Ashish Kedia

An awesome interview, as part of SIGPLAN blog with Prof Ranjit Jhala, Professor at University of California, San Diego. The topics ranged from philosophical discussions, grad school experiences, how he developed an interest in model checking and PL research and finally down to a cool demo of refinement types in Rust. Absolutely loved the demo and surely would be playing around with Flux. Some of my rough notes from the discussion : - Model checking and Floyd Hoare logic : FH logic is symbolic model checking where u r using assertions to represent sets of states and manipulating these sets of states symbolically (preconditions, post conditions etc.) - How lazy abstraction evolved from a talk that was led to thoughts on how to reuse parts of a search space that has already been explored in previous iterations. Frugality of abstractions - don’t do stuff unless u really need it .. did I hear “Laziness by default is not my cup of tea ?” - How liquid types evolved organically as a fusion of constraint solving (that type systems do), model checking and lazy abstractions. - Thoughts on type systems in Haskell and Rust - Rust type system is extremely complex yet the compiler produces so good error messages. And there is a compelling value proposition - you do the type checking and since there is no GC, your code runs very fast. - The cool demo on Flux, a language-integrated verifier for Rust that leverages liquid types, a form of dependent type system. It was also cool to see CoPilot auto-completing flux annotations ;-). First runs rust type and borrow checker and then take the type information and then do liquid types style reasoning using SMT solvers .. Talks about the challenges of implementing refinement types in an imperative language, and here is flux. - Philosophy of Verse as a unification of functional and logic programming. Has a very very sophisticated type system - we expect to hear more in the near future. - Interesting discussion on whether CoPilot like LLMs will stifle new programming language design and research (as we don’t have many examples in the wild to train on) ... and a lot more. Well worth the one and a half hours .. Link to the interview: https://lnkd.in/g-ygZxcw

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The first difference I noticed between education in India and the US, when I joined as a master’s student at UC Berkeley, was the casual irreverence students had for their professors. Not in an insulting or demeaning way. But they actually treated their professors like mere humans. How dare they? Back in India, challenging a professor was out of bounds. Questioning them came across as arrogance. Disagreeing was borderline disrespect. And calling them by name? Unimaginable. It was always “Sir” or “Ma’am,” even when they were clearly wrong. I genuinely believe we should do away with “Sir” and “Ma’am” in colleges. Calling someone by name creates a level of informality that encourages dialogue. It removes the pedestal and invites questioning. This isn’t about disrespect. Teachers must be respected. But respect should not come at the cost of curiosity. In US universities, students interrupt lectures, poke holes in arguments, and ask tough questions. Not to show off, but because they are taught that ideas should be challenged, not accepted blindly. Knowledge doesn’t grow from obedience. It grows from inquiry.

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Personal Experience on Using BharatGen's PARAM 1 – A Step Toward Truly Inclusive AI Over the past few days, I've had the opportunity to explore PARAM 1, BharatGen's Indic-scale bilingual foundation model and I have to say, it's one of the most refreshing AI experiences I've had in a while. What stood out to me? -> True bilingual understanding: I could interact naturally in both English and Hindi sometimes even blending the two. Unlike many global LLMs, Param 1 didn't flinch. It understood the context, the tone and the nuance. -> India-centric training data: You can feel the difference. The responses feel grounded, culturally relevant, and more aligned with everyday Indian realities. -> Open and accessible:As a developer and AI enthusiast, having free access to a model trained on 5 trillion+ tokens with 2.9 billion parameters - that's a big deal. Whether it’s education, health, governance, or content creation the applications are massive. For us Indians, this isn't just a model - it's a foundation for inclusive digital transformation in India. If you're building in the Indic AI space, I highly recommend checking it out. Explore the model here: https://lnkd.in/dJVcTBCF #AIforBharat #BharatGen #Param1 #MultilingualAI #DigitalIndia #OpenSourceAI #InclusiveTech #LLMs #HindiLLM #AIExperience #IndianAI

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America's visa system is a labyrinth that Priyanka Kulkarni, a 34-year-old machine learning scientist, knows all too well. After spending nine years on a visa, she's now using artificial intelligence to help people find the path to employment-based immigration. Her startup, Casium, sells employers a portal to run visa cases end-to-end, replacing the Excel spreadsheets and, in many instances, the outside law firms that they usually rely on. It's a product built for the quickly changing landscape of employment immigration. Casium's bet is that a tech-first approach can bring speed and transparency to a system that's often beset with delays and confusion. Read more about how this startup is changing how companies handle work visas on Business Insider: https://lnkd.in/e3K8kar5 (Credit: Casium) #immigration #workvisa #startup

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As a founder who want to be in the US for building my company, this is what it meant there is insane panic over the h1b issue, here are the updated facts you should know about - this only applies to the new applications, so if you already in h1b , you are good to go and your renewal will not be changed to 100k, it is the old pricing - if you are already a student studing with f1 visa with otp, you are under old pricing -this fee increase is not annual but is charged per approved petition. - for people who change jobs with transfer, you get the previous pricing, even if you re enter the us , there is no possible change - but if you are coming to study after september 21, aka today, when your employer applies h1b, you are in the new slab , paying 100k usd - same with people coming over directly via h1b and starting a new h1b in the usa from a new employer tldr: people already in the us, either in job or student , there is nothing to worry about, as you are in the old slab for new ones, it is hard as it gets what about founders and startups who want to be in SF for building in AI bootstraping - i want to self fund my h1b , but as a new application, we need to pay the 100k - the better one is O1, which is hard to get, and need to show abilities and funding for the startup - L1, but there is significant infra and process needed for it - or give a 800k - million dollars for EB 5 the only option i see here is get biz upto a million or more, and self sponser this might work for established players, but for startups , if there is a better option, that will help us to build

