Andrew Ng

I wrote in today's edition of The Batch about the new F-1 visa policy, and want to share that message here as well. #StudentBan

Announcing: Agentic Document Extraction!

PDF files represent information visually - via layout, charts, graphs, etc. - and are more than just text. Unlike traditional OCR and most PDF-to-text approaches, which focus on extracting the text, an agentic approach lets us break a document down into components and reason about them, resulting in more accurate extraction of the underlying meaning for RAG and other applications. Watch the video for details.

Agentic Document Extraction just got much faster! From previous 135sec median processing time down to 8sec. Extracts not just text but diagrams, charts, and form fields from PDFs to give LLM-ready output. Please see the video for details and some application ideas.

Math for Machine Learning and Data Science is now available on Coursera! Taught by Luis Serrano, this gives an intuitive understanding of the most important math concepts for AI.

I’ve often said “don’t worry about it” when it comes to math, because math shouldn’t hold anyone back from making progress in ML. And, understanding some key topics in linear algebra, calculus, and prob & stats will help you better get learning algorithms to work.

This specialization was designed with numerous interactive visualizations to help you see how the math works. Math isn’t about memorizing formulas; it’s about sharpening your intuition. I hope you enjoy the specialization!

Had an insightful conversation with Geoff Hinton about AI and catastrophic risks. Two thoughts we want to share:
(i) It's important that AI scientists reach consensus on risks-similar to climate scientists, who have rough consensus on climate change-to shape good policy.
(ii) Do AI models understand the world? We think they do. If we list out and develop a shared view on key technical questions like this, it will help move us toward consensus on risks.

I learned a lot speaking with Geoff. Let’s all of us in AI keep having conversations to learn from each other!

Building and deploying a machine learning model usually takes months. How can you go from starting a project to training and deploying your model in minutes? Here's a 3min overview of the LandingLens platform.

Our new short course, “Knowledge Graphs for RAG” is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications.

In this course, taught by Andreas Kollegger of Neo4j, you’ll 
- Explore how knowledge graphs work by building a graph of public financial documents from scratch
- Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot
- Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems

Sign up here! https://lnkd.in/gZx2Kie5

I’ve been thinking about how to accelerate how all of us build and deploy ML, and have some ideas I want to share. I hope you’ll join me on this interactive livestream next Wednesday to chat over some ideas! https://lnkd.in/gYYzR_K

Announcing the Data-Centric AI competition! I’m excited to invite you to participate in this new competition format, and see how you can improve an AI system only by refining the data it depends on! https://bit.ly/3vwE56i

The new ICE policy regarding F-1 visa international students is horrible and will hurt the US, students, and universities. Pushes universities to offer in-person classes even if unsafe or with no pedagogical benefit, or students to leave the US amidst pandemic and risk inability to return.

Here's the text of the policy. This puts the US, students and universities in a lose-lose-lose situation. https://lnkd.in/gx4E85S

Everyone should learn to code with AI! At AI Fund, everyone - not just engineers - can vibe code or use AI assistance to code. This has been great for our creativity and productivity. I hope more teams will empower everyone to build with AI. Please watch the video for details.

Announcing “Generative AI for Software Development,“ a new specialization on Coursera! Taught by my friend and longtime DeepLearning.AI instructor Laurence Moroney. Using GenAI for software development goes well beyond using chatbots for code generation. This 3-course series shares current best practices for AI use through the entire software development lifecycle: From design and architecture to coding, testing, deployment, and maintenance.

You'll learn to use LLMs as your thought partner, pair programmer, documentation specialist, security analyst, and performance optimization expert. There's a lot that anyone that writes software can gain from using GenAI, and this will show you how!

Please sign up here to get started! https://lnkd.in/gJZ_j88K

New Course: ACP: Agent Communication Protocol

Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research’s BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM.

Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework.

The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system.

In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP.

Through hands-on exercises, you’ll build:
- A RAG agent with CrewAI and wrap it inside an ACP server.
- An ACP Client to make calls to the ACP server you created.
- A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent.
- A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers.
- An agent that uses MCP to access tools and ACP to communicate with other agents.

You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents.

ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework.

