1/Large language models like Galactica and ChatGPT can spout nonsense in a confident, authoritative tone. This overconfidence – which reflects the data they’re trained on – makes them more likely to mislead.
AI
-
WordfixerBot: Free AI Grammar and Text Processing Tool
By
–
WordfixerBot by @austinnguyen00 is a free AI tool that checks your grammar and paraphrases, summarizes, and compares text. https://
futurepedia.io/tool/wordfixer
bot
… #productivity #Researcher #ArtificialIntelligence -
Robotics Transformer 1: Multi-Task Robot Learning Model
By
–
Introducing the Robotics Transformer 1, a multi-task model that tokenizes robot inputs and outputs actions to enable efficient inference at runtime. Learn how it improves zero-shot generalization to new tasks, environments and objects → https://t.co/hnsKvCJjmP pic.twitter.com/g9QFXzjs9T
— Google AI (@GoogleAI) 13 décembre 2022Introducing the Robotics Transformer 1, a multi-task model that tokenizes robot inputs and outputs actions to enable efficient inference at runtime. Learn how it improves zero-shot generalization to new tasks, environments and objects → https://
goo.gle/3Yxomnt -
Self-ranking method improves academic paper evaluation system
By
–
This is an obvious critique, which the paper deals with. It makes you rank your own papers against each other. This turns out to make things work out. Check out the paper!
-
Key AI Benchmarks for Language Model Evaluation
By
–
Benchmarks:
– MMLU (massively multitask language understanding): https://
arxiv.org/abs/2009.03300
– BBH (Big-Bench Hard): https://
arxiv.org/abs/2210.09261
– TyDiQA (typographically diverse QA): https://
arxiv.org/abs/2003.05002
– MGSM (multilingual grade school math): https://
arxiv.org/abs/2210.03057 -
Code-Davinci-2 vs Text-Davinci-3: Instruction Tuning and PPO Performance
By
–
– code-davinci-2 > text-davinci-3 means that their instruction finetuning overall hurts performance on academic benchmarks
– text-davinci-3 > text-davinci-2 means that PPO improves performance -
Text-davinci-003 Instruction Following vs Academic Benchmark Performance
By
–
@OpenAI
's text-davinci-003 follows instructions better. Is it also better on academic benchmarks? Summary:
– text-davinci-3 beats text-davinci-2, but is not as good as code-davinci-2
– it is behind @GoogleAI
's PaLM and Flan-U-PaLM Full results: https://
arxiv.org/abs/2210.11416 App D -
Neural Networks Introduction: 7-Minute Quick Start Guide
By
–
Get started with Neural Networks in 7 minutes!
— Satya Mallick (@LearnOpenCV) 13 décembre 2022
Watch the full video here: https://t.co/wDHSvFKuLJ #learnopencv #opencv #computervision #artificialintelligence #neuralnetworks #python #ai #deeplearning #machinelearning pic.twitter.com/6dbnXlXZYtGet started with Neural Networks in 7 minutes!
Watch the full video here: https://
youtube.com/watch?v=gsgt_u
XvjmM
… #learnopencv #opencv #computervision #artificialintelligence #neuralnetworks #python #ai #deeplearning #machinelearning -
Python Debugging Cheat Sheet for Developers
By
–
Python debugging cheat sheet. @HaroldSinnott @SourabhSKatoch
