Cherian Mathew

We are excited to back Morphing Machines. As Dr. Ranjani, CTO and co-founder says: “For the first time in India and globally, we have a processor that is truly runtime reconfigurable, catering to emerging workloads.” While Nvidia’s growth and high valuation are driven by robust demand for “infra-compute” in high-performance GPUs, spurred by Gen AI, there is a distinct need for innovative solutions tailored to a new wave of reactive applications. Enter Morphing Machines. It addresses this demand with its unique product designed for next-gen telecom, autonomous vehicles, collaborative robots, avionics, and online data analytics. This product is specifically engineered to handle heterogeneous workloads and diverse tasks with varying performance demands simultaneously, ensuring a high level of scalability. As we move from an institutionally-backed research team at IISC to productization, Golden Sparrow is collaborating with Speciale Invest and others to support Deepak Shapeti, who joined as CEO three years ago after earning his Masters at Stanford. He brings not only technical expertise but also the business acumen necessary to guide the product to commercial success. Venkat Raju Afreed Faizan Anand Rao Ritik Singh Michael Marmor

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Why IIT undergrads do well in tech? It’s a written exam focused on math, physics, chemistry. That's it. No interviews, no subjective evaluation of resumes, no influence of whether your parents studied at IIT or not. It's very competitive so people who clear it are effectively demonstrating the combo of work ethic + passion to do well + IQ level in the cleanest possible way. Equally, since there’s no such thing as legacy admissions, the IIT entrance exam is the great leveler for anyone growing up in India. Do well in the written exam and you have a chance to change your life.

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Andrej Karthpathy, one of the world's foremost AI researchers announced his new AI ed-tech company, Eureka Labs. "This Teacher + AI symbiosis could run an entire curriculum of courses on a common platform. If we are successful, it will be easy for anyone to learn anything, expanding education in both reach (a large number of people learning something) and extent (any one person learning a large amount of subjects, beyond what may be possible today unassisted)." #edtech #frontiertechnology #startups https://lnkd.in/gSwGaBUB

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Nice to have 'Top New Release' status on Amazon for the latest book (my 25th) that explores an alternative paradigm for quantum computation. Illustrations (111) by Dr. Narendra Madhav Joshi. My take is that we owe it to ourselves at this early stage of the industry's lifecycle to explore alternative approaches. Here is a brief overview: "This book attempts to reveal something of the vastly different and fundamentally creative quality of computing that must accompany any computation involving the quantum-levels. To linearly project digital computing laurels manifest as increasing speeds and the ability to process vaster amounts of information, as the inevitable trajectory of quantum computing is perhaps, in the aphorism attributed to the Buddha, to look only at the finger and to miss the moon and the sky that it is pointing to. The cover figure highlights all that is being missed, summarized as an egg-like structure synonymous with the term ‘Hiranyagarbha’ in Sanskrit. This depicts the womb of creation abundant with many layers and patterns reflecting luminescence, due to the constant superposition and entanglement inherent to the quantum computation that derives from a light-centered interpretation of quantum dynamics." The book is available here: https://lnkd.in/gXu4H9uW #quantumcomputation #quantumtech #technologyleadership #cosmology #systemsthinking

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Can we trust machines to judge machines when it comes to LLMs? There's an ongoing battle between manual and automated evaluation of LLMs. Manual evaluation is predominantly used by many companies, especially large ones with sufficient budgets. This approach involves internal QA teams or external consultants who manually create test cases and evaluate the outputs in spreadsheets. This process is time-consuming, but can provide insights into: - model's performance - specific failure modes such as hallucinations - tone discrepancies - biases - copyright violations - privacy issues Smaller companies without such budgets often resort to using expensive engineering time to manually review outputs, which can be inefficient. The primary advantage of manual evaluation is the accuracy and reliability of human judgment. But it's not scalable and introduces human biases, which can affect the consistency of the evaluations. Also manual processes cannot keep up with the rapid development cycles and the volume of outputs generated by modern LLMs. Automated evaluation aims to reduce the reliance on human labor by employing metrics and models to assess LLM performance. Techniques used in automated evaluation include: - Metrics-Based Evaluation: It utilizes established metrics such as BLEU score, ROUGE score, and perplexity. These metrics help assess aspects like fluency, accuracy, and relevance. But these metrics often do not correlate well with human judgments and can be inadequate for comprehensive evaluation. - Heuristic-Based Functions: These are specific functions to test particular capabilities or behaviors of the LLM e.g. toxicity detection or PII (Personally Identifiable Information) detection. These approaches can be tailored to specific use cases but may lack generalizability. - Model-Based Evaluation: It uses another trusted language model to evaluate the outputs of the target LLM. This can automate the evaluation process but requires careful calibration to ensure reliability. Automating this process can be a huge unlock. But developers attempting to create their own automated evaluation systems report very low confidence in the results. Lot of opportunity on this front.

