While global headlines fixate on AI’s existential questions, Taiwan is quietly tackling a more practical one: How can artificial intelligence actually improve real industries, right now?
That was the animating spirit behind “AI Everyday: Seeing the Next Step for Taiwan’s Industry,” a forum hosted by the National Development Council (NDC) and held in Taipei on October 17. The event—featuring policymakers, technologists, and entrepreneurs—marked a shift in the country’s AI narrative from research hype to deployment reality.
From “Why AI” to “How AI”
Richard Lee, CEO of the Asia Silicon Valley Development Agency (ASVDA) Executive Center, framed the challenge succinctly:
“We must promote AI, but not for the sake of AI.”
That’s not just rhetoric. ASVDA has cataloged nearly 300 smart application cases across industries—from manufacturing to logistics—and the takeaway is clear: AI is not a standalone sector. It’s an enabling layer that cuts through semiconductors, sensors, networks, and data, creating entirely new business models in its wake.
Lee pointed to Taiwan’s upcoming “AI New Ten Major Construction Projects” as a blueprint not only to strengthen its semiconductor dominance but to infuse AI into every industrial sector—from agriculture to defense. The message was simple: Taiwan doesn’t just want to build AI chips; it wants to build an AI-powered economy.
The Academic Framework: Perception, Generation, and Inference
For those seeking structure amid the buzzwords, Professor Yun Nung Chen of National Taiwan University offered a crisp taxonomy of how AI actually behaves—through Perception, Generation, and Inference.
- Perception: Machines that can “see” and “hear” via computer vision or medical imaging.
- Generation: Systems that create—marketing copy, synthetic media, or even game NPCs that talk like humans.
- Inference: Algorithms that predict outcomes, from consumer behavior to drug efficacy.
Chen’s core insight wasn’t technical—it was managerial. In the AI era, humans evolve from operators to supervisors. “We must learn to give commands, review drafts, and correct errors,” she said, likening AI to a capable but fallible assistant.
Her warning was equally direct:
“The future will not be one where AI replaces humans, but where people who use AI replace those who do not.”
That reframes AI literacy not as a technical skill, but as a new baseline for employability.
Defense Innovation: Building Drones the Hard Way
One of the most striking case studies came from Aiseed, a homegrown defense tech startup. Co-founder Monica Lee recounted the company’s decision to take the harder road—developing indigenous drone technology from scratch rather than relying on imported components.
By independently designing both hardware and AI-driven flight software, Aiseed has already field-tested drones that can navigate GPS interference, avoid obstacles, and adapt in real-time—critical capabilities in modern defense.
Lee drew a line in the sand for Taiwan’s role in the global supply chain:
“Taiwan should not be limited to OEM or ODM work. We play a critical role in both supply and R&D.”
Her comments echoed a broader national priority—“Sovereign AI”—the idea that Taiwan must not only export semiconductors but also own the intelligence built upon them.
AI That Cares: Smart Elder Support
While defense projects grab headlines, Taiwan’s aging population presents a different urgency—and a different kind of AI challenge.
Hugo Lin, CEO of Humetrics, described a system that uses AI-powered under-bed sensors to monitor elderly patients. The system tracks sleep patterns, movement, heart rate, and respiration, flagging potential risks such as nighttime wandering or respiratory anomalies.
But Lin’s key point wasn’t about the data—it was about the user experience.
“The frontline doesn’t want to look at massive amounts of data; they want direct case suggestions and alerts.”
In other words, AI’s job isn’t to overwhelm professionals with information—it’s to distill it into actionable insights. It’s a subtle but essential principle in designing AI for real-world impact.
Making 3D Everyday
Then there’s Optiqb, a company tackling a very different frontier: 3D display technology. CEO Jye Lin believes the problem with 3D isn’t capability—it’s accessibility.
His company’s approach is elegantly simple: a screen protector + app combo that uses an AI-powered visual engine to convert 2D images into glasses-free 3D in real time. It’s low-power, affordable, and designed for mass adoption across education, entertainment, and video conferencing.
As Lin put it:
“3D may not be an essential need, but a phone screen protector is.”
By embedding the technology into something ordinary, Optiqb could make immersive 3D as common as scrolling through a feed.
The Bigger Picture: AI as Infrastructure
Taken together, the forum’s message was clear: AI’s true power lies in integration, not isolation.
Whether it’s Aiseed’s autonomous drones, Humetrics’ clinical AI, or Optiqb’s 3D-on-demand interface, Taiwan’s emerging AI ecosystem isn’t just about new inventions—it’s about embedding intelligence into existing systems.
It’s an approach that mirrors the country’s semiconductor strategy: build the invisible foundations on which entire industries depend.
As Richard Lee summarized, the ultimate goal of Taiwan’s AI movement isn’t just ubiquity—it’s utility:
“The future of AI is not about creating more dazzling technology, but about enabling it to penetrate every sector—becoming as natural as air and water.”
Why It Matters
At a time when Silicon Valley obsesses over general AI and billion-dollar model training runs, Taiwan’s focus on applied, interoperable AI is refreshingly pragmatic. The country is positioning itself not as an AI hype hub, but as an AI enabler—a nation that turns intelligence into infrastructure.
If successful, Taiwan won’t just keep pace with the AI revolution—it may set the benchmark for what sustainable, sovereign, and human-centered AI adoption looks like.
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