronomists in Sfax, Tunisia, Orange has developed a system that analyzes multispectral satellite images to train an AI that detects diseased trees. Why multispectral? Because a diseased tree does not
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AI to save sick olive trees in Tunisia
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[Thread] 40% of agricultural land in Tunisia is allocated to olive trees. But some trees are sick! And to save them, we can use #AI. I was at @orange for #OpenTechDays today and I'm going to show you #AI projects that blew my mind! #Publicité
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Keras 3 Release Breaks Python Package Test Suites
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I remember when I first started training deep learning models and my whole twitter timeline was the author of keras tweeting about how no one wants to use pytorch keras 3 came out today and the only reason I heard about it is because it broke one of my python package test suites https://
x.com/fchollet/statu
/fchollet/status/1047570406570307584
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Fine-tuning T5 Model on Stack Overflow Questions for Automatic Tagging
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🚀Blog-tastic Tuesdays🚀https://t.co/qhCLiOlguT
— Satya Mallick (@LearnOpenCV) 28 novembre 2023
T5, or Text to Text Transfer Transformer, was a groundbreaking language model from Google that could perform multiple tasks. In this week's article, we fine-tune the T5 model on Stack Overflow questions for automatic tag… pic.twitter.com/Q552FyhfpoBlog-tastic Tuesdays https://
learnopencv.com/fine-tuning-t5/ T5, or Text to Text Transfer Transformer, was a groundbreaking language model from Google that could perform multiple tasks. In this week's article, we fine-tune the T5 model on Stack Overflow questions for automatic tag -
Model Parameters Training Steps and Downstream Performance
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those too, but i think more importantly i'm asking about the relationship between model parameters, training steps (whatever that means in the embeddings case), and downstream performance
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Stable Video Diffusion Model by Stability AI Explained
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Good morning fellow AI enthusiast! This week's iteration focuses on the new Stable Diffusion model by @StabilityAI
! It is an open-source video generation model based on the stable diffusion architecture for videos. We dive into how it works https://
open.substack.com/pub/louisbouch
ard/p/stable-video-diffusion-svd-explained?r=25qlky&utm_campaign=post&utm_medium=web
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Scaling Laws for Language Models Explained
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no, scaling laws like the scaling laws for language models: