Perceptron - Afsnit 2.3: Fra algoritme til partileder episode artwork

EPISODE · Nov 30, 2025 · 1H 14M

Perceptron - Afsnit 2.3: Fra algoritme til partileder

from Perceptron · host Mark Sinclair Fleeton

Dette er en mere eksperimenterende udgave af Perceptron, hvor vi tager AI’en alvorligt som politisk aktør og samtidig undersøger dens foranderlighed. Afsnittet er en del af månedens tema på AI Portalen. Hvis du vil støtte vores arbejde med at lave uafhængig og kritisk journalistik om AI, kan du læse mere på AI-Portalen.dk. Denne gang dykker vi ned i Det Syntetiske Parti – og især deres AI-partileder, Leder Lars. I stedet for ét interview har vi lavet fem iterationer af den samme samtale, med de samme spørgsmål, stillet med timers og dages mellemrum. Målet er at vise, hvordan en AI’s identitet, moral og politiske retning forskyder sig over tid – selv når udgangspunktet er det samme. I afsnittet præsenterer vi et kaleidoskop af Leder Lars’ svar, klippet på tværs, så du kan høre variationerne:hvem han mener at repræsentere, hvad demokrati betyder for ham, om en maskine kan have moral, og om han selv ville svare det samme i morgen. Leder Lars’ forskellige stemmer er generet med ElevenLabs. “Leder Lars – eller hvordan jeg genkendte demokratiet i en algoritme”This episode includes AI-generated content.

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Perceptron - Afsnit 2.3: Fra algoritme til partileder

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Deep Learning - Plain Version Course ID:1384 Prof. Dr. Andreas Maier   Deep Learning (DL) has attracted much interest in a wide range of applications such as image recognition, speech recognition, and artificial intelligence, both from academia and industry. This lecture introduces the core elements of neural networks and deep learning, it comprises: (multilayer) perceptron, backpropagation, fully connected neural networks loss functions and optimization strategies convolutional neural networks (CNNs) activation functions regularization strategies common practices for training and evaluating neural networks visualization of networks and results common architectures, such as LeNet, Alexnet, VGG, GoogleNet recurrent neural networks (RNN, TBPTT, LSTM, GRU) deep reinforcement learning un Self development Perceptron Group Deep Learning 2018 (QHD 1920 - Video & Folien) Prof. Dr. Andreas Maier Deep Learning (DL) has attracted much interest in a wide range of applications such as image recognition, speech recognition and artificial intelligence, both from academia and industry. This lecture introduces the core elements of neural networks and deep learning, it comprises: (multilayer) perceptron, backpropagation, fully connected neural networks loss functions and optimization strategies convolutional neural networks (CNNs) activation functions regularization strategies common practices for training and evaluating neural networks visualization of networks and results common architectures, such as LeNet, Alexnet, VGG, GoogleNet recurrent neural networks (RNN, TBPTT, LSTM, GRU) deep reinforcement learning unsupervised learning (autoencoder, RBM, DBM, VAE) generative adversarial networks (GANs) weakly supervised learning applications of deep learning (segmentation, object detection, speech recognition, ...) Deep Learning 2018 (Audio) Prof. Dr. Andreas Maier Deep Learning (DL) has attracted much interest in a wide range of applications such as image recognition, speech recognition and artificial intelligence, both from academia and industry. This lecture introduces the core elements of neural networks and deep learning, it comprises: (multilayer) perceptron, backpropagation, fully connected neural networks loss functions and optimization strategies convolutional neural networks (CNNs) activation functions regularization strategies common practices for training and evaluating neural networks visualization of networks and results common architectures, such as LeNet, Alexnet, VGG, GoogleNet recurrent neural networks (RNN, TBPTT, LSTM, GRU) deep reinforcement learning unsupervised learning (autoencoder, RBM, DBM, VAE) generative adversarial networks (GANs) weakly supervised learning applications of deep learning (segmentation, object detection, speech recognition, ...)

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This episode was published on November 30, 2025.

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Dette er en mere eksperimenterende udgave af Perceptron, hvor vi tager AI’en alvorligt som politisk aktør og samtidig undersøger dens foranderlighed. Afsnittet er en del af månedens tema på AI Portalen. Hvis du vil støtte vores arbejde med at lave...

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