Argh, my son just figured out how to steal chocolate from the pantry, and made a brown, gooey mess.
Worried about how hard it is to align AI? Right now I feel I have better tools to align AI with human values than align humans with human values, at least in the case of my 2 year old. Parenting would be easier if we could run RLHF on our kids!
ConvNets Match Vision Transformers at Scale
paper page: https://t.co/0ks69LNcDw
Many researchers believe that ConvNets perform well on small or moderately sized datasets, but are not competitive with Vision Transformers when given access to datasets on the web-scale. We challenge this belief by evaluating a performant ConvNet architecture pre-trained on JFT-4B, a large labelled dataset of images often used for training foundation models. We consider pre-training compute budgets between 0.4k and 110k TPU-v4 core compute hours, and train a series of networks of increasing depth and width from the NFNet model family. We observe a log-log scaling law between held out loss and compute budget. After fine-tuning on ImageNet, NFNets match the reported performance of Vision Transformers with comparable compute budgets. Our strongest fine-tuned model achieves a Top-1 accuracy of 90.4%.
The resume that got me referred to top MNC's and landed up a high paying job.
What things you should keep in mind while making a resume?
Here's a thread👇
Download 698-page PDF >> Everything You Always Wanted To Know About Mathematics*
(*But didn’t even know to ask)
A Guided Journey Into the World of Abstract Mathematics and the Writing of Proofs
https://t.co/JLsDOmqmQY
Stanford University has just opened full access to CS224U.
One of their immensely popular graduate-level Natural Language Understanding course taught by Professor Christopher Potts.
Checkout link below 👇
We just released "Large Language Models with Semantic Search”, built with @cohere, and taught by @JayAlammar and @SerranoAcademy. Search is a key part of many applications. Say, you need to retrieve documents or products in response to a user query; how can LLMs help? You’ll learn about (i) Embeddings, to retrieve a collection of documents loosely related to a query, and (ii) LLM assisted re-ranking, to rank them precisely according to relevance. You’ll also go through code showing how to tie all this together to build a complete search system for retrieving relevant Wikipedia articles. Please check it out!
https://t.co/H4O0xKBhOO