Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing (NLP), including text generation, translation, summarization, and code synthesis, as a consequence of which revolutionizing a wide range of AI applications [10, 56, 45]. Models such as OpenAI's ChatGPT series, Google's Gemini, and Anthropic's Claude have been widely deployed in commercial systems, including search engines, customer support, software development tools, and personal assistants [45, 55, 3]. However, as their capabilities grow, so do their attack surfaces and the potential for misuse [51, 77, 50]. While the scale and specific nature of these vulnerabilities are new, the fundamental challenge of ensuring that powerful AI systems operate safely and align with human intent is a longstanding concern in the AI community. Foundational work, such as the identification of concrete problems in AI safety long before the current LLM era, laid the groundwork for understanding issues like reward hacking and negative side effects that remain highly relevant today [1]. The susceptibility arises because the models are trained on vast, yet imperfectly curated, datasets containing potentially harmful content, and because they interact with users through open-ended prompts that can be manipulated [48, 17, 16]. Researchers and practitioners are increasingly concerned that these systems can be manipulated, misused, or even behave in misaligned and potentially deceptive ways [25, 42, 6]. Consequently, the security and alignment of LLMs have become critical areas of study, requiring an understanding of emergent threats and robust, multi-faceted defenses [17, 70, 43].

Speech emotion recognition plays a crucial role in human-computer interactions. However, most speech emotion recognition research is biased toward English-speaking adults, which hinders its applicability to other demographic groups in different languages and age groups. In this work, we analyze the transferability of emotion recognition across three different languages--English, Mandarin Chinese, and Cantonese; and 2 different age groups--adults and the elderly. To conduct the experiment, we develop an English-Mandarin speech emotion benchmark for adults and the elderly, BiMotion, and a Cantonese speech emotion dataset, YueMotion. This study concludes that different language and age groups require specific speech features, thus making cross-lingual inference an unsuitable method. However, cross-group data augmentation is still beneficial to regularize the model, with linguistic distance being a significant influence on cross-lingual transferability. We release publicly release our code at https://github.com/HLTCHKUST/elderly_ser.

Computer scientist Pascale Fung can imagine a rosy future in which polyglot AI helpers like ChatGPT bridge language barriers. In that world, Indonesian store owners fluent only in local dialects might reach new shoppers by listing their products online in English. "It can open opportunities," Fung says--then pauses. She's spotted the bias in her vision of a more interconnected future: The AI-aided shopping would be one-sided, because few Americans would bother to use AI translation to help research products advertised in Indonesian. "Americans are not incentivized to learn another language," she says.

Hong Kong (CNN Business)Before the pandemic, Ka Tim Chu, teacher and vice principal of Hong Kong's True Light College, looked at his students' faces to gauge how they were responding to classwork. Now, with most of his lessons online, technology is helping Chu to read the room. An AI-powered learning platform monitors his students' emotions as they study at home. The software, 4 Little Trees, was created by Hong Kong-based startup Find Solution AI. While the use of emotion recognition AI in schools and other settings has caused concern, founder Viola Lam says it can make the virtual classroom as good as -- or better than -- the real thing.

Universities in China and elsewhere in Asia are belatedly joining global alliances to promote ethical practices in artificial intelligence or AI, which were previously being studied in university research centres in a fragmented way. Countries like South Korea, Japan, China and Singapore are making huge investments in AI research and development, including the AI interface with robotics and are in some areas rapidly narrowing the gap with the United States. But crucially there are still no international guidelines and standards in place for ethical research, design and use of AI and automated systems. China's universities in particular are turning out a large number of researchers specialising in AI. Whereas in the past they would head for Silicon Valley in the US, many are now opting to stay in the country to work for home-grown technology giants such as Alibaba, Tencent and Baidu – companies which gather and use huge amounts of consumer data with few legal limits.

