May 17, 2020

Facebook to create three new European digital skills hubs

Facebook
Digital Transformation
Digital skills gap
Digital hub
Ben Mouncer
2 min
Digital skills
Facebook has announced that it will be opening three new digital skills hubs across Europe in the aim of training one million workers and small business...

Facebook has announced that it will be opening three new digital skills hubs across Europe in the aim of training one million workers and small business by 2020.

The facilities, known as community skills hubs, will be built in Spain, Poland and Italy, in the hope of tackling the digital skills gap challenge presented to companies across the continent.

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The European Commission shows that, while nine out of 10 jobs will require digital skills in the future, 44% of Europe's population between 16 and 74 years do not have adequate digital skills.

Working closely with local organisations, the centres will offer specific training in digital skills, media literacy and online safety.

"People are worried that the digital revolution is leaving people behind and we want to make sure that we’re investing in digital skills to get people the skills they need to fully participate in the digital economy," said Sheryl Sandberg, Facebook’s COO, to Reuters.

Facebook has also said it will increase the number of PhD fellows at its AI research centre in Paris from 10 to 40 at a cost of €10mn. 

Further, the US social media and tech giant has also partnered with Freeformers to offer tailored training to people across France, the UK and Germany. Since 2011, it has invested more than $1bn in small businesses globally.

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Jun 11, 2021

Google AI Designs Next-Gen Chips In Under 6 Hours

Google
AI
Manufacturing
semiconductor
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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