被誉为“下一个马斯克”！19岁MIT辍学创业，5年成就73亿美元独角兽，这位华裔男孩成为全球最年轻的亿万富翁！（附视频&演讲稿）

When most people think about AI,  they picture a sci-fi dystopian future, with man versus machine. 

Terminator,  Skynet, Black Mirror, Blade Runner, Westworld. 

But as someone who is working  on the most ambitious AI projects in the world,  every day, I can tell you that is far from reality. 

To me, it’s the contrary of that. 

AI enhances and even supercharges humanity. 

Let me explain why. 

There are many reasons why AI will never replace humans. 

AI always has, and always will, rely on humans. 

That’s one of the reasons  that I was actually inspired to start an AI company. 

That and my background have had a huge impact on me  and why I started Scale. 

My parents were brilliant scientists of Los Alamos,  who accomplished a lot in advancing their field. 

That inspired me to use science and technology  to have a real impact on the world. 

My dad was a physicist, and my mom was an astrophysicist,  both at the top of their field,  who made meaningful contributions to plasma fluid dynamics  and the beginnings of the universe. 

Their scientific work will have meaningful impact  on how we understand and perceive our world. 

And I wanted to work on something as impactful  or even more impactful than that. 

That’s why I decide to become a programmer,  so I can make a difference in the world. 

Growing up as a programmer,  despite how powerful computers are,  you quickly realize how limited they are. 

In particular, they lack judgment and intelligence. 

Programming is the art of giving clear robotic instructions  to computers to accomplish simple objectives. 

It’s all black and white, and there’s no gray area. 

As I learned about AI, it was clearly transformational. 

It changed the game. 

It equipped computers with intelligence, and I knew I wanted to be deeply involved. 

I was studying AI at MIT and slowly became more and more excited  about all the potential applications of AI for solving more nuanced problems. 

For example, there was one class project  where I worked on applying AI to human emotions. 

The goal was to take picture of human expressions  and ultimately identify and understand the emotion through very subtle signals  in facial expressions. 

Using AI, we built an algorithm  that was able to detect intent with 80% accuracy and efficacy. 

We were extremely proud of that. 

It was the start of using AI to do entirely new things using computers. 

That’s when I realize the implications of AI  and how it could tackle the gray areas that involve judgment or intelligence. 

You see, AI needs humans to teach it individual values,  nudge it to find thoughtful outcomes,  and ensure that human intentions and values are aligned with the AI. 

It was a revelation. 

Before, coding was like a black-and-white film  versus watching in technicolor. 

What’s more, AI has the potential to take away the repetition in our lives,  meaning that new and fresh ideas will matter more  and ultimately enable us to be more human. 

But, to power AI, you need powerful data,  which was especially hard to come by at that time, in 2016, while I was at MIT. 

I realized that nobody was building anything with AI outside of school. 

It’s unusual for MIT students to not be building something. 

Mechanical engineering majors are building catapults in the lawn,  electrical engineering majors are building robots,  and computer science majors are building apps for their friends to use. 

But nobody was building anything using AI. 

That’s when I discovered what a bottleneck data can be  to building meaningful and powerful AI systems. 

You can't treat data as an afterthought. 

Bad data or lack of data results in bad AI. 

I even realize this in my personal life. 

I put a camera inside my fridge to gather data,  to tell me when to refill my groceries and what I needed to buy. 

That’s when I realized just how much data I needed  to actually be able to successfully predict what to purchase. 

There’s no way I could create enough data  to be successful with the algorithms on my own. 

But it did help me discover that my roommate was stealing my food. 

  At that point, I realized  that this was going to be a pivotal problem for AI. 

Building large-scale, high-quality datasets  to power every single application. 

This was the impetus behind starting Scale:  quality data, to create reliable AI outcomes,  requires human insight and guidance. 

If you think about the core setup of AI, the algorithms need data,  and data needs humans. 

To ensure data is accurate,  an expert human is often required. 

Only humans can understand the context and nuance  to properly annotate the data to be fed to algorithms. 

Humans are the one who teach the algorithms what to do. 

They’re the ones making the decisions, they guide them. 

If something happens, here’s what you should do. 

And AI learns from that and replicates it. 

We are teaching the AI our individual values  and nudging the AI to find thoughtful outcomes. 

Machines make mistakes. 

We have to teach them and incentivize them to tell the truth. 

This is why teaching the AI human intentions and values is so important. 

It’s through this process that we will ensure  that AI will have fair, ethical outcomes in line with human values. 

It’s this alignment that we must solve for. 

The constant alignment of AI to human intentions will always require humans. 

and human ideas and creativity can actually matter much more,  with the power of AI behind them. 

The long tale of real-world problems,  and the fact that there’s always unknown unknowns  means that humans will never be fully removed  from the AI development lifecycle. 

For example, I remember back in 2016  when chatbots were first starting to become a big thing. 

It was right when we were starting Scale. 

We were all thinking there's no way to build a fully automated system. 

