The mixture of happenstance and geostrategy that helped make a small European country key to the global semiconductor market is depicted in delicious detail in Chris Miller’s “Chip War.” The book couldn’t have been timed better. Miller, an associate professor of international history at the Fletcher School, uses a colorful cast of characters to tell the story of a truly pivotal industry’s formation, and explain why altering it in a meaningful way seems unlikely any time soon – regardless of mounting geopolitical pressure. Chips are coveted not least for the role they play in artificial intelligence tools seemingly poised to shake things up for just about everyone. The more we want them, though, the more expensive and difficult they are to make. It’s all gotten very complicated. Take the Dutch niche in the supply chain, for example – it’s based on one company’s machine “that took tens of billions of dollars and several decades to develop,” Miller writes, and uses light to print patterns on silicon by deploying lasers that can hit 50 million tin drops per second. It’s an industry full of such mind-bending extremes. In an interview with the Forum’s Radio Davos podcast, Miller marvelled at having recently visited a facility in the US being built with “seventh-biggest crane that exists in the world,” which will eventually assemble chips mounted with transistors “roughly the size of a coronavirus.” Nvidia, the company now most closely identified with chips powering artificial intelligence, features prominently in Miller’s book. The company traces its roots to a meeting at a 24-hour diner on the fringes of Silicon Valley, he writes. At a certain point it realised that its semiconductors used for video-game graphics could do a good job of training AI systems. Earlier this year, its market value increased by $184 billion in a single day. Nvidia’s chips aren’t made anywhere near […]
-Robert Le Busque, Regional Vice-President, Asia Pacific, Verizon Business In Australia, the rapid development of artificial intelligence (AI) has coincided with the Government’s announcement of the $15 billion National Reconstruction Fund (NRF), $1 billion of which will be dedicated to reinvigorating, accelerating, and building up advanced manufacturing capabilities. As businesses have already started adopting AI for various use cases, the Government has also recently launched a public consultation to seek advice on how to mitigate any potential risks and ensure it is applied responsibly. One of these uses cases is advanced manufacturing, which is a broad term that encompasses technologies applied to any manufacturing facility, including application software and AI tools to augment and generate higher value manufacturing output. Advanced manufacturing is also likely to use AI to embed faster and safer means of production and better operations management for industrial processes. Generating speed, safety and enhanced production Part of removing human error to make manufacturing safer and faster means reducing reliance on human observation. As machinery drivers and operators are among the top three occupation groups with the highest rate of work-related injuries in Australia at 6.5 percent in the 2021-2022 financial year, this makes a use case for AI analytics in augmenting workplace health and safety. High precision tracking, for example, detects faults in machinery micro-components at a scale previously unachievable – this improves productivity, output, and profitability for manufacturing companies. AI analytics can match workers’ physical position in relation to how they interact with machines, particularly heavy industrial machinery, facilitating a more comprehensive understanding of safety in workplace operations and injury. AI software can also manage workflows more effectively by identifying and allocating tasks. This can be taken one step further, with large site and heavy industrial manufacturing operations using digital twins to optimise these workflows – […]
Just like the world at large, the world of work shifts and changes over time. The future of work refers to an informed perspective on what businesses and other organisations need to know about how work could shift (given digitisation and other trends), plus how workforces and workplaces can prepare for those changes, big and small. When you think of the future of work, what do you picture? Offices that look more or less like today’s? Factories full of robots? Or something else entirely? While no one can predict the future with absolute certainty, the world of work is changing, just as the world itself is. Looking ahead at how work will shift, along with trends affecting the workforce and workplaces, can help you prepare for what is next: One in sixteen workers may have to switch occupations by 2030. That is more than 100 million workers across the eight economies studied—and the pandemic accelerated expected workforce transitions. Job growth will be more concentrated in high-skill jobs (for example, in healthcare or science, technology, engineering, and math [STEM] fields), while middle- and low-skill jobs (such as food service, production work, or office support roles) will decline. A few job categories could see more growth than others. The rise of e-commerce created demand for warehouse workers; investments in the green economy could increase the need for wind turbine technicians; aging populations in many advanced economies will increase demand for nurses, home health aides, and hearing-aid technicians; and teachers and training instructors will also continue to find work over the coming decade. But other types of jobs may be at risk: for example, as grocery stores increasingly install self-checkout counters, there may be a need for fewer clerks, and robotics used to process routine paperwork may lessen demand for some office workers. The future of work was shifting […]
Two management and technology experts show that AI is not a job destroyer, exploring worker-AI collaboration in real-world work settings. This book breaks through both the hype and the doom-and-gloom surrounding automation and the deployment of artificial intelligence-enabled—“smart”—systems at work. Management and technology experts Thomas Davenport and Steven Miller show that, contrary to widespread predictions, prescriptions, and denunciations, AI is not primarily a job destroyer. Rather, AI changes the way we work—by taking over some tasks but not entire jobs, freeing people to do other, more important and more challenging work. By offering detailed, real-world case studies of AI-augmented jobs in settings that range from finance to the factory floor, Davenport and Miller also show that AI in the workplace is not the stuff of futuristic speculation. It is happening now to many companies and workers. These cases include a digital system for life insurance underwriting that analyzes applications and third-party data in real time, allowing human underwriters to focus on more complex cases; an intelligent telemedicine platform with a chat-based interface; a machine learning-system that identifies impending train maintenance issues by analyzing diesel fuel samples; and Flippy, a robotic assistant for fast food preparation. For each one, Davenport and Miller describe in detail the work context for the system, interviewing job incumbents, managers, and technology vendors. Short “insight” chapters draw out common themes and consider the implications of human collaboration with smart systems.