Neural Networks
Herein lie some of my thoughts and resources about neural networks. Because I am work for a company that builds models for computer vision, I have a bit of a professional bias towards image models, but I have tried to represent my knowledge/opinions about a broader range of subjects here.
What do you think about generative "AI"?
tl;dr - mostly dancing bearware, some novel uses in responsibility laundering
Resources
Image models
- Stanford CS231n: Deep Learning for Computer Vision - excellent introductory course in computer vision (from kNN to VGGNet) focused on neural networks, with exercises done in Python (with numpy)
- How to trick a neural network into thinking a panda is a vulture - excellent exploration by Julia Evans (with Python source code) of an adversarial attack on an image classifier
Text models
For code
- Stephen Wolfram's "What Is ChatGPT Doing … and Why Does It Work?"
- 0xabad1dea's GitHub CoPilot risk assessment
For everything else
- Washington Post coverage of the data contained in the 'C4' dataset and how it influences the training of popular large models. Also allows users to check if arbitrary URLs are part of the dataset. (NOTE: C4 is not the only source of training text for the models being discussed, and the authors aren't doing a great job highlighting that, but it should still be pretty representative)
- How well does ChatGPT speak Japanese? - an April 2023 evaluation of GPT-3.5 and GPT-4 performance on Japanese language assessments. Also includes an interesting comparison of the number of tokens required to represent the "Lord's Prayer" in multiple languages. I found the results of the latter particularly surprising.
Misc.
- I gave a talk on the fundamentals of neural networks to Boston Python in March 2023
- 3blue1brown has an excellent series of lessons about the fundamentals of neural networks. Particularly interesting to me is the lesson on backpropagation for its excellent visualization of the process of adjusting neural network weights.
Lawsuits
The legal status of generative models and their implications for intellectual property in the US is something I'm trying to keep an eye on. The cases given below are of particular interest to me.
ANDERSEN v. STABILITY AI LTD.
GETTY IMAGES (US), INC. v. STABILITY AI, INC.
DOE 1 v. GITHUB, INC.
SILVERMAN v. OPENAI, INC.
MATA v. AVIANCA, INC. (closed)
Note: this case is not about machine learning textually, but is included in this list because it is a notable example of gross misuse of a language model by plaintiff's counsel to submit falsified documents to the court. This led to censure of plaintiff's counsel and dismissal of the case.