A panel of experts at the recent 2017 Wharton Global Forum in Hong Kong outlined their views on the future for artificial intelligence (AI), robots, drones, other tech advances and how it all might affect employment in the future. Their comments came in a panel session titled, "Engineering the Future of Business," with Wharton Dean Geoffrey Garrett moderating and speakers Pascale Fung, a professor of electronic and computer engineering at Hong Kong University of Science and Technology; Vijay Kumar, dean of engineering at the University of Pennsylvania, and Nicolas Aguzin, Asian-Pacific chairman and CEO for J.P.Morgan. A fundamental problem is that most observers do not realize just how vast an amount of data is needed to operate in the physical world -- ever-increasing amounts, or, as Kumar calls it -- "exponential" amounts. "To have electric power and motors and batteries to power drones that can lift people in the air -- I think this is a pipe dream.

Robots that serve dinner, self-driving cars and drone-taxis could be fun and hugely profitable. They are likely much further off than the hype suggests. A panel of experts at the recent 2017 Wharton Global Forum in Hong Kong outlined their views on the future for artificial intelligence (AI), robots, drones, other tech advances and how it all might affect employment in the future. The upshot was to deflate some of the hype, while noting the threats ahead posed to certain jobs. Their comments came in a panel session titled, "Engineering the Future of Business," with Wharton Dean Geoffrey Garrett moderating and speakers Pascale Fung, a professor of electronic and computer engineering at Hong Kong University of Science and Technology; Vijay Kumar, dean of engineering at the University of Pennsylvania, and Nicolas Aguzin, Asian-Pacific chairman and CEO for J.P.Morgan. Kicking things off, Garrett asked: How big and disruptive is the self-driving car movement? It turns out that so much of what appears in mainstream media about self-driving cars being just around the corner is very much overstated, said Kumar.

Try typing "the machines" into Google and chances are that one of the top results the artificial intelligence-powered search engine will return is the phrase: "The Machines are Coming". After a 2016 filled with high-profile advances in artificial intelligence (AI), leading technologists say this could be a breakout year in the development of intelligent machines that emulate humans. Asia, until now lagging Silicon Valley in AI, will play a bigger role as the field cements itself at the pinnacle of the technology world in 2017, the experts say. Pascale Fung, an AI researcher at the Hong Kong University of Science and Technology (HKUST), said several milestones have been reached in developing computers that are similar to the human brain. Speech and emotional recognition were among the areas "reaching new milestones", Fung said.

Try typing "the machines" into Google and chances are that one of the top results the artificial intelligence-powered search engine will return is the phrase: "The Machines are Coming". After a 2016 filled with high-profile advances in artificial intelligence (AI), leading technologists say this could be a breakout year in the development of intelligent machines that emulate humans. Asia, until now lagging Silicon Valley in AI, will play a bigger role as the field cements itself at the pinnacle of the technology world in 2017, the experts say. AI – technically, a computing field that involves the analysis of large troves of data to predict outcomes and patterns – is as old as modern computers but its esoteric nature means it has long endured caricatures of its actual potential – think for example, the 1960s space age cartoon The Jetsons, which featured a sentient robot maid and automated flying cars (both of which we are still waiting for, even 50 years on). Now, a confluence of factors has given rise to hopes that computers with human-like cognitive ability may soon be a reality.

Try typing "the machines" into Google and chances are that one of the top results the artificial intelligence-powered search engine will return is the phrase: "The Machines are Coming". After a 2016 filled with high-profile advances in artificial intelligence (AI), leading technologists say this could be a breakout year in the development of intelligent machines that emulate humans. Asia, until now lagging Silicon Valley in AI, will play a bigger role as the field cements itself at the pinnacle of the technology world in 2017, the experts say. AI – technically, a computing field that involves the analysis of large troves of data to predict outcomes and patterns – is as old as modern computers but its esoteric nature means it has long endured caricatures of its actual potential – think for example, the 1960s space age cartoon The Jetsons, which featured a sentient robot maid and automated flying cars (both of which we are still waiting for, even 50 years on). Now, a confluence of factors has given rise to hopes that computers with human-like cognitive ability may soon be a reality.