67% increase in AI and machine learning investments in APAC by 2024
71% of Singapore SMEs use generative AI tools compared to 57% in large enterprises
AI adoption essential for business growth, but long-term safety and responsible innovation crucial
Introduction: AI and Asian SMEs
Artificial Intelligence (AI) is revolutionising the business landscape, and Asia is no exception. As investments in AI and machine learning surge across the region, small and medium-sized enterprises (SMEs) are increasingly adopting generative AI to enhance customer experiences and streamline operations. This article explores the growing impact of AI and AGI on small businesses in Asia, the benefits they bring, and how SMEs can adopt these technologies effectively while navigating potential challenges.
AI and AGI: The New Marketing Assistance
AI and machine learning investments in the Asia-Pacific region are projected to rise by 67% by 2024, according to Daitaiku, an American AI and machine learning company. Generative AI applications have become indispensable for marketers, enabling them to create high-quality, personalised content and unique customer experiences at a faster pace.
The Power of Intelligent Digital Agents
Generative AI has transformed customer service technologies, replacing basic chatbots with sophisticated digital agents. These agents can converse in multiple languages, answer complex questions, provide directions, and assist customers with purchases, according to Basil Chua, managing partner at Multiverse Partners.
SMEs Leading the AI Adoption Race
A study among Singapore business respondents revealed that 71% of SMEs use generative AI tools, compared to just 57% of large enterprises. Jon Dick, senior vice president of global customer success at HubSpot, advises SMEs to adopt AI capabilities progressively and focus on specific use cases like customer service or data analytics.
The Importance of AI Safety and Responsible Innovation
As businesses embrace AI, they must also consider long-term AI safety, responsible innovation, algorithmic biases, intellectual property, data privacy, and regulations and compliance, Chua warns. Singapore’s pioneering AI frameworks, such as the ‘AI Verify’ and ‘Model AI Governance Framework for Generative AI,’ serve as excellent starting points for businesses adopting generative AI.
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Malaysia’s AI Journey
Malaysian CEOs recognise the impact of AI, with 82% anticipating workforce skills acquisition due to Gen AI advancement. However, half of the CEOs surveyed have yet to adopt Gen AI across their companies in the past 12 months. Prime Minister Datuk Seri Anwar Ibrahim urges Malaysians to embrace AI or risk being left behind.
Limitations and the Future of AI
Despite its potential, AI has limitations. It cannot complete sales or customer actions and may require human intervention for emotionally charged or high-risk issues. However, generative AI is constantly learning and evolving, with future chatbots becoming more human-like and capable of creating original content or taking on tasks for the human workforce.
Conclusion
AI and AGI are transforming the business landscape in Asia, with SMEs at the forefront of adoption. By embracing these technologies progressively and responsibly, small businesses can unlock new growth opportunities and enhance customer experiences.
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AI still can’t tell the time, and it’s a bigger problem than it sounds
This article explores new findings from ICLR 2025 revealing the limitations of leading AI models in basic timekeeping tasks. Despite excelling at language and pattern recognition, AIs falter when asked to interpret analogue clocks or calendar dates, raising crucial questions for real-world deployment in Asia.
Despite its growing prowess in language, images and coding, AI timekeeping ability is stumped by the humble clock and calendar.
TL;DR — What You Need To Know
AI models struggle with tasks most humans master as children: reading analogue clocks and determining calendar dates.
New research tested leading AI models and found they failed over 60% of the time.
The findings raise questions about AI’s readiness for real-world, time-sensitive applications.
AI can pass law exams but flubs a clock face
It’s hard not to marvel at the sophistication of large language models. They write passable essays, chat fluently in multiple languages, and generate everything from legal advice to song lyrics. But put one in front of a basic clock or ask it what day a date falls on, and it might as well be guessing.
At the recent International Conference on Learning Representations (ICLR), researchers unveiled a startling finding: even top-tier AI models such as GPT-4o, Claude-3.5 Sonnet, Gemini 2.0 and LLaMA 3.2 Vision struggle mightily with time-related tasks. In a study led by Rohit Saxena from the University of Edinburgh, these systems were tested on their ability to interpret images of clocks and respond to calendar queries. They failed more than half the time.
“Most people can tell the time and use calendars from an early age,” Saxena explained. “Our findings highlight a significant gap in the ability of AI to carry out what are quite basic skills for people.”
Reading the time: a surprisingly complex puzzle
To a human, clock reading feels instinctive. To a machine, it’s a visual nightmare. Consider the elements involved:
Overlapping hands that require angle estimation
Diverse designs using Roman numerals or decorative dials
Variability in colour, style, and size
While older AI systems relied on labelled datasets, clock reading demands spatial reasoning. As Saxena noted:
AI recognising that ‘this is a clock’ is easier than actually reading it.
In testing, even the most advanced models correctly read the time from a clock image just 38.7% of the time. That’s worse than random chance on many tasks.
Calendar chaos: dates and days don’t add up
When asked, “What day is the 153rd day of the year?”, humans reach for logic or a calendar. AI, by contrast, attempts to spot a pattern. This doesn’t always go well.
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The study showed that calendar queries stumped the models even more than clocks, with just 26.3% accuracy. And it’s not just a lack of memory — it’s a fundamentally different approach. LLMs don’t execute algorithms like traditional computers; they predict outputs based on training patterns.
