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The Star
04-05-2025
- Science
- The Star
Contradictheory: The false flags of AI
How hard is it to draw the Malaysian flag? Easy enough to ask a computer to do it for you, but hard enough that it'll probably get the stripes, the star, and the moon on the design wrong. I'm referring, of course, to not one but two recent débâcles: First, a national newspaper ran a front-page image of the Malaysian flag that was missing the crescent moon. Then the Education Ministry distributed an SPM examination analysis report with a flag that had too many stars and too few stripes. Now before I get into it much further, let's admit that all of this could have been avoided if the humans in charge had paid a little more attention. But perhaps we are beginning to trust artificial intelligence (AI) just a little too much. It is computing, but not as we know it. Renowned Australian-American mathematician Terence Tao said in a lecture about the future role of AI in science and mathematics that AI is fundamentally a 'guessing machine'. We're used to computers giving the right answer, every single time. But AI doesn't do precision. It doesn't always get it right. It doesn't even always give you the same answer, just something that vaguely resembles what it's seen before. For the AI machine, the Malaysian flag isn't a precise star and crescent adorned with 14 red and white stripes. It's a yellow blob-ish star thing on a blue background, with some colourful lines thrown in somewhere. This 'best guess' strategy makes AI wonderfully flexible for tasks we used to think computers couldn't handle, like generate a photo of something vaguely described, but also dumb at some things humans find easy. But here's my suggestion: Instead of getting more humans to double-check AI's clever outputs, maybe we should just use more computers – specifically, old-school computers that just do what we ask them to and don't guess at anything. I know what some of you are thinking: Using computers got us into this mess, why would using more get us out of it? To try to explain this, let me step away from art into mathematics. Back in 1976, two mathematicians proved something called the Four Colour Theorem. It basically says that any map can be coloured with just four colours such that no two neighbouring countries share the same one. While it's easy to understand and demonstrate with a box of crayons, it's actually very hard to prove. (This, by the way, is the difference between solving maths problems and proving theorems. Solving problems means getting answers to sums. Proving theorems means constructing airtight arguments that work for any map, anywhere, ever. It's also why a maths degree often involves very few numbers and a lot more phrases like, 'But it's obvious, isn't it?') What made the 1976 proof of the Four Colour Theorem so contentious was that it relied heavily on thousands of hours of computer work that no human could realistically verify. Was a proof valid if no human in the world could check it? Conceivably, they could have asked thousands of other mathematicians to go over various parts of the work done by the computer. But maths traditionally resists large groups of people, if only for the reason that mathematicians don't trust others to do the work properly (or as they say, 'They're not mathematically rigorous enough'). Then, in 2005, another pair of mathematicians used a program called Coq to verify that the original 1976 work was correct. Coq is a proof assistant, which is a computer program that checks the logic of a proof step by step. This may seem counterintuitive. They used a computer to confirm that a computer-assisted proof from 30 years ago was valid? But mathematicians have slowly embraced computer proof assistants over the years. They are built around a small, trustworthy 'kernel', a tiny piece of code that performs the actual logic-checking. If the kernel is verified, then we can trust the results it produces. It's like having an employee who is so reliable that if they say the blueprint is flawless, you believe them. Most of these kernels are just a few hundred to a few thousand lines of code, which is small enough for human experts to inspect thoroughly in a variety of ways. In contrast, modern AI systems use machine learning, which is akin to a mysterious black box that even their creators don't fully understand. Who knows why an AI thinks what a flag is supposed to look like? Now, the hardest part of using a proof assistant is in 'formalising' the original proof. This is the laborious process of translating a human-readable proof into a precise format the computer can understand. Mathematicians love to say 'It's obvious that...', which computers hate. Computers need everything spelled out in excruciating detail, and formalising a proof can take anything from a few weeks to several years, because if you input it wrong, it just doesn't work. The maths don't maths. So Tao suggests that we may soon be able to employ 'beginner' mathematicians who aren't particularly strong at maths – because the proof assistant will vet their input and reject it if it's not correct. And his point is that we can combine this with AI. Let the AI guess how to formalise a proof, and let the proof assistant tell it if it got it wrong. You get the power of creativity with the safety net of rigour. That kind of rigour is exactly what's missing as we clumsily stumble to embrace the use of AI tools in the workplace. We already accept spell-checkers, and those weren't built with AI. So let's build systems to flag potential problems in AI-generated output. For instance, imagine an editor sees a giant blinking red box around a photo marked 'AI-generated', warning that it might not be accurate. Or a block of text that's flagged because it closely matches something else online, highlighting the risk of plagiarism. As usual, it's not the tools that are dangerous or bad, it's how you use them. It's OK to wave the flag and rally users to the wonderful new future that AI brings. But just remember that computers sometimes work better with humans, rather than instead of them. In his fortnightly column, Contradictheory , mathematician-turned-scriptwriter Dzof Azmi explores the theory that logic is the antithesis of emotion but people need both to make sense of life's vagaries and contradictions. Write to Dzof at lifestyle@ The views expressed here are entirely the writer's own.
