chetna bansal

@shashwatology sir, could you pls tell who’s the flautist in Aakhri Ishq song?🙏

@Theds_diary @Shivendrak Thanks

Is there a way to identify the root composition/raga/maqam of a song just by listening to it? @Shivendrak-sir, any tips?

Microsoft launched the best course on Generative AI! The free 21 lesson course is available on Github and will teach you everything you need to know to start building Generative AI applications.

क्या मिला? क्या ऐसे गुनाह किया था उन 26 लोगों ने..? कौन सा इंसाफ हुआ? आँखें तो देखी होंगी मारते वक़्त, चीखें सुनी होंगी? इंसान ना सही, अंदर के जानवर ने तो रोका होगा एक बार? कब ख़त्म होगा या कभी ख़त्म होगा भी? कब सूखेगी मिट्टी कश्मीर की, कब ये लाल रंग छूटेगा..? 💔😔

How to vibe code (practical guide):

@Saadaadesigns i love you!! I literally love this brand.. perfect for office wear.. you solved a big problem of mine.. i love your fits, your material, your branding.. everything. I Love You Saadaa♥️

✍️✨♥️ #amateur #shayari #poetry #ishq #love #randomthought

Proud of fixing something today that I didn’t break🤓 #SGian #ShreyaGhoshal #LataMangeshkar #MusicIsLife @shreyaghoshal 🫠🛐

I’ve noticed that many GenAI application projects put in automated evaluations (evals) of the system’s output probably later — and rely on humans to judge outputs longer — than they should. This is because building evals is viewed as a massive investment (say, creating 100 or 1,000 examples, and designing and validating metrics) and there’s never a convenient moment to put in that up-front cost. Instead, I encourage teams to think of building evals as an iterative process. It’s okay to start with a quick-and-dirty implementation (say, 5 examples with unoptimized metrics) and then iterate and improve over time. This allows you to gradually shift the burden of evaluations away from humans and toward automated evals. I wrote previously in The Batch about the importance and difficulty of creating evals. Say you’re building a customer-service chatbot that responds to users in free text. There’s no single right answer, so many teams end up having humans pore over dozens of example outputs with every update to judge if it improved the system. While techniques like LLM-as-judge are helpful, the details of getting this to work well (such as what prompt to use, what context to give the judge, and so on) are finicky to get right. All this contributes to the impression that building evals requires a large up-front investment, and thus on any given day, a team can make more progress by relying on human judges than figuring out how to build automated evals. I encourage you to approach building evals differently. It’s okay to build quick evals that are only partial, incomplete, and noisy measures of the system’s performance, and to iteratively improve them. They can be a complement to, rather than replacement for, manual evaluations. Over time, you can gradually tune the evaluation methodology to close the gap between the evals’ output and human judgments. For example: - It’s okay to start with very few examples in the eval set, say 5, and gradually add to them over time — or subtract them if you find that some examples are too easy or too hard, and not useful for distinguishing between the performance of different versions of your system. - It’s okay to start with evals that measure only a subset of the dimensions of performance you care about, or measure narrow cues that you believe are correlated with, but don’t fully capture, system performance. For example if, at a certain moment in the conversation, your customer-support agent is supposed to (i) call an API to issue a refund and (ii) generate an appropriate message to the user, you might start off measuring only whether or not it calls the API correctly and not worry about the message. Or if, at a certain moment, your chatbot should recommend a specific product, a basic eval could measure whether or not the chatbot mentions that product without worrying about what it says about it. So long as the output of the evals correlates with overall performance, it’s fine to measure only a subset of things you care about when starting. The development process thus comprises two iterative loops, which you might execute in parallel: - Iterating on the system to make it perform better, as measured by a combination of automated evals and human judgment; - Iterating on the evals to make them correspond more closely to human judgment. As with many things in AI, we often don’t get it right the first time. So t’s better to build an initial end-to-end system quickly and then iterate to improve it. We’re used to taking this approach to building AI systems. We can build evals the same way. To me, a successful eval meets the following criteria. Say, we currently have system A, and we might tweak it to get a system B: - If A works significantly better than B according to a skilled human judge, the eval should give A a significantly higher score than B. - If A and B have similar performance, their eval scores should be similar. Whenever a pair of systems A and B contradicts these criteria, that is a sign the eval is in “error” and we should tweak it to make it rank A and B correctly. This is a similar philosophy to error analysis in building machine learning algorithms, only instead of focusing on errors of the machine learning algorithm's output — such as when it outputs an incorrect label — we focus on “errors” of the evals — such as when they incorrectly rank two systems A and B, so the evals aren’t helpful in choosing between them. Relying purely on human judgment is a great way to get started on a project. But for many teams, building evals as a quick prototype and iterating to something more mature lets you put in evals earlier and accelerate your progress. [Original text: deeplearning.ai/the-batch/issu… ]

Hey Siri, how do I stop listening to Koi Naa on loop? Siri: Why would you want that you tiny, stupid human!!!! @shreyaghoshal what spell have you cast, please iska tod bhi batao.. can’t come out of the loop! #KoiNaa #ShreyaGhoshal #BhoolChukMaaf

Shreya Ghoshal- naam hi kaafi hai to elevate a song♥️✨ #Harnoor #KoiNaa #bhoolchukmaaftrailer @shreyaghoshal 🛐 Koi Naa (From "Bhool Chuk Maaf") music.youtube.com/watch?v=kbMj7H… via @YouTubeMusic

