Get live statistics and analysis of Andrew Ng's profile on X / Twitter

Co-Founder of Coursera; Stanford CS adjunct faculty. Former head of Baidu AI Group/Google Brain. #ai #machinelearning, #deeplearning #MOOCs

962 following1M followers

The Thought Leader

Meet Andrew Ng, a titan in the AI world, who seamlessly blends academic insights with groundbreaking digital initiatives. As a co-founder of Coursera and former lead at Google Brain and Baidu AI Group, his tweets pack wisdom that's both profound and practical. From advocating for educational joy to critiquing visa policies, Andrew isn't just sharing knowledge—he's shaping the future.

Impressions
2.5M-237.9k
$469.70
Likes
24.5k-1.9k
56%
Retweets
3.3k-267
8%
Replies
942-93
2%
Bookmarks
14.7k-111
34%

Andrew, you're out here teaching people the wonders of AI while still expecting students to fight through homework without a virtual assistant or an abundance of coffee. You might need to revise that vision of joyful learning!

One of Andrew's biggest achievements includes co-founding Coursera, which has transformed online education by making it more accessible to millions worldwide.

To democratize access to AI education, making cutting-edge knowledge available to everyone while driving innovation that positively impacts society.

Andrew believes in the transformative power of education, the necessity for innovation in technology, and the ethical responsibility that comes with AI development. He champions the importance of inclusivity in tech—a principle evident in his courses.

Andrew's strengths include his deep expertise in AI, his ability to communicate complex ideas simply, and his commitment to making education accessible through innovative methods.

A potential weakness could be occasionally coming off as overly task-focused, which might limit interpersonal engagements and connections beyond professional networking.

To further grow his audience on X, Andrew could embrace engaging multimedia content—like short video clips or live Q&A sessions—to complement his tweets. This would help him connect more personally with his followers and keep the conversation lively!

Fun fact: Andrew once wished for homework to be so engaging that students would turn to it instead of ChatGPT—an ambitious goal for sure, but we all know AI can be quite the distractor!

Top tweets of Andrew Ng

Thank you to the entire OpenAI team, including esp @sama @gdb @miramurati, for your incredible contributions to AI. It is tragic that the foolish decisions of a few people are hurting everyone. We love and are rooting for every one of you.❤️

833k

I'm teaching a new course! AI Python for Beginners is a series of four short courses that teach anyone to code, regardless of current technical skill. We are offering these courses free for a limited time. Generative AI is transforming coding. This course teaches coding in a way that’s aligned with where the field is going, rather than where it has been: (1) AI as a Coding Companion. Experienced coders are using AI to help write snippets of code, debug code, and the like. We embrace this approach and describe best-practices for coding with a chatbot. Throughout the course, you'll have access to an AI chatbot that will be your own coding companion that can assist you every step of the way as you code. (2) Learning by Building AI Applications. You'll write code that interacts with large language models to quickly create fun applications to customize poems, write recipes, and manage a to-do list. This hands-on approach helps you see how writing code that calls on powerful AI models will make you more effective in your work and personal projects. With this approach, beginning programmers can learn to do useful things with code far faster than they could have even a year ago. Knowing a little bit of coding is increasingly helping people in job roles other than software engineers. For example, I've seen a marketing professional write code to download web pages and use generative AI to derive insights; a reporter write code to flag important stories; and an investor automate the initial drafts of contracts. With this course you’ll be equipped to automate repetitive tasks, analyze data more efficiently, and leverage AI to enhance your productivity. If you are already an experienced developer, please help me spread the word and encourage your non-developer friends to learn a little bit of coding. I hope you'll check out the first two short courses here! deeplearning.ai/short-courses/…

774k

I think AI agentic workflows will drive massive AI progress this year — perhaps even more than the next generation of foundation models. This is an important trend, and I urge everyone who works in AI to pay attention to it. Today, we mostly use LLMs in zero-shot mode, prompting a model to generate final output token by token without revising its work. This is akin to asking someone to compose an essay from start to finish, typing straight through with no backspacing allowed, and expecting a high-quality result. Despite the difficulty, LLMs do amazingly well at this task! With an agentic workflow, however, we can ask the LLM to iterate over a document many times. For example, it might take a sequence of steps such as: - Plan an outline. - Decide what, if any, web searches are needed to gather more information. - Write a first draft. - Read over the first draft to spot unjustified arguments or extraneous information. - Revise the draft taking into account any weaknesses spotted. - And so on. This iterative process is critical for most human writers to write good text. With AI, such an iterative workflow yields much better results than writing in a single pass. Devin’s splashy demo recently received a lot of social media buzz. My team has been closely following the evolution of AI that writes code. We analyzed results from a number of research teams, focusing on an algorithm’s ability to do well on the widely used HumanEval coding benchmark. You can see our findings in the diagram below. GPT-3.5 (zero shot) was 48.1% correct. GPT-4 (zero shot) does better at 67.0%. However, the improvement from GPT-3.5 to GPT-4 is dwarfed by incorporating an iterative agent workflow. Indeed, wrapped in an agent loop, GPT-3.5 achieves up to 95.1%. Open source agent tools and the academic literature on agents are proliferating, making this an exciting time but also a confusing one. To help put this work into perspective, I’d like to share a framework for categorizing design patterns for building agents. My team AI Fund is successfully using these patterns in many applications, and I hope you find them useful. - Reflection: The LLM examines its own work to come up with ways to improve it. - Tool use: The LLM is given tools such as web search, code execution, or any other function to help it gather information, take action, or process data. - Planning: The LLM comes up with, and executes, a multistep plan to achieve a goal (for example, writing an outline for an essay, then doing online research, then writing a draft, and so on). - Multi-agent collaboration: More than one AI agent work together, splitting up tasks and discussing and debating ideas, to come up with better solutions than a single agent would. I’ll elaborate on these design patterns and offer suggested readings for each next week. [Original text: deeplearning.ai/the-batch/issu…]

