I could talk for hours about the future of AI, but right now, I’m really looking at multimodal AI, synthetic data, companion AI, GPU shortage mitigation, hyperpersonalization, and AI enterprise readiness trends (from training to LLM ops to quick deploy templates).
LLMS
-
AI-Powered Delivery Text Interface with Auto-Translation
By
–
One example among many is the delivery personnel have a text-like interface which includes scripted messages, which are auto-translated into appropriate display language for both sides of the text chain.
-
Fleuret’s Deep Learning Introduction for STEM Readers
By
–
François Fleuret's Homepage https://
bit.ly/48mIxJC
This is a short introduction to deep learning for readers with a STEM background, originally designed to be read on a phone screen. #AI #MachineLearning #DeepLearning #LLMs #DataScience -
How to understand LLM and AI without Machine Learning?
By
–
Hello, I have a probably stupid question but how can you be knowledgeable about LLM and AI without knowing anything about Machine Learning? Please.
-
AI Model Training Data Requirements and Generalization
By
–
> No Samivel data was used in the training. I can't tell if you're joking… What are the chances of it "just knowing" without training data?
-
Deep Learning Course: François Fleuret’s UNIGE Slides and Virtual Machine
By
–
UNIGE 14×050 – Deep Learning https://
bit.ly/46DOK2q
Slides and virtual machine for François Fleuret's Deep Learning Course #AI #MachineLearning #DeepLearning #LLMs #DataScience -
Adaptive Prompting Approach Improves LLM Few-Shot Performance
By
–
Introducing a new approach for adaptive prompting of #LLMs that train with unlabeled samples + pseudo-demonstrations generated by the model itself to close the gap between few-shot and 0-shot performance on reasoning, NLU and language generation tasks. → https://t.co/ZtSEZOxNCc pic.twitter.com/5H7YE0usc3
— Google AI (@GoogleAI) 2 novembre 2023Introducing a new approach for adaptive prompting of #LLMs that train with unlabeled samples + pseudo-demonstrations generated by the model itself to close the gap between few-shot and 0-shot performance on reasoning, NLU and language generation tasks. → https://
goo.gle/3tY00IB -
Understanding LLM Word Prediction: Key Knowledge for AI Progress
By
–
What makes this knowledge pivotal is its relevance to how LLMs predict the next word of a sentence or respond to inquiries. Equipped with this understanding, you'll be able to make strides in the AI space with ease.
-
Understanding LLM Architecture: Deep Dive into Transformers
By
–
If you want to have a comprehensive look at how LLMs function and understand their architecture better, don't wait, dig into the full article! https://
louisbouchard.substack.com/p/how-do-llms-
work-the-transformer
… -
Understanding LLM Architecture: Transformers Tokenizers and Attention
By
–
Deepen your understanding of LLM architecture with an interview by AI educator, @jay . It covers generative aspects of transformers’ architecture, such as tokenizers, attention, and feed-forward networks. Ideal for beginners and enthusiasts.