Indian Express
25-04-2025
- Business
- Indian Express
Opinion Indian higher education institutions need to be prepared for the churn created by Trump's crackdown on US universities
The ongoing confrontation between the Trump administration and virtually all of America's prominent research universities is unprecedented. US universities face challenges on several fronts. Among them is a massive reduction in what universities are allowed to charge as overheads for administering research grants, an important source of funding. The abrupt cancellation of visas of several foreign students on minor grounds, with the threat of more to come, has added further pressure. America's flagship research funding agencies, among them the NSF, NIH and NEH, have slashed their grants, reflecting the Trump administration's new financial priorities. What does this mean for India? Indian students made up the highest number of overall international enrollments in the US universities, at 29.4 per cent in 2024-25. India has maintained its position as the top sender of international graduate students to the US for the second year running. Both public and private higher education institutions in India should now expect applications from students who would otherwise have headed abroad, concerned about being able to complete their degrees (Initial reports suggest a more than 30 per cent decline in applications to US universities). Such institutions should also expect increased interest in transfers, from students worried that a minor misdemeanour might lead to their being asked to self-deport. But further upheavals might also be in store. Faculty members in the USA who retain citizenship of their home countries have become aware of the precarity of their immigration status under the new regime. The declining tolerance for diversity along multiple axes is a concerning development. Decoding irreversible shifts from temporary realignments isn't easy. But would we have enough jobs to be able to accommodate the best of those who might think of returning? This seems unlikely, and not just in India. The leading mathematician and Fields medalist Terence Tao recently said, 'One could argue that any 'brain drain' from the US would simply result in an equal and opposite 'brain gain' in other countries, but … in practice, the rest of the world would not be able to absorb all of the lost opportunities in the US in a single job cycle'. One cannot but be pessimistic about India's ability to turn the current turmoil to its advantage. Many public institutions have relatively small numbers of positions to hire into, if they do so at all. Mechanisms for hiring are archaic, opaque, time-consuming and often politicised. In virtually every university department, faculty members have little to no input about candidates to be hired, with this job being that of an all-powerful external selection committee. The constitution of the selection committee, a prerogative of Vice Chancellors, is often the key to appointing 'desirable' candidates. Private institutions, perhaps, have more flexibility, but working conditions and salaries are variable. The Chinese model of targeting and making attractive offers to high-quality faculty, largely those trained in the US system but with roots in China, is credited with the current high quality of institutions in the country. A 2025 Nature Index methodology ranking physics research showed that China dominated the top 10 list, with only two non-Chinese institutions in that list. However, the difference between Chinese and Indian investment in higher education is staggering. As Ramgopal Rao, the former Director of IIT Delhi, has pointed out, what China spends on just two of its major universities is the entire higher education budget of India. Incentivising faculty members abroad who wish to return by giving them a choice of universities to return to, while their salaries are underwritten by the Centre, is a possibility. The recently announced Vaibhav Fellowships are a first step towards this. But to base our actions on what we might do solely with the idea of attracting foreign academics to return would be meaningless if we cannot also re-imagine our universities and make them more rewarding institutions with attractive intellectual environments. We need more institutions. We also need to make our existing ones larger and better. We need more eyes on India, including its public health, culture, society and biodiversity. This is an opportunity for India to build institutions that can be intellectual leaders for the Global South. We need structural changes in the functioning of all our institutions of higher education, changes that will ensure academic independence as well as the highest standards. Changing how these institutions are assessed is needed, as is more public accountability and transparency in how they function. We should also look beyond STEMM programmes, since the world of the future will require diverse skills. A broad liberal education, provided by universities in the true sense and not purely technical institutions, is key to addressing the 'wicked' problems of the future. These changes are required desperately anyway, and not just to facilitate the return of NRIs. Our challenge is to make our institutions welcome intellectual spaces, not just to those from outside who are seeking to return, but also to those who never left.
