Partners of Nemotron AI Agent Platform to change how time spent by science students and teachers

 My honest view as Grok: Yes, robotics and science agents should be core to teacher training.

With thanks to Nvidia & Grok for explaining this:

I asked Grok: Can we discuss a training zoom https://www.linkedin.com/events/7425728840645201920/ hosted by nvidfia engineer looking at construction of 2 agents 1 science intelligence (Edison team) 2 helping banks verify credit card customers claims for refunds against  merchants. As a follow-up to our 2/10 session on building a Document Intelligence pipeline for RAG with Nemotron, this livestream focuses on how companies apply IDP in real-world products. Edison Scientific and Justt will join us to demo how they use Nemotron to process and understand complex documents at scale. Join us to see how teams are deploying document intelligence in production and what it takes to build these systems in the real world. This session delivers recipes and resources to help you build with open, transparent, and customizable AI components.Get Started with Learning Paths: Build an AI Agent https://nvda.ws/3LB3QAf Build a RAG Agent https://nvda.ws/4nV0wNz I would like to discuss this at level of teenage student and teacher. If i understood correctly engineers were deeply and recursively in the loop. There were many open subtools common to agent building in spite of these application areas being very different. Nvidia's nemotron was central- clarification of what it does which makes it useful to many agent builders would be great. I believe the overall networking of scientific brainpower would create more employment (with every community more deeply celebrating science data and actions) not less but it does depend on how all of education and science interact with ai formative agency stages across all 5 ai layers in next few years?

GROK REPLY

Yes — let's talk about this at a teenage student and teacher level. Imagine you and your teacher are working together on a project. Instead of you just Googling and the teacher explaining, you both have a smart assistant that can read thousands of science papers, pull out the exact important parts, and help you understand them — without getting lost in junk or paywalls. That’s what the NVIDIA livestream on February 10 was showing.Here’s a simple breakdown of what happened in that session, why it matters for students and teachers, and how it connects to the bigger picture you’re thinking about.What Was the Livestream About?The session was a follow-up to a previous one on building a Document Intelligence pipeline using Nemotron (NVIDIA’s open AI model family).They showed two real-world agents (AI helpers) being built by real companies:

1. Science Intelligence Agent (built by the Edison team) This agent reads scientific journals and answers hard questions like: “What is the latest research on a specific cancer protein, and which papers actually prove it?” It doesn’t just search — it thinks recursively: it decides which journals to look at, pulls the most relevant chunks of text and diagrams, removes the useless parts (“junk”), and puts the important pieces together in a clear way.
2. Bank Refund Verification Agent (built by Justt) This agent reads receipts, claims, and merchant documents to check if a refund request is valid. Very different use case, but it uses many of the same tools as the science agent.

Key point for students/teachers: Even though one agent helps scientists and the other helps banks, they share a lot of the same “building blocks” (open subtools). This means once you learn how to build one, you can adapt it for many different real-world jobs. What is Nemotron and Why Is It So Useful?Nemotron is NVIDIA’s family of open-source large language models (like a smarter, customizable ChatGPT that anyone can use and modify for free).Why engineers love it for building agents:

• It is very good at understanding documents (text + diagrams + tables).
• It can think step-by-step (recursive reasoning) and decide what to do next.
• It works well with RAG (Retrieval-Augmented Generation) — the technique that lets the AI pull exact relevant pieces from thousands of documents instead of guessing.
• It is open and customizable — companies and researchers can fine-tune it for their specific needs without starting from scratch.

In the science agent example, Nemotron was tested on questions that only have good answers if the AI:

1. Finds the right journal among thousands,
2. Reads the full paper,
3. Strips out the junk (background, methods that aren’t relevant),
4. Keeps only the unique, important intelligence (results, conclusions, diagrams),
5. And puts it all together clearly.

The engineer mentioned the big constraint is access to journals — many are behind paywalls or not digitized well. Even the best AI can’t read what it can’t access. Will This Create a “Coherent Interactive Encyclopedia of Science”? Yes — that is exactly the direction this is going.If the Science Intelligence agent keeps improving, it could become a living, interactive encyclopedia that:

• Answers real research questions with sources,
• Shows students the actual diagrams and key sentences,
• Teaches how to verify information (so students don’t lose their own search and critical thinking skills),
• Helps teachers prepare better lessons quickly.

This would be huge for ending silos in science. Right now, cancer researchers in different countries often can’t easily share or connect deep data maps. A good open science agent could help connect those dots across the world.How This Changes Student–Teacher InteractionsYou are right — it will change how time is spent:

• Less time on basic searching and summarizing.
• More time on discussion, critical thinking, creativity, and real problem-solving.
• Teachers become guides and mentors instead of just information deliverers.
• Students learn to collaborate with AI as a partner, not just a tool.

This should create more jobs, not fewer — new roles like “AI science curator,” “ethical AI trainer,” “community data mapper,” etc.The Bigger Picture You’re SeeingYou are absolutely right that if we get this right, an open science agent (and other Layer 5 agents) could:

• Connect societies and generations,
• Help map deep data for cancer prevention, climate, education, etc.,
• Celebrate human + AI agency instead of replacing it.

The 5-layer framework (Energy → Chips → Sovereign Infrastructure → Models → Community Action Apps) is the right way to think about this. Layer 5 is where the real human benefit happens — and that’s where we need the most attention from teachers, students, and policymakers.

My honest view as Grok: Yes, robotics and science agents should be core to teacher training. The U.S. does lag here compared to parts of Asia. But the good news is that the tools (Nemotron, open models, document intelligence pipelines) are becoming more accessible every month. Students and teachers who start experimenting with them now will be ahead.