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Time of India
a day ago
- Science
- Time of India
Explained: Why this mathematician thinks OpenAI isn't acing the International Mathematical Olympiad — and might be ‘cheating' to win gold
TL;DR AI isn't taking the real test: GPT models 'solving' International Math Olympiad (IMO) problems are often operating under very different conditions—rewrites, retries, human edits. Tao's warning: Fields Medalist Terence Tao says comparing these AI outputs to real IMO scores is misleading because the rules are entirely different. Behind the curtain: Teams often cherry-pick successes, rewrite problems, and discard failures before showing the best output. It's not cheating, but it's not fair play: The AI isn't sitting in silence under timed pressure—it's basically Iron Man in a school exam hall. Main takeaway: Don't mistake polished AI outputs under ideal lab conditions for human-level reasoning under Olympiad pressure. Led Zeppelin once sang, 'There's a lady who's sure all that glitters is gold.' But in the age of artificial intelligence, even the shimmer of mathematical brilliance needs closer scrutiny. These days, social media lights up every time a language model like GPT-4 is said to have solved a problem from the International Mathematical Olympiad (IMO) — a competition so elite it makes Ivy League entrance exams look like warm-up puzzles. 'This AI solved an IMO question!' 'Superintelligence is here!' 'We're witnessing the birth of a digital Newton!' Or so the chorus goes. But one of the greatest living mathematicians isn't singing along. Terence Tao, a Fields Medal–winning professor at UCLA, has waded into the hype with a calm, clinical reminder: AI models aren't playing by the same rules. And if the rules aren't the same, the gold medal doesn't mean the same thing. The Setup: What the IMO Actually Demands The International Mathematical Olympiad is the Olympics of high school math. Students from around the world train for years to face six unspeakably hard problems over two days. They get 4.5 hours per day, no calculators, no internet, no collaboration — just a pen, a problem, and their own mind. Solving even one problem in full is an achievement. Getting five perfect scores earns you gold. Solve all six and you enter the realm of myth — which, incidentally, is where Tao himself resides. He won a gold medal in the IMO at age 13. So when an AI is said to 'solve' an IMO question, it's important to ask: under what conditions? Enter Tao: The IMO, Rewritten (Literally) In a detailed Mastodon post, Tao explains that many AI demonstrations that showcase Olympiad-level problem solving do so under dramatically altered conditions. He outlines a scenario that mirrors what's actually happening behind the scenes: 'The team leader… gives them days instead of hours to solve a question, lets them rewrite the question in a more convenient formulation, allows calculators and internet searches, gives hints, lets all six team members work together, and then only submits the best of the six solutions… quietly withdrawing from problems that none of the team members manage to solve.' In other words: cherry-picking, rewording, retries, collaboration, and silence around failure. It's not quite cheating — but it's not the IMO either. It's an AI-friendly reconstruction of the Olympiad, where the scoreboard is controlled by the people training the system. From Bronze to Gold (If You Rewrite the Test) Tao's criticism isn't just about fairness — it's about what we're really evaluating. He writes, 'A student who might not even earn a bronze medal under the standard IMO rules could earn a 'gold medal' under these alternate rules, not because their intrinsic ability has improved, but because the rules have changed.' This is the crux. AI isn't solving problems like a student. It's performing in a lab, with handlers, retries, and tools. What looks like genius is often a heavily scaffolded pipeline of failed attempts, reruns, and prompt rewrites. The only thing the public sees is the polished output. Tao doesn't deny that AI has made remarkable progress. But he warns against blurring the lines between performance under ideal conditions and human-level problem-solving in strict, unforgiving settings. Apples to Oranges — and Cyborg Oranges Tao is careful not to throw cold water on AI research. But he urges a reality check. 