Key Highlights
Here are the main points from what we talked about with agentic AI: Andrew Ng is a globally recognized leader in artificial intelligence, and he is talking about a big move toward agentic AI. On Medium, Andrew Ng has written several notable articles exploring the future of AI, including pieces like "Why AI Is the New Electricity" and "A Strategical AI Guide for Tech Leaders" which share his forward-thinking perspectives and practical advice for embracing transformative technologies.
Andrew Ng is a leader in artificial intelligence, and he is talking about a big move toward agentic AI. Agentic AI is about building systems that plan, do tasks, and make choices on their own. If you'd like to explore andrew ng's published research papers on artificial intelligence or agentic AI, you can find many of them on the arXiv repository, Google Scholar, or the website of his academic institutions. These platforms host a comprehensive collection of andrew ng's work on ai.
Agentic AI is about building systems that plan, do tasks, and make choices on their own.
This change in machine learning will bring new challenges and chances for developers.
Companies will see big changes in how they work and how much they get done.
Ng gives ways to learn more about this change using platforms like Coursera.
The next steps in AI development will be about making and running these smart agents.
Introduction
The world of artificial intelligence is always changing. Now, there is a new area to watch: agentic AI. This is not just about AI that can guess or sort things. It is about AI that can do things and take action. Andrew Ng, who is a big name in machine learning, is talking about this important change and what it could mean next. If you are a developer or you work in business, you need to know about this update. It will be important for you. Are you ready to see how this new kind of AI could change the way you work and come up with new ideas?
Who is Andrew Ng? A Brief Profile and AI Contributions
Andrew Ng is known all over the world as a leader in artificial intelligence and machine learning. He has had a big impact on the way people use AI today. Andrew is the co-founder of Coursera, and he also led the Google Brain project before, serving as an adjunct professor at Stanford University. He played a key role in making AI technologies better and helping more people get to use machine learning and the latest AI tools.
His work goes from leading deep learning studies to helping millions around the world learn about AI and related fields. At Stanford University and several tech companies, Ng always moves computer science forward. He has built a strong name for himself in the global AI community.
Good Read: AI Myths VS Facts A Quick GuideCareer Highlights: From Stanford to DeepLearning.AI
Andrew Ng started out in the world of artificial intelligence with a strong base in learning. He finished his studies at Carnegie Mellon University, MIT, and the University of California. Later, he joined Stanford University in 2002 as a professor of computer science. In his early work and years at Stanford, Andrew did important work and helped many students. His passion for teaching and research in artificial intelligence soon reached people all around the world.
A big moment in his career was when he started Coursera. It began with his well-liked online machine learning course, which was one of the first massive open online courses. Over time, it grew into one of the largest places for online education in the world. By setting up Coursera, he showed he cared about giving everyone a chance to learn about AI. He also worked in academics. Also, he started and led the Google Brain project. That team helped make new progress in deep learning on a large scale. Both the founding of Coursera and the Google Brain project show how much he has given to AI and online education.
Today, Ng still works to bring new ideas and growth in the field of AI. He does this through companies like DeepLearning.AI and Landing AI. Ng also gives tips and thoughts to the AI community every week in his newsletter called "The Batch." You Can Sign Up To The Batch Here >> His work shows a strong drive to push AI technologies forward and help people learn how to use AI.
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Andrew Ng has played a big role in machine learning. He made big changes with some important projects. At Google Brain, he helped lead a group that built one of the largest artificial neural networks of that time. This network surprised many people because it could learn to spot cats in YouTube videos without any help. It was able to do this by using deep learning algorithms that didn't need someone to explain what to look for. This showed the power of these new machine learning tools and his influence at companies like Baidu.
His way of thinking goes beyond one project. Ng has led many efforts that helped shape the AI field. These plans include:
Google Brain: Helped start large-scale deep learning. This set a model for how big tech companies develop and research AI. Learn More About Google Brain >>
Coursera: Made AI education open to everyone through its MOOC offerings. This lets millions of people learn about machine learning and deep learning. Learn More About Coursera >>
Landing AI: Aims to bring working AI solutions to businesses. It mostly helps manufacturing and industrial areas use automation. Learn More About Landing.AI >>
Ng has played a big part in growing ai technologies. He always works for the responsible use of ai. He has helped out in many areas like robotics and reinforcement learning. Ng also uses ai to help solve problems with climate change. This has made him an important person in how ai is moving forward.
