TLDR Index
TLDR AI

TLDR AI 2024-02-21

5 sections18 entrieslinks blockcomplete parse

Parse notes

  • Subscription/account footer truncated before parsefooter_truncated

01HEADLINES & LAUNCHES

GPT-4 KNOWLEDGE CUTOFF IS DECEMBER 2023

According to the new documentation on the OpenAI platform, the training data for GPT-4 models has been updated to include information up to December 2023.

platform.openai.com1 min read

SCRIBE $25M SERIES B

Scribe has raised a Series B funding round led by Redpoint Ventures to accelerate its AI-driven platform, which automates the creation of visual step-by-step guides and facilitates knowledge sharing within organizations. Over a million teams use Scribe, including 97% of the Fortune 100. The company is expanding its AI features to make workplace information even more accessible.

scribehow.com4 min read

02RESEARCH & INNOVATION

GENERATIVE REPRESENTATIONAL INSTRUCTION TUNING

The Contextual team has trained and released a model that both generates text and embeddings. It dramatically outperforms single specialist models. The model is an interesting take on the multi-modal trend where the output modality is an embedding.

arxiv.org24 min read

DEEPDIVE: MAMBA THE HARD WAY

Sasha Rush has released an annotated tutorial for accelerating Mamba with custom Triton kernels. It doesn’t scale yet due to a bug in the Triton compiler, but it is an extreme illustration of the technology and great for those looking to dive deeper into the state space Transformer alternative world.

srush.github.io30 min read

ENHANCING FINE-TUNING WITH LORA+

This paper presents LoRA+, an advancement over the existing Low-Rank Adaptation (LoRA) method for fine-tuning large models. LoRA+ achieves better performance and faster fine-tuning, without increasing computational demands, by using different learning rates for key components in the process.

arxiv.org25 min read

03ENGINEERING & RESOURCES

Sponsored

TONIC TEXTUAL: NAMED ENTITY RECOGNITION AND SYNTHESIS FOR UNSTRUCTURED DATA

Train your models with high-quality and realistic data, without worrying about sensitive data leakage. Tonic Textual [11] provides named entity recognition and sensitive data synthesis from free-text, PDF, and Word documents [11]. Cleanse data of all sensitive information, and use a few lines of SDK code to start working with synthetic data in minutes. See how Tonic scans your text in real time

tonic.aiSponsor

tonic.ai

3D OBJECT FROM AS FEW AS 4 PICTURES

This repository allows you to take four images and turn them into a high-quality 3d representation with Gaussian Splatting.

github.comGitHub repository

GENERATING IMAGES AT ANY RESOLUTION

The Flexible Vision Transformer (FiT) is a novel architecture designed to create images at any resolution and aspect ratio. Unlike traditional models, FiT treats images as sequences of variable-sized tokens, allowing it to adapt to different image sizes more effectively during training and inference.

github.comGitHub repository

BOOSTING AI'S DEFENSE AGAINST ADVERSARIAL ATTACKS

This project introduces a new method to strengthen multi-modal models like OpenFlamingo and LLaVA against visual adversarial attacks. By fine-tuning the CLIP vision encoder in an unsupervised manner, the authors effectively shield these models from manipulative image attacks, enhancing their reliability and security in real-world applications without the need for retraining the entire model.

github.comGitHub repository

04MISCELLANEOUS

NEW LLM BENCHMARK

Preeminent researcher Nicholas Carlini has released the benchmark he uses for evaluating large language model performance. Interestingly, it has GPT-4 further ahead than most other benchmarks.

nicholas.carlini.com12 min read

STRATEGIES FOR AN ACCELERATING FUTURE

The recent advancements in AI, particularly with Google's Gemini offering a context window of over a million tokens and Groq's hardware enabling almost instantaneous responses from GPT-3.5 models, signify a major leap forward in practical AI applications and underscore the urgency for leaders to understand and adapt to AI's rapidly evolving landscape.

oneusefulthing.org5 min read

HOW TO LOSE AT GENERATIVE AI

Generative AI, while hyped, is likely to disappoint most startups because it favors incumbents with data advantages, existing workflows, and the ability to integrate AI into these systems without major overhauls. Despite venture capital flowing into the GenAI space, startups focusing on prompt engineering and UX improvements at the workflow layer are essentially prepping the market for incumbents who can easily adopt and integrate AI innovations into their dominant platforms, suggesting a challenging path ahead for startups aiming to capture significant value in the Generative AI domain.

medium.com7 min read