Phone Thiri Yadana
Singapore, Singapore
780 followers
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780 followers
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Phone Thiri Yadana shared thisJust completed the "Data Storytelling for Business" course, and it was a real eye-opener! Thanks to Neil Lyas for making the learning process both insightful and enjoyable. Excited to apply these new skills to craft compelling stories with data! #DataStorytelling #ContinuousLearning #ocbccampus #StoryIQ
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Phone Thiri Yadana shared thisIt was an enjoyable learning experience with Six Sigma Academy Amsterdam! #SixSigmaAcademy #WhiteAndYellowBeltSpecialist
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Phone Thiri Yadana shared thisDay 2 shift at Our Innovation Booth yesterday. Come and explore our Employer Brand Showcase till 7th March. Time: 10am – 5pm Venue: 65 Chulia Street, OCBC Centre, Level 1 #OpportunityStartsHere #OCBCCareers #EmployerBrandShowcase
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Phone Thiri Yadana shared thisThe long-waited book by Ali Abdaal, "Feel-Good Productivity" has finally landed on my doorstep! Having preordered the hardcover edition weeks ago, I can't wait to delve into the insights and strategies Ali has shared. Huge congratulations to Ali on this incredible achievement! 🎉 If you're on the lookout for practical tips and valuable insights, I highly recommend grabbing a copy. # Feel-Good Productivity
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Phone Thiri Yadana shared thissuper helpful and clear representation of ML model. check out the original one for details.Phone Thiri Yadana shared thisHow does the inner workings of a Machine Learning model work? It begins with the crucial step of Exploratory Data Analysis (EDA), where we uncover the hidden patterns and correlations within our initial dataset. Techniques like PCA and LDA help to simplify the complexity before we proceed. The datasets are then split into training set, where algorithms like SVM, KNN, and Random Forest learn to make predictions. The remaining forms the test set, providing the ground for unbiased evaluation. Hyperparameter optimization and feature selection are key to refining our model. But the real moment comes when we evaluate the model's performance - using metrics like MCC for classification and RMSE for regression. The outcome? Predicted values that bring us closer to understanding and forecasting real-world phenomena. What's your go-to method for feature selection? And after all the fine-tuning, how do you ensure your model isn't overfitting?
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Phone Thiri Yadana shared this"What are the Functional and Non-Functional requirements of a Human?" Out of curiosity, I just put the prompt while doing some reading and learning today. An interesting answer by #chatgpt
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Phone Thiri Yadana shared thisFinally, I have completed "Python for Machine Learning & Data Science Masterclass". It took longer than expected as I needed to some breaks during the course. Thanks Jose Marcial Portilla for this Amazing Masterclass. #machinelearning #datascience #python Note: Due to Udemy's certificate system, actual hours and date are not reflected. When I started this course, it was on-going class and udemy created the snapshot of that completion point only. https://lnkd.in/gSTm4Mpr
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Phone Thiri Yadana reacted on thisPhone Thiri Yadana reacted on thisI recently received an email titled “An 18-year-old’s dilemma: Too late to contribute to AI?” Its author, who gave me permission to share this, is preparing for college. He is worried that by the time he graduates, AI will be so good there’s no meaningful work left for him to do to contribute to humanity, and he will just live on Universal Basic Income (UBI). I wrote back to reassure him that there will still be plenty of work he can do for decades hence, and encouraged him to work hard and learn to build with AI. But this conversation struck me as an example of how harmful hype about AI is. Yes, AI is amazingly intelligent, and I’m thrilled to be using it every day to build things I couldn’t have built a year ago. At the same time, AI is still incredibly dumb, and I would not trust a frontier LLM by itself to prioritize my calendar, carry out resumé screening, or choose what to order for lunch — tasks that businesses routinely ask junior personnel to do. Yes, we can build AI software to do these tasks. For example, after a lot of customization work, one of my teams now has a decent AI resumé screening assistant. But the point is it took a lot of customization. Even though LLMs can handle a much more general set of tasks than previous iterations of AI technology, compared to what humans can do, they are still highly specialized. They’re much better at working with text than other modalities, still require lots of custom engineering to get it the right context for a particular application, and we have few tools — and only inefficient ones — for getting our systems to learn from feedback and repeated exposure to a specific task (such as screening resumés for a particular role). AI has stark limitations, and despite rapid improvements, it will remain limited compared to humans for a long time. AI is amazing, but it has unfortunately been hyped up to be even more amazing than it is. A pernicious aspect of hype is that it often contains an element of truth, but not to the degree of the hype. This makes it difficult for nontechnical people to discern where the truth really is. Modern AI is a general purpose technology that is enabling many applications, but AI that can do any intellectual tasks that a human can (a popular definition for AGI) is still decades away or longer. This nuanced message that AI is general, but not that general, often is lost in the noise of today's media environment. [Truncated for length. Full text: https://lnkd.in/gAuQcZ8M ]
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Phone Thiri Yadana reacted on thisPhone Thiri Yadana reacted on thisMy new book just came out! 🎉😊 Kindle & e-book available now, print within 1–2 weeks. You can get it at: https://homl.info (you'll also find free online content there) Play with the notebooks at: https://lnkd.in/gc9WgdfB Hope you'll find it useful!
