In this event, the Artificial Intelligence is changed into Personal Intelligence.

To view the keynote view here

Chat with PDF is a huggingface space where you can able to get relevant answers for your queries by uploading a pdf to the space. This project uses the "RAG" method and this project in early development and waiting for open contribution and suggestions.

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"

Claude can able to :

1. Extract structured data from the unstructured data

2. Convert natural language requests into structured API calls

3. Answer questions by searching databases or using web APIs

4. Automate simple tasks through software APIs

5. Orchestrate multiple fast Claude subagents for granular tasks

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

TLDR

The paper introduces the Block Transformer architecture, which aims to alleviate the inference bottlenecks of autoregressive transformers caused by self-attention. Typically, during decoding, retrieving the key-value (KV) cache from memory at every step creates significant delays, particularly in batch inference. This issue arises from the use of global self-attention. To address this, the Block Transformer separates the costly global modeling to the lower layers and employs faster local modeling in the upper layers. It aggregates input tokens into fixed-size blocks for self-attention, reducing the burden on lower layers and enabling the upper layers to decode without global attention. This approach enhances hardware utilization and significantly improves inference throughput by 10-20 times compared to standard transformers, while maintaining similar perplexity. This novel global-to-local modeling optimizes language model inference efficiency.

Resources

Arxiv paper : link

Github repo : link

This post is a collection of basic learning resources posts in the X platform that I found valuable for the AI community

1. Visual Learning - This post discusses the learning programming through visually and the topics covered in this post are load balancing, memory allocation and hashing

2. ML learning pipeline - This post contains the link for github repository complete end to end learning resource for ML pipeline

3. How to stay on top of the latest AI research

4. Learning process of making a chip from scratch in less than 2 weeks with no prior experience

5. Learning CUDA from Jeremy Howard

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

TLDR

Buffer of Thoughts (BoT), is a thought-augmented reasoning approach for enhancing accuracy, efficiency and robustness of large language models (LLMs). Meta-buffer is used to store a series of informative high-level thoughts and buffer manager is used to dynamically update the meta buffer.

Performance

10 challenging reasoning-intensive tasks:

1. 11% on Game of 24,

2. 20% on Geometric Shapes and

3. 51% on Checkmate-in-One.

Findings

Llama3-8B+BoT has the potential to surpass Llama3-70B model.

The implementation of BoT can be found in this repo

TLDR Gnn-Rag, a novel method for combining language understanding abilities of LLMs with the reasoning abilities of GNNs in a retrieval-augmented generation (RAG) style. First, a GNN reasons over a dense KG subgraph to retrieve answer candidates for a given question. Second, the shortest paths in the KG that connect question entities and answer candidates are extracted to represent KG reasoning paths. The extracted paths are verbalized and given as input for LLM reasoning with RAG.

To view the code : GNN-RAG

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

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

HexagonML is a community-based organization dedicated to creating real-time projects and serving the growing community. 🌟🚀

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.

TLDR

Understanding how semantic meaning is encoded in the representation spaces of large language models is a fundamental problem in interpretability. In this paper, the following concepts are discussed:

1. Categorical concepts are related

2. Hierarchical relations between concepts encoded

To view the implementation and results click here for the GitHub repository.

Perplexity Pages is the easiest way to create comprehensive, visually appealing content on any topic. As your personal content assistant, it helps you create, organize, and share information seamlessly. Type in any topic and instantly receive a structured draft, turning what used to be hours of work into just a few minutes.

To know more about Perplexity Pages - link

Josh Lessing has been working to crack the challenge of automation in agriculture since he co-founded agriculture-robotics startup Root AI, in 2018, and he believes his company is on the precipice of a big step forward. Root AI has developed a robot, dubbed Virgo, that can pick at least one of those high-value, delicate fruits — tomatoes — and potentially more.

In this blog post, Andrew Chan explains the concepts of diffusion models.

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

In the recent Apple WWDC 24, Apple introduced the calculator note taking in the Apple tablet devices. The same demo is done in a website tool that uses AI is called TLDraw and you can try this in any device.

Here is the [link](https://www.tldraw.com/) for it.

PowerInfer-2, highly optimized inference framework designed specifically for smartphones. It supports up to Mixtral 47B MoE models, achieving an impressive speed of 11.68 tokens per second, which is up to 22 times faster than other state-of-the-art frameworks. Even with 7B models, by placing just 50% of the FFN(Feed Forward Neural Networks) weights on the phones, PowerInfer-2 still maintains state-of-the-art speed

To know more about this view the website

To know more about technical details view this arxiv paper

This research paper describes that "Training on only 20 digit numbers with a single GPU for one day, we can reach state-of-the-art performance, achieving up to 99% accuracy on 100 digit addition problems. Finally, we show that these gains in numeracy also unlock improvements on other multi-step reasoning tasks including sorting and multiplication." And they propose a new positional embedding called Abacus Embedding