Deepthi Sigireddi

💙 Excited to share my latest post on TheNewStack on addressing the staging bottleneck problem impacting Engineering teams building Microservices: https://lnkd.in/gM_hB_wC #stagingenvironments #kubernetes #microservices #testingstrategies #developerexperience

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🔥Middleware Gets a MASSIVE Makeover! 🔥 Check out the details here: https://lnkd.in/gPu9Tkvr PS: Feels like Bruce Banner becoming the HULK!!!! 💪 #productupdate #version2 #2024updates #observability #middleware

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https://lnkd.in/gyFRCkg9 Good read if you are working on #LLM #optimization. Explains what to optimize based on popular GPU and open source configurations like A10 and llama etc. Looks like for server side inferencing there are quite a few options, like sequence batching, speculative decoding etc. Will be interesting to see how they get applied on client side inferencing using NPUs. Though the article is focused on #GPU, applicable to #NPU as well. Hope someone shares the same for few standard available NPUs in the market.

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Are LLMs capable of Artificial General Intelligence (AGI)? I came across this article in the NY Times: https://lnkd.in/geXXbi8B (for non-subscribers here's the paper it talks about https://lnkd.in/gP-m7sxA) The upshot is that researchers are finding strong evidence that language is a very distinct part of the human brain whose purpose is to communicate and that it is not intrinsically involved in reasoning. This gets at a very fundamental question about the use of LLMs as the basis for Artificial General Intelligence. Can a model based on language be used as the basis for AGI? Time will tell I'm sure, but when I've asked this question to researchers I know, they generally say yes. However, I'm not sure what the basis is for this belief. CAVEAT: I'm not in the AI field, nor have I delved deeply into the scholarly articles. I have a fairly rudimentary understanding of LLMs, but would be happy to learn from those who know more. One of the fundamental articles shaping my personal views on LLM based AI is Ted Chiang's excellent piece "ChatGPT is a blurry JPEG of the web" (https://lnkd.in/gmkRBe6A). For me, it captured the idea that LLMs are just summarizing what we know. To be sure, they are capable of really cool things, but are they really doing any thinking? More important are they even capable of thinking? Again, time will tell, but I'm increasingly skeptical. The kicker for me is that they have no common sense. One of the reasons we find LLMs so amusing is they crazy ways they go off the rails (aka hallucinations). I think that these are largely because there's no common sense 'boundary'. When a human's thoughts start to get carried away, we have ways of reigning them in by asking ourselves "is this actually a reasonable thought?" Indeed, it can be quite alarming to encounter people who communicate about ideas that we find to be outside the bounds of common reason. It feels like this 'framework of rationality' is very important but I don't think it's something you can just use as guard rails. It seems like you need some kind of other AI to be able to generate that framework. What do you think? Are LLMs capable of AGI? Or are they like our own language processing center, just a small piece of a much larger 'intelligence framework'? [Edited to use AGI instead of GenAI so as not to be confused with Generative AI] [Edit: Thanks to Tareq Alkhatib here is a direct link to the paper in question: https://lnkd.in/g9b7Q66c]

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Here is what AI is thinking about numbers 😀 Very basic question but it’s trained to read the numbers like a text using tokenisation. May be it’s time for AI to learn how to read numbers otherwise it will be a big blow for computations. #metaai #ai #mathemetics

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