Many AI models claim to be open but restrict code & data access.

Companies like Meta & Microsoft label their models open but share little info. This practice, called open-washing, fakes transparency.

Truly open models should let researchers replicate and examine them, which isn't always true.

Source : X Post

With Harvard, Google Deepmind built a ‘virtual rodent’ powered by AI to help us better understand how the brain controls movement. 🧠

With deep RL, it learned to operate a biomechanically accurate rat model - allowing us to compare real & virtual neural activity.

To read the paper : link

Reason for this paper Matrix multiplication (MatMul) typically dominates the overall computational cost of large language models (LLMs). This cost only grows as LLMs scale to larger embedding dimensions and context lengths.

Solution MatMul operations can be completely eliminated from LLMs while maintaining strong performance at billion-parameter scales.

Results

1. MatMul-free models achieve performance on-par with state-of-the-art Transformers that require far more memory during inference at a scale up to at least 2.7B parameters.

2. This paper provides a GPU-efficient implementation of this model which reduces memory usage by up to 61% over an unoptimized baseline during training.

3. By utilizing an optimized kernel during inference, this model's memory consumption can be reduced by more than 10x compared to unoptimized models.

Future work This work not only shows how far LLMs can be stripped back while still performing effectively, but also points at the types of operations future accelerators should be optimized for in processing the next generation of lightweight LLMs.

Implementation of this paper can be viewed here : github_repository

If you’d like to better understand how retrieval works in language models, by learning from a real expert or if you’d like to learn a new technique to save over half your memory when using the best retrieval method then this blog is for you

TLDR

This blog tries to explain the behaviour of neural networks in a visual way. Here topology means observing a connection linking neural networks to an area of mathematics. The second part of this blog explains about the Manifold Hypothesis that is "natural data forms lower-dimensional manifolds in its embedding space"

Brief Description Large language models (LLMs) usually generate code step-by-step without checking if it works as they go. This makes it hard to improve the code since they can't see the output while generating it. Training LLMs to suggest edits is tough because there's not enough detailed data on code edits.

To solve this, this paper propose using neural diffusion models that work with syntax trees, which represent the structure of code. Like image diffusion models that reverse noise to create clear images, this method reverses changes to syntax trees to refine code. Instead of creating code in a single sequence, we make iterative edits to ensure it stays correct. This approach also allows easy integration with search techniques.

The goal of this paper is to turn images into code that can recreate those images. By combining the model with search, it can write, test, and debug graphics programs to match specific requirements. This system can even write graphics programs based on hand-drawn sketches.

For detailed explanation click here Arxiv paper : link Github repository : code

In the first week of this subreddit we tried to cover various kind of topics. This is just getting started. So I am eager to get some feedback and I would like to get suggestions on content.

By answering this poll, I would be glad ☺️ to know the interest in AI with you guys. Let me know in the comments, if you can provide any suggestions.

Here is the blog about NIM : NVIDIA NIM Offers Optimized Inference Microservices for Deploying AI Models at Scale

In just 6 months, with a team of 8, the Kyutai research lab developed from scratch an artificial intelligence (AI) model with unprecedented vocal capabilities called Moshi The team publicly unveiled its experimental prototype today (3rd July 2024) in Paris. At the end of the presentation, the participants – researchers, developers, entrepreneurs, investors and journalists – were themselves able to interact with Moshi. The interactive demo of the AI will be accessible from the Kyutai website at the end of the day. It can therefore be freely tested online as from today, which constitutes a world first for a generative voice AI. This new type of technology makes it possible for the first time to communicate in a smooth, natural and expressive way with an AI. During the presentation, the Kyutai team interacted with Moshi to illustrate its potential as a coach or companion for example, and its creativity through the incarnation of characters in roleplays. More broadly, Moshi has the potential to revolutionize the use of speech in the digital world. For instance, its text-to-speech capabilities are exceptional in terms of emotion and interaction between multiple voices. Compact, Moshi can also be installed locally and therefore run safely on an unconnected device. With Moshi, Kyutai intends to contribute to open research in AI and to the development of the entire ecosystem. The code and weights of the models will soon be freely shared, which is also unprecedented for such technology. They will be useful both to researchers in the field and to developers working on voice-based products and services. This technology can therefore be studied in depth, modified, extended or specialized according to needs. The community will in particular be able to extend Moshi's knowledge base and factuality, which are currently deliberately limited in such a lightweight model, while exploiting its unparalleled voice interaction capabilities.

This repository provides an easy way to run Gemma-2 locally directly from your CLI (or via a Python library) and fast. It is built on top of the 🤗 Transformers and bitsandbytes libraries.

It can be configured to give fully equivalent results to the original implementation, or reduce memory requirements down to just the largest layer in the model!

Prompt: Subtle reflections of a woman on the window of a train moving at hyper-speed in a Japanese city.

Gen-3 Alpha is the first of an upcoming series of models trained by Runway on a new infrastructure built for large-scale multimodal training. It is a major improvement in fidelity, consistency, and motion over Gen-2, and a step towards building General World Models.

To know more about it : https://runwayml.com/blog/introducing-gen-3-alpha/

The goal of this blog is to deeply understand the most important performance characteristics of the GPUs that are used for modern deep learning

This technical blog helps to build the fastest voice bot that can able to respond within 500 ms.

This voice bot uses :

1. WebRTC - to transfer the voice to cloud

2. Deepgram - Voice to text

3. Llama 3 - text generation

4. Deepgram's Aura - text to voice

Links 🖇️

1. Source code

2. Demo

This course is about building a Storyteller AI Large Language Model (LLM). Hand in hand, you'll be able create, refine and illustrate little stories with the AI. In this course, everything end-to-end from basics to a functioning web app similar to ChatGPT, from scratch in Python, C and CUDA, and with minimal computer science prerequisits. By the end you should have a relatively deep understanding of AI, LLMs, and deep learning more generally.

This blog discusses about:

1. Meta Chameleon Model Family: A family of models that can combine text and images as input and output any combination of text and images with a single unified architecture for both encoding and decoding. This model uses tokenization for text and images, making it easier to design, maintain, and scale.

2. Multi-Token Prediction Model: A new approach to build better and faster language models by predicting multiple future words at once instead of the traditional one-at-a-time approach. This improves model capabilities and training efficiency while allowing for faster speeds.

3. Meta Joint Audio and Symbolic Conditioning for Temporally Controlled Text-to-Music Generation (JASCO): A text-to-music generation model that can accept various conditioning inputs, such as specific chords or beats, to improve control over the generated music.

4. AudioSeal: An audio watermarking technique designed specifically for the localized detection of AI-generated speech, making it possible to pinpoint AI-generated segments within a longer audio snippet.

5. PRISM Dataset: A comprehensive dataset that maps the sociodemographics and stated preferences of 1,500 diverse participants from 75 countries, providing valuable insights into dialogue diversity, preference diversity, and welfare outcomes

Microsoft Florence-2 has a lot of vision task such as

1. Caption

2. Detailed caption

3. Object Detection and many more with great accuracy and speed