Recent events have demonstrated how crucial resilience (e.g. of supply chains) is for our society. Semantic technologies can play a crucial here.
We will organize the #D2R2 (Linked Data-driven Resilience Research) #workshop at @email@example.com
in May 2023 in Crete. We are looking forward to your contribution. Submission deadline is March 9. Check more details on our event page! https://d2r2.aksw.org #ESWC23 #CoyPu_Project #Resilience #LinkedData #cfp
The artificial-intelligence chatbot ChatGPT can write fake abstracts that scientists have trouble distinguishing from those written by humans. Increasing sophistication of chatbots could undermine research integrity and accuracy, researchers fear.
I found the papers "Scaling Laws for Neural Language Models" (OpenAI, 2020) and "Training Compute-Optimal Large Language Models" (DeepMind, 2022) interesting:
They do a LOT of experiments training large language models (causal transformers) with varying hyperparameters, in particular model size, shape, batch size, and training data set size over many orders of magnitude. 1/?
DeepMind's paper refutes this last claim, and finds that both are equally useful.
The differences between DeepMind & OpenAI's papers matter in terms of forecasting how big LLMs need to get. They arrived at these different conclusions because DeepMind did more learning rate tuning. This blog post https://severelytheoretical.wordpress.com/2022/07/18/thoughts-on-the-new-scaling-laws-for-large-language-models/ hypothesizes that DeepMind's paper might also be not doing enough hyperparameter tuning, and the scaling law may be less severe, perhaps not even a power law.
On #TheDataExchangePod I speak with Mark Chen, Research Scientist at OpenAI. We discuss the evolution of DALL·E, key research developments that led to DALL·E 2, data sources, safety measures, ML models needed for its success. #machinelearning #dalle2 #dalle #AI #generativeai https://thedataexchange.media/exploring-dalle-2/
Very interesting essay on LLMs, their limitations, and their future by @yoavgo!
The latest issue of 'Ahead of AI' is now available!
This edition covers my top 10 papers of the year, as well as trends in the AI industry, notable developments in open source projects, and my personal yearly review routine.
Check it out at the link below and have a happy new year!
How good of a BERT can one get in ONE DAY on ONE GPU?
With all the recent studies about scaling compute up, this paper takes a refreshing turn and does a deep dive into scaling down compute.
It's well written, stock full of insights. Here is my summary and my opinions.
With the advent of #ChatGPT, everyone is talking about large language models. But how do they work? Initially, such models were trained to complete sentences.
But they exhibit exciting capabilities that can be invoked by feeding them "prompts."
Read our Prompt Engineering Guide for a quick overview of the current state of this field.
Scikit-learn 1.2 is out: https://github.com/scikit-learn/scikit-learn/releases/tag/1.2.0
Was an eventful December & I totally missed the new release of my favorite #machinelearning library!
My personal highlights are around the HistGradientBoostingClassifier (if you haven't used it yet, it's a LightGBM impl that works really well)
It now supports
1. interaction constraints (in trees, features that appear along a particular path are considered as "interacting")
2. class weights
3. feature names for categorical features
😮 Exciting times:
Surprised to see a #ChatGPT style AI model integrated with Web search so soon!
The new #YouChat provides links to sources, but just like other AI models also makes many mistakes.
Will be interesting to see how people use it.
Hey, I am just signed up a few days ago and want to introduce myself.
I am a #machinelearning researcher focusing on deep neural nets. My passion is sharing all kinds of stuff about machine learning & open source. (Some of you may know me from my books “Python Machine Learning” and “Machine Learning with PyTorch and Scikit-Learn”.)
I love to teach others, and am currently working as Lead AI Educator at Lightning AI, and also an Assistant Prof of Statistics at the University of Wisconsin-Madison.
We've been working on new https://prodi.gy workflows that let you use the OpenAI API to kickstart your annotations, via zero- or few-shot learning. We've just published the first recipe, for NER annotation 🎉 https://github.com/explosion/prodigy-openai-recipes
Here's what, why and how. 🧵
Let's say you want to do some 'traditional' NLP thing, like extracting information from text. The information you want to extract isn't on the public web — it's in this pile of documents you have sitting in front of you.
Please donate to the Internet Archive if you can.
We are a bargain! Serving millions every day with books music video and web archives.
Please help keep everything freely available.
great post about chatgpt, reviewing everything known about how its emergent abilities might have come about https://yaofu.notion.site/How-does-GPT-Obtain-its-Ability-Tracing-Emergent-Abilities-of-Language-Models-to-their-Sources-b9a57ac0fcf74f30a1ab9e3e36fa1dc1
QOTO: Question Others to Teach Ourselves
An inclusive, Academic Freedom, instance
All cultures welcome.
Hate speech and harassment strictly forbidden.