When Scale AI 'teaches' artificial intelligence

Started when its founder was still a student, Scale AI is now an indispensable link in the learning journey of artificial intelligence models. The company does not create AI, but it is the place where AI understands the human world.

The input data is neatly organized before being used to train the AI.

Scale AI doesn’t make the headlines often, nor is it one of the tech companies that makes products that users can actually touch. But for AI developers, it’s an integral part of the entire model training process.

Scale AI’s work happens quietly behind the scenes, where raw data is processed by humans and transformed into lessons for machines. Thanks to that, new intelligent systems can gradually understand the language, images, emotions and behaviors that people display in the real world .

Who is Scale AI and what do they do?

Compared to OpenAI, Google, or Meta, Scale AI is a relatively quiet player. The company doesn’t directly create chatbots that can talk like real people or self-driving cars that can read traffic situations, but it plays a crucial role in helping those technologies become smarter every day.

Scale AI was founded in 2016 when founder Alexandr Wang was still a student. Instead of going down the algorithm development path, Wang chose a different path: building a specialized data processing platform to serve the training of artificial intelligence .

In this world, data is the raw material. But raw data like unclassified images, unorganized conversations, or unclear videos are often messy and have no direct value to machines.

Scale AI’s job is to clean, categorize, and label that massive amount of data. That means designing both systems and teams to identify and organize every little detail in a photo, a paragraph, or a video shot.

For example, for a self-driving car to learn to stop at the right place, each camera frame must be clearly identified where there is a crosswalk, where there is a traffic light, where there is a pedestrian. With millions of such data, artificial intelligence can learn the behavior accurately.

Thanks to such data preparation steps, models like ChatGPT, Claude or virtual assistants in cars can understand natural language, accurately recognize images in real-world environments and respond in a human-like manner.

Want to teach AI to be smart, have to start from the smallest thing

No matter how complex an AI model is, it is just an empty skeleton without data to feed it. Unlike humans who can learn from experience and intuition, machines can only repeat what they have seen before. That is why training data plays a decisive role in creating an effective model or not.

For a chatbot to understand how humans ask questions, it must have been exposed to millions of conversations. For a car to recognize pedestrians in the rain, it must have seen hundreds of thousands of similar photos. All those real-world examples must be correctly labeled for the computer to learn from. Without the right labels, the AI ​​will get it wrong. Without enough diverse data, it will react poorly in real-world environments.

This is why Scale AI’s work is so important. They don’t just collect data, they make sure it’s organized in a way that’s accurate, diverse, and learnable, so that future models can react like a person would.

A classic example is in the field of self-driving cars. To train a car to handle unexpected situations like a person crossing the street or a motorbike going the wrong way, the artificial intelligence model needs to see tens of thousands of similar situations.

Such data cannot be readily available, nor can it be left to the machine to learn on its own. Someone must prepare, organize, and ensure its accuracy before the artificial intelligence can begin the learning process.

That’s where Scale AI comes in. They create lessons, not from textbook knowledge but from billions of carefully refined real-world examples. Every stream of data that passes through their hands becomes a building block of modern AI cognition.

From the lab to the streets, data remains king

Scale AI is not just limited to text, it is also involved in training computer vision for self-driving cars. Technology companies such as Tesla, Toyota and General Motors have all collaborated with Scale AI to teach cars to recognize pedestrians, read traffic signs and handle unexpected situations.

In addition, Scale AI also supports other fields such as defense, satellites and maps. They process images from cameras, radars and photos taken from space to help models recognize terrain, classify objects or detect risks early. A satellite image may seem like just a mountain scene, but through the hands of the Scale AI team, it can become a data set that helps the machine predict the direction of wildfires.

The expansion into many fields shows that Scale AI is not just a supplementary tool but is becoming a core part of how artificial intelligence learns the world. As the world continues to race to create smarter models, it is companies like Scale AI that are quietly laying a solid foundation for that race.

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THANH THU

Source: https://tuoitre.vn/khi-scale-ai-day-hoc-cho-tri-tue-nhan-tao-20250616095516101.htm