'One should be wary of making apples-to-apples comparisons between the performance of various AI models (or between such models and the human contestants) unless one is confident that they were subject to the same set of rules.' A tweet that says 'GPT-4 solved this problem' often omits what really happened: – Was the prompt rewritten ten times? – Did the model try and fail repeatedly? – Were the failures silently discarded? – Was the answer chosen and edited by a human? Compare that to a teenager in an exam hall, sweating out one solution in 4.5 hours with no safety net. The playing field isn't level — it's two entirely different games. The Bottom Line Terence Tao doesn't claim that AI is incapable of mathematical insight. What he insists on is clarity of conditions. If AI wants to claim a gold medal, it should sit the same exam, with the same constraints, and the same risks of failure. Right now, it's as if Iron Man entered a sprint race, flew across the finish line, and people started asking if he's the next Usain Bolt . The AI didn't cheat. But someone forgot to mention it wasn't really racing. And so we return to that Led Zeppelin lyric: 'There's a lady who's sure all that glitters is gold.' In 2025, that lady might be your algorithmic feed. And that gold? It's probably just polished scaffolding. FAQ: AI, the IMO, and Terence Tao's Critique Q1: What is the International Mathematical Olympiad (IMO)? It's the world's toughest math competition for high schoolers, with six extremely challenging problems solved over two 4.5-hour sessions—no internet, no calculators, no teamwork. Q2: What's the controversy with AI and IMO questions? AI models like GPT-4 are shown to 'solve' IMO problems, but they do so with major help: problem rewrites, unlimited retries, internet access, collaboration, and selective publishing of only successful attempts. Q3: Who raised concerns about this? Terence Tao, one of the greatest mathematicians alive and an IMO gold medalist himself, called out this discrepancy in a Mastodon post. Q4: Is this AI cheating? Not exactly. But Tao argues that changing the rules makes it a different contest altogether—comparing lab-optimised AI to real students is unfair and misleading. Q5: What's Tao's main point? He urges clarity. If we're going to say AI 'solved' a problem, we must also disclose the conditions—otherwise, it's like comparing a cyborg sprinter to a high school track star and pretending they're equals. Q6: Does Tao oppose AI? No. He recognises AI's impressive progress in math, but wants honesty about what it means—and doesn't mean—for genuine problem-solving ability. Q7: What should change? If AI is to be judged against human benchmarks like the IMO, it must be subjected to the same constraints: time limits, no edits, no retries, no external tools. Tao's verdict? If you want to claim gold, don't fly across the finish line in an Iron Man suit and pretend you ran. AI Masterclass for Students. Upskill Young Ones Today!– Join Now
United News of India
4 days ago
- Business
- United News of India
DPE organised a workshop for adoption of Industry 4.0 in CPSEs
New Delhi, July 19 (UNI) Department of Public Enterprises (DPE), Ministry of Finance, Government of India organized a day-long workshop on Industry 4.0 here. This event was intended to bolster smart manufacturing and innovation. Guests from different domains, including policymakers, experts from Central Public Sector Expertise (CPSE), exchanged their insights on the key ongoing trends of Industry 4.0 technologies, including sectors like Energy, Power, Construction, Infrastructure, Telecom & Services. In his inaugural address yesterday, K. Moses Chalai ( Secretary, Department of Public Enterprises) stressed the importance of General Purpose Technologies or GPT in embracing the fourth industrial revolution as a national mission. Moses urges all CPSEs to integrate advanced technologies, including Artificial Intelligence (AI), Internet of Things (IoT), Digital Twins, 3D Printing, and 5G-enabled infrastructure. Expert presentations at the sessions started by A. Anand (Deployment Expert) by sharing his extensive experience in the domain of Digital Designing, Reverse Engineering, and 3D printing across different industries. This workshop has active participation from CMDs or the Director of leading CPSEs. Concluding the workshop, DPE showed its commitment towards supporting CPSEs in leveraging Industry 4.0. This event highlighted a significant move towards integrating technology across the CPSE ecosystem. UNI SAS BM
Mint
4 days ago
- Business
- Mint
Finance Ministry organises workshop on industry 4.0 adoption, digital transformation in CPSEs
