How chips, data and algorithms powered the AI revolution

详细摘要

Artificial intelligence (AI) has become an increasingly prominent part of our daily lives, influencing how we work, communicate, and access information. From generating music and…

Artificial intelligence (AI) has become an increasingly prominent part of our daily lives, influencing how we work, communicate, and access information. From generating music and academic writing to solving complex problems, AI is advancing rapidly, often in ways that would have seemed unimaginable just a decade ago. Its growing influence has also sparked widespread debate about its potential, limitations, and implications for humanity.

Much has already been written and debated about AI. Yet understanding how we arrived at this point requires looking beyond the software applications that have captured public attention. Having spent three decades working as a semiconductor technologist in California's Silicon Valley, a centre of global technological innovation, I have witnessed many of the developments that made today's AI revolution possible. At the heart of this transformation lies semiconductor hardware, the computing engine that has enabled AI to evolve from a largely experimental technology into a powerful force shaping industries worldwide.

Artificial intelligence broadly refers to machines performing tasks that normally require human intelligence. Modern AI systems use sophisticated algorithms and specialised computing hardware, including AI accelerators, to process information, recognise patterns, and generate outputs that can resemble human work. These capabilities now extend to writing, music, images, software development, and increasingly complex decision-making.

However, AI did not emerge overnight. Efforts to develop intelligent machines date back to the 1940s, when scientists began exploring the possibility of building computers capable of performing narrowly defined tasks. Over the following decades, advances in computing power and software gradually expanded what machines could accomplish.

The 1990s and 2000s marked an important period in this development. Computers became increasingly powerful, enabling researchers to create more sophisticated systems.

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