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Phone Thiri Yadana liked thisPhone Thiri Yadana liked thisA mentor once told me that the best career decisions are the ones that have you running towards something, not away from something. That’s exactly how I feel today. After almost six years at Gloat, tomorrow will be my last day. From the InMail I sent to Ben Reuveni back in 2019 asking if he wanted Gloat to come to APAC, to onboarding the week before COVID in that tiny Tel Aviv office above a gas station, it’s been an incredible journey. As Gloat’s first APAC employee, I had the privilege of helping build the region from the ground up, joining the senior leadership team, and later leading across International and Global roles. I’m proud of the contributions me and my teams have made, helping expand Gloat’s global footprint, deepen partnerships with world-class customers, and shape how the company shows up to the world. I’ve learned so much and am grateful to have worked alongside such talented, passionate people. I’ve made lifelong friends, lost what little hair I had left, and feel nothing but gratitude for the ride. I’ve now been offered an opportunity that was hard to refuse, I will be joining a company that’s solving one of the biggest and most challenging problems of the AI era. I can’t wait to share more on that soon. To every Gloater, thank you for the collaboration, trust, and belief. I’m deeply proud of what we built together and hope my fingerprints will always be on parts of the journey. Brian McCarthy Shlomo Weiss Noam Mordechay Danny Shteinberg Amichai Schreiber Amir Avraham Ruslan Tovbulatov Esty Yehuda Mike Worthington Michael Tripodi Jeff Schwartz Yoni Friedman Niv Friedman Joel Taylor Chad Hinkle Sebastian Ritz Brian Hershey Craig Townsend Shane Counihan Reed Mettler Anat Gil Donna Geva Moti Gabay Chris Smith Tim Holloway Riddhika Rathod Torie Kessler Aditya Kaushish Manoj Prabakaran Smita Bhattacharya Nathan Siler Ron Shabtai Mac Krauss Ira Goldfarb Elinor Yudan
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Phone Thiri Yadana reacted on thisPhone Thiri Yadana reacted on thisI accidentally conducted the weirdest social experiment about AI in public. Last week, I set up shop like a Peanuts character and offered free AI training in the park to anyone who wanted it. I chatted with… a 19-year-old worried about her college major, a 4-year entrepreneur craving less admin work, sisters in their 60s from Florida asking about data privacy, a young data professional wondering if his job is safe, a non-profit CEO dealing with AI skeptics, a mid-career guy wanting to pivot into wellness content, a startup director that wanted to nerd out on spreadsheets, and so many more. I’m gonna be real - no one stopped for the first hour 😆 And I get it. I was a weird stranger in a park, AI is an intimidating topic, and people didn’t realize it was free. I added a “for free” sign. Two people stopped at my table within minutes. I had a steady stream of AI questions for the next 4 hours. We each have a responsibility to help the people around us understand this paradigm shift. It’s been my privilege to teach you all AI for the last decade. Grateful you’re all here - stay curious 🙏
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Phone Thiri Yadana liked thisPhone Thiri Yadana liked thisHalfway through my Newsletter Hero 66-day build in public challenge. Some days I crushed it. Some days I wanted to quit. This is my story and what I’ve learned from showing up every single day 👇 #ship66
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Phone Thiri Yadana liked thisPhone Thiri Yadana liked thisThe future of work is one where AI becomes so natural we hardly notice it. After a week in San Francisco exploring the latest in enterprise technology, I left with the clear impression that AI is no longer an add-on. It’s now being built directly into platforms and designed to natively interoperate with agents from across the enterprise through multi-agent orchestration. Key moments that stood out: 1. Adaptive interfaces: UIs that surface insights, suggest next steps, and generate content in the flow of work. 2. Composable agents: low-code tools and developer features that allow teams to build AI agents which hand off tasks across HR, Finance, IT, and communication channels, unlocking new ways to reimagine the flow of work. 3. Humans in the loop: ensuring oversight, judgment, and values remain at the centre. With AI handling the transactional work, our role shifts to reimagining outcomes, steering decisions, and elevating the human contributions that give work meaning. So what uniquely human work will we choose to focus on? On a separate note, it was also energising to reconnect with familiar faces so far from Singapore, proof that even in a tech-driven future, human connection remains essential.