The Shift Toward Agentic AI Explained by Andrew Ng
Andrew Ng talks a lot about what comes next for artificial intelligence. He says the next big thing is agentic AI. This new kind of AI is different from the old models. The earlier AI models mostly worked to process information. But agentic AI is about building systems that can do tough, multi-step tasks on their own.
Ng says this change is a big step for artificial intelligence. Deep learning, which utilizes CPU cores, gave AI its "brain." Agentic workflows add the power to "act." He talks about his ideas in research papers and also in community talks. Ng helps guide the AI community. This new way will change how people use and talk to artificial intelligence.
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Agentic AI is not the same as older AI models. The older ways just answer questions or point out what is in a picture. Agentic AI can do much more. It be able to plan and do a set of steps to reach a goal. With agentic AI, there is a big change. One example is in marketing. A normal ai might tell you what a good marketing plan is. But agentic AI can go further. It can write emails, check how well they work, and then change the plan if needed. This makes it much more useful.
Andrew Ng, the founder of DeepLearning.AI, says that agentic AI stands out because it can act on its own and aims to reach certain goals. This type of AI can take a complex ask, split it into smaller steps, and finish each step, one by one. You will see agentic AI has a few important traits:
Planning: This means coming up with a way to get a job done.
Tool Use: This is being able to use things outside of itself, like code interpreters or web browsers.
Self-Correction: This is the skill to look at how it is doing the work and change the plan if it needs to.
This makes artificial intelligence act as more than just a tool. Now, it can help people as a partner. The ai uses deep learning and new ways of understanding what people want. So, it can follow orders and do things well. With this, artificial intelligence is much more useful and strong than before.
Why Is the AI Industry Moving Toward Agentic Models?
The way the industry is turning to agentic AI makes sense. It started with learning how to handle and understand big data. After that, the next step is to let ai use what it knows and take action. Companies want more automation, better speed, and ai that solves problems well.
Andrew Ng, the director of the Stanford Artificial Intelligence Laboratory, talks about how today's AI has some limits. A large language model can do a lot, but it still needs people to guide it at each step if the task is hard. Agentic models try to make AI so they do not need people as much. The main reasons for this change are:
Increased Efficiency: You can use automation to manage whole workflows and not just single tasks.
Solving Complex Problems: You can fix tricky problems that need you to do several steps one after the other.
Enhanced User Experience: These tools help make digital assistants smarter and give people better help.
This trend comes from many years of work in reinforcement learning. Because of today's strong AI systems, it is becoming real for people to use now. The AI community, which has many people from the original Google Brain team, keeps putting out research papers. These papers help agentic tools and ideas get better.
How Agentic AI Impacts Developers
The growth of agentic AI is happening now, and it is changing how developers work. You will not only make machine learning models anymore. Now, you will be in charge of building smart agents that do hard jobs. Because of this, you need to learn new things. There are many new chances coming in that space of ai and machine learning.
The way artificial intelligence is changing means you will work with systems that do more on their own and have more tasks. It's important to know how to make, check, and handle these ai technologies. The good news is that there are already resources from leaders in online education to help you get ready for what's next in artificial intelligence.
New Opportunities for Developers Working with Agentic AI
For the people who work as developers, the rise of agentic AI brings many new chances. Now, you do not only need to learn how to train neural networks for one task. You can also make advanced systems. These use many ai technologies to reach big goals. Because of this, there is now more need for developers who know how to plan workflows and set up autonomous systems.
You can get started with agentic AI now by checking out open-source frameworks. There are also new platforms made for this. Andrew Ng gives updates about these new tools. You can find the latest features for developers using his tips.
Building Autonomous Agents: Creating agents that can do tasks such as coding, research, or handling projects.
