#ts

I love the last paragraph, especially this sentence:
> Unfortunately, I can’t continue my debate with Clive Granger. I rather hoped he would come to accept my point of view.

Rob J Hyndman - The difference between prediction intervals and confidence intervals
https://robjhyndman.com/hyndsight/intervals/

#data

Quite useful.

I use pyarrow a lot and also a bit of polars. Mostly because pandas is slow. With the new 2.0 release, all three libraries are seamlessly connected to each other.

https://datapythonista.me/blog/pandas-20-and-the-arrow-revolution-part-i

datapythonista blog

pandas 2.0 and the Arrow revolution (part I)

Introduction At the time of writing this post, we are in the process of releasing pandas 2.0. The project has a large number of users,...

#ai

The performance is not too bad. But…given this is about academic topics, it sounds terrible to have this level of hallucination.

https://bair.berkeley.edu/blog/2023/04/03/koala/

The Berkeley Artificial Intelligence Research Blog

Koala: A Dialogue Model for Academic Research

The BAIR Blog

#ai

https://github.com/dair-ai/Prompt-Engineering-Guide/blob/main/guides/prompts-advanced-usage.md

GitHub

Prompt-Engineering-Guide/guides/prompts-advanced-usage.md at main · dair-ai/Prompt-Engineering-Guide

🐙 Guides, papers, lecture, notebooks and resources for prompt engineering - dair-ai/Prompt-Engineering-Guide

#ai

Generate complicated 3D models by jotting down your ideas:

https://opus.ai/demo

#ai

A lot of big names signed it. (Not sure how they verify the signee though)

Personally, I'm not buying it.

https://futureoflife.org/open-letter/pause-giant-ai-experiments/

Future of Life Institute

Pause Giant AI Experiments: An Open Letter - Future of Life Institute

We call on all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4.

#dl

I am experimenting with torch 2.0 and searching for potential training time improvements in lightning. The following article provides a very good introduction.

https://lightning.ai/pages/community/tutorial/how-to-speed-up-pytorch-model-training/

Lightning AI

How to Speed Up PyTorch Model Training

Learn how to improve the training performance of your PyTorch model without compromising its accuracy.

#ml

Pérez J, Barceló P, Marinkovic J. Attention is Turing-Complete. J Mach Learn Res. 2021;22: 1–35. Available: https://jmlr.org/papers/v22/20-302.html

#misc

This is how generative AI is changing our lives. Now thinking about it, those competitive advantages from our satisfying technical skills are fading away.

What shall we invest into for a better career? Just integrated whatever is coming into our workflow? Or fundamentally change the way we are thinking?

#agi

http://arxiv.org/abs/2303.12712

This is crazy.

#misc

https://twitter.com/MushtaqBilalPhD/status/1637715463399456768?t=dCY5NP4Ddd_HtTj9PUugxQ&s=19


(Fact check needed)

#dl

https://github.com/Lightning-AI/lightning/releases/tag/2.0.0

You can compile (torch 2.0) LightningModule now.

import torch
import lightning as L
model = LitModel()
# This will compile forward and {training,validation,test,predict}_step
compiled_model = torch.compile(model)
trainer = L.Trainer()
trainer.fit(compiled_model)

GitHub

Release Lightning 2.0: Fast, Flexible, Stable · Lightning-AI/lightning

Lightning AI is excited to announce the release of Lightning 2.0 ⚡

Highlights
Backward Incompatible Changes

PyTorch
Fabric


Full Changelog

PyTorch
Fabric
App


Contributors

Over the last coupl...

#ml

https://mlcontests.com/state-of-competitive-machine-learning-2022/

Quote from the report:

Successful competitors have mostly converged on a common set of tools — Python, PyData, PyTorch, and gradient-boosted decision trees.

Deep learning still has not replaced gradient-boosted decision trees when it comes to tabular data, though it does often seem to add value when ensembled with boosting methods.
Transformers continue to dominate in NLP, and start to compete with convolutional neural nets in computer vision.

Competitions cover a broad range of research areas including computer vision, NLP, tabular data, robotics, time-series analysis, and many others.
Large ensembles remain common among winners, though single-model solutions do win too.

There are several active machine learning competition platforms, as well as dozens of purpose-built websites for individual competitions.
Competitive machine learning continues to grow in popularity, including in academia.

Around 50% of winners are solo winners; 50% of winners are first-time winners; 30% have won more than once before.

Some competitors are able to invest significantly into hardware used to train their solutions, though others who use free hardware like Google Colab are also still able to win competitions.

ML Contests

The State of Competitive Machine Learning | ML Contests

We summarise the state of the competitive landscape and analyse the 200+ competitions that took place in 2022. Plus a deep dive analysis of 67 winning solutions to figure out the best strategies to win at competitive ML.

Great thread on how a communication failure contributed to SVB’s collapse.
https://twitter.com/lulumeservey/status/1634232322693144576

#ml

Releasing the Skynet

https://github.com/internet-explorer-ssl/internet-explorer

GitHub

GitHub - internet-explorer-ssl/internet-explorer: Internet Explorer explores the web in a self-supervised manner to progressively…

Internet Explorer explores the web in a self-supervised manner to progressively find relevant examples that improve performance on a desired target dataset. - GitHub - internet-explorer-ssl/interne...

#dl

Hmm I'm talking to ... a paper?

https://github.com/mukulpatnaik/researchgpt

GitHub

GitHub - mukulpatnaik/researchgpt: A LLM based research assistant that allows you to have a conversation with a research paper

A LLM based research assistant that allows you to have a conversation with a research paper - mukulpatnaik/researchgpt

#misc

豆瓣 for research papers.

https://42papers.com/