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.” MastodonBooks
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 hypothesisTextual inversion ★
Pretrained modelsZero-shot learningMercator projection
People mentioned
Ted Underwood UIUC
Chip Huyen
Aurélien Géron
Chapters
00:00 · Introduction00:40 · Machine learning02:36 · Spam and scams15:57 · Adversarial attacks20:50 · Deep learning revolution23:06 · Transformers31:23 · Language models37:09 · Zero-shot learning42:16 · Prompt engineering43:45 · Training costs and hardware47:56 · Open contributions51:26 · BERT and Stable Diffusion54:42 · Tokenization59:36 · Latent space01:05:33 · Ethics01:10:39 · Fine-tuning and pretrained models01:18:43 · Textual inversion01:22:46 · Dimensionality reduction01:25:21 · Mission01:27:34 · Advice for beginners01:30:15 · Books and papers01:34:17 · The lab notebook01:44:57 · Thanks
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Show notes, transcripts, and past episodes at gettingsimple.com/podcast.
Thanks to Andrea Villalón Paredes for editing this interview.
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A Loop to Kill For songs by Steve Combs under CC BY 4.0.
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