- The AI Master's Forum, hosted by the Yonglin Foundation and Commonwealth Magazine, discusses how AI will shape the future of mankind.
- Distinguished guests and speakers include Chairman Guo Xiaolin, President Lin Qi Hong, Shelly Guo, Qi Chi-Hung Lim, Dr. Li Jie, Dr. Andrew Ng, Dr. Jay Lee, Terry Guo, and Sam Altman.
- The Yonglin Foundation has a history of promoting education, philanthropy, and healthcare initiatives.
- The forum explores the next technological revolution for mankind, following the industrial and computer revolutions.

- The forum is focused on the importance of AI to the future of Taiwan
- The event has reached full capacity with over a thousand attendees
- Dr. Andrew Ng is the first speaker and is introduced as a leader in AI
- AI is described as a collection of tools, with supervised learning and generative AI being the most important
- Supervised learning is good at labeling things and has many applications
- Generative AI is set to be the next big development in AI
- Large language models are built by repeatedly predicting the next word using supervised learning
- Generative AI is changing AI application development and is exciting for both consumers and developers
- Building a production AI system typically takes 6 to 12 months.

- Building a production commercial AI system typically takes 6 to 12 months, but with prompting, it can be done in a matter of hours or days.
- This has led to a flourishing of AI applications, with many people being able to build AI systems in a week that used to take months.
- The speaker demonstrates how to build a sentiment classifier using OpenAI tools with just a few lines of code.
- There are significant opportunities in AI, with supervised learning being massive and generative AI showing potential for significant growth in the next three years.
- The speaker emphasizes that AI technologies are general purpose and can be applied to many different tasks and applications.
- Low-code and data-centric AI tools are enabling users to customize and build AI systems for specific projects, expanding the value of AI beyond the consumer internet sector.
- The speaker believes that starting new companies or integrating AI into existing workflows are efficient ways to pursue AI opportunities.
- The AI stack includes hardware, and the semiconductor industry, particularly TSMC and UMC, is doing well.

- TSMC and UMC are performing well, but the speaker doesn't invest in them due to capital intensity and concentration.
- Building service and cloud infrastructure are also profitable, but the speaker doesn't invest in them either.
- The speaker sees potential in the developer tools market, but it is hyper-competitive.
- The application layer is crucial for the success of infrastructure and tooling.
- The speaker gives an example of partnering with a relationship expert to apply AI to romantic relationship coaching.
- The speaker discusses their process of building startups, starting with idea validation and recruiting a CEO.
- They mention a successful case study of using AI to make shipping more fuel efficient.
- The speaker emphasizes their expertise in AI and working with partners in different application sectors.
- They mention risks of AI, such as bias and job disruption, but believe in our ability to control it.
- The speaker believes AI can be a key part of solving existential risks to humanity.
- The fireside chat will be moderated by Dr. Chi-Hung Lin and will include Mr. Mark Chen from OpenAI.

- Sam Altman, CEO of OpenAI, is a prominent figure in the AI revolution and Silicon Valley.
- Altman's vision is to make energy and intelligence more accessible and affordable for all.
- He is involved in various ventures, including OpenAI, Helion Energy, WorldCoin, and Retro.
- Altman believes that the AI revolution will bring positive transformation and unlimited opportunities for human creativity.
- He anticipates rapid progress in AGI and expects each year to bring new possibilities.
- Mark, also from OpenAI, shares the optimism about AI's future and believes it will bring about positive change.
- Altman and Mark have been impressed by the enthusiasm and creativity of people around the world in adopting AI technologies.
- OpenAI is focused on research in multimodal and reasoning capabilities for AI models.
- Altman believes that if AI models can reason like humans, they can contribute to scientific progress and transform the world.
- OpenAI started as a non-profit but transitioned to a profit-making company to support the expensive infrastructure required for AI development.
- The company aims to balance capitalism and societal benefit through a capped profit model and governance by a non-profit organization.
- Altman acknowledges the concerns about energy consumption and aims to reduce the cost of energy and intelligence to near-zero.

