"Watermarking Language Models" paper and GPTZero EXPLAINED | How to detect text by ChatGPT?

AI Coffee Break with Letitia ยท Beginner ยท๐Ÿง  Large Language Models ยท3y ago
Did ChatGPT write this text? We explain two ways of knowing whether AI has written a text: GPTZero and Watermarking language models. โ–บ Sponsor: Cohere ๐Ÿ‘‰ https://t1p.de/22srn UPDATE: Further research about the Reliability of Watermarks for LLMs! https://arxiv.org/abs/2306.04634 shows that watermarking works even when watermarked text is re-written by humans or paraphrased by another non-watermarked LLM. Check out our daily #MachineLearning Quiz Questions: https://www.youtube.com/c/AICoffeeBreak/community โžก๏ธ AI Coffee Break Merch! ๐Ÿ›๏ธ https://aicoffeebreak.creator-spring.com/ ๐Ÿ“œ Watermarking LMs paper: Kirchenbauer, John, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers and Tom Goldstein. โ€œA Watermark for Large Language Models.โ€ (2023). https://arxiv.org/abs/2301.10226 ๐Ÿ“– Tom Goldsteinโ€™s tweet summary: https://twitter.com/tomgoldsteincs/status/1618287665006403585 ๐Ÿ”— GPTZero: https://gptzero.me/ ๐Ÿ”— First version of GPTZero (easy and free to try out): https://etedward-gptzero-main-zqgfwb.streamlit.app/ Thanks to our Patrons who support us in Tier 2, 3, 4: ๐Ÿ™ Dres. Trost GbR, Siltax, Edvard Grรธdem, Vignesh Valliappan, Mutual Information, Mike Ton Outline: 00:00 Did ChatGPT generate this text? 01:14 Cohere [Sponsor] 02:43 How does GPTZero work: Perplexity explained 05:42 GPTZero โ€œBurstinessโ€ 07:08 Robust detection with watermarking 07:46 Language modelling decoding explained 09:21 Watermarking explained 11:05 โ€œsoftโ€ watermark 12:49 Attacking watermarks โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€ ๐Ÿ”ฅ Optionally, pay us a coffee to help with our Coffee Bean production! โ˜• Patreon: https://www.patreon.com/AICoffeeBreak Ko-fi: https://ko-fi.com/aicoffeebreak โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€ ๐Ÿ”— Links: AICoffeeBreakQuiz: https://www.youtube.com/c/AICoffeeBreak/community Twitter: https://twitter.com/AICoffeeBreak Reddit: https://www.reddit.com/r/AICoffeeBreak/ YouTube: https://www.youtube.com/AICoffeeBreak #AICoffeeBreak #MsCoffeeBean #MachineLearning #AI #researchโ€‹ Music ๐ŸŽต : Sunset n Be
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1 AI Coffee Break - Channel Trailer
AI Coffee Break - Channel Trailer
AI Coffee Break with Letitia
 2 How to check if a neural network has learned a specific phenomenon?
How to check if a neural network has learned a specific phenomenon?
AI Coffee Break with Letitia
 3 A brief history of the Transformer architecture in NLP
A brief history of the Transformer architecture in NLP
AI Coffee Break with Letitia
 4 Our paper at CVPR 2020 - MUL Workshop and ACL 2020 - ALVR Workshop
Our paper at CVPR 2020 - MUL Workshop and ACL 2020 - ALVR Workshop
AI Coffee Break with Letitia
 5 The Transformer neural network architecture EXPLAINED. โ€œAttention is all you needโ€
The Transformer neural network architecture EXPLAINED. โ€œAttention is all you needโ€
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 6 Transformer combining Vision and Language? ViLBERT - NLP meets Computer Vision
Transformer combining Vision and Language? ViLBERT - NLP meets Computer Vision
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 7 Pre-training of BERT-based Transformer architectures explained โ€“ language and vision!
Pre-training of BERT-based Transformer architectures explained โ€“ language and vision!
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 8 GPT-3 explained with examples. Possibilities, and implications.
GPT-3 explained with examples. Possibilities, and implications.
AI Coffee Break with Letitia
 9 Adversarial Machine Learning explained! | With examples.
Adversarial Machine Learning explained! | With examples.
AI Coffee Break with Letitia
 10 BERTology meets Biology | Solving biological problems with Transformers
BERTology meets Biology | Solving biological problems with Transformers
AI Coffee Break with Letitia
 11 Can a neural network tell if an image is mirrored? โ€“ Visual Chirality
Can a neural network tell if an image is mirrored? โ€“ Visual Chirality
AI Coffee Break with Letitia
 12 The ultimate intro to Graph Neural Networks. Maybe.
The ultimate intro to Graph Neural Networks. Maybe.