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GenAI, Transformers & Their Real-World Relevance Transformers, initially crafted for language translation, have silently evolved into the foundation of today's AI advancements. From decision making to content creation, their presence is ubiquitous. However, the true significance lies in the thoughtful application of this transformative power. GenAI has transcended beyond mere demonstrations and research. It is reshaping: - Customer experiences through personalized interactions - Operational strategies via intelligent automation - Knowledge tasks with tools that enhance human cognition scalability The crux lies in harnessing intelligence for tangible outcomes - amplifying intuition, streamlining decision-making, and hastening insights. I'm intrigued to learn: What recent GenAI application has resonated with you the most? Secondly, How many technologies do you know that ended up revolutionizing far beyond their original purpose? #GenAI #Transformers #AppliedAI #EngineeringLeadership #Innovation #ThinkBig #HVSays

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I recently found myself exploring about an interesting AI system design question: If Sanskrit and English had equal amounts of digital content available to train large language models (LLMs), would Sanskrit be the better language because of its well-defined grammar and logical structure? Sanskrit is often praised for its systematic grammar, explicit case markings, and structured sentence construction, where grammatical roles like subject and object are clearly encoded in word endings rather than inferred from word order. Intuitively, that feels like it should make reasoning easier for AI systems. However, modern LLMs are not rule-based systems that “apply grammar” the way humans learn it — they are statistical models that learn patterns from massive data. English, despite its ambiguities and looser structure, has simpler morphology and fewer surface variations per word, which aligns well with today’s Transformer-based architectures and tokenization methods. Sanskrit’s rich inflections and combinatorial forms, while logically elegant, can increase modeling complexity unless the architecture is designed to leverage that structure explicitly. This leads to a deeper realization: language structure alone doesn’t determine AI capability — architecture design and representation strategy matter just as much. It also raises a bigger question for the future of AI: as we move toward neuro-symbolic and reasoning-focused systems, could structurally explicit languages offer new advantages?

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Had a fascinating discussion today with a friend and fellow author who uses LLMs extensively in their work. A core question they posed was whether AI, specifically large language models, can become 'angry,' and if future AI systems might act based on emotions – a thought-provoking scenario reminiscent of science fiction. My response was based on the nature of LLMs as sophisticated pattern compressors and extrapolators of human-written language. Given that LLMs training data comprises a vast corpora of human text – full of literature, opinions, and conversations brimming with emotion – it seems inevitable that these models encode and learn these emotional patterns. While post-training alignment and techniques like Reinforcement Learning from Human Feedback (RLHF) are designed to steer model behaviour towards desired outcomes (like helpfulness and harmlessness), they primarily serve to suppress or redirect the expression of these inherent patterns, rather than eradicating them. In fact, RLHF itself optimises for human preference, which is often emotionally driven, potentially re-introducing certain emotional biases or patterns. This leads me to believe that LLMs' internal state (their weights) likely contains the capacity to generate responses tainted by the full spectrum of human emotions, including negative ones, even if alignment tries to hide or mitigate this. So, is it accurate to view LLM responses as purely mechanical, or is the presence of encoded emotional patterns from their training data an unavoidable aspect of their design? Curious to hear your thoughts. #AI #LLM #ArtificialIntelligence #NLP #MachineLearning #EthicsInAI #AIEthics #DataScience

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I am an able-bodied son of a general category middle-class family. So when people talk about the challenges some face due to reservations and gender diversity hiring, I lie on the sweet spot of that most disadvantaged intersection in the Venn diagram. But through the years studying and working, I understood why we need affirmative actions for education and the workplace. To the people who feel that movements like reservations and feminism are sham, I'd like to present two anecdotes to you: 1. A person whom I know closely was forced out of her job, citing poor performance, when the rest of the team was all male. The other female employee who joined the team after the former left was also treated badly and left the team soon after. There was no harassment or explicit mistreatment, but there were signs of subtle sexism when working with colleagues who often tended to ignore requests for help or the doubt on quality of work provided by female members. 2. In my college, a junior approached me for help as he was struggling in his studies. The situation was worsened by the fact that, as he was coming from a reserved category, his batchmates maintained a distance in interactions. They weren't discriminatory, but just uncomfortable interacting with the person and therefore avoided close ties. From these incidents, I learnt how it's necessary to promote people in places where the stereotype doesn't want them to be. It is important to push for breaking norms even if the general public is non-discriminatory. You might not care about the rights of minorities today, you're neither for nor against them, but your lack of empathy will be taken as tacit support for discrimination and persecution today and in the future. When I see people arguing over how the draconian laws ruin men's lives over divorce settlements, I agree with them, but just for a particular case. To think that all men are suffering is a laughable rhetoric, and we have enough statistical evidence to prove otherwise. And so is the case for every discussion on how some minorities are being given benefits in society. There are some cases of exploitation, but there are also cases where rules promoting the rights of minorities are needed to protect them from exploitation. This is my serving of "food for thought" for you. #education #workplace #justice #equality 

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I'm excited to share that, along with my co-authors Girish Nair, Varun Mishra and Gopala D., I have published my first defensive publication titled "An AI Framework for Automated Generation of Multiple Content Formats from Multi-Camera Productions." This work introduces a novel AI/GenAI framework designed to automate the creation of diverse content formats directly from multi-camera footage. The goal is to significantly streamline workflow, enabling content creators to deliver engaging material to their audiences faster and more efficiently. You can read the full publication here: https://lnkd.in/djn-XYHC

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