May 17, 2020

Chinese startup TuSimple to test driverless trucks in Arizona in 2018

Callum Rivett
2 min
Chinese technology startup TuSimple will challenge Tesla and Waymo when they bring their autonomous truck project to Arizona's roads in a 2018 test.


Chinese technology startup TuSimple will challenge Tesla and Waymo when they bring their autonomous truck project to Arizona's roads in a 2018 test.

Tests in the Chinese province of Hebei have already been approved and will be carried out in October 2017, with an agreement signed with the Caofeidian government to conduct trials on the highways within the district.

Having already completed a 200-mile Level 4 test drive between Yuma, Arizona and San Diego in June, TuSimple ranks alongside Uber and Waymo in the autonomous industry.

Tesla, meanwhile, has planned to conduct Level 5 tests - which do not need a human at all - but have not yet finalised a date.


Commercial services will begin in 2019, with a 20-mile route in Shanghai linking the port and warehousing, whilst a 120-mile stretch of highway between Phoenix and Tucson will be the first roadway in America to host the technology.

The main benefit of autonomous driving is safety - with the US estimating that 96% of all road traffic accidents are caused by human error, having AI could reduce the number of annual fatalities drastically.

China estimates that 25,000 people are killed by trucks every year, with 30% of all truck drivers suffering from fatigue early in the morning and at night.

Costs would also decrease by around 40% if autonomous commercial trucks were adopted into the industry, helping to alleviate the shortage of drivers.


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Jun 11, 2021

Google AI Designs Next-Gen Chips In Under 6 Hours

3 min
Google AI’s deep reinforcement learning algorithms can optimise chip floor plans exponentially faster than their human counterparts

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. 



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