AI Coffee Break with Letitia
 13 Can language models understand? Bender and Koller argument.
Can language models understand? Bender and Koller argument.
AI Coffee Break with Letitia
 14 GANs explained | Generative Adversarial Networks video with showcase!
GANs explained | Generative Adversarial Networks video with showcase!
AI Coffee Break with Letitia
 15 What nobody tells you about MULTIMODAL Machine Learning! ๐Ÿ™Š THE definition.
What nobody tells you about MULTIMODAL Machine Learning! ๐Ÿ™Š THE definition.
AI Coffee Break with Letitia
 16 Multimodal Machine Learning models do not work. Here is why. Part 1/2 โ€“ The SYMPTOMS
Multimodal Machine Learning models do not work. Here is why. Part 1/2 โ€“ The SYMPTOMS
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 17 Why Multimodal Machine Learning models do not work. Part 2/2 โ€“ The CAUSES
Why Multimodal Machine Learning models do not work. Part 2/2 โ€“ The CAUSES
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 18 An image is worth 16x16 words: ViT | Vision Transformer explained
An image is worth 16x16 words: ViT | Vision Transformer explained
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 19 AI understanding language!? A roadmap to natural language understanding.
AI understanding language!? A roadmap to natural language understanding.
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 20 "What Can We Do to Improve Peer Review in NLP?" ๐Ÿ‘€
"What Can We Do to Improve Peer Review in NLP?" ๐Ÿ‘€
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 21 The curse of dimensionality. Or is it a blessing?
The curse of dimensionality. Or is it a blessing?
AI Coffee Break with Letitia
 22 PCA explained with intuition, a little math and code
PCA explained with intuition, a little math and code
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 23 Data-efficient Image Transformers EXPLAINED! Facebook AI's DeiT paper
Data-efficient Image Transformers EXPLAINED! Facebook AI's DeiT paper
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 24 OpenAI's DALL-E explained. How GPT-3 creates images from descriptions.
OpenAI's DALL-E explained. How GPT-3 creates images from descriptions.
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 25 Leaking training data from GPT-2. How is this possible?
Leaking training data from GPT-2. How is this possible?
AI Coffee Break with Letitia
 26 OpenAIโ€™s CLIP explained! | Examples, links to code and pretrained model
OpenAIโ€™s CLIP explained! | Examples, links to code and pretrained model
AI Coffee Break with Letitia
 27 Transformers can do both images and text. Here is why.
Transformers can do both images and text. Here is why.
AI Coffee Break with Letitia
 28 UMAP explained | The best dimensionality reduction?
UMAP explained | The best dimensionality reduction?
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 29 NVIDIA Jarvis (now NVIDIA Riva) meets Ms. Coffee Bean
NVIDIA Jarvis (now NVIDIA Riva) meets Ms. Coffee Bean
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 30 Transformer in Transformer: Paper explained and visualized | TNT
Transformer in Transformer: Paper explained and visualized | TNT
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 31 [RANT] Adversarial attack on OpenAIโ€™s CLIP? Are we the fools or the foolers?
[RANT] Adversarial attack on OpenAIโ€™s CLIP? Are we the fools or the foolers?
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 32 Pattern Exploiting Training explained! | PET, iPET, ADAPET
Pattern Exploiting Training explained! | PET, iPET, ADAPET
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 33 Deep Learning for Symbolic Mathematics!? | Paper EXPLAINED
Deep Learning for Symbolic Mathematics!? | Paper EXPLAINED
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 34 FNet: Mixing Tokens with Fourier Transforms โ€“ Paper Explained
FNet: Mixing Tokens with Fourier Transforms โ€“ Paper Explained
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 35 Are Pre-trained Convolutions Better than Pre-trained Transformers? โ€“ Paper Explained
Are Pre-trained Convolutions Better than Pre-trained Transformers? โ€“ Paper Explained
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 36 "Please Commit More Blatant Academic Fraud" โ€“ A fellow PhD student's response.
"Please Commit More Blatant Academic Fraud" โ€“ A fellow PhD student's response.
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 37 Scaling Vision Transformers? How much data can a transformer get? #Shorts
Scaling Vision Transformers? How much data can a transformer get? #Shorts
AI Coffee Break with Letitia
 38 How cross-modal are vision and language models really? ๐Ÿ‘€ Seeing past words. [Own work]
How cross-modal are vision and language models really? ๐Ÿ‘€ Seeing past words. [Own work]
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 39 Charformer: Fast Character Transformers via Gradient-based Subword Tokenization +Tokenizer explained
Charformer: Fast Character Transformers via Gradient-based Subword Tokenization +Tokenizer explained
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 40 Positional embeddings in transformers EXPLAINED | Demystifying positional encodings.