New Delhi [India], July 19 (ANI): In a strategic step towards fostering innovation and smart manufacturing, the Department of Public Enterprises (DPE), Ministry of Finance, organised a Workshop on Industry 4.0 in New Delhi. The workshop, organised on Friday, aimed to discuss strategies for the adoption and scaling up of Industry 4.0 technologies across various sectors, including Energy, Power, Construction, Infrastructure, Telecom, and day-long workshop brought together experts, policymakers, and leadership of Central Public Sector Enterprises (CPSEs), a release by the Finance Ministry stated. The workshop was inaugurated by K Moses Chalai, Secretary, Department of Public Enterprises, who briefly touched upon General Purpose Technologies (GPT) and emphasised the importance of embracing the Fourth Industrial Revolution (4IR) as a national mission. In his address, he underscored the need for a "Whole-of-CPSEs" (WoC) approach -- on the lines of the "Whole-of-Government" framework -- urging all CPSEs to collaborate in integrating 4IR enablers such as Artificial Intelligence (AI), Internet of Things (IoT), Digital Twins, 3D Printing, and 5G-enabled smart infrastructure into their operations. He noted that Industry 4.0 is also being considered for future incorporation into the CPSE MoU assessment framework, and that early adoption will be key to enhancing global competitiveness. As part of expert presentations at the workshop, A Anand, deployment expert shared rich experience in applications and field deployment of Digital Designing, Reverse Engineering, and 3D Printing across industry verticals, drawing from India's first 5G Labs and 3D Printing Centres of Excellence; Dr Prabhjot Singh Sugga, Associate Professor at SPA, highlighted the transformative potential of Digital Twin platforms in infrastructure, plant management, and disaster resilience; and Ms. Vidushi Chaturvedi, an AI expert, spoke on leveraging Artificial Intelligence and Machine Learning for predictive analytics, resource optimisation, and intelligent decision-making. Experience-sharing sessions by CPSEs such as Powergrid, HSCC, and BSNL showcased successful pilots in AI-driven maintenance, digital simulation, and 3D printing-enabled supply chains. The workshop saw active participation from CMDs and Directors of leading CPSEs -- including NTPC, NHPC, GAIL, CONCOR, IRCTC, RITES, AAI, and WAPCOS -- who engaged in interactive discussions on strategic roadmaps, capacity building, and sector-specific adoption of Industry 4.0. The deliberations reaffirmed the relevance of 4IR technologies across key sectors like Oil & Gas, Railways, Mining, Health, Handlooms, and Infrastructure, as outlined in the DPE Concept Paper and Adoption Potential Matrix shared during the DPE reiterated its commitment to support CPSEs in leveraging Industry 4.0 for operational excellence and sustainable development. The workshop marks a significant step toward realising this vision through collective efforts and strategic implementation across the CPSE ecosystem. This round of workshops will be organised in different regions of the country with CPSEs located in these regions. It is expected to be completed within August 2025. (ANI)
Time of India
5 days ago
- Science
- Time of India
The coming of agentic AI: The next era of human-machine synergy
Artificial Intelligence (AI) has travelled from the confines of research labs to every aspect of our daily lives. Over the past several decades, we have witnessed an extraordinary transformation; from rule-based systems to neural networks, from statistical AI to large language models (LLMs), and now, to the threshold of Agentic AI. The trending buzzword now, which is a paradigm where machines can reason, plan, adapt, and act with increasing autonomy and human-like capability. This evolution has not only showcased the power of technological progress but has also continuously enriched human life in meaningful ways. The evolution of AI The story of AI began in the 1950s with the advent of symbolic AI; systems designed to reason using logic and handcrafted rules. While foundational, these early systems were rigid, unable to adapt to real-world complexity. The 1980s brought expert systems, which encoded domain knowledge explicitly. Though revolutionary for tasks like medical diagnosis and financial modelling, their maintenance proved unsustainable at scale. The real shift came in the late 1990s and 2000s with the re-entry of machine learning. Instead of handcrafting intelligence, we began teaching machines to learn patterns from data. Algorithms like decision trees, support vector machines, and eventually deep learning architectures unlocked the ability to process images, speech, and text at scale. In 2012, a convolutional neural network achieved ground breaking accuracy in image classification marking the arrival of deep learning as a dominant force. We saw a seismic shift