Developing Agentic Frameworks: Helping to improve open-source tools that support these systems.
Integrating Agents into Products: Adding agentic features to current software and apps.
This change lets you move past older deep learning and NLP tools. Now, you can get into exciting areas like robotics that interact with people and digital helpers that work automatically. More and more people will want developers who have the skills to use these ai technologies. The need for experts in ai and robotics will get bigger every year.
Essential Skills for the Agentic AI Era
To do well in the age of agentic AI, developers must build more skills. A solid base in machine learning is still very important. Now, some new skills matter as much as old ones. It is not enough to know how models work. You also need to learn how to make them work together as one group or system with ai.
Andrew Ng has some great courses on Coursera. You can start with his famous "Machine Learning" and "Neural Networks and Deep Learning" classes. These will help you learn the basics and get you ready for agentic AI. Build your skills in areas like machine learning, deep learning, neural networks, and more.
System Design: Setting up workflows so that AI agents can make plans and get tasks done.
Prompt Engineering: Writing clear steps to help guide how the agent acts.
Tool Integration: Knowing how to link AI agents with outside APIs, databases, and other software tools.
You need more than technical skills. You should also know a lot about NLP and how responsible AI works. You will need to keep learning with ai education platforms. These will help you keep up as ai technologies change over time.
Business Implications of Agentic AI: Insights from Andrew Ng
The move to agentic AI is not only for those who make software. It can change how every business works. Andrew Ng says these AI tools can boost productivity a lot. They can do full jobs, not just small tasks. So, companies can give hard work to AI agents, and human teams can spend more time on ideas and planning.
Agentic AI can help with marketing, sales, operations, and customer service. It makes many slow and manual tasks faster and easier. The ai community is working hard to make these tools better every day. If businesses start to use ai now, they can get ahead of their rivals. This is a good time to think about how agentic workflows can change your company.
Transforming Workflows and Productivity Through Agentic Tools
Agentic AI tools are changing the way we work. Now, you can use an AI assistant that will do more than just write a report. It can help you gather data, check the numbers, make visual charts, and send the finished document to the right people in your team. With this kind of help, your job gets easier and you can get more tasks done in less time.
Andrew Ng's company, DeepLearning.AI, helps people learn how to use ai technologies. The goal is to teach the skills needed in real life, including applications in healthcare. This way, people can build and use ai to change how things work. There are groups like Landing AI that are already using what they learn to fix business problems. A market research example shows how this can help in the real world.
Workflow Stage |
Before Agentic AI |
With Agentic AI |
|---|---|---|
Data Collection |
Manually searching for reports and articles |
Agent autonomously scours the web for relevant data |
Analysis |
Analyst reads and synthesizes information |
Agent summarizes key findings and identifies trends |
Reporting |
Manually creating charts and writing summaries |
Agent generates a full report with visuals and text |
This change in workflows helps businesses be faster and smarter. The ai community is putting out new research papers all the time. These show how artificial intelligence works and how ai helps people get more done.
Adapting Teams and Projects for the Agentic AI Shift
Bringing agentic AI into your work is not only about using new tools. It asks teams to change how they think and work. Project managers will not just give jobs to people now. They will also plan how humans and AI agents work together. This mix of people and AI can really help teams get better results and think of new ideas.
To keep up, teams need to spot which ways of working can use automation. These are often jobs where there are many steps, and those steps get done again and again, but some choices need to be made along the way. When people begin to build using agentic AI tools, they will have to change how their projects are set up. Some big steps to help change include:
Identifying Pilot Projects: Start with small and safe projects. Use these to test how agentic workflows with ai work.
Upskilling Your Team: Put money into training. This will help your team know and work well with agentic ai.
Fostering Collaboration: Build a culture where people see ai agents as helpers. They should not feel that ai will replace them.
If your teams change how they work early, they will get more from these strong artificial intelligence tools. This way, you can keep up as the ai community builds smarter agentic systems.
