May 17, 2020

375mn jobs to be displaced by automation by 2030 – McKinsey

McKinsey
McKinsey Global Institute
Automation
AI
Jonathan Dyble
2 min
Automation
The McKinsey Global Institute has predicted that 375mn workers will be forced to differentiate occupationally by 2030 as a result of the rise of automat...

The McKinsey Global Institute has predicted that 375mn workers will be forced to differentiate occupationally by 2030 as a result of the rise of automated solutions.

Within the report, named “Jobs lost, jobs gained: workforce transitions in a time of automation”, McKinsey highlights that many firms will turn to automated solutions such as robotics and AI due to the enhanced benefits that they offer.

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“The unprecedented level of digital change our businesses are experiencing is having a sizeable impact on the skills the workforce and the evolving job market require for the economy of tomorrow,” said Paul Cant, Vice President of EMEA at BMC Software.

Jobs are at risk across a number of sectors, from machine operation to data collection, due to the varied application of emerging automated technologies.

According to McKinsey, at least 30% of work in 60% of organisations can be carried out successfully using automated processes – something that will likely revolutionise over half of current jobs once implemented.

As a result, the firm’s calculated scenarios suggest that anywhere between 75mn and 375mn workers (3% to 14% of the global workforce) will have to change occupation by 2030, with automated alternatives likely to either completely or partially displace many jobs.

Even those who do not have to change occupation will likely have to change in some way in order to incorporate and work alongside machinery – something that Cant believes will have to be enforced by employers.

Our research has revealed that 88% of office workers globally strongly believe that employers must instill an innovative culture to retain staff, thus improving employees’ productivity and future career prospects given the increasing digitisation of roles and responsibilities,” Cant continues.

“For companies to thrive during this period of ultra-swift technological change, business leaders must ensure constant efforts are being made to skill up workers through the offering of training courses to meet the demands and pace of this digital era.””

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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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