CM3: A Causal Masked Multimodal Model of the Internet (Paper Explained w/ Author Interview)

Yannic Kilcher · Beginner ·🧠 Large Language Models ·4y ago
#cm3 #languagemodel #transformer This video contains a paper explanation and an incredibly informative interview with first author Armen Aghajanyan. Autoregressive Transformers have come to dominate many fields in Machine Learning, from text generation to image creation and many more. However, there are two problems. First, the collected data is usually scraped from the web and uni- or bi-modal and throws away a lot of structure of the original websites, and second, language modelling losses are uni-directional. CM3 addresses both problems: It directly operates on HTML and includes text, hyperlinks, and even images (via VQGAN tokenization) and can therefore be used in plenty of ways: Text generation, captioning, image creation, entity linking, and much more. It also introduces a new training strategy called Causally Masked Language Modelling, which brings a level of bi-directionality into autoregressive language modelling. In the interview after the paper explanation, Armen and I go deep into the how and why of these giant models, we go over the stunning results and we make sense of what they mean for the future of universal models. OUTLINE: 0:00 - Intro & Overview 6:30 - Directly learning the structure of HTML 12:30 - Causally Masked Language Modelling 18:50 - A short look at how to use this model 23:20 - Start of interview 25:30 - Feeding language models with HTML 29:45 - How to get bi-directionality into decoder-only Transformers? 37:00 - Images are just tokens 41:15 - How does one train such giant models? 45:40 - CM3 results are amazing 58:20 - Large-scale dataset collection and content filtering 1:04:40 - More experimental results 1:12:15 - Why don't we use raw HTML? 1:18:20 - Does this paper contain too many things? Paper: https://arxiv.org/abs/2201.07520 Abstract: We introduce CM3, a family of causally masked generative models trained over a large corpus of structured multi-modal documents that can contain both text and image tokens. Our new causally mask
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Playlist

Uploads from Yannic Kilcher · Yannic Kilcher · 0 of 60

← Previous Next →
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
Yannic Kilcher
 3 Reinforcement Learning with Unsupervised Auxiliary Tasks
Reinforcement Learning with Unsupervised Auxiliary Tasks
Yannic Kilcher
 4 Attention Is All You Need
Attention Is All You Need
Yannic Kilcher
 5 git for research basics: fundamentals, commits, branches, merging
git for research basics: fundamentals, commits, branches, merging
Yannic Kilcher
 6 Curiosity-driven Exploration by Self-supervised Prediction
Curiosity-driven Exploration by Self-supervised Prediction
Yannic Kilcher
 7 World Models
World Models
Yannic Kilcher
 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
Yannic Kilcher
 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
Yannic Kilcher
 18 S.H.E. - Search. Human. Equalizer.
S.H.E. - Search. Human. Equalizer.
Yannic Kilcher
 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
Yannic Kilcher
 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
Yannic Kilcher
 36 LeDeepChef 👨‍🍳 Deep Reinforcement Learning Agent for Families of Text-Based Games
LeDeepChef 👨‍🍳 Deep Reinforcement Learning Agent for Families of Text-Based Games
Yannic Kilcher
 37 The Visual Task Adaptation Benchmark
The Visual Task Adaptation Benchmark
Yannic Kilcher
 38 IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
Yannic Kilcher
 39 AlphaStar: Grandmaster level in StarCraft II using multi-agent reinforcement learning
AlphaStar: Grandmaster level in StarCraft II using multi-agent reinforcement learning
Yannic Kilcher
 40 SinGAN: Learning a Generative Model from a Single Natural Image
SinGAN: Learning a Generative Model from a Single Natural Image
Yannic Kilcher
 41 A neurally plausible model learns successor representations in partially observable environments
A neurally plausible model learns successor representations in partially observable environments
Yannic Kilcher
 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
Yannic Kilcher
 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
Yannic Kilcher
 44 NeurIPS 19 Poster Session
NeurIPS 19 Poster Session
Yannic Kilcher
 45 Go-Explore: a New Approach for Hard-Exploration Problems
Go-Explore: a New Approach for Hard-Exploration Problems
Yannic Kilcher
 46 Reformer: The Efficient Transformer
Reformer: The Efficient Transformer
Yannic Kilcher
 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
Yannic Kilcher
 48 Turing-NLG, DeepSpeed and the ZeRO optimizer
Turing-NLG, DeepSpeed and the ZeRO optimizer
Yannic Kilcher
 49 Growing Neural Cellular Automata
Growing Neural Cellular Automata
Yannic Kilcher
 50 NeurIPS 2020 Changes to Paper Submission Process
NeurIPS 2020 Changes to Paper Submission Process
Yannic Kilcher
 51 Deep Learning for Symbolic Mathematics
Deep Learning for Symbolic Mathematics
Yannic Kilcher
 52 Online Education - How I Make My Videos
Online Education - How I Make My Videos
Yannic Kilcher
 53 [Rant] coronavirus
[Rant] coronavirus
Yannic Kilcher
 54 Axial Attention & MetNet: A Neural Weather Model for Precipitation Forecasting
Axial Attention & MetNet: A Neural Weather Model for Precipitation Forecasting
Yannic Kilcher
 55 Agent57: Outperforming the Atari Human Benchmark
Agent57: Outperforming the Atari Human Benchmark
Yannic Kilcher
 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
Yannic Kilcher
 58 POET: Endlessly Generating Increasingly Complex and Diverse Learning Environments and Solutions
POET: Endlessly Generating Increasingly Complex and Diverse Learning Environments and Solutions
Yannic Kilcher
 59 Evaluating NLP Models via Contrast Sets
Evaluating NLP Models via Contrast Sets
Yannic Kilcher
 60 [Drama] Who invented Contrast Sets?
[Drama] Who invented Contrast Sets?
Yannic Kilcher

Related AI Lessons

⚡
I Tried 10 ChatGPT Resume Prompts. Here's What Actually Got Me Interviews.
Learn how to use ChatGPT prompts to improve your resume and get more interview callbacks
Dev.to AI
 ⚡
How does indirect prompt injection work? #tech
Indirect prompt injection is a technique used in AI to manipulate model outputs by injecting prompts indirectly, and understanding how it works is crucial for developing secure AI systems.
Dev.to AI
 ⚡
A Unified View of AI Evolution: From Machine Learning to LLMs, RAG, and Fine-Tuning
Learn about the evolution of AI from machine learning to LLMs, RAG, and fine-tuning, and how to apply these concepts in practice
Dev.to · Naimul Karim
 ⚡
OpenAI Just Unleashed GPT-5.5 — And It Signals the Next Phase of AI
OpenAI's GPT-5.5 signals a shift towards practical AI applications in the real world
Medium · AI

Chapters (14)

Intro & Overview
6:30 Directly learning the structure of HTML
12:30 Causally Masked Language Modelling
18:50 A short look at how to use this model
23:20 Start of interview
25:30 Feeding language models with HTML
29:45 How to get bi-directionality into decoder-only Transformers?
37:00 Images are just tokens
41:15 How does one train such giant models?
45:40 CM3 results are amazing
58:20 Large-scale dataset collection and content filtering
1:04:40 More experimental results
1:12:15 Why don't we use raw HTML?
1:18:20 Does this paper contain too many things?
Up next
5 Levels of AI Agents - From Simple LLM Calls to Multi-Agent Systems
Dave Ebbelaar (LLM Eng)
Watch →