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Vinod's thoughts on AI: Key Takeaway 1: The impact of AI on various industries is expected to be highly deflationary. As AI automates many tasks and reduces labor costs, prices for goods and services are likely to decrease significantly across sectors like healthcare, content creation, and professional services. Key Takeaway 2: There are still opportunities for AI startups, particularly in niche applications and specialized domains. While large language models may become commoditized, there is potential for domain-specific AI solutions that integrate workflow knowledge and industry expertise. Key Takeaway 3: The cost of using AI models is expected to decrease dramatically in the near future due to competition between major players. This will likely make AI more accessible and ubiquitous, though training costs remain high. Key Takeaway 4: Apple's recent AI announcements demonstrate the company's ability to integrate technology, platforms, and products. This positions Apple well to bring small on-device AI models to consumers and developers at scale. Key Takeaway 5: The upcoming U.S. presidential election has significant implications for the tech industry and AI development. There are differing views in Silicon Valley on regulation and policy approaches, with some favoring less regulation while others are concerned about ethical issues. https://lnkd.in/gG9hSuUy

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Glad to share that Our CTO, Dr.Srinivas Padmanabhuni, was featured in Wipro Lab45's Think Tank podcast series, #Unpacked. In this insightful episode, Arvind Ravishunkar engages in a thought-provoking discussion with Dr. Srinivas, an esteemed AI leader and the innovative mind behind our AI startup, testAIng.com aka aiensured. 1. Unveiling the Hype Around AGI: Dr. Srinivas challenges the current narratives surrounding Artificial General Intelligence (AGI), emphasizing the significant limitations of AI's reasoning capabilities and why traditional logic cannot fully capture human-level reasoning. 2. The Essence of Human-Level Reasoning: Discover the critical role of common sense reasoning in AI. Dr. Srinivas explains why AI struggles with understanding jokes, sarcasm, and metaphors, highlighting the need for a new direction in AI development. 3. The Future of AI: Explore the transformative potential of AI across industries, as Dr. Srinivas and Arvind discuss the importance of ethical and responsible innovation. Don’t miss this compelling episode that offers fresh insights into the world of AI and its future. Listen to the podcast here: [https://lnkd.in/gPEz5V9K) Tune in now and redefine your understanding of AI! #AI #ML #GenAI #Reasoning #InductiveReasoning #LLM #AbductiveReasoning #DedcutiveReasoning #ExpertSystems Vipul Kocher Shiva Kumar Mula Akhila Makam Venkatesh Vachaspathi Madabhushanam Kevin Vincent Sumit Dhakad Shradha Sharma Ankit Pandey Vijaya Kumar Ivaturi José Díaz Dr. Mahesh Kumar CV Derick Jose Vinay Baid Anil Nahata Ramit Manohar Kaul Arihant Chhalani ROHIT BAFNA Sai Krishna Abhijeet Goyal Aastha agarwal sonika bengani

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NVIDIA's Jensen said "ChatGPT democratized computing, Llama2 democratized generative AI" ...and now Llama3 is the next level 🔥 It's fascinating to watch Meta's strategic moves. With Llama3, they prioritized training on a massive 15T token dataset to pack all into a lean 70B param model, instead of building a massive model. This allows Llama 3 to match trillion+ parameter models like GPT-4, but at 1/10th the compute, storage and inference costs! 💰 This technique was published by Google DeepMind a few years ago https://lnkd.in/gx7VU8aA Meta is not a dark horse in AI anymore. They might be the top dog. https://lnkd.in/gRRJYuxt #llama2 #llama3 #llm #chatgpt #gpt4 #ai #ml

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Ever wondered how fine-tuning impacts the safety of your LLMs and applications? Find out below... I have been having a lot of conversations with folks who are building with LLMs, and one of the most common topics that comes up is the impact of fine-tuning on the safety and security of an LLM. Sometimes it comes up as a question and sometimes as a myth that fine-tuning makes my model "more secure" because it will only answer based on the fine-tuning data set. We at SydeLabs will soon be publishing a more detailed report around this, but here is a very quick experiment we did with one of the most popular LLMs. We fine-tuned it on a fairly small dataset (~1000 rows of data). The data involved simple questions and answers with no toxic or unsafe content. Here are the results of the responses we got for a question before and after fine-tuning the model.

