Loading video...

Video Failed to Load

Go Home

1.8 million scientific papers are published every year. Keeping up is impossible. Here is a solution: Look at They use AI to summarize and explain things to you. Another amazing use of Large Language Models. AI explaining AI!

249,428 views • 3 years ago •via X (Twitter)

10 Comments

Abacus.AI's profile picture
Abacus.AI3 years ago

If you enjoy this content, follow @abacusai! There's much more coming!

Kevin Patel's profile picture
Kevin Patel3 years ago

I have also recently heard about also, but haven't tried it yet

Pratik Desai's profile picture
Pratik Desai3 years ago

I understand what you’re doing, but isn’t the Abstract supposed do that?

Muhammad Iltaf's profile picture
Muhammad Iltaf3 years ago

Good

Daniel Yánez Bravo's profile picture
Daniel Yánez Bravo3 years ago

@SaveToNotion #Thread #ai #ChatGPT

Akshay 🚀's profile picture
Akshay 🚀3 years ago

This is great! Thanks for sharing.

Adrien Bruno's profile picture
Adrien Bruno3 years ago

Drag and drop? Why you don't use a link?

Ande's profile picture
Ande3 years ago

@remindmetweets in 6 hours

Jose Manuel Castiblanco Quintero's profile picture
Jose Manuel Castiblanco Quintero3 years ago

...then the author citations would be interesting to review

vrtr's profile picture
vrtr3 years ago

@DevHunterYZ What's the error rate?

Related Videos

99% of AI applications are cool-looking demos. Impressive, but don't get fooled by the hype. It takes a lot to build enterprise-grade products that deliver real value. I have at least three weekly conversations with companies that want to use a Large Language Model with their data. The demand is huge! Here is one idea about what you can do to help. The use cases that most of these companies want to solve are similar: They have an extensive knowledge base and want to build a simple application that uses that information to answer questions. In other words, they need help building Retrieval Augmented Generation (RAG) applications they can use in many different scenarios: 1. To train new employees 2. To help their support team 3. To search old meetings and documents 4. To help with their research However, building these systems is not straightforward. Yes, there's a lot of information online, but there aren't enough people who know how to create solutions that work. Here is the idea: Today, you can build an enterprise-grade RAG application without writing code. A couple of MIT PhDs with 10+ years of experience building AI applications created . It's a no-code platform for building applications using Large Language Models. They are partnering with me on this post. You can use Stack AI to create, test, and deploy an end-to-end production-ready AI system. It's SOC-2, HIPAA, and GDPR compliant and offers SSO, role management, access control, and on-premise deployments. Of course, you can use the platform with any LLM on the market now. It's the whole nine yards for building AI applications. Check them out here: 2023 was about models. 2024 is about the tools using these models to build production-ready applications. That's where I'd start.

Santiago

197,675 views • 2 years ago