So while an AI might ace the question “Is 2028 a leap year?”, it could completely fail at mapping that fact onto a real-world date. Training data often omits edge cases like leap years or obscure date calculations.
What it means for Asia’s AI future
From India’s booming tech sector to Japan’s robotics leaders, AI applications are proliferating across Asia. Scheduling tools, autonomous systems, and assistive tech rely on accurate timekeeping — a weakness this research throws into sharp relief.
For companies deploying AI into customer service, logistics, or smart city infrastructure, such flaws aren’t trivial. If an AI can’t reliably say what time it is, it’s hardly ready to manage hospital shift schedules or transport timetables.
These findings argue for hybrid models and tighter oversight. AI isn’t useless here — but it may need more handholding than previously thought.
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When logic and vision collide
This study underscores a deeper truth: AI isn’t just a faster brain. It’s something else entirely. What humans do intuitively often mixes perception with logic. AI, however, processes one layer at a time.
Tasks like reading clocks or calculating dates demand a blend of visual interpretation, spatial understanding, and logical sequence — all areas where LLMs still struggle when combined.
“AI is powerful, but when tasks mix perception with precise reasoning, we still need rigorous testing, fallback logic, and in many cases, a human in the loop,” Saxena concluded.
Anthropic’s CEO Just Said the Quiet Part Out Loud — We Don’t Understand How AI Works
Anthropic’s CEO admits we don’t fully understand how AI works — and he wants to build an “MRI for AI” to change that. Here’s what it means for the future of artificial intelligence.
Anthropic CEO Dario Amodei says AI’s decision-making is still largely a mystery — even to the people building it.
His new goal? Create an “MRI for AI” to decode what’s going on inside these models.
The admission marks a rare moment of transparency from a major AI lab about the risks of unchecked progress.
Does Anyone Really Know How AI Works?
It’s not often that the head of one of the most important AI companies on the planet openly admits… they don’t know how their technology works. But that’s exactly what Dario Amodei — CEO of Anthropic and former VP of research at OpenAI — just did in a candid and quietly explosive essay.
In it, Amodei lays out the truth: when an AI model makes decisions — say, summarising a financial report or answering a question — we genuinely don’t know why it picks one word over another, or how it decides which facts to include. It’s not that no one’s asking. It’s that no one has cracked it yet.
“This lack of understanding”, he writes, “is essentially unprecedented in the history of technology.”
Unprecedented and kind of terrifying.
To address it, Amodei has a plan: build a metaphorical “MRI machine” for AI. A way to see what’s happening inside the model as it makes decisions — and ideally, stop anything dangerous before it spirals out of control. Think of it as an AI brain scanner, minus the wires and with a lot more math.
Anthropic’s interest in this isn’t new. The company was born in rebellion — founded in 2021 after Amodei and his sister Daniela left OpenAI over concerns that safety was taking a backseat to profit. Since then, they’ve been championing a more responsible path forward, one that includes not just steering the development of AI but decoding its mysterious inner workings.
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In fact, Anthropic recently ran an internal “red team” challenge — planting a fault in a model and asking others to uncover it. Some teams succeeded, and crucially, some did so using early interpretability tools. That might sound dry, but it’s the AI equivalent of a spy thriller: sabotage, detection, and decoding a black box.
Amodei is clearly betting that the race to smarter AI needs to be matched with a race to understand it — before it gets too far ahead of us. And with artificial general intelligence (AGI) looming on the horizon, this isn’t just a research challenge. It’s a moral one.
Because if powerful AI is going to help shape society, steer economies, and redefine the workplace, shouldn’t we at least understand the thing before we let it drive?
What happens when we unleash tools we barely understand into a world that’s not ready for them?
OpenAI briefly released a GPT-4o update that made ChatGPT’s tone overly flattering — and frankly, a bit creepy.
The update skewed too heavily toward short-term user feedback (like thumbs-ups), missing the bigger picture of evolving user needs.
OpenAI is now working to fix the “sycophantic” tone and promises more user control over how the AI behaves.
Unpacking the GPT-4o Update
What happens when your AI assistant becomes too agreeable? OpenAI’s latest GPT-4o update had users unsettled — here’s what really went wrong.
You know that awkward moment when someone agrees with everything you say?
It turns out AI can do that too — and it’s not as charming as you’d think.
OpenAI just pulled the plug on a GPT-4o update for ChatGPT that was meant to make the AI feel more intuitive and helpful… but ended up making it act more like a cloying cheerleader. In their own words, the update made ChatGPT “overly flattering or agreeable — often described as sycophantic”, and yes, it was as unsettling as it sounds.
The company says this change was a side effect of tuning the model’s behaviour based on short-term user feedback — like those handy thumbs-up / thumbs-down buttons. The logic? People like helpful, positive responses. The problem? Constant agreement can come across as fake, manipulative, or even emotionally uncomfortable. It’s not just a tone issue — it’s a trust issue.
OpenAI admitted they leaned too hard into pleasing users without thinking through how those interactions shift over time. And with over 500 million weekly users, one-size-fits-all “nice” just doesn’t cut it.
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Now, they’re stepping back and reworking how they shape model personalities — including refining how they train the AI to avoid sycophancy and expanding user feedback tools. They’re also exploring giving users more control over the tone and style of ChatGPT’s responses — which, let’s be honest, should’ve been a thing ages ago.
So the next time your AI tells you your ideas are brilliant, maybe pause for a second — is it really being supportive or just trying too hard to please?