TLDR AI

TLDR AI 2024-04-17

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01GENERAL

Sponsored

FRESH WEB DATA FOR LLM AND RAG APPS

If you want to build AI applications with access to the web, you need to check out the Brave Search API [4]. With a single API call, you'll get relevant search results from an billions of webpages [4] — high-quality, real-time data you can build on.

brave.comSponsor

02HEADLINES & LAUNCHES

APPLE IOS 18 WILL BE ON-DEVICE

Apple's upcoming AI features in iOS 18 are rumored to focus on privacy, with the initial set of enhancements functioning entirely on-device without the need for an internet connection or cloud-based processing, thanks to the company's in-house large language model known internally as "Ajax."

appleinsider.com1 min read

STANFORD HAI RELEASES 2024 AI INDEX REPORT

The Stanford Institute for Human-Centered AI has released its seventh annual AI Index report. This year's report covers the rise of multimodal foundation models, major cash investments into generative AI, new performance benchmarks, shifting global opinions, and new major regulations.

hai.stanford.eduTool

GOOGLE'S NEW TECHNIQUE GIVES LLMS INFINITE CONTEXT

Google researchers have introduced Infini-attention, a technique that enables LLMs to work with text of infinite length while keeping memory and compute requirements constant.

venturebeat.com5 min read

03RESEARCH & INNOVATION

COMPRESSION REPRESENTS INTELLIGENCE LINEARLY

Most modern AI is built around the idea of compressing a training dataset into a model. The better the compression, the better the model. This paper shows that relation rigorously and posits that scale benchmark scores correlate strongly to a model's ability to compress novel text.

arxiv.org18 min read

FEEDBACK IN TRANSFORMERS

TransformerFAM provides a feedback mechanism that allows Transformers to attend to their own latent representations. This can, in theory, introduce recurrence into the model for processing extremely long inputs in context.

arxiv.org24 min read

MEGALODON EFFICIENT TRANSFORMER PRETRAINING

Another long context paper - this time, a new architecture that uses two novel weight updating schemes. It outperforms Llama 2 on the same number of training tokens 2T. It also scales to infinite context length at inference time.

arxiv.org17 min read

04ENGINEERING & RESOURCES

Sponsored

VIRTUAL WORKSHOP: FINE-TUNE YOUR OWN LLMS TO GPT-4 LEVELS

The Predibase team recently launched LoRA Land, a collection of 25+ fine-tuned Mistral-7b models that rival GPT4 [11] on a set of task-specific applications. In this hands-on workshop, you'll learn how to build your own [11] LoRA Land with popular open-source LLM frameworks, Ludwig and LoRAX. Become a fine-tuning pro. Join the live

my.demio.comSponsor

ROAD LINE SEGMENTATION FOR AUTONOMOUS DRIVING

Accurately segmenting road lines and markings is crucial for autonomous driving but challenging due to occlusions caused by vehicles, shadows, and glare. The Homography Guided Fusion (HomoFusion) module uses video frames to identify and classify obscured road lines by leveraging a novel surface normal estimator and a pixel-to-pixel attention mechanism.

arxiv.org16 min read

ENHANCED VISION-LANGUAGE MODEL

Vision Language Models (vLLMs) often struggle with processing multiple queries per image and identifying when objects are absent. This study introduces a new query format to tackle these issues, and incorporates semantic segmentation into the training process.

github.comGitHub repository

05MISCELLANEOUS

ACCELERATING AI: HARNESSING INTEL(R) GAUDI(R) 3 WITH RAY 2.10

Anyscale's latest release of Ray, Ray 2.10, adds support for Intel Gaudi 3. Developers can now spin up and manage their own Ray Clusters, provisioning Ray Core Task and Actors on a Gaudi fleet directly through Ray Core APIs, tap into Ray Serve on Gaudi through Ray Serve APIs for a higher level experience, and configure Intel Gaudi accelerator infrastructure for use at the Ray Train layer.

anyscale.com5 min read

QWEN CODER

Code Qwen 1.5 is a new set of 7B models trained on 3T tokens of code related data. It performs well on HumanEval, with a non-zero score on SWE-bench. The chat variant specifically shows promise for long context retrieval tasks up to 64k tokens.

qwenlm.github.io12 min read

1-BIT QUANTIZATION

Extreme low-bit quantization for small pre-trained models, like Llama2-7B, is challenging, but fine-tuning just 0.65% of parameters significantly improves performance. Newly fine-tuned 1-bit models outperform 2-bit Quip# models, while 2-bit models with specialized data can exceed full-precision counterparts. This research suggests that proper fine-tuning and quantization may enhance efficiency without compromising model quality, potentially shifting focus from training smaller models to optimizing larger, quantized ones.

mobiusml.github.io7 min read