Abu Dhabi unveils Artificial Intelligence University
The first of its kind university, named the Mohamed...
Abu Dhabi has established a graduate level research-based artificial intelligence (AI) university.
The first of its kind university, named the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) will give graduate students, businesses and governments the ability to stimulate the AI space with access to advanced AI systems across the world.
HH Sheikh Mohamed bin Zayed Al Nahyan, Crown Prince of Abu Dhabi and Deputy Supreme Commander of the UAE Armed Forces commented that the establishment "echoes the UAE’s pioneering spirit, and paves the way towards a new era of innovation and technological advancement that benefits the UAE and the world"
Experts have predicted that AI’s contribution to the UAE’s GDP will rise 14% by 2030 and could contribute nearly US$16tn (AED58.7tn) to the global economy.
“As such, the Mohamed bin Zayed University of Artificial Intelligence is an open invitation from Abu Dhabi to the world to unleash AI’s full potential. The University will bring the discipline of AI into the forefront, moulding and empowering creative pioneers who can lead us to a new AI-empowered era,” commented Dr. Sultan Ahmed Al Jaber, chairman of the MBZUAI board of trustees.
The university’s board of trustees, include: Sir Michael Brady, MBZUAI's interim president, and professor of oncological imaging at the University of Oxford; Anil K. Jain, professor at Michigan State University; Andrew Chi-Chih Yao, dean of the Institute for Interdisciplinary Information Sciences at Tsinghua University; Kai-Fu Lee, technology executive and venture capitalist; Daniela Rus, director of Massachusetts Institute of Technology Computer Science, and Artificial Intelligence Laboratory; and Peng Xiao, chief executive office of Group 42.
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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.