Strong law and order are essential for nurturing a healthy society and vibrant economy. Clear, consistently enforced laws preserve peace, safeguard citizens, and uphold justice, creating the security and predictability that inspire confidence among residents, investors, and entrepreneurs. With reduced risks of crime and disputes, investment, innovation, and growth thrive. A well-functioning legal framework also enables diverse communities to live harmoniously, ensuring universal adherence to shared rules. Ultimately, sound law and order underpin social stability and prosperity, elevating quality of life by fostering safe, fair, and reliable conditions for multifaceted development.
Strong law and order are essential for nurturing a healthy society and vibrant economy. Clear, consistently enforced laws preserve peace, safeguard citizens, and uphold justice, creating the security and predictability that inspire confidence among residents, investors, and entrepreneurs. With reduced risks of crime and disputes, investment, innovation, and growth thrive. A well-functioning legal framework also enables diverse communities to live harmoniously, ensuring universal adherence to shared rules. Ultimately, sound law and order underpin social stability and prosperity, elevating quality of life by fostering safe, fair, and reliable conditions for multifaceted development.
A Wharton economist ran a randomized controlled trial on almost a thousand high school students in Turkey.
The result was so brutal for the AI-in-education narrative that it had to be peer-reviewed by PNAS before people would believe it.
Her name is Hamsa Bastani. She teaches operations and information at the Wharton School at the University of Pennsylvania, and the study she published in 2025 alongside her co-authors is one of the cleanest experiments anyone has run on what AI actually does to learning when you remove it from the equation and check what is left.
The setup was a randomized controlled trial, the same methodology used in clinical drug trials. Nearly a thousand high school math students in Turkey were split into three groups and put through four sessions of ninety minutes each. One group practiced with GPT Base, a standard ChatGPT-4 interface that could answer any question directly. One group practiced with GPT Tutor, a version of the same model that had been prompted to guide students with hints rather than hand them the answer. One group practiced with nothing but their textbook and their own head.
During the practice sessions, the AI groups looked like a miracle. The GPT Base group solved 48% more problems than the students working alone. The GPT Tutor group solved 127% more. Every administrator looking at those numbers would have written a press release about the transformative power of AI in education and moved on.
Then the actual exam came, and AI was not allowed.
The students who had practiced with GPT Base scored 17% worse than the students who had practiced alone. Seventeen percent worse, despite having solved nearly half again as many problems in the sessions leading up to it. The students who had struggled the most, who had sat with the confusion and worked through it without a tool to rescue them, were now the only ones who could actually do the math when it counted.
Bastani's team read through the chat logs to understand what had actually been happening during the practice sessions, and the answer was exactly what the exam results had already implied. The GPT Base group had not been learning. They had been extracting answers and moving on, and every moment that felt like understanding was actually the model doing the cognitive work while the student's brain waited for the next problem to arrive. The paper describes it precisely: without guardrails, students attempt to use GPT-4 as a crutch during practice, and subsequently perform worse on their own.
The detail that should follow every conversation about AI in education is the one buried in the post-test survey results. The students who had relied on AI the most during practice were also the most confident they had understood the material. The tool had not just failed to teach them. It had convinced them they had learned something they had not, which is a different kind of failure entirely and a much harder one to correct because the student has no idea it is happening.
The crutch had made them confident and weak at the same time.
Hello. How are you? Thank you. I love you. Please.
Some of the most frequently translated phrases of the past 20 years!
Google Translate began twenty years ago with a mission to help people understand one another, regardless of the language they speak. What started as a small experiment has become a global tool that helps over 1 billion users every month.
In that time Translate has evolved from simple pattern matching to true understanding. In 2006, it relied on statistical machine learning to look for patterns in small word clusters. By 2016, we pioneered a shift to neural networks to move beyond literal word-for-word translations, and today weโre using our powerful Gemini models to make Translate even more helpful.
We are moving from text to fluid, real-time conversations. With our latest models, you can even use your headphones as a personal interpreter that preserves your original tone and cadence - itโs an amazing experience!
One of the interesting things about AI is that as we make progress, we begin to take it for granted. If you met a person who could translate across a hundred languages faster than any human can, you would be so impressed. Today, one product does that for nearly 250 languages, and we kind of just shrug.
Being able to say thank you in 250 languages is not something I take for granted. So to the 1 billion who use Google Translate - merci, dhanyavaad, arigatล, gracias, and thank you! Letโs see what the next 20 years will bring.
Why many JEE toppers struggle at IITs?
The thing is, JEE has ZERO connection with real mind development.
Coaching institutes only train you to โget the solution,โ not to think, question, or create.
Curiosity-based learning is what actually builds a powerful mind.
"After Operation Sindoor, I led a team of 8 MPs from eight states, five parties and three religions to explain India's position to the world. That diversity itself was a message: India speaks with one voice on national security," says Shashi Tharoor at Harvard
โน80 water in Kedarnath carried by porters/mules through tough terrain with short seasons & high costs. Not justifying overcharging, but understand the reality.
Respect the hard work.
@AskAnshul Factors for sufferings of #Bollywood are :
1. Their apathy to human & family values as well as social & #national#values.
2. Internet has become much more relevant alternative to what Bollywood could have become by working harder and smarter.
...and many more...