How Andrew Ng Shares Agentic AI Updates and Knowledge
Andrew Ng thinks it is good to share knowledge. He leads the way in artificial intelligence and the development of AI education. He works to tell people about new changes in AI, like agentic AI. He uses many channels to reach out. His aim is to help everyone understand hard topics, no matter their background.
Ng shares useful ideas in many ways, including his experiences from London. You can learn from him through his online machine learning course. He also talks often on social media. You can read research papers that come from his teams. A lot of people get to find help from Ng. He works hard so that developers, business leaders, and people who are keen on learning have what they need. This helps many people to get into and understand the new world of machine learning.
Courses, Articles, and Talks on the Shift Toward Agentic AI
To learn about agentic AI, Andrew Ng says you should first get to know artificial intelligence and deep learning. His main courses on Coursera are a good starting point. These courses show you the key ideas behind the AI systems people use now.
While there are not many courses yet that focus only on agentic AI, Ng shows a good way to start learning about it on his platforms. He says you first need to know the main ideas. You can find some good things to read about this with the resources below:
His basic online machine learning course on Coursera has reached millions of people.
Specializations in deep learning from DeepLearning.AI cover things like neural networks, facial recognition, and speech recognition.
His weekly newsletter, "The Batch," often talks about new trends and studies in agentic AI.
By learning from his formal AI education and keeping up with his regular updates, you can get a full view of this fast-growing field. He shares what he knows from his time at Stanford University and from working in the industry. You get both the basic ideas and the hands-on tips you need from him about AI and learning at Stanford.
Good Read: AI Agents Explained, see definitions, types & ExamplesWhere to Follow Andrew Ng's Latest Agentic AI Work Online
Connecting with Andrew Ng and the ai community is easy because he is active online. Andrew Ng likes to share ideas and talk with people who want to learn about ai. If you want the latest updates on his work with agentic ai, you can follow him on several places where he often shares news and thoughts.
You can reach out to him on social media. He often talks there about new deep learning research papers and what may be next for artificial intelligence, including his involvement in the Stanford Autonomous Helicopter project. If you want to see more of his work, you can go to these channels.
LinkedIn: Ng, the CEO of Landing AI, often shares updates. He talks about new courses and some trends in the AI world. Andrew Ng's Linkedin >>
DeepLearning.AI: His company's site and newsletter, "The Batch," is where you can read his latest ideas and learning material.
Coursera: You can sign up for his courses and follow his instructor profile. He gives news about new lessons from there.
If you follow him on these sites, you will get direct access to his knowledge. You can be part of the bigger talk in the ai community. This is a good way to keep up with fast changes in agentic ai.
Conclusion
Andrew Ng talks about agentic AI as a big change that will shape how developers and businesses work. When developers use these new AI models, they can find fresh ways to do things and boost how much they get done. This time of agentic AI means developers and other people need to learn new skills and learn to adjust, and it gives everyone the chance to do well in this area as it grows. As Ng shares more about AI in his courses and articles, it's important that people keep learning about these updates if they want to use agentic AI. You can read more about agentic AI and see what Andrew Ng says online, or you can sign up for his courses to keep up with changes.
Frequently Asked Questions
What courses does Andrew Ng recommend for understanding agentic AI?
Andrew Ng says you should start with a strong base of knowledge. He suggests taking his "Machine Learning" and "Deep Learning" specialization courses on Coursera. These will help you get the key ideas about artificial intelligence, including natural language processing. It will also make it easier for you to learn and build bigger agentic systems.
How can developers start building with agentic AI today?
Developers can start working with agentic AI by trying open-source frameworks like CrewAI or using some special toolkits. Andrew Ng says it is good to jump into projects that use several steps to plan and use tools, using ideas from deep learning and artificial intelligence. You can make agents that work on tasks by themselves and help in real situations.
Where does Andrew Ng publish insights about agentic AI and developer trends?
Andrew Ng talks about many topics on different platforms. He often posts on LinkedIn and shares news in his weekly newsletter "The Batch." Andrew also creates new courses for DeepLearning.AI, where he serves as chief scientist. His advocacy helps the AI community know about new things in artificial intelligence.
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