A twenty-two-year-old is taking over Silicon Valley’s artificial intelligence department with his startup that’s focused on identifying the difference between a car and a trash bin.
The three-year-old startup called Scale AI Inc. is providing a range of businesses from automotive to outlets such as Amazon the machine-learning technology that cuts time for more efficient processes.
The startup announced Monday that it had closed a $100 million Series C round of financing led by Founders Fund with participation from Accel, Coatue Management, Index Ventures, Spark Capital, Thrive Capital, Instagram founders Kevin Systrom and Mike Krieger and Quora CEO Adam D’Angelo.
This funding will bring Scale’s valuation past $1 billion.
“It takes billions or tens of billions of examples to get AI systems to human-level performance,” says Alexandr Wang, Scale’s co-founder and chief executive officer. “There is a huge gap between the handful of giant companies that can afford to do all this training and the many that can’t.”
Mainly, Scale’s main job is to help companies through their intuitive machine-learning technology that differentiates specific objects from one another.
For the autonomous-car industry, this technology could drastically help cars develop smarter and more efficient ways for it to function.
Similar to Tesla’s auto-park feature, it requires slowly on AI technology that determines where the sidewalk is and what other obstacles are there that the car should avoid. All of this intensive AI is working in the background while drivers type a text message on their phone.
For another company, Scale’s AI could be used to making common sense language patterns. In this application, technology will be able to understand text written by humans the way it was meant to be understood—a skill that machines do not inherently know unless taught.
However, this is not an easy task to do. In the past, companies had to manually accomplish this by hiring thousands of people to go through millions of individual data.
Typically, a worker sees an image pop up on a computer screen and use a mouse to trace the outline of all the cars and categorize them in the software. They will also have to do the same over other objects in the image such as streets buildings, and so forth.
For others, it could mean that workers manually input how specific words relate to one another and what they could all mean based on the set of words attached to it.
Overall, the process could last between a few minutes to a few hours—a method that used to be highly inefficient.
Now, companies would instead hire Scale to do all of that heavy lifting than spend millions of dollars trying to develop their own manually.
To help Scale, companies provide data from their API, and the startup puts its resources to work labeling the text, audio, pictures, and video so that its customers’ machine learning models can be trained.
Notably, Scale’s AI is not doing all the work per se. As with any budding AI-learning system, it needs to be trained for it to be 100% efficient on its own.
Currently, Scale employs about 30,000 contract workers, to double-check the work of their AI and ensure that their data is of good quality. Thus, machine learning models perform smoothly.
Scale AI Inc. has around 100 employees, according to Wang, but the 30,000 contractual employees play a crucial role in making sure the company is a top-notch performer in the field. “The humans are pretty critical to what we’re doing because they’re there to make sure that all the data we provide is really high quality,” Wang says.
The start-up’s intimate balance between machine and human counterparts have resulted in seamless operations.
Scale AI Inc. has attracted big-name customers in the self-driving car field, including Alphabet Inc.’s Waymo, General Motors Co.’s Cruise, and Uber Technologies Inc.
Now, Scale is looking to sell its wares to just about any company developing AI technology.
Newer customers of Scale include OpenAI, a research company that uses the service for language processing, and Standard Cognition, which is building software to automate the checkout process at retailers similar to Amazon Go.
Wang grew up in New Mexico and is the son of two physicists. During his teenage years, Wang excelled at coding competitions and got job offers from tech companies as a high schooler. This put him on a path to graduate early, work in Silicon Valley, and eventually start his own through Scale.