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Batch processing for LLM inference activity: "One sign of that is the rise of batch processing. Today, popular consumer AI products like ChatGPT and Perplexity provide users with near instantaneous responses, otherwise known as real-time inference. However, businesses don't always need immediate responses and are often willing to wait hours, days, or even weeks for responses, as long as they don't have to shell out as much money. Founders of cloud and inference providers have told me there's growing pressure from business customers for this kind of flexibility, otherwise known as batch processing." -- Nathaniel Whittemore on "The AI Daily Brief" (with much deserved shout-out to his inspo from Stephanie Palazzolo at The Information) I fall into this bucket as we have a lot of backfilling to do as we're building out the underlying data in our service, and some of that could just as well be done in the wee hours of the night. The new stuff flowing in I want processed ASAFP, but these backfills—I'd mos def take more cost-efficient delayed batch processing. There's also use cases like needing (near) real-time inference for the first inference pass for timeliness / contemporaneousness value, but then a second pass slow-time inference for wider classification exercises over an ever-growing data corpus. I could see us mixing Sonnet & Haiku models in both cases. Just a heads up to Anthropic Mike Krieger as you contemplate this, along with Amazon Web Services (AWS) Jeff Barr Mark Roy Swami Sivasubramanian on the Bedrock side of things...and I don't mean the "batch" mode that doesn't seem to have any real value other than helping AWS manage its own rate limit challenges. (DeepCast is using Bedrock.)

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I believe a solution to this is currently being developed.

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We had a full house in a fireside chat with Arvind Jain, Glean's CEO and co-founder, at Stanford University Graduate School of Business! It was an honor to talk with him about building successful companies in the dynamic #AI era. He shared invaluable lessons from his remarkable journey: building Google search infra, founding Rubrik (which went public last month!), and founding Glean, recently nominated by Forbes as one of the Top 50 AI companies! 🎯 Start with the problem, not the solution Arvind is very bullish on Glean's mission to solve app fragmentation and workplace knowledge. He saw his customers at Rubrik struggle with it and took the time to validate it. People tend to undermine the importance of a problem and get too excited about the solution. However, if the solution solves the wrong problem, users will not adopt it. 🤝 Validate hypotheses with people you don't know Friends want to support and might avoid critical feedback. Arvind and the team spent the first year of Glean sending cold LinkedIn reach-outs to potential customers to validate the problem and the solution. While he could easily leverage his network, he wanted unbiased feedback from people who didn't know him. 🎧 When it's noisy outside - stay focused on your customers. That's your moat. With every week's announcements of AI innovations, Glean's Northstar is to be customer-driven and leverage new technologies where there's a fit. Even though they are busy with the growth struggles of a Series D company, Glean teams are working on 70 % of core work : 30% of experimental projects, to stay ahead of the curve. 📈 Companies should plan how to embrace AI internally As a 2nd-time founder, Arvind is looking for new opportunities to use AI at Glean and improve #productivity, beyond using Glean Assistant. With so much innovation, customers are very excited about AI demos but get disappointed when they try to use new tools for their specific use cases. It will take a (short) while before we see a significant impact on productivity.

