Tech unicorn Scale AI raises $100mn in Series C funding
The company’s Series C funding round was led b...
San Francisco AI and machine learning startup Scale AI has announced the raising of $100mn of funding.
The company’s Series C funding round was led by venture capitalists Founders Fund, with the support of Accel, Coatue Management, Index Ventures, Spark Capital, Thrive Capital, Kevin Systrom, Mike Krieger and Adam d’Angelo. That investment values the business at more than $1bn, making it the latest tech Unicorn.
In a blog post, Scale’s 22 year old CEO Alexandr Wang hailed the company’s meteoric rise in the past three years and set out his philosophy that AI development represents a fundamental shift from traditional software, owing to the importance of data over code. Dropping out of the Massachusetts Institute of Technology, Wang previously worked at Quora before founding Scale in 2016.
Scale specialises in providing labelled datasets, critical to the construction of machine learning models. Developers send raw data to the company via its API, and Scale then uses a mixture of machine learning and manual labour to label and then return the data, opening up machine learning to a wider audience of companies.
Scale’s clients include the likes of Waymo, OpenAI, Nuro and Lyft, with its training data being used across a wide variety of industries with machine learning opportunities. These include autonomous vehicles, language processing, mapping and robotics.
Google AI Designs Next-Gen Chips In Under 6 Hours
In a Google-Nature paper published on Wednesday, the company announced that AI will be able to design chips in less than six hours. Humans currently take months to design and layout the intricate chip wiring. Although the tech giant has been working in silence on the technology for years, this is the first time that AI-optimised chips have hit the mainstream—and that the company will sell the result as a commercial product.
“Our method has been used in production to design the next generation of Google TPU (tensor processing unit chips)”, the paper’s authors, Azalea Mirhoseini and Anna Goldie wrote. The TPU v4 chips are the fastest Google system ever launched. “If you’re trying to train a large AI/ML system, and you’re using Google’s TensorFlow, this will be a big deal”, said Jack Gold, President and Principal Analyst at J.Gold Associates.
Training the Algorithm
In a process called reinforcement learning, Google engineers used a set of 10,000 chip floor plans to train the AI. Each example chip was assigned a score of sorts based on its efficiency and power usage, which the algorithm then used to distinguish between “good” and “bad” layouts. The more layouts it examines, the better it can generate versions of its own.
Designing floor plans, or the optimal layouts for a chip’s sub-systems, takes intense human effort. Yet floorplanning is similar to an elaborate game. It has rules, patterns, and logic. In fact, just like chess or Go, it’s the ideal task for machine learning. Machines, after all, don’t follow the same constraints or in-built conditions that humans do; they follow logic, not preconception of what a chip should look like. And this has allowed AI to optimise the latest chips in a way we never could.
As a result, AI-generated layouts look quite different to what a human would design. Instead of being neat and ordered, they look slightly more haphazard. Blurred photos of the carefully guarded chip designs show a slightly more chaotic wiring layout—but no one is questioning its efficiency. In fact, Google is starting to evaluate how it could use AI in architecture exploration and other cognitively intense tasks.
Major Implications for the Semiconductor Sector
Part of what’s impressive about Google’s breakthrough is that it could throw Moore’s Law, the axion that the number of transistors on a chip doubles every five years, out the window. The physical difficulty of squeezing more CPUs, GPUs, and memory on tiny silicon die will still exist, but AI optimisation may help speed up chip performance.
Any chance that AI can help speed up current chip production is welcome news. Though the U.S. Senate recently passed a US$52bn bill to supercharge domestic semiconductor supply chains, its largest tech firms remain far behind. According to Holger Mueller, principal analyst at Constellation Research, “the faster and cheaper AI will win in business and government, including with the military”.
All in all, AI chip optimisation could allow Google to pull ahead of its competitors such as AWS and Microsoft. And if we can speed up workflows, design better chips, and use humans to solve more complex, fluid, wicked problems, that’s a win—for the tech world and for society.