- The goal is to reduce the cost of energy and intelligence to near 0
- Fusion and solar energy are potential solutions to meet the enormous energy needs
- OpenAI believes in deploying AI technology iteratively and learning from mistakes
- International cooperation is important in addressing the risks and impacts of AI
- Bias and misinformation are concerns, but techniques can be used to mitigate them
- Technology, including AI, can be a force for equalizing society
- Regulation is seen as necessary for powerful AI systems, but overregulation is also a concern
- OpenAI does not fear regulation and believes some version of it is important.

- The discussion touches on the potential inequities caused by AI, with examples of code generation and medical diagnosis.
- The importance of hard work and conviction in achieving success in AI development is emphasized.
- The speaker reflects on the education systems in Taiwan and the United States.
- The story of a manager at OpenAI who took on the challenge of learning a new skill is shared.
- The ability to control AI models and localize them to specific needs is discussed.
- The keynote speaker, Dr. Jay Lee, is introduced as a leading expert in industrial big data.
- The speech will focus on real examples and implementation of AI in industry.
- The future trends of a data-centric economy and the abundance of data are mentioned.

- The AI Master's Forum discusses the impact of AI on the future of mankind
- The forum is focused on the importance of AI for Taiwan
- Dr. Andrew Ng, a leader in AI, discusses the importance of supervised learning and generative AI
- Building AI systems has become faster and more accessible with low-code and data-centric tools
- The speaker sees potential in the developer tools market and emphasizes the importance of the application layer
- OpenAI CEO, Sam Altman, believes in the positive transformation and unlimited opportunities brought by the AI revolution
- OpenAI is focused on research in multimodal and reasoning capabilities for AI models
- OpenAI aims to reduce the cost of energy and intelligence to near-zero and believes in iterative deployment and international cooperation
- Regulation is seen as necessary for powerful AI systems, but overregulation is a concern
- The discussion addresses potential inequities caused by AI and emphasizes the importance of hard work and conviction in AI development
- Dr. Jay Lee, an expert in industrial big data, will focus on real examples and implementation of AI in industry.

- The future trends of a data-centric economy involve determining how much data is useful and usable.
- Non-traditional asymmetric machine learning is a cost-effective approach to solving problems using AI.
- The development of AI has progressed from natural language processing to machines, robots, and various industries.
- The challenge lies in assembling the best knowledge models from different sources of data to simplify learning.
- The goal is to avoid problems and improve decision-making based on large datasets.
- The focus is on better data representation and taking action based on useful and usable data.
- The concept of intelligent metrology systems involves using data-centric systems as measurement systems.
- Transfer learning is a common approach for training models when collecting all the data is not feasible.
- Behavior analysis can be used to train systems, as seen in Tesla's insurance program.
- The future holds the potential for utilizing various sensors in phones and electric vehicles.

- The future of technology involves automatically generating label data based on sensors and moving equipment.
- A case study in high volume manufacturing, specifically semiconductors, shows the use of a new digital twin method to eliminate trial and error and improve calibration.
- The formation of the FRI in 2018 aimed to make manufacturing more transparent and predictable through the connection and monitoring of machines.
- The stream of quality concept, which combines material flow, people flow, process flow, money flow, information flow, and technology flow, allows for better traceability and prediction in manufacturing.
- In the wind turbine industry, ranking turbines based on wind speed changes and using machine learning can identify low-performance turbines and improve maintenance efficiency.
- In hospitals, the use of sensors and data analysis can help identify high-risk patients and customize treatment plans.
- The analysis of rocket data can help detect abnormalities and improve the life cycle system for reusable rockets.

- The speaker discusses the importance of the 0.1 second delay in valve opening and the use of dynamic time warping to develop a reference baseline.
- The speaker mentions the use of non-traditional machine learning and a similarity-based approach to improve machine performance.
- The speaker emphasizes the need for selecting usable data and highlights the challenges of maintaining AI software in the future.
- The speaker mentions their experience teaching industrial AI and the importance of practical application and discipline in learning.
- The speaker mentions their published books and invites the audience to visit their website for more information.