There’re so many different conversations that can have so many different pathways. 

It’s hard to build AI systems that can properly handle all these possibilities. 

For chatbots to work, there’re humans behind it who make the decisions once,  and from there, the chatbots can replicate that over and over again. 

That’s again why it’s impossible for AI to improve  or change without human input. 

Let’s take you to the front lines of AI. 

The things that AI automates first are not what you might expect. 

An unintuitive example is the weather. 

Humans have tried for many millennia  to crack the code of how to predict the weather. 

It’s especially hard for meteorologists  because there are so many different small things  that can cause massive impacts on the weather. 

It's the butterfly effect. 

Different elements react to one another in unexpected ways. 

There’re so many inputs that affect the weather,  way more data than any person would be able to comprehend on their own. 

That’s why we need AI to analyze the vast oceans of data  and provide more accurate, nuanced, and comprehensive analysis. 

At the moment,  AI can already provide extremely accurate short-term predictions,  including for critical storms and floods. 

So, it’s not what humans perceive to be the simplest task  that AI will automate first, but rather where we have the most data. 

The use cases the brightest minds are focusing on  are much more positive than what you might think. 

Much more so than Terminator or Westworld. 

That’s again why I think AI will be a supercharger for humanity. 

Unlike the movies, AI developers aren’t focusing their attention  on building replacements for humans. 

They’re building tools to help free up our time and energy  to focus on what human can uniquely solve. 

A good example about how AI can be used in practice is health care. 

According to the Association of American Medical Colleges,  the United States could see an estimated shortage  of between 38,000 and 124,000 physicians by 2034. 

AI could save doctors’ time with rogue tasks  and ultimately enable them to serve more patients and help more people. 

Health care is full of repetitive tasks which are right for AI. 

When a patient is sick, they go through all kinds of tests  which produce all sorts of data: blood tests, imagery,  lab results, X-rays, etc. 

Doctors then analyze all this data to make decisions about a case. 

AI can analyze all this data proactively  and go through a list of possibilities  by cross-referencing against all prior data in cases. 

It can identify when something isn’t right long before a physician can  and flag it to a physician, if it requires more attention. 

With AI, doctors are still integral to the process,  but it takes less time to get a diagnosis. 

You have to wait several weeks  for your case to go from one doctor to another. 

The AI will supercharge, finding a diagnosis faster. 

Similarly, in the field of drug discovery, it’s all about using complex data:  experiment data, patient data,  protein simulations and far more  to guide a more efficient process  of solving diseas through new drugs and compounds. 

Recent advancements in AI  have dramatically sped up the scientific process  by allowing us to process and make us of more  data than ever before. 

Another good example, and potentially more concrete, is the Russia-Ukraine war. 

We've all seen the images of tanks lining up ready to enter Kiev. 

AI can help assess satellite imagery with superhuman speed and precision,  so Ukrainian forces can respond faster. 

At Scale,  we’re using our platform to do damage assessment  in key areas affected by the war. 

We’ve rapidly analyzed over 2,000 square kilometers of Ukraine,  identifying over 370,000 structures,  including thousands not previously available via other datasets. 

We focused on Kiev, Kharkiv and Dnipro,  in which we provided some data directly to government and users. 

We also made the data publicly available to the broader AI community via Scale. 

We can also use this data to  maximize allocations of resources,  people or commodities. 

It’s clear satellite data can be extremely useful  in these types of situations. 

Thanks to satellite data, AI can analyze if planes or tanks  have been moved from one place to another. 

This is called change detection. 

Algorithms can constantly be monitoring for this kind of data,  and if it notices a change or movement,  it will alert a human to further investigate,  otherwise known as predictive modeling. 

AI can also help us understand the economic impacts of war. 

We can use AI to track farmland in Ukraine  and measure the agricultural damage in real time. 

Ukraine is a major food supplier for much of the world. 

Understanding these impacts is absolutely critical. 

In conclusion, AI is not something to be feared,  but it’s a tool that can be used to better understand…  that needs to be better understood,  and has the potential to transform our lives for the better. 

AI enables us to make use and sense of massive amounts of data  that has historically been beyond human capacity. 

It allows us to add intelligence and nuance  to automated systems that will dramatically improve humanity. 

Areas like health care and agriculture. 

This then allows humans to do what they do best. 

Take this information, put it into context with sensitivity,  to strategize and act in a timely manner. 

AI is a supercharger for humanity. 

When AI is better than humans,  it makes humans better. 

AI will automate repetitive tasks  that don’t require constant human judgment or creativity,  which frees us up to explore and focus on fresher, newer ideas. 

AI will enable us to be even more creative and more idea-driven,  which I personally find incredibly exciting. 

It allows us to embrace the generative aspects of human nature,  so we can run faster with ideas  and build better and more powerful solutions  to the world’s biggest problems. 

That’s why I believe that human-led AI is the path forward,  and I’m proud to usher all of us into a future with human-led AI. 

Thank you.