New theory: ChatGPT is lazy because it's winter ⛄️

In today’s email:

💰 How much AI Researchers make at OpenAI, Anthropic, Inflection, Amazon, Tesla, etc
📚 A quick intro guide into HuggingFace for non-technical people
📣 Meta just launched a demo of Audiobox
🧰 7 new AI-powered tools and resources. Make sure to check the online version for the full list of tools.

As ChatGPT gets “lazy,” people test the “winter break hypothesis” as the cause

In late November 2023, ChatGPT users began noticing that ChatGPT-4 seemed "lazier," refusing some tasks or giving simplified results. OpenAI acknowledged the issue but was unsure of the cause. Some speculated about the "winter break hypothesis," suggesting that ChatGPT-4 might mimic seasonal patterns in human productivity, although this is unproven.

An interesting observation arose when a user noted that ChatGPT-4's responses were shorter when given a December date compared to a May date, although this wasn't conclusively proven. This speculation reflects the complex and unpredictable nature of large language models (LLMs) like GPT-4.

There's also a history of perceived "laziness" in ChatGPT, with varying responses over time, making it a subject of debate and ongoing research. The situation highlights the evolving and often unpredictable nature of LLMs and the challenges in understanding and managing their behavior.

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French startup Mistral AI just released Mixtral, an open-source 45B parameter AI model.

Mistral AI has launched a beta version of their platform offering generative model services to developers. The platform includes three chat endpoints and an embedding endpoint, each with different performance and pricing.

Generative Endpoints:

Mistral-tiny: Serves the Mistral 7B Instruct v0.2 model, effective in English, with a 7.6 score on MT-Bench.
Mistral-small: Offers the Mixtral 8x7B model, supporting English, French, Italian, German, Spanish, and code, with an 8.3 score on MT-Bench.
Mistral-medium: Provides a prototype model with high performance, supporting the same languages and code as Mistral-small, scoring 8.6 on MT-Bench.

Embedding Endpoint:

Mistral-embed: Features a 1024-dimensional embedding model designed for retrieval, achieving a 55.26 score on MTEB.

API Specifications:

The API is compatible with popular chat interfaces and offers Python and JavaScript client libraries.
It includes features for setting moderation levels on model outputs.

Meta just launched a demo of Audiobox, its foundation model for generating highly realistic, voices, sound effects, and music.

Audiobox is Meta’s new foundation research model for audio generation. It can generate voices and sound effects using a combination of voice inputs and natural language text prompts — making it easy to create custom audio for a wide range of use cases. The Audiobox family of models also includes specialist models Audiobox Speech and Audiobox Sound, and all Audiobox models are built upon the shared self-supervised model Audiobox SSL.