- AI is a magnet for young talent and attracts the smartest and most ambitious individuals.
- AI literacy is increasing, with even 12-year-olds using AI technology effectively.
- The trend in technology is for computing to become easier and more intuitive over time.
- Large language models will continue to improve and integrate domain knowledge.
- The compute power for AI models will continue to increase, with significant investments in GPUs.
- Scaling laws show that making computers bigger and throwing more data at them improves AI systems.
- AI will evolve as a platform and be integrated into various verticals.
- Entrepreneurs are looking for critical factors to go beyond current projections in AI growth.

- The future growth of AI depends on critical factors such as open AI and the involvement of subject matter experts in various industries.
- It takes time to find all the ways to apply AI, but subject matter experts who already have concrete ideas can make progress rapidly when working with AI teams.
- AI is being used to build valuable companies and accelerate progress in various industries.
- OpenAI focuses on improving generative AI through a coordinated effort and multiplication of different teams' contributions.
- AI has the potential to contribute to the healthcare industry, particularly in the areas of cancer and aging.
- AI can automate tasks in healthcare and other sectors, leading to increased efficiency and lower costs.

- Radiologists have multiple tasks beyond reading X-rays that can be automated or augmented by AI
- Computationally improving drug development, especially for cancer, is important and supported by various agencies
- AI has the potential to change the doctor-patient relationship and provide more accessible healthcare
- Taiwan has the opportunity to excel in the AI industry and improve healthcare
- The majority of people are optimistic about the future with AI, but opinions vary in the US
- Taiwan's semiconductor industry is strong, but traditional industries may face challenges with AI adoption.

- Future trends in a data-centric economy involve determining the usefulness and usability of data.
- Non-traditional asymmetric machine learning is a cost-effective approach to problem-solving with AI.
- AI development has progressed from natural language processing to various industries and robots.
- The challenge lies in assembling knowledge models from different data sources to simplify learning.
- The goal is to improve decision-making based on large datasets and avoid problems.
- Intelligent metrology systems use data-centric systems as measurement systems.
- Transfer learning is commonly used for training models when collecting all data is not feasible.
- Behavior analysis can train systems, as seen in Tesla's insurance program.
- The future may utilize various sensors in phones and electric vehicles.
- Automatically generating label data based on sensors and moving equipment is the future of technology.
- A case study in high volume manufacturing shows the use of a new digital twin method to improve calibration.
- The FRI aims to make manufacturing more transparent and predictable through machine connection and monitoring.
- The stream of quality concept allows for better traceability and prediction in manufacturing.
- Machine learning can improve wind turbine maintenance efficiency by ranking turbines based on wind speed changes.
- Sensors and data analysis can help identify high-risk patients and customize treatment plans in hospitals.
- Rocket data analysis can detect abnormalities and improve reusable rocket life cycles.
- The speaker emphasizes the importance of selecting usable data and maintaining AI software in the future.
- AI is attracting young talent and increasing AI literacy.
- Computing will become easier and more intuitive over time.
- Large language models will continue to improve and integrate domain knowledge.
- AI compute power will increase with investments in GPUs.
- Scaling laws show that making computers bigger and throwing more data at them improves AI systems.
- AI will evolve as a platform and be integrated into various industries.
- The future growth of AI depends on factors like open AI and involvement of subject matter experts.
- AI is being used to build valuable companies and accelerate progress in various industries.
- OpenAI focuses on improving generative AI through a coordinated effort.
- AI has potential in the healthcare industry, particularly in cancer and aging.
- AI can automate tasks and increase efficiency and lower costs in healthcare and other sectors.
- AI can automate or augment tasks beyond reading X-rays for radiologists.
- Computational improvement in drug development, especially for cancer, is supported by various agencies.
- AI has the potential to change the doctor-patient relationship and provide more accessible healthcare.
- Taiwan has the opportunity to excel in the AI industry and improve healthcare.
- Opinions about the future with AI vary in the US.
- Taiwan's semiconductor industry is strong, but traditional industries may face challenges with AI adoption.