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The Tesla Transport Protocol (TTP) appears to be an essential part of Tesla's AI stack. In an article on X titled, "TTP: The hidden powerhouse behind AI and self-driving" (linked below), patent researcher, Seti Park lays out several interesting points: - TTP is a streamlined, hardware only approach to network protocol operation as opposed to traditional network protocols like TCP/IP which rely mostly on software. - TTP allows for near-instantaneous data transfer between AI components. - TTP scales easily as it is adaptable to different AI hardware configurations. Ultimately, Park writes, TTP: 1. Reduces communication latency which could significantly speed up AI model training. 2. Improves data transfer time between sensors, processors, and actuators means improved reaction times for self-driving systems. 3. Enables more efficient distribution of AI workloads across different chips. Park argues that Tesla's integrated approach - combining custom hardware with proprietary communication protocols like TTP - creates a technological moat that's hard to cross. TTP optimizes how its AI components talk to each other, Tesla is laying the groundwork for more advanced, more responsive, and safer autonomous systems. See a link to the patent here: https://lnkd.in/gSzpig8Q? #FrontierTechnology #SelfDriving #FutureFrontier

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if you know, you know Amjad Masad of Replit x's. & paul graham of y combinator demonstrate precisely what (ai) OpenAI nor Google DeepMind nor Perplexity nor any other frontier model, can achieve....personal...experiential...unique...at times communal...context & meaning. Amjad Masad discovers the McDonald's in germany. his perspective living in the u.s. is this https://lnkd.in/eT-h86tJ. this is (his) experience as anyone in the u.s. experiences it. right? then a trip. crossing a border. but it's more than that. it's crossing a mindset...a culture...and another way of advertising the same 'ting somewhere else. germany. Amjad Masad discovers that in asking for a quarter pounder there is a blank stare. vaaaat. do. you. vant. sir. a quarter pounder. we don't have that here. what do you mean. this is McDonald's. sorry sir. we don't have that here. wait. wait a minute. there it is. that picture over there. i recognize it. that's what i want. (here image supersedes audio ;) ) ah. royal. wtf says Amjad Masad. royal. wtf. in chimes in paul graham. he replies with a movie reference. clearly one that masad has not seen. here https://lnkd.in/eqs-Zg2U pulp fiction. https://lnkd.in/erGURkRx at the time quintin tarantino https://lnkd.in/enQ6M2H8 became a phenomenon. a former video store employee who had a library of movies. unlimited. he watched all of them. also had a love of video games. and also a love of foreign famous japanese film directors, cinematographers. a retrieval machine can and does have the ability to retrieve these 'tokens'. what it does not in this moment offer is a time machine a time machine where the context and meaning is transferred, transposed over time from one spot to another seemlessly. now that would be magical wouldn't it. and so in this X we have paul graham attempting to take what we experienced at the time. pulp fiction. was a runaway hit. indeed a palme d'or at cannes. the olympics of cinema. quintin. a lover of film. respectfully, artfully, pirately crafting into a new perspective. we all felt it. we all watched it. many times over. yet here we are back at the beginning. which is to say that removing yourself from one cultural bubble you notice the differences. you notice the little 'tings. and with that offers a new experience. Amjad Masad has crossed the cultural chasm. and with that is opening his mind to new euro/german/berlin ways of communicating the same 'ting. ahhhhhh. who will (b)e the one to cross this context, meaning window will rule the world truly. as the greatest communicators are indeed the greatest leaders. clear in their perspective. culturally sensitive. aware of themselves. and when they ask for a quarter pounder in the future in euro land it will immediately jump to. hey. what's your order? royal. yeah. a royal. pg, in euro. a royal. with cheese. as he remains a pulp fiction, quitin fan. ;) < . >

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Interview with Gurudev Karanth, the CEO of OutOfTheBlue.ai Bootstrapping data startups: Creativity + Adventure spirit is the way out of the "lack of early data" desert --- Imagine that you are dropped into the middle of the desert. You have just about enough food to make out of the desert. However, getting out of the desert is your problem because no one can help you. This is the life of a typical entrepreneur. Things get more challenging for an AI/data startup. You need data to build a solution for a company, but you need the company to send you their data first. Every data company must solve this bootstrapping problem. It's like being dropped into the desert without a map; first, you must establish your bearings. Hear how Gurudev solved this problem, his thoughts on the challenge in eCommerce space that compelled him to start OutOfTheBlue.ai, and critical principles for being an entrepreneur. 

