[ML News] Cedille French Language Model | YOU Search Engine | AI Finds Profitable MEME TOKENS

Yannic Kilcher · Advanced ·📄 Research Papers Explained ·4y ago
#mlnews #cedille #wmt Only the greatest of news from the world of Machine Learning. OUTLINE: 0:00 - Sponsor: Weights & Biases 1:50 - Cedille - French Language Model 3:55 - Facebook AI Multilingual model wins WMT 5:50 - YOU private search engine 10:35 - DeepMind's Open-Source Arnheim 12:10 - Company sued for using AI to make website more accessible 18:05 - Alibaba DAMO Academy creates 10 Trillion M6 model 21:15 - AMD MI200 Family 22:30 - State of AI report 2021 24:15 - Andrew Ng's Landing AI raises 57M 25:40 - Cerebras raises 250M 26:45 - Microsoft's Varuna: Scalable Training of Huge Models 28:15 - Laura Ruis reproduces Extrapolation Paper 29:05 - Ian Charnas' Real-Life Punchout 30:00 - Helpful Things 33:10 - AI finds profitable Meme-Tokens 34:55 - This Sneaker Does Not Exist Sponsor: Weights & Biases https://wandb.com References: Cedille - French Language Model https://en.cedille.ai/ https://github.com/coteries/cedille-ai https://app.cedille.ai/ https://en.wikipedia.org/wiki/Cedilla Facebook AI Multilingual model wins WMT https://ai.facebook.com/blog/the-first-ever-multilingual-model-to-win-wmt-beating-out-bilingual-models/ YOU private search engine https://you.com/ https://youdotcom.notion.site/FAQ-8c871d6c99d84e02955fda772a1da8d4 DeepMind's Open-Source Arnheim https://deepmind.com/research/open-source/open-source-arnheim-a-learnable-visual-grammar-for-generating-paintings https://twitter.com/OriolVinyalsML/status/1459231774068854785 https://github.com/deepmind/arnheim https://colab.research.google.com/github/deepmind/arnheim/blob/master/arnheim_2.ipynb Company sued for using AI to make website more accessible https://www.wired.com/story/company-tapped-ai-website-landed-court/ https://archive.ph/kdvOM Alibaba DAMO Academy creates 10 Trillion M6 model https://pandaily.com/alibaba-damo-academy-creates-worlds-largest-ai-pre-training-model-with-parameters-far-exceeding-google-and-microsoft/ https://www.infoq.cn/article/xIX9lekuuLcXewc5iphF AMD MI200 Family ht
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1 Imagination-Augmented Agents for Deep Reinforcement Learning
Imagination-Augmented Agents for Deep Reinforcement Learning
Yannic Kilcher
 2 Learning model-based planning from scratch
Learning model-based planning from scratch
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 3 Reinforcement Learning with Unsupervised Auxiliary Tasks
Reinforcement Learning with Unsupervised Auxiliary Tasks
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 4 Attention Is All You Need
Attention Is All You Need
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 5 git for research basics: fundamentals, commits, branches, merging
git for research basics: fundamentals, commits, branches, merging
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 6 Curiosity-driven Exploration by Self-supervised Prediction
Curiosity-driven Exploration by Self-supervised Prediction
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 7 World Models
World Models
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 8 Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
Yannic Kilcher
 9 Stochastic RNNs without Teacher-Forcing
Stochastic RNNs without Teacher-Forcing
Yannic Kilcher
 10 What’s in a name? The need to nip NIPS
What’s in a name? The need to nip NIPS
Yannic Kilcher
 11 BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Yannic Kilcher
 12 Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Yannic Kilcher
 13 GPT-2: Language Models are Unsupervised Multitask Learners
GPT-2: Language Models are Unsupervised Multitask Learners
Yannic Kilcher
 14 Neural Ordinary Differential Equations
Neural Ordinary Differential Equations
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 15 The Odds are Odd: A Statistical Test for Detecting Adversarial Examples
The Odds are Odd: A Statistical Test for Detecting Adversarial Examples
Yannic Kilcher
 16 Discriminating Systems - Gender, Race, and Power in AI
Discriminating Systems - Gender, Race, and Power in AI
Yannic Kilcher
 17 Blockwise Parallel Decoding for Deep Autoregressive Models
Blockwise Parallel Decoding for Deep Autoregressive Models
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 18 S.H.E. - Search. Human. Equalizer.
S.H.E. - Search. Human. Equalizer.