- The speaker worked with Meti and discussed how many companies, including Sake, are not interested in AI.
- The speaker believes that AI can augment processes and make work easier.
- According to a poll, 45% of people think AI will make their lives easier, 45% think it will make work easier, and 10% think it will help them make more money.
- The speaker believes AI won't be able to replicate human intuition and interest in other humans.
- Another speaker mentions that AI cannot break fundamental laws of physics and there are still many things it cannot do.
- Trustworthiness and the potential harm AI can cause are important considerations.
- Humans are social creatures and social networks will still be valuable.
- AI can potentially help solve global warming by finding solutions and reducing carbon emissions.
- The efficiency of AI systems in terms of carbon emissions is compared to alternatives like driving to a library.
- The speaker suggests studying climate geoengineering and using AI-powered drones for deployment.

- AI-powered drones and climate modeling can help reduce climate change
- Global warming is a fact and awareness is important
- Al Gore's foundation provides evidence-based information on carbon generation
- AI can drive evidence-based actions to address climate change
- LLM hallucinations are a difficult problem in large language models
- Reinforcement Learning from Human Feedback and browsing can help reduce hallucinations
- The AI Master's Forum concludes with gratitude to the panelists and audience participation

- AI can augment processes and make work easier
- 45% of people think AI will make their lives easier, 45% think it will make work easier, and 10% think it will help them make more money
- AI cannot replicate human intuition and interest in other humans
- Trustworthiness and potential harm of AI are important considerations
- Social networks will still be valuable as humans are social creatures
- AI can potentially help solve global warming by finding solutions and reducing carbon emissions
- AI-powered drones and climate modeling can help reduce climate change
- Al Gore's foundation provides evidence-based information on carbon generation
- Reinforcement Learning from Human Feedback and browsing can help reduce hallucinations in AI models

ChatGPT之父唯一現身！AI正改變生活、學習與工作的樣態，這一場革命又急又猛，改寫市場遊戲規則，有人晉升贏家，也有人害怕被AI取代。永齡基金會將於9月25日重磅推出「人工智慧如何形塑人類未來」大師論壇，授權天下雜誌直播。論壇壓軸邀請到ChatGPT之父，Open AI執行長奧特曼(Sam Altman)加入與談，與兩位華人AI大師--入選2023《時代雜誌》AI領域百大影響力人物、執教於史丹佛大學的吳恩達（Dr. Andrew Ng），以及工業大數據專家、馬里蘭大學講座教授暨工業人工智慧中心主任李傑（Dr. Jay Lee），同場探討AI如何跨領域顛覆世界，人類又該如何並肩向未來大步前行。

►按小鈴鐺通知 搶先看，精采獨家全面掌握！
=================================
更多精采內容請見：

◎天下雜誌：http://www.cw.com.tw
◎天下雜誌IG http://bit.ly/2R6jfL6
◎天下雜誌網路書店：https://www.cwbook.com.tw/

You you you you you you you you you you you You The following is a presentation of the AI Master's Forum.

Ladies and gentlemen, welcome to the AI Master Forum hosted by the Yonglin Foundation and the Commonwealth Magazine.

Today, we have invited an international AI master to discuss with us how AI will shape the future of mankind.

Today, we will stand on the shoulders of giants and feel the power of AI across the field.

Let's move forward together towards the future.

Ladies and gentlemen, we are going to stand on the shoulders of giants today.

Together, we're going to explore the revolutionary power of AI and how the AI revolution is going to redefine our tomorrow.

Next, I'm going to bring to the stage our distinguished guests and speakers.

First, let's welcome the Founder of Yonglin Foundation, Chairman Guo Xiaolin, and the Host of the Gourmet Talk, President Lin Qi Hong, President of National Yangming University.

Let's welcome Shelly Guo, CEO of the Yonglin Foundation, and our moderator today, Qi Chi-Hung Lim, president of National Yangming Chao-Tung University.

