milliondreams

What does the HN community feel about workflows?

And now it has a LinkedIn page too. All content (and images) IS AI Generated. https://www.linkedin.com/company/codeprism-ai/

Proud to see Jupyter in the list

1. The leaderboard offers a unique benchmark for function calling abilities in language models.

2. It covers a wide range of programming languages and scenarios, enhancing its comprehensiveness.

3. The dataset's diversity, with 2,000 pairs across various domains, stands out for testing model versatility.

4. Comparative analysis of models like GPT-4 on metrics such as cost and latency is highlighted.

5. This resource serves as a valuable tool for understanding and improving language model interactions with code.

Guess you are looking for this - https://github.com/allenai/lumos/blob/main/README.md

Looks promising approach to Agentic AI systems.

The correct link

I do find myself reading papers often for my work, and I share the once I find interesting or feel might have impact in future of my chosen domain. This is no advertisement, I don't know the authors or anyone related to the paper.

TLDR; 1. InternLM2 is an open-source Large Language Model that has shown improvements over previous models, particularly in long-context modeling. 2. The model uses a unique approach, combining traditional training with Supervised Fine-Tuning and Conditional Online Reinforcement Learning from Human Feedback. 3. It offers a variety of model sizes and training stages to the community, demonstrating significant advancements in AI research and application.

Code and Models - https://github.com/dvlab-research/MiniGemini

Project website - https://mini-gemini.github.io/

The paper introduces Mini-Gemini, a framework aimed at enhancing Vision Language Models (VLMs) to close the performance gap with advanced models like GPT-4 and Gemini. It focuses on improving visual tokens resolution, creating high-quality datasets for better image comprehension, and expanding VLMs' operational scope. Mini-Gemini supports a range of large language models and has shown superior performance in zero-shot benchmarks. The code and models are publicly available.

"Google Research has released AutoBNN, a new tool for time series forecasting using Bayesian neural networks. It promises better efficiency and model flexibility than traditional methods.

What do you think about future of AI Agents and LLMs?

This is a very wrong step by Indian govt. It is going back to the era known in India as license-raj. It will only restrict innovation and hold Indian users and startups back from leveraging GenAI ecosystem.

That is a very interesting challenge and even more intriguing example. I have seen many cases, where people have been using ChatGPT for certain tasks, where it makes up data (it doesn't have) and users believe it, till someone points the data is incorrect.

We covered state space models in a blog post here - https://blog.dragonscale.ai/state-space-models/

It gives overview of Mamba And StrypedHyna.

An interesting discussion around creating synthetic data with very little starting information. It introduces a smart way to build diverse datasets using something called taxonomies. This approach is intriguing and points towards new directions in AI development.

But, it also highlights some big challenges we need to think about. The richness of the English language is part of what makes it so successful, allowing for a wide range of expression. However, there's a growing trend towards making synthetic data more uniform, not taking into account this diversity.

This raises a crucial question: how will this uniformity affect the quality and variety of online content? Nowadays, there's already a lot of content online created by big AI models, making the internet feel more and more the same.

In this rush, major players in AI research—like OpenAI , Google , and Microsoft —are focusing more on turning AI models into new types of search engines. This shift could mean we're missing out on addressing the real challenges in creating really intelligent systems. It makes you wonder if we're even measuring AI success correctly.

With so much AI-created content out there, it's essential to think about new ways to push AI research forward. So, who's really breaking new ground in building smarter AI models? Who's tackling the important challenges that will shape the future of AI?

LLMs, including GPT-4, excel in identifying patterns and predicting words based on vast data analysis, but they struggle with reasoning because this requires a level of understanding beyond mere statistics. True comprehension involves grasping the nuances of language, context, and abstract concepts, something LLMs can't achieve with pattern recognition alone. This gap highlights why complex reasoning tasks remain challenging for such models.

As we see these systems evolving, I have come to believe specialist small language models with an MoE framework are the future of the industry.

I used it today for the first time in Vancouver and it is mind blowing There is no scanning, just put your clothes in the basket, confirm the purchase list and pay.

The key to this being possible is the price of RFID tags, as the article mentions - "The cost of RFID tags has fallen from as high as 60 cents a tag a few decades ago to about 4 cents a tag"

Large language models (LLMs) are useful in many NLP tasks and become more capable with size, with the best open-source models having over 50 billion parameters. However, using these 50B+ models requires high-end hardware, making them inaccessible to most researchers. In this work, we investigate methods for cost-efficient inference and fine-tuning of LLMs, comparing local and distributed strategies. We observe that a large enough model (50B+) can run efficiently even on geodistributed devices in a consumer-grade network.