Tag: Future of Computing

  • To Chu Zhiqin, Diamonds Are Forever – and for Everything

    To Chu Zhiqin, Diamonds Are Forever – and for Everything

    “One of the biggest limitations in fields like AI is power consumption, and the key bottleneck is that their semiconductors are based on silicon.”
    — Professor Chu Zhiqin

    Most people know diamonds as age-old symbols of eternal love, but to the University of Hong Kong’s Professor Chu Zhiqin, they’re the key to our technological future.

    It’s no exaggeration to say that that we’re living in the age of silicon. The element has become the building block of modernity, powering the semiconductors in everything from precision medical equipment to our phones. But silicon is far from an ideal conductor, and scientists have long dreamed of fashioning superior alternatives out of more suitable materials like diamonds.

    The challenge lies in the very attributes that make diamonds so desirable: their durability and toughness. How can you grow diamonds that aren’t just better conductors than silicon, but also have the flexibility needed for the next generation of computing devices, including both power-intensive uses like AI and difficult-to-manufacture wearables? 

    That’s where Professor Chu comes in. His research – for which he was just named a finalist in the prestigious Falling Walls Science Breakthrough awards – is revolutionising the field of diamond-based semiconductors, heralding the future arrival of flexible yet durable chips with more than 10 times the thermal conductivity of their silicon counterparts.

    The Future of Semiconductors

    In theory, diamonds are almost the perfect vehicles for a future “fourth generation” of semiconductors. They are relatively easy to grow – all you need is hydrogen, methane, and electricity – strong, and can handle far higher wattages and more heat than silicon. 

    The trick is in manufacturing. While the basic process is well established, researchers have long struggled to produce diamond structures suitable for next-generation devices like wearables: ones that are flexible without sacrificing durability. 

    Cracking the code took Professor Chu and his team over a year of hard work. But they finally developed a trade secret process that allows them to reliably produce high-quality, micrometre-thin diamond semiconductors that can be rolled and flexed like sheets of paper. 

    “That’s the beauty of being a scientist: You know more about your field than anyone else in the world. If you dig long enough and hard enough, no one will be better at it than you.”
    — Professor Chu Zhiqin

    The Next Generation of Computing

    The barriers to replication are steep: Although the team’s work has already spawned imitators, the competing diamonds tend to crack quickly, Professor Chu says, making them unsuitable for industrial uses.

    The next challenge is commercialisation. Professor Chu is trying to interest manufacturers – always difficult with any new material – and is focusing his energies on high-upside, future-centric sectors like heat spreaders for GPUs and electric vehicles. If they succeed, they could radically increase the efficiency of some of the world’s most important products. 

    Among the developments Professor Chu is keeping his eye on are new AI-based measuring tools. Although accumulating training data remains difficult, AI offers more efficient, dynamic measurements. “Everything can be analysed by AI,” Professor Chu says. “It tells us what the best measurement parameters are.”

    “Basically, the way we’re doing measurements now is the Stone Age, but with AI, we can move forward.”
    — Professor Chu Zhiqin

    A Scientific Spirit

    But Professor Chu isn’t solely fixated on cutting-edge technologies. As the interview winds down, he muses on another significant challenge facing the field: cultivating the next generation of scientists.

    Many aspiring scientists find the process discouraging and gradually drift away from the field. “They think learning math is hard, and learning physics is boring,” he says, adding that they can’t see how they’re supposed to master everything.

    That’s an unfortunate misconception, Professor Chu believes. “Scientists actually are highly specialised,” he says. “It’s not that scientists can solve everything, but we can solve a few, very specific things.”

    This preoccupation is reflected in his lab’s work. When asked what his favourite device is, he doesn’t mention the powerful Zeiss microscope, but a miniature quantum mechanics learning kit developed by one of his graduate students, Madhav. The box allows teachers to show young students quantum mechanics in action, rather than theory, letting them experience for themselves what it’s like to be a scientist.

    It may seem small – or an unusual project for a lab of this stature – but it’s just one more way Professor Chu and his team are shaping the future of science.

  • Reimagining the Future of Work with the Centre for AI Management and Organization

    Reimagining the Future of Work with the Centre for AI Management and Organization

    “My hope is that when people think about issues like AI and organisation, they think about CAMO.” 
    — Professor Jin Li

    When Professor Jin Li launched the Centre for AI, Management, and Organization (CAMO) earlier this year, his immediate goal was filling a gap in the scholarship on artificial intelligence and its impact on the future of work.  

