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Bayesian Networks – Probabilistic Neural Network (PNN)

Analytics Vidhya

It uses conditional probabilities to improve the prior probabilities, which results in posterior probabilities. In simple terms, suppose you want to ascertain the probability of whether your friends […] The post Bayesian Networks – Probabilistic Neural Network (PNN) appeared first on Analytics Vidhya.

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Zoom Uses Customer Data for AI Training; Faces Legal Quandary

Analytics Vidhya

The controversy centers around its recent terms and conditions, sparking user outrage and raising pertinent questions about data privacy and consent. In a new twist of events, Zoom, the popular videoconferencing platform, is entangled in a legal predicament regarding using customer data for training artificial intelligence (AI) models.

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InternLM2

Hacker News

InternLM2 efficiently captures long-term dependencies, initially trained on 4k tokens before advancing to 32k tokens in pre-training and fine-tuning stages, exhibiting remarkable performance on the 200k ``Needle-in-a-Haystack" test.

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Rising ill-health and economic inactivity from long-term sickness: 2019 to 2023

Hacker News

Experimental statistics estimating the different health conditions of the working-age population and those economically inactive because of long-term sickness.

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How to Package and Price Embedded Analytics

Just by embedding analytics, application owners can charge 24% more for their product. How much value could you add? This framework explains how application enhancements can extend your product offerings. Brought to you by Logi Analytics.

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GPT-4 Can Almost Perfectly Handle Unnatural Scrambled Text

Hacker News

To investigate this, we first propose the Scrambled Bench, a suite designed to measure the capacity of LLMs to handle scrambled input, in terms of both recovering scrambled sentences and answering questions given scrambled context.

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Self-Consuming Generative Models Go MAD

Hacker News

We term this condition Model Autophagy Disorder (MAD), making analogy to mad cow disease. Our primary conclusion across all scenarios is that without enough fresh real data in each generation of an autophagous loop, future generative models are doomed to have their quality (precision) or diversity (recall) progressively decrease.