TLDR AI 2023-09-12

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In collaboration with Microsoft, thousands of open-domain books have been transcribed with state-of-the-art text-to-speech. They're available on most streaming platforms. Information about the process is included in the link.

Meta has been snapping up AI training chips and building out data centers in order to create a more powerful new chatbot it hopes will be as sophisticated as OpenAI’s GPT-4.

This project introduces NuPrompt, a new set of instructions in everyday language that helps self-driving cars understand and track objects around them better. It works really well!

The authors of this study have created a way to test how well vision-language models can also think logically about what they see.

Language model inference is generally slow because these models are heavily memory-bound. So people have introduced speculative decoding using a smaller draft model to “run ahead” and offer suggestions to the large model. This works well, but it’s complicated to implement, and finding a good draft model is tough. Instead, you can create a Medusa model that acts as its own draft by splitting off activations from various internal stages. This speeds up Llama 2x without compromising performance.

RestGPT aims to construct a large language model-based autonomous agent to control real-world applications, such as movie databases and music players.

PointLLM is a tool that helps language models understand 3D objects, not just text and 2D images.

OpenAI faces a lawsuit from writers alleging illegal use of their copyrighted works to train ChatGPT. They argue that ChatGPT's capabilities to summarize and analyze their content indicate direct training on their works, constituting copyright infringement. This follows similar suits from other authors against OpenAI.

The debate surrounding large language models concerns whether they possess human-like reasoning abilities or rely on memorization and pattern-matching, with studies showing mixed results. Evaluating LLMs for true abstract reasoning is challenging due to their tendency to excel in tasks related to their training data, necessitating further research for clarification.

This article shares seven practical patterns for integrating large language models into systems: measuring performance, adding recent knowledge, fine-tuning for tasks, caching to save time/cost, ensuring quality output, managing errors, and harnessing user feedback.

OpenAI has confirmed that AI writing detectors are ineffective.

Convert your random voice notes and unstructured text notes into structured text summaries using AI.

HelpKit AI turns your Notion knowledge base into a smart, 24/7 AI assistant that provides precise and instant answers to your users.