Experience
Languages
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English
Full professional proficiency
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Japanese
Limited working proficiency
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Burmese
Native or bilingual proficiency
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Spanish
Elementary proficiency
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John (ジョン) Mangan (マンガン)
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BIG 3 - LLM : What’s the Difference in Japan? ChatGPT. Claude. Gemini. From a consumer view they look similar. From a strategy view they are very different. To most users they solve similar problems. But in Japan the competitive advantage for each company is shaped by how Japanese enterprises actually adopt technology. OpenAI (ChatGPT) - Advantage: distribution and enterprise rollout speed ChatGPT remains the most widely used tool among business professionals. Through partnerships like Microsoft Azure and the SoftBank JV route OpenAI’s strength in Japan is scale plus enterprise enablement. The edge is not just the model. It is how fast it can be operationalized inside large organizations. Anthropic (Claude) - Advantage: enterprise trust and SI alignment Claude’s positioning around safety and controlled deployment aligns well with Japan’s structured governance culture. Partnerships with firms like NRI and Bedrock routes suggest a strategy built around regulated industries and long term enterprise trust. Less consumer dominance. More enterprise credibility. Google (Gemini) - Advantage in Japan: ecosystem embedding For companies already running Google Workspace Gemini becomes the lowest friction path to AI adoption. Rather than competing as a standalone tool Google’s strength is integration across Workspace Cloud Android and Search. The advantage is infrastructure ownership. These are not just different models. They are different strategic bets. And in Japan where enterprise adoption is cautious relationship driven and compliance heavy that difference matters. With the pace of AI moving this fast, how might these strategic positions shift over the next 6–12 months? #AIinJapan #EnterpriseAI #LLM #GenerativeAI #AIStrategy
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Sandy Lai
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Humza Iftekhar
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Stuart M. Basefsky
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Staffing Industry Analysts (SIA) AI adoption outpaces training in HR departments [16 July 2025] https://lnkd.in/gWhpab4p Job-specific AI training lags behind the technology’s adoption in HR departments, according to a survey released July 15 by General Assembly, part of the Adecco Group. HR departments have widely adopted AI, with 82% of HR professionals now using it at work. But just 30% have received comprehensive, job-specific AI training. The lack of targeted training leaves many HR professionals to learn AI on their own, leading to mixed feelings of confidence and effectiveness. While 69% said AI has freed up time for more strategic work, 41% reported working fewer hours because of AI adoption. “AI is transforming every aspect of work, but to harness its full potential, we need to upskill every department, especially HR,” General Assembly CEO Daniele Grassi said in a press release. Grassi noted that HR teams who receive job-specific AI training are 35% more likely to say they are very or completely confident in their AI skills at work than those who learn AI on their own. “HR teams are on the frontlines of talent development and employee upskilling, and they must understand emerging workplace technologies,” Grassi said. HR teams are using AI most commonly for: Analyzing employee feedback, 46% Writing job descriptions, 46% Designing training materials, 45% Still, the survey found that formal training hasn’t kept pace. Outside the 30% who received job-specific training, 26% received no formal AI training at all, 18% received basic training covering concepts but not real applications, 14% sought training on their own and 12% received generic AI training that wasn’t useful for HR tasks. Among Gen Z respondents, 38% said they have received no formal AI training. Respondents working in healthcare were the most likely to report no AI training, with 32% noting this. HR executives in professional services and finance were most likely to have received comprehensive, job-specific AI training at 46% and 39%, respectively. Role-specific training strongly correlated with confidence in using AI: 85% of those who received it said they feel very or completely confident in their skills. HR professionals also said they are eager for more practical, hands-on AI training tailored to their specific roles. Seventy percent want interactive workshops focused on HR use cases, 63% want regular training updates as AI tools evolve and 59% of non-AI users said they would need hands-on, HR-specific training to get started. The top areas in which HR professionals want AI training in include workforce planning, 47%;, designing training materials, 46%; and writing employee communications, 42%. Additionally, 41% of professionals at companies mandating technology use say AI has created more work for them and pulled them away...AND MORE.