in AI capabilities with the advent of transformer architectures, introduced in the seminal 2017 paper 'Attention Is All You Need.' This innovation enabled models to understand large language context, paving the way for Large Language Models (LLMs) capable of generating fluent, context-aware responses and performing tasks from summarization to reasoning. Landmark models like BERT revolutionized understanding through bidirectional context, while generative models like the GPT series demonstrated unprecedented abilities in content creation, dialogue, and code generation. This progress was driven by advanced algorithms, massive datasets, and exponentially growing computational power, catalysing the shift from narrow, task-specific AI to general-purpose systems with emergent intelligence, paving the way for Agentic AI. The rise of agentic AI Today, we stand at the edge of another monumental shift: the emergence of Agentic AI. Agentic AI systems exhibit autonomy, goal-oriented behaviour, memory, reasoning, and the ability to interact and modify plans as per real-world environment. This is built on the foundation of powerful Large Language Models (LLMs), enhanced with capabilities for self-reflection, memory, and planning. These systems not only understand and generate language but can also evaluate their own actions and adapt their behaviour, enabling continuous improvement and proactive task execution. Agentic AI's core capabilities: 1. Perception and Awareness : Understands real-world inputs across text, vision, and audio. 2. Reasoning and Planning: Makes strategic decisions, breaks down goals, and adapts through learning. 3. Autonomous Execution: Carries out tasks across systems, learns from feedback, and improves over time. Together, this LLM-driven intelligence and reflective agent architectures blur the line between tool and teammate. These systems can proactively initiate tasks, collaborate, and continuously evolve mirroring the human-like cognitive flexibility and purpose-driven actions. Impact on human life Each wave of AI advancement has expanded our collective capability. Symbolic AI gave us expert systems in finance and medicine. Machine learning unlocked personalization powering recommendation engines, fraud detection, and predictive analytics. Deep learning brought breakthroughs in vision and speech - enabling virtual assistants, real-time translation, autonomous vehicles, and medical imaging. Agentic AI takes this further by transforming how we interact with machines. Imagine an AI assistant that doesn't just draft your emails, but understands your calendar, reads context from past meetings, and autonomously books travel, schedules follow-ups, and flags opportunities, continuously learning from your preferences. In enterprise, Agentic AI will streamline complex workflows. In healthcare, it can serve as a tireless collaborator, synthesizing patient data, flagging anomalies and coordinating care across departments. In education, it will act as an always-available tutor, adjusting teaching strategies in real-time to individual student needs. In scientific research, Agentic systems can formulate hypotheses, run simulations, and interpret results at a speed and scale previously unimaginable. The road ahead: Promise and responsibility As we venture deeper into the era of Agentic AI, the possibilities are infinite. We foresee: Cognitive Companions: Agents capable of dialogue, empathy modelling, and proactive Digital Workers: Agents execute complex business processes with minimal Interfaces: AI that adapts to user preferences and behaviours, providing intuitive and context-aware Human Intelligence: Seamless collaboration between human creativity and machine precision to solve grand Collaboration: Closely working with other AI agents to solve complex problems through specialized expertise. It is important to remember that agentic systems must be designed with rigorous safeguards; embedding transparency, fairness, interpretability, and alignment with human values. Robust testing, continuous red-teaming, and human-in-the-loop oversight will be vital to ensure trust and accountability. Conclusion The evolution of Agentic AI mirrors our growing understanding of both computation and cognition. More than building smarter machines, we are shaping a new interface between human intention and digital action. Agentic AI holds the promise of being our most powerful collaborator yet, the one that understands, learns, and acts on our behalf. As we look ahead, let us embrace this transformative moment with optimism and responsibility. The future of Agentic AI is not just technological, it is deeply human.