Positional embeddings in transformers EXPLAINED | Demystifying positional encodings.
AI Coffee Break with Letitia
 41 Adding vs. concatenating positional embeddings & Learned positional encodings
Adding vs. concatenating positional embeddings & Learned positional encodings
AI Coffee Break with Letitia
 42 Self-Attention with Relative Position Representations โ€“ Paper explained
Self-Attention with Relative Position Representations โ€“ Paper explained
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 43 Saddle points vs. local minima in high dimensional spaces | โ“ #AICoffeeBreakQuiz #Shorts
Saddle points vs. local minima in high dimensional spaces | โ“ #AICoffeeBreakQuiz #Shorts
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 44 What is the model identifiability problem? | Explained in 60 seconds! | โ“ #AICoffeeBreakQuiz #Shorts
What is the model identifiability problem? | Explained in 60 seconds! | โ“ #AICoffeeBreakQuiz #Shorts
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 45 Data leakage during data preparation? | Using AntiPatterns to avoid MLOps Mistakes
Data leakage during data preparation? | Using AntiPatterns to avoid MLOps Mistakes
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 46 Is today's AI smarter than YOU? #Shorts
Is today's AI smarter than YOU? #Shorts
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 47 Convolution vs Cross-Correlation. How most CNNs do not compute convolutions. | โ“ #Shorts
Convolution vs Cross-Correlation. How most CNNs do not compute convolutions. | โ“ #Shorts
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 48 Why do we care about cross-correlations vs convolutions | โ“ #AICoffeeBreakQuiz #Shorts
Why do we care about cross-correlations vs convolutions | โ“ #AICoffeeBreakQuiz #Shorts
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 49 The convolution is not shift invariant. | Invariance vs Equivariance | โ“ #AICoffeeBreakQuiz #Shorts
The convolution is not shift invariant. | Invariance vs Equivariance | โ“ #AICoffeeBreakQuiz #Shorts
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 50 How to increase the receptive field in CNNs? | #AICoffeeBreakQuiz #Shorts
How to increase the receptive field in CNNs? | #AICoffeeBreakQuiz #Shorts
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 51 What is tokenization and how does it work? Tokenizers explained.
What is tokenization and how does it work? Tokenizers explained.
AI Coffee Break with Letitia
 52 Foundation Models | On the opportunities and risks of calling pre-trained models โ€œFoundation Modelsโ€
Foundation Models | On the opportunities and risks of calling pre-trained models โ€œFoundation Modelsโ€
AI Coffee Break with Letitia
 53 How modern search engines work โ€“ Vector databases explained! | Weaviate open-source
How modern search engines work โ€“ Vector databases explained! | Weaviate open-source
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 54 Eyes tell all: How to tell that an AI generated a face?
Eyes tell all: How to tell that an AI generated a face?
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 55 Swin Transformer paper animated and explained
Swin Transformer paper animated and explained
AI Coffee Break with Letitia
 56 Data BAD | What Will it Take to Fix Benchmarking for NLU?
Data BAD | What Will it Take to Fix Benchmarking for NLU?
AI Coffee Break with Letitia
 57 SimVLM explained | What the paper doesnโ€™t tell you
SimVLM explained | What the paper doesnโ€™t tell you
AI Coffee Break with Letitia
 58 Generalization โ€“ Interpolation โ€“ Extrapolation in Machine Learning: Which is it now!?
Generalization โ€“ Interpolation โ€“ Extrapolation in Machine Learning: Which is it now!?
AI Coffee Break with Letitia
 59 Do Transformers process sequences of FIXED or of VARIABLE length? | #AICoffeeBreakQuiz
Do Transformers process sequences of FIXED or of VARIABLE length? | #AICoffeeBreakQuiz
AI Coffee Break with Letitia
 60 The efficiency misnomer | Size does not matter | What does the number of parameters mean in a model?
The efficiency misnomer | Size does not matter | What does the number of parameters mean in a model?
AI Coffee Break with Letitia

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Chapters (9)

Did ChatGPT generate this text?
1:14 Cohere [Sponsor]
2:43 How does GPTZero work: Perplexity explained
5:42 GPTZero โ€œBurstinessโ€
7:08 Robust detection with watermarking
7:46 Language modelling decoding explained
9:21 Watermarking explained
11:05 โ€œsoftโ€ watermark
12:49 Attacking watermarks
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