logo
Cheap Chinese tours won't save Hong Kong's tourism – DW – 06/11/2025

Cheap Chinese tours won't save Hong Kong's tourism – DW – 06/11/2025

DW2 days ago

Hong Kong's Golden Bauhinia Square, anchored by the sculpture gifted by Beijing in 1997 to mark the city's return after more than 150 years of colonial rule, is a magnet for budget tour groups from mainland China, most of whom leave without an overnight stay. Visitor numbers have climbed since the end of COVID-19 restrictions, yet per-capita spending is still far below pre-pandemic levels. Mainland China's economic slowdown has intensified the squeeze: frugal tourists are battering retailers and forcing many shops to close. Analysts argue that Hong Kong must pivot to distinctive, high-quality experiences if it hopes to stand out as a premier destination rather than a quick, low-cost stop.
This video summary was created by AI from the original DW script. It was edited by a journalist before publication.

Orange background

Try Our AI Features

Explore what Daily8 AI can do for you:

Comments

No comments yet...

Related Articles

AI Isn't Fully Automated — It Runs on Hidden Human Labor
AI Isn't Fully Automated — It Runs on Hidden Human Labor

Int'l Business Times

timea day ago

  • Int'l Business Times

AI Isn't Fully Automated — It Runs on Hidden Human Labor

Welcome to Tech Times' AI EXPLAINED, where we look at the tech of today and tomorrow. Brought to you by Imagine this scenario, one that's increasingly common: You have a voice AI listen to your meeting at work, you get a summary and analysis of that meeting, and you assume AI did all the work. In reality, though, none of these tools work alone. PLAUD AI, Rabbit, ChatGPT, and more all rely on a layer of human labor that most of us don't hear about. Behind that clean chat interface on your phone or computer, there are data labelers that tag speech samples, contractors that rate AI answers , and testers feeding the system more examples to learn from. Some are highly trained while others focus on more of the tedious aspects of the work. No matter what, though, your AI isn't just automated - it's a complex blend of code and human effort. Without it, your AI wouldn't work at all. The Invisible Workforce Behind Everyday AI AI tools don't just appear out of thin air, of course. They learn similarly to the way we do: by example. That learning process often relies on what's called human-in-the-loop (HITL) training. As data-annotation company Encord says in a blog post: "In machine learning and computer vision training, Human-in-the-Loop (HITL) is a concept whereby humans play an interactive and iterative role in a model's development. To create and deploy most machine learning models, humans are needed to curate and annotate the data before it is fed back to the AI. The interaction is key for the model to learn and function successfully," the company wrote. Annotators, data scientists, and data operations teams play a significant role in collecting, supplying, and annotating the necessary data, the post continued. The amount of human input varies with how involved the data is and how much human interaction it will be expected to offer. Of course, as with many business activities, there are ethical concerns. Many content moderators complain of low pay and traumatic content to review. There can also be a language bias in AI training , something researchers and companies are likely working on to solve as AI becomes more complex and global. Case Study: PLAUD AI Various ways users wear the PLAUD Note device—on a wristband, clipped to a lapel, or hanging as a pendant—highlighting its flexibility for hands-free voice capture throughout the day. PLAUD AI PLAUD AI's voice assistant offers an easy, one-button experience. Just press a button, speak, and then let it handle the rest. As the company said on its website , the voice assistant lets you "turn voices and conversations into actionable insights." Behind the scenes, this "magic" started with pre-trained automatic speech recognition (ASR) models like Whisper or other custom variants , that have been refined with actual user recordings. The models not only have to transcribe words, but also try to understand the structure, detect speakers , and interpret tone of voice. The training involves hours and hours of labeled audio and feedback from real conversations. It's likely that every time you see an improvement in the output, it's thanks to thousands of micro-adjustments based on user corrections or behind-the-scenes testing. According to reviewers, PLAUD AI leverages OpenAI's Whisper speech-to-text model running on its own servers. There are likely many people managing the PLAUD AI version of the model for its products, too. Every neat paragraph that comes out of the voice assistant likely reflects countless iterations of fine-tuning and A/B testing by prompt engineers and quality reviewers. That's how you get your results without having to deal with all that back-end work yourself. Case Study 2: ChatGPT and The ChatGPT logo represents one of the most widely used AI assistants—powered not just by models, but by human trainers, raters, and user feedback. ilgmyzin/Unsplash When you use ChatGPT, it can feel like an all-knowing helpful assistant with a polished tone and helpful answers. Those are based, of course, on a foundation of human work. OpenAI used reinforcement learning from human feedback , or RLHF, to train its models. That means actual humans rating responses so the system could learn what responses were the most helpful or accurate, not to mention the most polite. "On prompts submitted by our customers to the API, our labelers provide demonstrations of the desired model behavior and rank several outputs from our models," wrote the company in a blog post . "We then use(d) this data to fine-tune GPT‑3." a popular online voice transcription service, also relies on human work to improve its output. It doesn't use RLHF like OpenAI does, but it does include feedback tools for users to note inaccurate transcriptions, which the company then uses to fine-tune its own models. The company also uses synthetic data (generated pairs of audio and text) to help train its models, but without user corrections, these synthetic transcripts can struggle with accents, cross talk, or industry jargon; things only humans can fix. Case Study 3: Rabbit R1's Big Promise Still Needs Human Help The Rabbit R1 made a splash with its debut: a palm-sized orange gadget promising to run your apps for you, no screen-tapping required. Just talk to it, and it's supposed to handle things like ordering takeout or cueing up a playlist. At least, that's the idea. Rabbit says it built the device around something called a Large Action Model (LAM), which is supposed to "learn" how apps work by watching people use them. What that means in practice is that humans record themselves doing things like opening apps, clicking through menus, or completing tasks and those recordings become training data. The R1 didn't figure all this out on its own; it was shown how to do it, over and over. Since launch, people testing the R1 have noticed that it doesn't always feel as fluid or "intelligent" as expected. Some features seem more like pre-programmed flows than adaptive tools. In short, it's not magic—it's a system that still leans on human-made examples, feedback, and fixes to keep improving. That's the pattern with almost every AI assistant right now: what feels effortless in the moment is usually the result of hours of grunt work—labeling, testing, and tuning—done by people you'll never see. AI Still Relies On Human Labor For all the talk of artificial intelligence replacing human jobs, the truth is that AI still leans hard on human labor to work at all. From data labelers and prompt raters to everyday users correcting transcripts, real people are constantly training, guiding, and cleaning up after the machines. The smartest AI you use today is only as good as the humans behind it. For now, that's the part no algorithm can automate away. Originally published on Tech Times