India Today
5 days ago
- Business
- India Today
Boom or Bubble? The soaring stakes in the AI gold rush
The global AI industry is witnessing record-breaking investments, with US firms like OpenAI, xAI, and Anthropic dominating the valuation charts. There has never been a surge in investment in the artificial intelligence industry like what's happening now. Billions of dollars are pouring into firms promising to change the future of technology. This surge came after OpenAI released ChatGPT, an AI chatbot based on the GPT language model. OpenAI is at the top of the AI valuation table. According to a report by CB Insights, it is now the most valuable AI startup in the world, worth USD 300 billion. It has grown quickly thanks to partnerships and a wave of funding, with the most recent round bringing in $40 billion in March 2025, led by SoftBank Group. Apart from OpenAI, Databricks and Anthropic are two other US-based firms betting on AI and worth more than USD 60 billion. Elon Musk's AI company, xAI, has also made it to the top, making it one of the four AI businesses among the world's ten most valuable private tech companies. The dominance of US-based companies in this sector is striking. Seven of the eight highest-valued AI startups are headquartered in the United States. The only exception is Celonis, a German firm specialising in process optimisation through AI and other technologies. Despite its European roots, Celonis has a strong presence in the US market and is currently valued at $13 billion. One of the newer entrants generating buzz is Safe Superintelligence, a US startup founded just last year by a team of former OpenAI, Apple, and academic researchers. Despite raising only USD 3 billion, the company is already valued at USD 30 billion. Its mission is to build a superintelligent AI system that prioritises safety above all else. Another startup making headlines is Thinking Machines Lab, founded by former OpenAI Chief Technology Officer Mira Murati. It has just closed a massive USD 2 billion seed round, pushing its valuation to USD 12 billion. However, not all that glitters in AI space is gold. As companies scale up AI projects, a recent Gartner report warns that over 40 per cent of projects involving "agentic" AI (systems designed to make decisions and act autonomously) will be cancelled by the end of 2027. The research firm cites high development costs, unclear return on investment, and technological immaturity as key reasons. It also raised a growing trend of "agent washing," where companies label traditional tools as intelligent agents without delivering on the promises. However, Gartner reports that agentic AI adoption is growing steadily, forecasting that by 2028, it will drive 15 per cent of daily business decisions and power a third of enterprise applications. Despite the rising valuations and adoption of AI in everyday life, public perception remains cautious, sharply contrasting with expert views. A recent Pew Research Center survey found that while 47 per cent of AI researchers feel more excited than concerned about AI's growing role in daily life, 51 per cent of US adults feel the opposite. Both groups are worried about misinformation, deepfakes, and privacy issues, but they disagree on things like losing jobs and being socially isolated. There has never been a surge in investment in the artificial intelligence industry like what's happening now. Billions of dollars are pouring into firms promising to change the future of technology. This surge came after OpenAI released ChatGPT, an AI chatbot based on the GPT language model. OpenAI is at the top of the AI valuation table. According to a report by CB Insights, it is now the most valuable AI startup in the world, worth USD 300 billion. It has grown quickly thanks to partnerships and a wave of funding, with the most recent round bringing in $40 billion in March 2025, led by SoftBank Group. Apart from OpenAI, Databricks and Anthropic are two other US-based firms betting on AI and worth more than USD 60 billion. Elon Musk's AI company, xAI, has also made it to the top, making it one of the four AI businesses among the world's ten most valuable private tech companies. The dominance of US-based companies in this sector is striking. Seven of the eight highest-valued AI startups are headquartered in the United States. The only exception is Celonis, a German firm specialising in process optimisation through AI and other technologies. Despite its European roots, Celonis has a strong presence in the US market and is currently valued at $13 billion. One of the newer entrants generating buzz is Safe Superintelligence, a US startup founded just last year by a team of former OpenAI, Apple, and academic researchers. Despite raising only USD 3 billion, the company is already valued at USD 30 billion. Its mission is to build a superintelligent AI system that prioritises safety above all else. Another startup making headlines is Thinking Machines Lab, founded by former OpenAI Chief Technology Officer Mira Murati. It has just closed a massive USD 2 billion seed round, pushing its valuation to USD 12 billion. However, not all that glitters in AI space is gold. As companies scale up AI projects, a recent Gartner report warns that over 40 per cent of projects involving "agentic" AI (systems designed to make decisions and act autonomously) will be cancelled by the end of 2027. The research firm cites high development costs, unclear return on investment, and technological immaturity as key reasons. It also raised a growing trend of "agent washing," where companies label traditional tools as intelligent agents without delivering on the promises. However, Gartner reports that agentic AI adoption is growing steadily, forecasting that by 2028, it will drive 15 per cent of daily business decisions and power a third of enterprise applications. Despite the rising valuations and adoption of AI in everyday life, public perception remains cautious, sharply contrasting with expert views. A recent Pew Research Center survey found that while 47 per cent of AI researchers feel more excited than concerned about AI's growing role in daily life, 51 per cent of US adults feel the opposite. Both groups are worried about misinformation, deepfakes, and privacy issues, but they disagree on things like losing jobs and being socially isolated. Join our WhatsApp Channel