Please sign up here: https://lnkd.in/g4gES9CF

I'm very excited to welcome Ted Greenwald to the deeplearning.ai team! Ted is a former Wall Street Journal editor, and will be leading a new editorial function to share with you the most important stories in AI. Stay tuned!

Next week on June 30, I’ll be with my Machine Learning Engineering for Production (MLOps) Specialization co-instructors Robert Crowe and Laurence Moroney, as well as Chip Huyen and Rajat Monga, in a live event to talk about MLOps. Hope to see you there!

Congratulations to the #Stanford2020 class that just graduated today! An online commencement wasn't what anyone had envisioned, but I am excited to see what you will accomplish and the contributions you'll make to this chaotic world. Proud of all of you! https://lnkd.in/gfmJUqT #stanford

Just finished writing final few chapters of Machine Learning Yearning book draft, on how to organize and strategize your ML projects. Will send out soon -- sign up at http://mlyearning.org if you want a copy!

What are the most important topics to study for building a technical career in AI? I share my thoughts on this in The Batch.

Ian Goodfellow, Anima Anandkumar, Alexei Efros, Sharon Zhou and I will be speaking at GANs for Good, an online panel, on September 30th at 10am PDT. This is to celebrate the launch of DeepLearning.AI's new Generative Adversarial Networks Specialization. Come join us! https://bit.ly/3hPfLpy

You can also sign up to get course updates: https://bit.ly/2FZixuU

Love seeing the data-centric AI development movement growing! Starting this month, FourthBrain (online AI bootcamp and AI Fund portfolio company) will be teaching data-centric approaches to MLOps!

What rules regarding publishing papers would be fair, when it relates to work done by researchers working for companies? I ask this question in this week's The Batch, and would love to hear your thoughts. https://lnkd.in/gUx6piK

I’ve been following the Data-Centric AI competition leaderboard with excitement. Right now Wei Jing is in the lead, followed closely by AryanTyagi. Bi2i and Svpino are tied for third place. Anyone want to take them on?

I hope we can empower everyone to build with AI. Starting from K-12, we should teach every student AI enabled coding, since this will enable them to become more productive and more empowered adults. But there is a huge shortage of computer science (CS) teachers. I recently spoke with high school basketball coach Kyle Creasy, who graduated with a B.A. in Physical Education in 2023. Until two years ago, he had never written a line of Python. Now — with help from AI — he not only writes code, he also teaches CS. I found Kyle’s story inspiring as a model for scaling up CS education in the primary- and secondary-school levels.

Kyle’s success has been with the support of Kira Learning (an AI Fund portfolio company), whose founders Andrea Pasinetti and Jagriti Agrawal have created a compelling vision for CS education. In K-12 classrooms, teachers play a huge social-emotional support role, for example, encouraging students and helping them when they stumble. In addition, they are expected to be subject-matter experts who can deliver the content needed for their subject. Kira Learning uses digital content delivery — educational videos, autograded quizzes, and AI-enabled chatbots to answer students' questions but without giving away homework answers — so the teacher can focus on social-emotional support. While these are still early days, it appears to be working!

A key to making this possible is the hyperpersonalization that is now possible with AI (in contrast to the older idea of the flipped classroom, which had limited adoption). For example, when assigned a problem in an online coding environment, if a student writes this buggy line of Python code

best_$alty_snack = 'potato chips'

Kira Learning’s AI system can spot the problem and directly tell the teacher that $ is an invalid character in a variable name. It can also suggest a specific question for the teacher to ask the student to help get them unstuck, like “Can you identify what characters are allowed in variable names?” Whereas AI can directly deliver personalized advice to students, the fact that it is now helping teachers also deliver personalized support will really help in K-12.

Additionally, agentic workflows can automate a lot of teachers’ repetitive tasks. For example, when designing a curriculum, it’s time-consuming to align the content to educational standards (such as the Common Core in the United States, or the AP CS standard for many CS classes). Having an AI system carry out tasks like these is already proving helpful for teachers.

Since learning to code, Kyle has built many pieces of software. He proudly showed me an analysis he generated in matplotlib of his basketball players’ attempts to shoot three-pointers (shown above), which in turn is affecting the team’s strategy on the court. One lesson is clear: When a basketball coach learns to code, they become a better basketball coach!

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