This AI Creates Infinite NFTs
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Frank
- May 9, 2022
- AI
- ai art
- ai created nft
- ai nft generator
- artificial intelligence
- Arxiv
- bored ape yacht club
- bored apes
- Deep Learning
- explained
- gan nft
- generative adversarial network
- goodfellow
- how to create nft
- Machine Learning
- machine learning nft
- Neural Networks
- nft gan
- nft generator
- opensea nft
- Paper
- Schmidhuber
- StyleGAN
- stylegan2
Yannic shows us how to build an AI that can create as many bored apes as we want. Fungibility for everyone, indeed.
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[ML News] New ImageNet SOTA | Uber’s H3 hexagonal coordinate system | New text-image-pair dataset
Yannic provides the latest news in machine learning in this video. Time Stamps: 0:00 – Intro 0:20 – TruthfulQA benchmark shines new light on GPT-3 2:00 – LAION-400M image-text-pair dataset 4:10 – GoogleAI’s EfficientNetV2 and CoAtNet 6:15 – Uber’s H3: A hexagonal coordinate system 7:40 – AWS NeurIPS 2021 DeepRacer Challenge 8:15 – Helpful Libraries […]
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GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton’s Paper Explained)
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Frank
- March 3, 2021
- AI
- artificial intelligence
- Arxiv
- attention mechanism
- Capsule Networks
- capsule networks explained
- column
- Computer Vision
- consensus algorithm
- Deep Learning
- deep learning tutorial
- explained
- Geoff Hinton
- geoff hinton capsule networks
- geoff hinton neural networks
- Geoffrey Hinton
- geoffrey hinton deep learning
- geoffrey hinton glom
- glom model
- Google AI
- Google Brain
- hinton glom
- introduction to deep learning
- Machine Learning
- Neural Networks
- Schmidhuber
- transformer
Yannic Kilcher covers a paper where Geoffrey Hinton describes GLOM, a Computer Vision model that combines transformers, neural fields, contrastive learning, capsule networks, denoising autoencoders and RNNs. GLOM decomposes an image into a parse tree of objects and their parts. However, unlike previous systems, the parse tree is constructed dynamically and differently for each input, […]
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Explaining the Paper: Hopfield Networks is All You Need
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Frank
- August 10, 2020
- AI
- artificial intelligence
- Arxiv
- attention
- attention is all you need
- Bert
- binary
- continuous
- Deep Learning
- energy function
- error
- explained
- exponental
- gru
- hochreiter
- hopfield
- hopfield network
- key
- lse
- LSTM
- Machine Learning
- metastable
- Neural Networks
- Paper
- pattern
- query
- retrieval
- RNN
- routing
- Schmidhuber
- separation
- store
- transformer
- update rule
- value
Yannic Kilcher explains the paper “Hopfield Networks is All You Need.” Hopfield Networks are one of the classic models of biological memory networks. This paper generalizes modern Hopfield Networks to continuous states and shows that the corresponding update rule is equal to the attention mechanism used in modern Transformers. It further analyzes a pre-trained BERT […]
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Marcus Hutter on Universal Artificial Intelligence, AIXI, and AGI
Lex Fridman interviews Marcus Hutter ,a senior research scientist at DeepMind and professor at Australian National University. Throughout his career of research, including with JĂĽrgen Schmidhuber and Shane Legg, he has proposed a lot of interesting ideas in and around the field of artificial general intelligence, including the development of the AIXI model which is […]
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