How does Amazon’s record weekend compare with Singles’ Day?
Amazon judged its success using the...
Amazon has said that yesterday’s Cyber Monday sale was the “biggest” shopping day in the company’s history.
Amazon judged its success using the metric of “items ordered worldwide”, saying that independent sellers were particular beneficiaries having sold more items on the day than in any other 24-hour period.
The manufactured “holiday” was specifically created to promote online shopping as opposed to Black Friday. With the rise of ecommerce that distinction has largely been lost, with both days inevitably leading to online shopping bonanzas.
Particular successful were Amazon’s own stable of devices, such as its Echo Dot smart speaker and Fire TV Stick 4K, highlighting the company’s actual strength as a technology rather than ecommerce firm. Amazon also revealed a broader trend for the purchase of smart devices from any manufacturer. Clearly the appetite for AI has well and truly filtered into the general populace.
“We’re focused on making this holiday season more convenient than ever for our customers, especially given how short this holiday shopping season will be,” said Jeff Wilke, CEO Worldwide Consumer, Amazon. “We are thrilled that customers continue to come to Amazon in record numbers to discover what they need and want for the holidays.”
While Amazon has not released sales figures, the event is likely not on the scale of Alibaba’s bumper Singles’ Day last month. In many ways comparable to Cyber Monday, the constructed Chinese phenomenon of Singles’ Day has been capitalised upon by the ecommerce giant to drive huge amounts of sales. One hour after going live, $12bn was generated by the occasion. By the end, over $38bn had been raised, prompting Fan Jiang, President of Alibaba subsidiaries Taobao and Tmall to say: “Today we showed the world what the future of consumption looks like for brands and consumers,” said Fan Jiang, President of Taobao and Tmall. “We are meeting the growing demand of Chinese consumers and helping them upgrade their lifestyles, while introducing new users to our digital economy from across China and around the world.”
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.