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 19 Reinforcement Learning, Fast and Slow
Reinforcement Learning, Fast and Slow
Yannic Kilcher
 20 Adversarial Examples Are Not Bugs, They Are Features
Adversarial Examples Are Not Bugs, They Are Features
Yannic Kilcher
 21 I'm at ICML19 :)
I'm at ICML19 :)
Yannic Kilcher
 22 Population-Based Search and Open-Ended Algorithms
Population-Based Search and Open-Ended Algorithms
Yannic Kilcher
 23 XLNet: Generalized Autoregressive Pretraining for Language Understanding
XLNet: Generalized Autoregressive Pretraining for Language Understanding
Yannic Kilcher
 24 Conversation about Population-Based Methods (Re-upload)
Conversation about Population-Based Methods (Re-upload)
Yannic Kilcher
 25 Reconciling modern machine learning and the bias-variance trade-off
Reconciling modern machine learning and the bias-variance trade-off
Yannic Kilcher
 26 Learning World Graphs to Accelerate Hierarchical Reinforcement Learning
Learning World Graphs to Accelerate Hierarchical Reinforcement Learning
Yannic Kilcher
 27 Manifold Mixup: Better Representations by Interpolating Hidden States
Manifold Mixup: Better Representations by Interpolating Hidden States
Yannic Kilcher
 28 Processing Megapixel Images with Deep Attention-Sampling Models
Processing Megapixel Images with Deep Attention-Sampling Models
Yannic Kilcher
 29 Gauge Equivariant Convolutional Networks and the Icosahedral CNN
Gauge Equivariant Convolutional Networks and the Icosahedral CNN
Yannic Kilcher
 30 Auditing Radicalization Pathways on YouTube
Auditing Radicalization Pathways on YouTube
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 31 RoBERTa: A Robustly Optimized BERT Pretraining Approach
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Yannic Kilcher
 32 Dynamic Routing Between Capsules
Dynamic Routing Between Capsules
Yannic Kilcher
 33 DEEP LEARNING MEME REVIEW - Episode 1
DEEP LEARNING MEME REVIEW - Episode 1
Yannic Kilcher
 34 Accelerating Deep Learning by Focusing on the Biggest Losers
Accelerating Deep Learning by Focusing on the Biggest Losers
Yannic Kilcher
 35 [News] The Siraj Raval Controversy
[News] The Siraj Raval Controversy
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 36 LeDeepChef 👨‍🍳 Deep Reinforcement Learning Agent for Families of Text-Based Games
LeDeepChef 👨‍🍳 Deep Reinforcement Learning Agent for Families of Text-Based Games
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 37 The Visual Task Adaptation Benchmark
The Visual Task Adaptation Benchmark
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 38 IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
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 39 AlphaStar: Grandmaster level in StarCraft II using multi-agent reinforcement learning
AlphaStar: Grandmaster level in StarCraft II using multi-agent reinforcement learning
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 40 SinGAN: Learning a Generative Model from a Single Natural Image
SinGAN: Learning a Generative Model from a Single Natural Image
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 41 A neurally plausible model learns successor representations in partially observable environments
A neurally plausible model learns successor representations in partially observable environments
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 42 MuZero: Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
MuZero: Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
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 43 Reinforcement Learning Upside Down: Don't Predict Rewards -- Just Map Them to Actions
Reinforcement Learning Upside Down: Don't Predict Rewards -- Just Map Them to Actions
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 44 NeurIPS 19 Poster Session
NeurIPS 19 Poster Session
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 45 Go-Explore: a New Approach for Hard-Exploration Problems
Go-Explore: a New Approach for Hard-Exploration Problems
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 46 Reformer: The Efficient Transformer
Reformer: The Efficient Transformer
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 47 [Interview] Mark Ledwich - Algorithmic Extremism: Examining YouTube's Rabbit Hole of Radicalization
[Interview] Mark Ledwich - Algorithmic Extremism: Examining YouTube's Rabbit Hole of Radicalization
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 48 Turing-NLG, DeepSpeed and the ZeRO optimizer
Turing-NLG, DeepSpeed and the ZeRO optimizer
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 49 Growing Neural Cellular Automata
Growing Neural Cellular Automata
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 50 NeurIPS 2020 Changes to Paper Submission Process
NeurIPS 2020 Changes to Paper Submission Process
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 51 Deep Learning for Symbolic Mathematics
Deep Learning for Symbolic Mathematics
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 52 Online Education - How I Make My Videos
Online Education - How I Make My Videos
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 53 [Rant] coronavirus
[Rant] coronavirus
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 54 Axial Attention & MetNet: A Neural Weather Model for Precipitation Forecasting
Axial Attention & MetNet: A Neural Weather Model for Precipitation Forecasting
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 55 Agent57: Outperforming the Atari Human Benchmark
Agent57: Outperforming the Atari Human Benchmark
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 56 State-of-Art-Reviewing: A Radical Proposal to Improve Scientific Publication
State-of-Art-Reviewing: A Radical Proposal to Improve Scientific Publication
Yannic Kilcher
 57 Dream to Control: Learning Behaviors by Latent Imagination
Dream to Control: Learning Behaviors by Latent Imagination
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 58 POET: Endlessly Generating Increasingly Complex and Diverse Learning Environments and Solutions
POET: Endlessly Generating Increasingly Complex and Diverse Learning Environments and Solutions
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 59 Evaluating NLP Models via Contrast Sets
Evaluating NLP Models via Contrast Sets
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 60 [Drama] Who invented Contrast Sets?
[Drama] Who invented Contrast Sets?
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Chapters (17)

Sponsor: Weights & Biases
1:50 Cedille - French Language Model
3:55 Facebook AI Multilingual model wins WMT
5:50 YOU private search engine
10:35 DeepMind's Open-Source Arnheim
12:10 Company sued for using AI to make website more accessible
18:05 Alibaba DAMO Academy creates 10 Trillion M6 model
21:15 AMD MI200 Family
22:30 State of AI report 2021
24:15 Andrew Ng's Landing AI raises 57M
25:40 Cerebras raises 250M
26:45 Microsoft's Varuna: Scalable Training of Huge Models
28:15 Laura Ruis reproduces Extrapolation Paper
29:05 Ian Charnas' Real-Life Punchout
30:00 Helpful Things
33:10 AI finds profitable Meme-Tokens
34:55 This Sneaker Does Not Exist
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Microsoft Research Forum | Season 2, Episode 4
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