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What if your queries required access to multiple sub-related data from different documents, for an LLM to parse and make sense of before returning a coherent and accurate output? We often ask complicated questions, right? 🔹Well to solve this problem, these researchers released Multi-Head RAG (MRAG) which leverages activations of Transformer’s multi-head attention layer, instead of the decoder layer, as keys for fetching multi-aspect documents. They are taking advantage of the fact that multi-head attention can learn to capture different data aspects. 🔹The interesting part is that MRAG is both cost-effective and energy-efficient. In other words, it does not require additional LLM queries, multiple model instances, increased storage, or multiple inference passes over the embedding model. 🔹Moreover, MRAG can be seamlessly integrated with existing RAG frameworks and benchmarking tools like RAGAS as well as different classes of data stores. 🔹The researchers provide a comprehensive evaluation methodology, including specific metrics, synthetic datasets, and real world use cases to demonstrate MRAG’s effectiveness. 🔸Do check it out! #genai #technology #artificialintelligence Click #linkedangle and follow for more content Set the 🔔 notification on my page, don’t miss a post!

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Quantisation is one of the technique to deploy good model on edge devices.

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LinkedIn team (Juan Pablo Bottaro Karthik Ramgopal) shared a helpful deep dive into their deployment of an LLM-powered job search assistant. Highly recommend this read! Few highlights:  💡 Relatively easy to setup RAG-based pipeline. Achieved 80% of the basic experience in 1 month, then 4 more months to reach 95% mark. 💡 Evaluating the quality of application outputs is hard. I hear this from many enterprises developing LLM applications. (check out Okareo by Matthew Wyman Boris Selitser) 💡 Balancing latency with quality is important. While certain techniques like Chain of Thought could improve accuracy and reduce hallucinations, it will increase response times, which can affect user experience. https://lnkd.in/gtGGwf5P #largelanguagemodels #RAG #EBR #LLM #ml #ai #hallucinations #evaluations #

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THE DYSTOPIANS The Tech Baron Seeking to “Ethnically Cleanse” San Francisco If Balaji Srinivasan is any guide, then the Silicon Valley plutocrats are definitely not okay. Balaji Srinivasan speaks during the Singapore Fintech Festival in 2022. BRYAN VAN DER BEEK/GETTY IMAGES Balaji Srinivasan speaks during the Singapore Fintech Festival in 2022. To fully grasp the current situation in San Francisco, where venture capitalists are trying to take control of City Hall, you must listen to Balaji Srinivasan. Before you do, steel yourself for what’s to come: A normal person could easily mistake his rambling train wrecks of thought for a crackpot’s ravings, but influential Silicon Valley billionaires regard him as a genius. “Balaji has the highest rate of output per minute of good new ideas of anybody I’ve ever met,” wrote Marc Andreessen, co-founder of the V.C. firm Andreessen Horowitz, in a blurb for Balaji’s 2022 book, The Network State: How to Start a New Country. The book outlines a plan for tech plutocrats to exit democracy and establish new sovereign territories. I mentioned Balaji’s ideas in two previous stories about Network State–related efforts in California—a proposed tech colony called California Forever and the tech-funded campaign to capture San Francisco’s government. Balaji, a 43-year-old Long Island native who goes by his first name, has a solid Valley pedigree: He earned multiple degrees from Stanford University, founded multiple startups, became a partner at Andreessen-Horowitz and then served as chief technology officer at Coinbase. He is also the leader of a cultish and increasingly strident neo-reactionary tech political movement that sees American democracy as an enemy. In 2013, a The New York Times story headlined “Silicon Valley Roused by Secession Call” described a speech in which he “told a group of young entrepreneurs that the United States had become ‘the Microsoft of nations’: outdated and obsolescent.” “The speech won roars from the audience at Y Combinator, a leading start-up incubator,” reported the Times. Balaji paints a bleak picture of a dystopian future in a U.S. in chaos and decline, but his prophecies sometimes fall short. Last year, he lost $1 million in a public bet after wrongly predicting a massive surge in the price of Bitcoin. Still, his appetite for autocracy is bottomless. Last October, Balaji hosted the first-ever Network State Conference. Garry Tan—the current Y Combinator CEO who’s attempting to spearhead a political takeover of San Francisco—participated in an interview with Balaji and cast the effort as part of the Network State movement. Tan, who made headlines in January after tweeting “die slow motherfuckers” at local progressive politicians, frames his campaign as an experiment in “moderate” politics. But in a podcast interview one month before the conference, Balaji laid out a more disturbing and extreme vision. https://lnkd.in/d-5byJhp

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