818k

Most engaged tweets of Andrew Ng

After reading the @nytimes lawsuit against @OpenAI and @Microsoft, I find my sympathies more with OpenAI and Microsoft than with the NYT. The suit: (1) Claims, among other things, that OpenAI and Microsoft used millions of copyrighted NYT articles to train their models (2)…

943k

I think AI agentic workflows will drive massive AI progress this year — perhaps even more than the next generation of foundation models. This is an important trend, and I urge everyone who works in AI to pay attention to it. Today, we mostly use LLMs in zero-shot mode, prompting a model to generate final output token by token without revising its work. This is akin to asking someone to compose an essay from start to finish, typing straight through with no backspacing allowed, and expecting a high-quality result. Despite the difficulty, LLMs do amazingly well at this task! With an agentic workflow, however, we can ask the LLM to iterate over a document many times. For example, it might take a sequence of steps such as: - Plan an outline. - Decide what, if any, web searches are needed to gather more information. - Write a first draft. - Read over the first draft to spot unjustified arguments or extraneous information. - Revise the draft taking into account any weaknesses spotted. - And so on. This iterative process is critical for most human writers to write good text. With AI, such an iterative workflow yields much better results than writing in a single pass. Devin’s splashy demo recently received a lot of social media buzz. My team has been closely following the evolution of AI that writes code. We analyzed results from a number of research teams, focusing on an algorithm’s ability to do well on the widely used HumanEval coding benchmark. You can see our findings in the diagram below. GPT-3.5 (zero shot) was 48.1% correct. GPT-4 (zero shot) does better at 67.0%. However, the improvement from GPT-3.5 to GPT-4 is dwarfed by incorporating an iterative agent workflow. Indeed, wrapped in an agent loop, GPT-3.5 achieves up to 95.1%. Open source agent tools and the academic literature on agents are proliferating, making this an exciting time but also a confusing one. To help put this work into perspective, I’d like to share a framework for categorizing design patterns for building agents. My team AI Fund is successfully using these patterns in many applications, and I hope you find them useful. - Reflection: The LLM examines its own work to come up with ways to improve it. - Tool use: The LLM is given tools such as web search, code execution, or any other function to help it gather information, take action, or process data. - Planning: The LLM comes up with, and executes, a multistep plan to achieve a goal (for example, writing an outline for an essay, then doing online research, then writing a draft, and so on). - Multi-agent collaboration: More than one AI agent work together, splitting up tasks and discussing and debating ideas, to come up with better solutions than a single agent would. I’ll elaborate on these design patterns and offer suggested readings for each next week. [Original text: deeplearning.ai/the-batch/issu…]

818k

Had an insightful conversation with @geoffreyhinton 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!

1M

People with Thought Leader archetype

The Thought Leader

plastic surgery // klinik beyoutiful jl pakubuwono 6 no 5a telp 021 7211025

209 following1M followers
The Thought Leader
71 following1M followers
The Thought Leader

Scaling Computing 🖥️ Inventor of zkBridge & Expander

135 following965k followers
The Thought Leader

Director, School of Communication @IILMInstitute; Columnist, Gulf News. Recipient, IPI India award,Ramnath Goenka award for excellence in journalism. Dog lover

487 following1M followers
The Thought Leader

Tech Reporter

1k following1M followers
The Thought Leader

Sunshine, Yahoo, Google, San Franciscan, Wisconsinite, Geek.

441 following1M followers
The Thought Leader

football Player /African Golden Ball /Best Tunisian Player of The Century /Sport Minister /Analyst at Bein Sports

1k following970k followers
The Thought Leader

SUNY Distinguished Professor; Margaret W Wong Prof; Former Dean @UBSchoolofLaw; Senior Advisor on Constitutional Affairs to President of Kenya; NMG columnist.

1k following1M followers
The Thought Leader

Father, Husband, Brother, Son - Actor, Traveller, Petrolhead, Dreamer. Freedom (n.): To ask nothing. To expect nothing. To depend on nothing.

16 following1M followers
The Thought Leader

Perfil oficial para informações, divulgação de serviços e esclarecimentos. Aqui, você acompanha o nosso dia a dia! linktr.ee/minsaudebr

323 following1M followers
The Thought Leader

#Zimbabwe's former Minister of Higher & Tertiary Education, Science & Technology Development. "The unexamined life is not worth living" ~ Socrates

350 following990k followers
The Thought Leader

Ex-governador de SP, ex-prefeito de São Paulo, empresário, jornalista e um homem a serviço da família e do melhor para o Brasil.

293 following1M followers

Explore Related Archetypes

If you enjoy the thought leader profiles, you might also like these personality types:

Supercharge your 𝕏 game,
Grow with SuperX!

Get Started for Free