German drone industry takes off  – DW – 05/30/2025
German drone industry takes off  – DW – 05/30/2025

DW

timea day ago

  • DW

German drone industry takes off – DW – 05/30/2025

Carmakers shift to drone production Germany, long known for its automotive prowess, is witnessing a quiet but significant transformation. As traditional car production slows, a new industry is taking flight—literally. Across southern Germany, start-ups and engineers are pivoting from cars to combat drones. One such innovation is the 'Falke,' a drone designed for both civilian and military use, capable of high speeds and long-range missions. Its creators emphasize affordability and mass production, leveraging materials and techniques from the automotive sector. This shift is not isolated. Near Munich, drone developers are collaborating with car part suppliers, repurposing their expertise and infrastructure to meet the growing demand for military-grade drones. Ukraine war has changed German mindset The war in Ukraine has accelerated this trend. Companies like High Cat are already supplying drones to the front lines, designed to resist jamming and deliver real-time reconnaissance. These aren't just tech experiments—they're the result of serious engineering, often funded by private investors who once shied away from defense ventures. Even packaging companies are adapting, creating climate-controlled transport cases for drones. The transformation is industrial and cultural: Engineers and entrepreneurs are learning to think like soldiers, designing for battlefield conditions. Automotive suppliers, facing declining orders, are finding new life in defense manufacturing, applying their precision and scale to drone production. Tapping into the €500 billion fund Germany's defense sector is poised for further growth, thanks to a €500 billion special fund aimed at strengthening national security. This fund opens doors for manufacturers across the country, especially those with existing capabilities in high-precision production. Companies that once hesitated to enter the arms industry are now reconsidering, driven by both financial incentives and a growing sense of responsibility to defend democratic values. With the right support and strategic partnerships, Germany's factories could become key players in Europe's defense landscape—transforming economic uncertainty into industrial opportunity. This video summary was created by AI from the original DW script. It was edited by a journalist before publication.

Trump Admin's Plans to Push AI Across Government Sites Leaked on Code Sharing Website
Trump Admin's Plans to Push AI Across Government Sites Leaked on Code Sharing Website

Int'l Business Times

time2 days ago

  • Int'l Business Times

Trump Admin's Plans to Push AI Across Government Sites Leaked on Code Sharing Website

The Trump administration's plan to integrate artificial intelligence across federal agencies has been exposed through a leaked draft of a government-run website, revealing an initiative set to launch on July 4 that would track and promote AI use across departments. The early details were uncovered in code uploaded to GitHub by the General Services Administration's Technology Transformation Services (TTS), led by former Tesla engineer Thomas Shedd, according to 404 Media. The website, is described as a centralized platform offering integration with AI tools from OpenAI, Google, Anthropic, AWS Bedrock, and Meta's LLaMA. It also includes an analytics feature that will reportedly measure AI adoption rates by specific government teams. The project is part of a broader push by Shedd and the Department of Government Efficiency, spearheaded by Elon Musk, to rapidly embed AI technologies into government operations. Leaked audio from a TTS meeting in February revealed that Shedd wanted AI tools to write software, review contracts, and standardize usage across agencies—goals that internal staff reportedly viewed with widespread skepticism. Concerns raised by government employees include the potential for AI-generated code to introduce security flaws, create software bugs, or mistakenly recommend cancelling essential contracts. Despite these warnings, the GitHub page suggests that the initiative is moving forward, with set to launch on Independence Day. As of now, redirects to the White House homepage, and the staging version of the site is hosted quietly on The GSA has not commented publicly on the leak or the concerns surrounding the project. Originally published on Latin Times

DOWNLOAD THE APP

Get Started Now: Download the App

Ready to dive into the world of global news and events? Download our app today from your preferred app store and start exploring.
app-storeplay-store