    But Professor Li’s long-term vision is even more ambitious: Transforming the University of Hong Kong into a global leader in AI, Management, and Organisation research, positioning it at the forefront of a revolution that will shape the next half-century of work and corporate governance. In his own words, he hopes the centre will contribute the “grand ideas” and frameworks societies need to navigate the AI transition.  

    Drawing on the combined expertise of faculty from across the university – plus a board of advisers featuring members from the University of California, Berkeley, Columbia, the Massachusetts Institute of Technology, the University of Tokyo, and the London School of Economics – CAMO is well on its way to reaching that lofty goal. We asked Professor Li about what drew him to AI, how artificial intelligence is changing the future of work, and what’s next for CAMO.  

    The Pursuit of Happiness 

    When Professor Li was a young student growing up in Shanghai, his school emphasised that the “purpose of life is the search for excellence.” A top student at one of Shanghai’s best schools, Professor Li’s only weak subject was chemistry. The harder he worked and the more he struggled, the more he began to wonder whether there was more to life than excellence – that perhaps the real purpose of life was the search for happiness. 

    He found his answer in an introductory economics class at the California Institute of Technology (CalTech). As his professor explained that individuals maximise utility, he remembers thinking that this seemed an awful lot like maximising happiness. 

    “Now is the best time to study AI.” 
    — Professor Jin Li

    Although not directly involved in AI research at the time, a number of his friends and classmates would go on to play pivotal roles in an earlier wave of the AI revolution, and their work sparked his own interest in the field. Now, as he enters what he calls the “second curve” of life, he sees AI research as both important and a way to pursue a topic that has always interested him. 

    Out of the ‘Stone Age’

    Professor Li is fond of a famous line from Edward O. Wilson, in which the American biologist notes that we live in a world of “Palaeolithic emotions, medieval institutions… and god-like technology.” He envisions CAMO as contributing to the upgrading of those institutions to keep pace with technological change.  

    As an example, he points to the way many firms have struggled to update corporate governance and workplace norms for the AI age. Previously, the ideal firm was mid-sized: big enough to achieve economies of scale, but not so big that it becomes bogged down by bureaucracy – an example of what economists call the “U-shaped” relationship between size and efficiency. 

    “As economists, as management strategy scholars, we don’t have that much to do with technology. We cannot change human nature either. What we can do is to think about new organisations, new institutions.” 
    — Professor Jin Li

    Now, however, the AI boom has helped fuel the rise of both unicorns and tech giants like FAANG (Facebook, Apple, Amazon, Netflix, and Google). That tectonic shift has caught many companies off-guard, with CEOs and even consultants unsure of how to adapt. “Firms need to have a playbook,” says Professor Li. “They need to have a framework for how to move forward.” 

    The Future of Work 

    To help write that playbook, CAMO has already surveyed more than 100 C-suite leaders and 500 HR reps as it works on a practical guide for companies navigating the AI transition.  

    One of the centre’s current points of emphasis is identifying the jobs humans don’t want to do, helping firms to decide on automation priorities without exacerbating popular fears of AI “replacing” workers. This approach is also what sets CAMO apart. “There are very few centres that focus on organisation,” Li says. “It may sound cocky, but I don’t think there are more than three to five institutions in the world with the same calibre of people as us.” 

    In addition to laying the “intellectual foundation” for the study of AI and the future of work through his research at the centre, Professor Li is also hard at work on a new book about “The Great Compression.”  

    “I like to call incentive and knowledge the ‘yin’ and ‘yang’ of AI.” 
    — Professor Jin Li

    The access to knowledge promised by AI has spawned new incentives – to cheat, to game the system, to “shirk” – all of which managers must understand and learn to spot. Professor Li’s book will explore these two sides of AI, as well as their combination, which he identifies as “power.” In the process, he hopes to help managers better navigate both the opportunities and risks of the AI era. 

    If all that seems daunting, Professor Li would likely agree. When asked about the biggest challenges he’s facing, he replies quickly: “Time.” There are so many interesting and exciting potential projects, he explains, but the CAMO team must be selective and focused in its priorities. “It’s such an exciting time that I’m not getting enough sleep,” he says with a wry smile. “But now we’re being bombarded with so many interesting possibilities.”