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Charlie Lilburn
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Big Tech has officially broken APAC’s talent market, and nobody wants to admit it. Coupang, Alibaba, AWS, Google and Asia’s other tech giants have completely reshaped the region’s talent landscape. They’re mirroring Silicon Valley level salaries, offering RSUs that turn into life-changing wealth, and running engineering cultures where tech is the business. And yes... this has pushed salaries up across APAC. Manufacturers, FMCGs, logistics players and traditional MNCs simply cannot compete on cash. Big Tech offers: ▫️Equity ▫️Cutting-edge AI and innovation ▫️Global-scale problems ▫️Clean architecture ▫️Hyper-speed scaling Most companies can’t match this. But the thing is they don’t need to... Because the same engineers who join Big Tech - also leave Big Tech. And not because of money. They leave because they want: 🔸Real ownership 🔸Impact 🔸Influence 🔸Modern engineering, not just firefighting 🔸Work-life balance, not 80-hour sprints This is where traditional companies have the advantage. You can win top-tier tech talent by offering: ✔️ Autonomy over the roadmap ✔️ High-impact transformation work ✔️ Clear access to senior leaders ✔️ A healthier lifestyle and stability ✔️ Faster career growth than Big Tech can ever offer Big Tech wins on compensation. But non-tech companies can win on meaning, influence, and lifestyle....and that’s becoming the competitive edge. Do you think meaning and autonomy can outweigh compensation in today’s APAC tech market? #TechTalent #DigitalTransformation #APACTech #BigTech #TechHiring #FutureOfWork #TalentStrategy #Connexusglobal
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Skills-first hiring is accelerating globally, and Japan is no exception. For senior HR roles, credentials and titles are no longer the strongest signal. What matters is how leaders operate in practice — navigating ambiguity, localising global intent, and connecting people decisions to business risk and growth. In Japan’s constrained talent market, assessing this properly requires more than networks or CVs. It requires real insight into how HR leaders actually operate on the ground. #ExecutiveSearch #HumanResources Jason LewisGhil Vincent FernandezYvonne UmapasHiroshi KurosakaCeline LeeRocky ReynaldoYuki WadaAniketh NairJulian StephensonAlkis KotzampasisAida WangEdgar E. SalazarScott WallacePaul Cochrane
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Rahim Ismanov
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From Worst to Best “Firing” scenarios. With recent layoffs posts, wanted to share quick observations on how companies are handling "firing" in Japan. ❌ 1. Illogical Firing Something happens globally (HQ decision, restructuring, etc.), and you’re told to let people go, regardless of actual performance or potential. This is the most painful type of exit because it’s simply not fair. In many cases, companies end up getting sued and losing. ⚖️ 2. Real Firing When someone isn’t performing and the decision is based on facts and clear feedback. It’s tough, but usually understandable and often ends on relatively good terms. Hard, but fair. 💼 3. Voluntary Retirement The classic “soft landing.” The company initiates it, but gives employees the option to leave with compensation. A common formula: one month of salary per year worked, so 10 years = 10 months’ worth of pay. Not great, but at least respectful. 🎉 4. Happy Voluntary Retirement (best one?) Some employees walk away with 3–4 years’ worth of salary. Usually from major pharmaceutical or large Japanese corporates during restructuring (does not happen a lot). They usually weren’t even planning to leave and they just got "lucky" with the timing. I’m sure there are more variations out there. But one thing is clear: How a company lets people go says a lot about its values and leadership.
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Turyal Neeshat
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The latest Robert Walters Salary Survey lays out a clear picture of how quickly AI is changing workforce design in Hong Kong. And honestly, the scale of the shift is bigger than what most leaders expect. Here’s what stands out. Well over half of employers have already introduced AI into their operations, and nearly half of those implementations focus directly on optimising headcount. The immediate impact is showing up in admin, IT, and finance, roles that depend heavily on routine, repeatable tasks. But the story isn’t just about automation. Employers now expect serious reskilling needs: 53 percent believe a quarter of their workforce will need new skills, and another 26 percent predict reskilling for more than half their teams. That’s a massive structural shift. Employees aren’t sitting still either. Almost half have already taken AI-related courses, and many are actively scouting industries where new opportunities are emerging. Confidence is surprisingly high: 65 percent believe AI will improve their careers, which tells you people are starting to see AI as a catalyst rather than a threat. What this really means is that the competitive advantage is shifting toward organisations that can balance automation with real investment in people, critical thinking, data literacy, creativity, and ethical judgment. Technical skills matter, but the human layer is becoming the differentiator. For leadership teams, this is the moment to decide whether AI will become a cost-cutting tool or a capability-building engine. The companies that treat AI as a chance to elevate their workforce, not replace it, will come out ahead. #AI #AIAdoption #hytGenX
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