Let's welcome our 2 keynote speakers today, Dr.

It's a great honor to have the founder of Yonglin Foundation, the organizer of this forum, Mr.

Guo Taiming, and the father of ChatGPT, the founder of Open AI, Sam Altman, please welcome Terry Guo, founder of the Yongling Foundation, and the father of Chad Chibi Chin, co-founder of OpenAI, Sam Altman.

Let's firstly look at the cameras in front of you.

Guo Taiming to lead the audience to give a round of applause.

Thumbs up, everybody, for the second group photo.

Now, let's welcome the host, CEO of Yonglin Foundation, Shirley Guo.

Now for the opening remark, ladies and gentlemen, please welcome Shirley Guo, CEO of the Yonglin Foundation.

I am Shirley Goh, the CEO of Yonglin Foundation.

We have our speakers, invited guests, and everyone here today in person as well as online.

Thank you so much for taking the time to join us today.

Yonglin Foundation was founded in the year 2000, and Since then, we have always kept our eye on the future.

We have worked tirelessly to push for fundamental change and long-term impact in places of need here in Taiwan and around the world.

In education, we launched Yonglin School of Hope with a goal of equalizing basic education for underprivileged kids.

We have teachers, social workers, and created our own teaching materials and teaching training courses to support each and every 1 of our students.

As of today, we have helped over 160,000 students, many of whom have gone on to bigger and better jobs in society.

In philanthropy, we have stood in front of the front lines of disaster.

In 1999, the biggest earthquake in recent Taiwan history, where over 50,000 houses collapsed, we were there to help rebuild.

1 of the most disastrous typhoon wiped out entire towns, misplacing hundreds of families.

at the height of the COVID-19 pandemic, Yongning Foundation and Hong, and, I'm sorry, let me say that again.

The Yonlin Foundation and Foxconn Group donated 5 million doses of the life-saving vaccine to help protect the people of Taiwan.

In health, we have built a cancer hospital, the National Taiwan University Cancer Center.

It is a state of the art facility providing most advanced treatment practices available internationally.

The cancer center aimed to offer precise treatment and personalized regimen to all cancer patients.

At the verge of the next technological revolution for mankind, we are here.

We had the industrial revolution in the 1800s, the computer revolution in the 1900s and early 2000s.

And now we are facing the artificial intelligence revolution.

We understand the importance of AI to the future of the world, but most importantly, to the future of Taiwan.

For this very reason, we are hosting this forum today that is absolutely free to everyone and anyone who wants this knowledge.

We have over a thousand people here in this very room, and the entirety of this forum will be available via Yongning's YouTube channel and Facebook channel for those who are not able to join us here live today.

I was told that this event has reached full capacity in just a matter of a few days, so thank you for that.

But obviously, this is because of our esteemed speakers that are here today.

I know that every 1 of our speakers here today are already leaders in their respective worlds and are famous in their own right.

So it is my great honor to give each and every 1 of them a proper introduction.

Ng is Managing General Partner at AI Fund, CEO of Landing AI, founder of Deep Learning AI, chairman and co-founder of Coursera, and an adjunct professor at Stanford University.

He is also authoring and co-authoring over 200 research papers in machine learning, robotics, and related fields.

He was also the founding lead of the Google Brain Team and chief scientist at Baidu.

Eng now focuses his time primarily on his entrepreneur ventures, looking for the best ways to accelerate responsible AI practices in the larger global economy.

He holds degrees from Carnegie Mellon University, MIT, and the University of California at Berkeley.

In 2023, he was named 1 of Times Magazine's 100 most influential people in AI around the world.

Shirley Guo, CEO of the Yongling Foundation.

And ladies and gentlemen, the first keynote speaker today is Dr.

What I want to do is share with you today what I see as some exciting opportunities in AI.

So I've been saying for a few years that AI is the new electricity and that's because 1 of the difficult things about to understand about AI is a general purpose technology, meaning it's not useful just for 1 or 2 things.

