TLDR AI 2023-08-10

82% OF AMERICANS THINK WE SHOULD SLOW DOWN AI DEVELOPMENT

ANTHROPIC LAUNCHES IMPROVED VERSION OF ITS ENTRY-LEVEL LLM

INWORLD AI BECOMES THE BEST-FUNDED STARTUP IN AI X GAMING

UNDERSTANDING GROKKING

The PAIR group at Google has released a lovely explainer that goes deep into the topic of Grokking. Grokking is the dynamic process a model goes through during training that may point to a shift from memorizing to understanding. It isn't well understood in general, but this is a lovely introduction that covers much of the groundwork for this strange phenomenon.

PHOTOREALISTIC SYNTHETIC DATA IN UNREAL ENGINE

Don't have millions of photorealistic images to train your algorithm? Maybe you can generate them using PUG from Meta AI. It uses the powerful unreal game engine in a controllable way to generate synthetic image data for downstream training.

SIMPLE SYNTHETIC DATA REDUCES SYCOPHANCY

Sycophancy is when a model repeats and adopts a user's opinion. This happens more in larger models and instruction-tuned models. It can also occur in tasks when the opinion is irrelevant, leading to bubble-like behavior. Simple synthetic data fine-tuning can prevent this without harming overall performance.

A NEW METHOD FOR IMPROVING STUDENT NETWORKS IN COMPUTER VISION

Deep neural networks have excelled in computer vision, but faster inference times are needed. This paper introduces the Inter-Class Similarity Distillation method and an Adaptive Loss Weighting strategy for better knowledge transfer from a teacher network to a student one.

INTEGRATE PRIVATE DATA INTO LLMS WHILE PRESERVING PRIVACY

Generative AI systems have grown with the help of Large Language Models. The SimplyRetrieve open-source tool offers a user-friendly way to integrate private data into these systems without extra tuning using the Retrieval-Centric Generation approach. It promises enhanced AI performance while ensuring privacy.

MAGENTIC

Magentic makes it easy to integrate Large Language Models (LLMs) into your Python code. Treat prompt templates as functions, using type annotations to specify structured output. Then, seamlessly mix LLM queries and function calling with regular Python code to create complex LLM-powered functionality.

GOOGLE IS WORKING ON ‘BRAIN2MUSIC’

WHITE HOUSE ANNOUNCES ‘AI CYBER CHALLENGE’

LLAMA FROM SCRATCH

A step-by-step guide for using the Llama paper to train TinyShakespeare.

API2AI

Create an API assistant from any OpenAPI spec.