World Artificial Intelligence Conference roundup: robotaxis
The recent 2019 World Artificial Intelligence Conference in Shanghai f...
Didi Chuxing (DiDi) to allow customers in Shanghai to hail self-driving vehicles
The recent 2019 World Artificial Intelligence Conference in Shanghai featured a number of revelations, including the imminent launch of Microsoft’s HoloLens 2.
With events such as the conversation between Jack Ma and Elon Musk grabbing headlines, a number of eye-catching stories dropped under the radar. One such announcement came from Chinese transportation company Didi Chuxing (DiDi), which is to allow customers in Shanghai to hail self-driving vehicles via an app.
The pilot project comes after the receipt of permission from the Shanghai government to test its autonomous fleet in the Jiading district of the city. 30 different autonomous vehicles will be deployed, all at level four on the SAE scale, which is ranked out of five. The vehicles will still at times be piloted by humans however, with the company citing the complexity of the Shanghai environment.
The company has been active in developing vehicles in both China and the US, with areas of progress including “HD mapping, perception, behavior prediction, planning and control, infrastructure and simulation, labeling, problem diagnosis, vehicle modifications, connected car, and security.”
As reported by Autoblog, the service is scheduled to launch within a ‘couple’ of months, expand to Beijing and Shenzhen by 2020, and launch outside the country by 2021.
“Working with our auto-industry partners, DiDi has the potential to become the first business to realize large-scale robo-taxi service in China,” said Zhang Bo, CTO of DiDi and CEO of DiDi’s autonomous driving company.
The company has experienced something of a meteoric rise since its founding in 2012. According to Crunchbase, the company has raised $21.2bn, with investment from the likes of SoftBank, Toyota and Apple, who contributed $1bn in 2016. DiDi is also yet another example of regional rivals displacing the American originators of the ride-hailing business model, much as Grab has achieved in South East Asia.
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.