Idk what Shreya experienced while recording it but it sure sends me in an "ibaadat" mode.. specially the last 2 mins.. feels like she transcends me to a higher level- to the state of shunya! Thanks a ton, @DPMusic_Tweets @shreyaghoshal ♥️ Maahi Rokna Aaj music.youtube.com/watch?v=4kIS8Y…

I am so sorry that the U.S. is letting down our friends and allies. Broad tariffs, implemented not just against adversaries but also steadfast allies, will damage the livelihoods of billions of people, create inflation, make the world more fragmented, and leave the U.S. and the world poorer. AI isn’t the solution to everything, but even amidst this challenging environment, I hope our community can hold together, keep building friendships across borders, keep sharing ideas, and keep supporting each other. Much has been written about why high, widespread taxes on imports are harmful. In this letter, I’d like to focus on its possible effects on AI. One silver lining of the new tariffs is that they focus on physical imports, rather than digital goods and services, including intellectual property (IP) such as AI research inventions and software. IP is difficult to tax, because each piece of IP is unique and thus hard to value, and it moves across borders with little friction via the internet. Many international AI teams collaborate across borders and timezones, and software, including specifically open source software, is an important mechanism for sharing ideas. I hope that this free flow of ideas remains unhampered, even if the flow of physical goods is. However, AI relies on hardware, and tariffs will slow down AI progress by restricting access to it. Even though a last-minute exception was made for semiconductors, taxing imports of solar panels, wind turbines, and other power-generation and -distribution equipment will diminish the ability to provide power to U.S. data centers. Taxing imports of servers, cooling hardware, networking hardware, and the like will also make it more expensive to build data centers. And taxing consumer electronics, like laptops and phones, will make it harder for citizens to learn and use AI. With regard to data-center buildouts, another silver lining is that, with the rise of generative AI, data gravity has decreased because compute processing costs are much greater than transmission costs, meaning it’s more feasible to place data centers anywhere in the world rather than only in close proximity to end-users. Even though many places do not have enough trained technicians to build and operate data centers, I expect tariffs will encourage data centers to be built around the world, creating more job opportunities globally. Finally, tariffs will create increased pressure for domestic manufacturing, which might create very mild tailwinds for robotics and industrial automation. As U.S. Vice President J.D. Vance pointed out in 2017, the U.S. should focus on automation (and education) rather than on tariffs. But the U.S. does not have the personnel — or know-how, or supply chain — to manufacture many of the goods that it currently counts on allies to make. Robotics can be helpful for addressing a small part of this large set of challenges. Generative AI’s rate of progress in robotics is also significantly slower than in processing text, visual data, audio, and reasoning. So while the tariffs could create tailwinds for AI-enabled robotics, I expect this effect to be small. My 4-year-old son had been complaining for a couple of weeks that his shoes were a tight fit — he was proud that he’s growing! So last Sunday, we went shoe shopping. His new shoes cost $25, and while checking out, I paused and reflected on how lucky I am to be able to afford them. But I also thought about the many families living paycheck-to-paycheck, and for whom tariffs leading to shoes at $40 a pair would mean they let their kids wear ill-fitting shoes longer. I also thought about people I’ve met in clothing manufacturing plants in Asia and Latin America, for whom reduced demand would mean less work and less money to take home to their own kids. I don’t know what will happen next with the U.S. tariffs, and plenty of international trade will happen with or without U.S. involvement. I hope we can return to a world of vibrant global trade with strong, rules-based, U.S. participation. Until then, let’s all of us in AI keep nurturing our international friendships, keep up the digital flow of ideas — including specifically open source software — and keep supporting each other. Let’s all do what we can to keep the world as connected as we are able. [I had written this letter before the 90 day pause on the tariffs, but am sharing this here since many of the points are still relevant depends on what happens next.] Original text: deeplearning.ai/the-batch/issu…

Still catches me by surprise when I find myself in a women-majority room. The energy, the ease, the "vibe" is different-It shouldn’t feel rare, but it does. I love the change..& I hope one day it doesn’t feel like a surprise at all♥️ #WiDS2025 #IIMB #Bengaluru #Womenindatascience

Oh the percussions in this one.. what a fun rhythm ♥️✨ #AbidaParveen #Classics Main Nara-e-Mastana music.youtube.com/watch?v=bc_N3r… via @YouTubeMusic

Talking of versions, this is a special one♥️ An RJ in an interview once said ki "jab bhi kisi male ke paas se aap gana leti ho na to le hi leti ho buri tarah se... I like your version.." I agree🛐 @shreyaghoshal KABHI JO BAADAL BARSE (FEMALE Version) music.youtube.com/watch?v=uHxx_T…

This version♥️ Love it when a specific phrase/harkat/expression in a song comes to you out of the blue and us gane ki khushboo yaad dila jata hai.. for me it’s the onset of the antara- “surat os di…”♥️ @shahidmaliya ✨ Ikk Kudi music.youtube.com/watch?v=JvJXEf… via @YouTubeMusic

Perfect music for late night work♥️✨ #AbidaParveen #Ghazal #Magic Woh Humsafar Tha Magar music.youtube.com/watch?v=kySi4D… via @YouTubeMusic

Kuch ishq kiya, kuch kaam kiya.. #burningmidnightoil #peace #MusicIsLife #latenightvibe #nightowls #study #WorkLifeBalance