It's useful for a lot of different applications, like electricity.

If I ask you what is electricity good for, it's almost hard to answer that question because electricity is useful for so many different things.

And like electricity about 100 years ago, I think AI is now going to revolutionize every sector of the economy.

What I want to do is start off with a brief description of the technology landscape, and this will then lead into a discussion of what AI, what we can all do with AI.

So there's a lot of hype and excitement about AI.

And I think a good way to think about AI is as a collection of tools.

The 2 most important tools in AI are supervised learning, which is very good at labeling things, and the new entrant generative AI.

If you study AI you may have heard of some other tools as well, but today I want to focus on just the 2 most important which are supervised learning, good at labeling things, and generative AI.

So supervised learning is a technology that started to work really well about 10, 15 years ago, where we could take inputs and label them with an output.

Given input A, we could compute an output B.

For example, given an email, we can label it with whether or not it is spam.

The most lucrative application of this I've ever worked on is probably online advertising, where all the large ad platforms can input an ad and some information about a user and label if you're likely to click on the ad.

And for a single company like Google, this drives more than 100 billion US dollars a year.

I see we have also Appia here, a very successful MarTech Taiwanese company also making good use of this technology.

In self-driving cars, we have AI that can input a picture of what's in front of the car and label it with position of the other cars with driver assistance or self-driving 1 of the projects 1 of my teams worked on is given a ship route We can label it with how much fuel is going to consume use that for logistics My teams do a lot of work in manufacturing, where we can take a picture of a smartphone that's just been manufactured and tell us if there's a defect for inspection.

Or given a restaurant, if you want to monitor the reputation, you can look at the restaurant review and label that as a positive or negative sentiment for reputation tracking.

So 1 thing about AI, even supervised learning, is notice how many different applications it is useful for.

And when it started to work well 10, 15 years ago, it was a lot of work for many of us to go and find and build to these applications of supervised learning.

So just to walk through 1 concrete example, this is what building a supervised learning labeling project looks like.

If you want to build a restaurant reputation monitoring system, you start by getting data, so pieces of text like the oyster omelet.

And so given thousands of data points like this, you then have your label data.

Then you can train your AI model and then find a cloud service to run it.

And after that, you can input your best sentence I've ever had.

And it'll say, there's a positive sentiment.

So this is a typical workflow of an AI supervised learning project.

Now, I know there's a lot of excitement about generative AI.

And I think that generative AI was set up for success with some of the early progress in supervised learning.

In fact, I think the last decade was a decade of large-scale supervised learning.

We're starting about 15 years ago, 10, 15 years ago.

We found that If we built a very small AI model, a very small neural network, even as we fed it more and more data, it would not get much better.

But when I started the Google Brain team, the primary mission I set for the team is I said, let's just build really, really large neural networks with Google's compute infrastructure.

And we found that as we scale to very, very large neural networks, we could feed in more and more data, and we just keep on getting better.

I know Jensen Huang and Lisa Su were in town just recently, and I feel like GPU hardware has powered a lot of this rise of AI in the last decade.

How about if that was the last decade, I think this decade is turning out to be the decade of generative AI, which adds, doesn't replace, but adds to the tools we have in supervised learning, where we now have tools that can input a piece of text called a prompt, and if you run it once, it may say mango shaved ice, mother's braised pork rice, or tofu.

And I think that when Sam and his team released ChaiGPT in November less than a year ago, it was almost a magical moment.

But I want to take just a minute to demystify how the heart of large language models works.

It seems so magical, how does it actually work?

It turns out that the heart of Gent of AI, of these large language models, is they're built by repeatedly predicting what is the next word using supervised learning.

So for example, if an AI has read on the internet a lot of text, like a sentence like, my favorite food is tomato scrambled eggs, right?

Then it can use this as data and ask, if you see my favorite food is, predict what's the next word.

In this case, it is tomato, or if it sees my favorite food is tomato, what's the next word?

If it sees my favorite tomato is scramble, what's the next word?

