Channel News Asia
중도 성향
IT/기술
IT/기술 · "BOOSTS" · 총 8건
필터 보기현재 지수
50.3
0 = 부정 우세
50 = 중립
100 = 긍정 우세
최근 7일 기준 86,966건을 분석한 결과, 뉴스 심리지수는 50.2(균형)입니다. 긍정 4,282건(4.9%)·중립 80,552건(92.6%)·부정 2,132건(2.5%)이며, 중립 비중이 뚜렷하게 높습니다. 성향 지수는 종합 14.8(중도 균형)입니다.
New graduates’ careers are unfolding in an era when AI is not optional. The most successful engineers treat artificial intelligence as leverage, not competition. Here are seven tips to help keep young professionals in demand no matter how quickly the field’s tools evolve. 1. Master the fundamentals first. AI tools can help you code, but you still need strong fundamentals in: Data structures and algorithms for problem-solving. Operating systems, databases, and networking for system-level understanding. Core programming languages such as C++, Java, and Python. AI can autocomplete syntax, but if you don’t understand how things work under the hood, you’re likely to struggle to debug or optimize. 2. Learn how to work with AI, not against it. The best engineers will not try to out-code AI. Instead, they will learn to: Write clear prompts to generate better code snippets. Review and debug AI-generated code for accuracy, performance, and security. Use AI for productivity boosts while still exercising judgment. Think of AI as a teammate. The real skill is knowing when to trust it and when not to. 3. Build projects that showcase end-to-end thinking. Employers increasingly look for engineers who can design and build systems, not just solve problems. Create projects that show you can: Define requirements clearly. Use AI tools responsibly within the workflow. Deliver a product that scales and is maintainable. 4. Sharpen your system design skills early. Even junior engineers are now asked questions about basic system design with AI. Expect to explain to prospective employers: How you would responsibly integrate AI into a system. How to design fallbacks when AI fails. How to ensure scalability and reliability. 5. Develop strong communication skills. Today’s engineers don’t just code in isolation. You will be expected to: Explain design choices to teammates and stakeholders. Document decisions clearly. Collaborate effectively in cross-functional teams. This is one area where AI cannot replace you. Clear communication is a career accelerant. 6. Stay curious and keep learning. The tech industry moves fast, and AI is accelerating that pace. Cultivate habits such as: Following industry news, blogs, and open-source projects. Experimenting with new AI tools, frameworks, and libraries. Engaging in communities such as GitHub, IEEE Collabratec, LinkedIn, and Medium. Employers value engineers who keep themselves sharp and relevant. 7. Think beyond coding. AI will increasingly handle routine coding tasks. The differentiators for you will be: Problem-framing: Can you take a vague idea and turn it into a solution? Architectural judgment: Can you design systems that scale and last? Ethical awareness: Can you spot risks in AI use and address them responsibly? For more career advice, subscribe to the IEEE Spectrum Career Alert Newsletter. The biweekly newsletter features the latest information on jobs, education, management, and the engineering workplace.
Elderly people are particularly vulnerable, with experts warning of rising cases of “grandparent scams”.
The FBI said in April that Americans lost over US$893 million last year to AI-enabled hoaxes, including voice cloning scams.
Shanghai: China's electronics giant Huawei is using a new principle for its chip designing framework that focuses more on cutting transmission time than shrinking transistors. The company plans to use innovative technologies like LogicFolding based on this principle to continuously compress signal propagation delay and improve transistor density.The current chip design framework rests on Moore's law which dates back decades when Intel co-founder Gordon Moore posited in 1965 that the number of transistors on a microchip will double every two years.The Tau Scaling principle could be a revolutionary step in the future of chip designing as it shifts focus from geometric scaling to time scaling. The principle that governs modern advanced chips is to shrink the size of transistors to fit onto a microchip. But this mechanism may have a handicap. It may not be easy to shrink them beyond a point. This is where time scaling becomes useful as it makes cutting signal transmission time the underlying principle of future chip designs.Also Read: PLI 2.0: India bets big on making more of the smartphone at homeThe innovative core technologies like LogicFolding, which Huawei will use for its Kirin chips scheduled to launch in Fall 2026, will work on the Tau Scaling principle in order to drive up performance, energy efficiency, and transistor density."With the t Scaling Law, we look forward to working closely with scientists, engineers, and industry partners around the world to drive the sustainable development of the semiconductor and electronics industries," Huawei's semiconductor chief He Tingbo noted.Huawei's new chip design breakthrough will help the chip maker to sidestep the US sanctions that restrict access to advanced lithography machines from ASML.Also Read: Indian semicon firm Netrasemi plans mass production of its first chip this yearBy 2031, Huawei is aiming for high-end chips based on the t Scaling Law that are expected to feature a transistor density that is equivalent to 14 A (1.4 nm) processes."This is a breakthrough for Huawei, but it's not a threat for TSMC," Reuters quoted Nvidia CEO Jensen Huang, who was in Taipei on Thursday."TSMC has been using die stacking and 3D packaging for how long now? Almost 10 years. And so TSMC's technology is very advanced," he added.A Reuters report mentioned Bernstein analysts cautioning in a note that while stacking multiple chip layers boosts transistor density, there's risk of increasing power density and overheating chips.
Network-wide rollout boosts energy efficiency by 10.6%, cutting carbon emissions and operational costs without compromising user experience