May 17, 2020

Element AI secures largest Series A funding in AI history with $102m in investments

artifical intelligence
Callum Rivett
2 min
Element AI has secured funding from some of the world's biggest wealth funds
Element AI has secured the largest funding round in history for an artificial intelligence company, with the Quebec-based start-up securing $102m in Ser...

Element AI has secured the largest funding round in history for an artificial intelligence company, with the Quebec-based start-up securing $102m in Series A funding.

Microsoft Ventures, NVIDIA, Intel Capital and Development Bank of Canada are just four firms that participated in the funding round, as well as "several of the world’s largest sovereign wealth funds."

The funding will be used to help propel Element globally, investing in three new offices in Asia whilst recruiting an extra 250 employees for their Canada hub. This will allow Element to fund "AI-powered solutions for their customers in cybersecurity, fintech, manufacturing, logistics and transportation, and robotics."

Element will also target large-scale AI investment opportunities with the funding and aim to retain its position of the largest global artificial intelligence company in Canada.

Co-founded in October 2016 by Jean-François Gagné and Nicolas Chapados, Element works to deliver AI solutions to companies almost as a consultancy, helping to build artificial intelligence capabilities to those who have limited experience or cannot afford to invest in their own AI systems. 

“Artificial Intelligence is a 'must have' capability for global companies," said Mr Gagné. "This historic round of funding will help Element AI deliver profoundly powerful AI platforms for all, not just the few."

"Seasoned AI investors at Data Collective have understood and supported us to democratise the AI firepower reserved today for only the largest of tech corporations."

"Intel, Microsoft, and NVIDIA, as pioneers and champions of AI hardware and software, likewise understand that their businesses flourish when every company is empowered with world-class AI."



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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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