And it turns out that if you train a very large AI system on a very large number of words, hundreds of billions of words, or sometimes more than a trillion words, then you get a large language model like ChaiGPT or Googlebots or Bing Chats or others.

And I think that while many of you are familiar with tools like ChaiGPT as a consumer tool, and I think it's a fantastic consumer tool.

There's 1 other revolution, which is how generative AI is changing AI application development.

And I think I'm as excited, even more excited, about generative AI as a developer to, as a consumer to.

So if I wanted to build a restaurant-centered review system with supervised learning, this is what I would do.

Train an AI model, maybe it takes 3 months.

Deploy in the cloud, Maybe that takes me 3 months.

So a typical realistic timeline for building a production commercial AI system is maybe 6 to 12 months.

And many very good teams I worked with took about that long to build a system.

But in contrast with prompting, this is what the workflow looks like.

And then you can deploy the model in maybe hours or days.

What this means is that there are a lot of AI applications that used to take me 6 months to build, that today there are hundreds of people.

There are, sorry, hundreds of thousands of people, maybe millions of people, that can do in 1 week what used to take me 6 months to do.

And this is leading to a flourishing of AI applications.

Now, I know you were not expecting me to write code in this presentation, but that's what I'm going to do.

But this is all the code it takes to write a sentient classifier.

I'm going to import some tools from OpenAI, load my API key.

Then, I don't know, I really enjoyed the Yonglin Foundation meeting.

I've never run this before, so I really hope it works.

So it says, classify the text below the 3 dashes as having either positive or negative sentiment.

Right, And so, oh, thank goodness that worked.

But this is all the code it takes in order to build a sentiment classifier.

And this is revolutionary that So many people can now, with just a few lines of code, build and deploy a pretty good AI system.

1 of the things I've been working on over the last many months is work with many different industry partners to teach generative AI as a developer tool.

And I've been privileged to work with many of the industry experts.

So OpenAI, AWS, Lanchain, Lambda, Huckinface, Google, Microsoft, Ways and Bytes, Cohere, almost all the leading AI, generative AI companies.

I think we're probably working with all of them at this point, including many that have not yet been announced.

And I also want to mention that key to our developer education efforts is our Taiwan engineering team.

So deeplearning.ai, my team, is working on this generative AI application.

Our engineering team is actually here in Taiwan, and we couldn't have done it without them.

And I hope this will be our little contribution to the Taiwan engineering ecosystem as well in dissemination of knowledge.

Now, given this technology landscape, let me share a little bit about where I think are some significant AI opportunities.

The size of these circles shows what I think is the value of different AI technologies today in terms of value generated from applications compared to what the value would grow to 3 years from now.

For a single company like Google, worth more than $100 billion a year, and with millions of developers working on it, I think over the next 3 years, which is a very short time horizon, may double, go from massive to maybe even more massive.

Generative AI, very exciting, but it's small right now.

But I think it will much more than double over the next 3 years.

Sometimes people see this slide, and they think, I'm not excited about Generative AI.

No, I'm very excited about Generative AI.

I think it just will take time to figure out and build the applications.

If it continues to grow at this rate, then in 6 years, maybe it'll be even vastly bigger.

But the light-shaded region is where I think there are a lot of opportunities for developers, big companies, startups, everyone to identify and build exciting opportunities.

And 1 thing I hope you take away from this part of the presentation is AI technologies are general purpose technologies.

Supervised learning and generative AI, They're both general purpose technologies.

And this means they're useful for many different tasks, and many applications remain to be identified and built.

But there's 1 caveat, which is that there will be fads along the way.

I'm not sure how many of you remember the app Lenzer.

This is the app that you can upload 10, 15 pictures of yourself and draw a cool picture of you as a scientist or an astronaut.

Its revenues took off like that through last December, and then it did that.

And I think this is because Lenzer was the first of, unfortunately, what may be multiple fads.

There was a good idea, but it wasn't a long-term, sustainable business.

When I think about Lenzer, I'm reminded of when Steve Jobs, as well as Terry, gave us this.

