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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Show notes, transcripts, and past episodes at gettingsimple.com/podcast.

Thanks to Andrea Villalón Paredes for editing this interview.
Sleep and A Loop to Kill For songs by Steve Combs under CC BY 4.0.


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