Alex O’Connor—researcher and ML manager—on the latest trends of generative AI. Language and image models, prompt engineering, the latent space, fine-tuning, tokenization, textual inversion, adversarial attacks, and more.

Alex O’Connor got his PhD in Computer Science from Trinity College, Dublin. He was a postdoctoral researcher and funded investigator for the ADAPT Centre for digital content, at both TCD and later DCU. In 2017, he joined Pivotus, a Fintech startup, as Director of Research. Alex has been Sr Manager for Data Science & Machine Learning at Autodesk for the past few years, leading a team that delivers machine learning for e-commerce, including personalization and natural language processing.

Favorite quotes

• “None of these models can read.”


• “Art in the future may not be good, but it will be prompt.” Mastodon
  

Books

• 
  Machine Learning Systems Design by Chip Huyen


• 
  Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron

Papers

• 
  The Illustrated Transformer by Jay Alammar


• 
  Attention Is All You Need by Google Brain


• 
  Transformers: a Primer by Justin Seonyong Lee

Links

• 
  Alex in Mastodon ★


• 
  Training Dream Booth Multimodal Art on HuggingFace by @akhaliq
  


• 
  NeurIPS
  


• 
  arxiv.org: Where most papers get published


• 
  Nono’s Discord
  


• 
  Suggestive Drawing: Nono’s master’s thesis


• 
  Crungus is a fictional character from Stable Diffusion’s latent space

Machine learning models

• 
  Stable Diffusion
  


• 
  Arcane Style Stable Diffusion fine-tuned model ★


• 
  Imagen
  


• 
  DALL-E
  


• 
  CLIP
  


• 
  GPT and ChatGPT
  


• 
  BERT, ALBERT & RoBERTa
  


• 
  Bloom
  


• 
  word2vec
  


• 
  Mupert.ai and Google’s MusicLM
  


• 
  t-SNE and UMAP: Dimensionality reduction techniques


• 
  char-rnn
  

Sites

• 
  TensorFlow Hub
  


• 
  HuggingFace Spaces ★


• 
  DreamBooth
  


• 
  Jasper AI
  


• 
  Midjourney
  


• 
  Distill.pub ★

Concepts

• 
  High-performance computing (HPC)


• 
  Transformers and Attention
  


• 
  Sequence transformers
  


• 
  Quadratic growth
  


• 
  Super resolution
  


• 
  Recurrent neural networks (RNNs)


• 
  Long short-term memory networks (LSTMs)


• 
  Gated recurrent units (GRUs)


• 
  Bayesian classifiers
  


• 
  Machine translation
  


• 
  Encoder-decoder
  


• 
  Gradio
  


• 
  Tokenization ★


• 
  Embeddings ★


• 
  Latent space
  


• The distributional hypothesis


• 
  Textual inversion ★


• Pretrained models


• Zero-shot learning


• 
  Mercator projection
  

People mentioned

• 
  Ted Underwood UIUC


• 
  Chip Huyen
  


• 
  Aurélien Géron
  

Chapters

• 00:00 · Introduction


• 00:40 · Machine learning


• 02:36 · Spam and scams


• 15:57 · Adversarial attacks


• 20:50 · Deep learning revolution


• 23:06 · Transformers


• 31:23 · Language models


• 37:09 · Zero-shot learning


• 42:16 · Prompt engineering


• 43:45 · Training costs and hardware


• 47:56 · Open contributions


• 51:26 · BERT and Stable Diffusion


• 54:42 · Tokenization


• 59:36 · Latent space


• 01:05:33 · Ethics


• 01:10:39 · Fine-tuning and pretrained models


• 01:18:43 · Textual inversion


• 01:22:46 · Dimensionality reduction


• 01:25:21 · Mission


• 01:27:34 · Advice for beginners


• 01:30:15 · Books and papers


• 01:34:17 · The lab notebook


• 01:44:57 · Thanks

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