Developers built an app for $199 to do this, to turn on the flashlight.

And it was a good idea, but this too was a fact that wasn't a long-term sustainable business.

But on the flip side, the iPhone also allowed other smart entrepreneurs to build long-term sustainable businesses like Uber and Airbnb and Tinder.

And I think on top of Genesive AI platforms, view this as a developer too, we have opportunities to do that too, and that's what I'm excited about.

So the first trend is Genesive AI as a...

Oh, sorry, AI as a general purpose technology.

There's just 1 more trend I want to share with you, and I only share this because I think this will impact many industries that Taiwan is strong in, which is, and this addresses why AI isn't more widespread yet, right?

So a bunch of us, we've been talking about AI for 10, 15 years now, but its value is still very concentrated in consumer internet.

If you were to take all current and potential projects, current and potential AI projects, and sort them in decreasing order of value, you get a curve that looks like this, where to the left are the most valuable projects, like online advertising and web search, where you can write 1 AI piece of software, if you're Google, say, and apply that to a billion users and generate massive economic value.

So about 10, 15 years ago, my friends and I figured out a recipe for how to build projects like that.

We hire 100 engineers, have them all work on 1 project, apply it to 100 million or a billion users, and this turns out to be very valuable.

But once you go outside the internet sector, almost no 1 has 100 million users that you can apply 1 AI system to.

Instead, these are the types of projects I've been working on.

We're working with a factory that makes pizzas, and we're taking pictures of the pizza, and the factory needed to take pictures of the pizza to make sure that the cheese is evenly spread.

This turns out to be about a $5 million project for the factory, but you can't hire 100 engineers to work on a $5 million project.

Or agriculture, I know there's a lot of agriculture in Taiwan obviously, but we're working with a company that makes agricultural machinery, and we figured out that if we can find out how tall the wheat is with cameras, with computer vision, and chop off the wheat at the right height, then we get more food for the farmer to sell, and it's better for the environment.

But this is another $5 billion project that the old recipe is not economical for.

Or inspection, material grading, a lot of these $5 billion projects.

So where's to the left of this curve is a small number of billion dollar projects, $100 million projects, we know how to do that.

In other sectors, we see tens of thousands of these $5 million projects and the struggle has been how do we get all of these tens of thousands of projects economically built?

Fortunately, the trend in AI has been to have better tools, specifically low-code, sometimes no-code tools, that enable the user to do the customization.

So what this means is instead of me having to do work to figure out what to do with pictures of pizza, now the IT department of the pizza factory can build an AI system using their own images and realize that $5 million worth of value.

And by the way, these pictures of pizza, they don't exist on the internet.

And so Google and Bing do not have access to these pictures either.

And the key technology to do this is prompting, as well as a technology called data-centric AI, in which you can let the, say, IT department, the factory, write prompts to provide data and this lowers the barrier.

And I'm sharing this because I think this is an important part of the recipe to take a lot of the value of AI, which has so far been concentrated in consumer software internet and push it to the rest of the economy, which is even bigger than the tech industry.

I felt 5 years ago that many valuable opportunities are now possible in AI because of supervised learning, but I feel even more strongly about it because of generative AI.

And the puzzle I wanted to solve 5 years ago was, how do I get many of these projects built?

And even though I had led AI teams in big companies like Google and Baidu, I couldn't imagine how I could operate a team inside a big tech company to go after the very diverse set of opportunities that AI now makes possible.

I think starting new companies is an efficient way to do this, which is why I started AI Funds, which is a venture studio.

But of course, to incumbent companies, there are also many opportunities to integrate AI into existing workflows.

And often incumbent companies have a huge distribution advantage.

But concretely, where are the opportunities?

At the lowest level is the hardware layer.

And I know Jensen Huang and Lisa Su are just in town.

See all 天下雜誌 video｜深度人物專訪 transcripts on Youtube

Open AI 創辦人Sam Altman來台！獨家直播AI大師論壇－人工智慧如何形塑人類未來