Naren Chittar

Looking forward to this panel where Divakar Tantravahi, Meduri Ravi Kumar, and Cosimo Spera will talk about the latest AI technologies that are changing product and customer support, along with their real world examples and practical strategies. If you have not registered, use this link - https://lnkd.in/gdTMgryA #ai #businessoutcomes #product #customer

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Please join me on June 25th San Francisco, CA, USA for a panel titled "A BLUEPRINT FOR SCALABLE & RELIABLE ENTERPRISE AI/ML SYSTEMS" w/ moderation by Hira Dangol from Bank of America and my fellow panelists Rama Akkiraju from Nvidia and Nitin Aggarwal from Google. Recent breakthroughs in AI technology have captured the world's attention, and now it's time to address the challenge of creating truly robust and reliable AI systems. AIQCon's mission is to harness the power of our community to develop new GOLD STANDARDS for AI quality. The global movement is bringing together renowned leaders and professionals from across the industry to establish reliable AI quality solutions and standards. If you're working on AI systems, you won't want to miss this one! Use the discount code "Community" for a 15% discount on registration. Register at https://lnkd.in/gqRE48fC. #AI #EnterpriseAI #AIQuality #AIQCon https://lnkd.in/gqRE48fC

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https://lnkd.in/dJW9sZpx SelfExtend introduces a novel approach to expanding the context window of LLMs. It leverages a two-tiered attention mechanism—grouped attention for long-range dependencies and neighbor attention for local interactions—both derived from the model's original self-attention. This method allows seamless context window extension without requiring fine-tuning, and extensive experiments have demonstrated its effectiveness across various benchmarks.

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Hallucinations in LLMs are inevitable, no seriously! Josh Fritzsche and I have worked hard at Voze to find a way to tune, prompt and even plead 🙏 with LLMs to prevent them from the occasional 🍄 hallucination. It seemed like the better we have done the harder the edge cases have been to solve and eventually we realized that they tend to frequently happen when LLMs predict or make anticipations on "nothing". I remember at one point nearly going insane with frustration because when LLMs had a hallucination, they were EPIC! 😱 Then we pushed through it and benefited for the effort! How did we solve this common problem? We didn't (Not completely), we got better at identifying the issues as they happened. Once we identified in the data we were able to start looking for patterns. The same catalysts that cause it to happen once if left unchanged caused it over and over again. Our solution is astoundingly simple. Once you know that it's likely to happen..... let it! Accept it will 🍄, but also be preemptive in not focusing on it and use alternative data sources, generators or providers. Change the conditions that cause it but continue to monitor it as it happens. As Dan Caffee is fond of saying to me on a regular bases. "Dusty we need the system to be adaptive and provide mechanisms to learn from it's past." Well he is right! All we needed was data and the people that contribute to it's quality. Huge props 👏 to strong support from Kathy and Janelles audit teams. We were armed with a knowledge and a desire to learn from past mistakes. Armed with confidence that we could correct these problems we could focus on the data to prevent their impact going forward. When doing millions of LLM (Generative AI) calls regularly Josh and I have learned that hallucinations will happen. We also know exactly how we will handle and prevent it going forward! Failure is not doing anything and accepting defeat.

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A couple of recent college graduates from India against AI finetuning giants like Anyscale and Run:ai? I met Arko C at a NASSCOM event last October. He was building Xylem AI (YC S24) , a platform that allows you to train and deploy LLMs in production. So naturally most Indian VCs rejected him. Because how could a team of three recent college grads compete with the likes SV startups like Anyscale that have raised hundreds of millions of dollars, have teams of experienced AI researchers and a stash of H100 GPUs? Arko hustled and closed two Fortune 500 companies for paid contracts. I personally know unicorns that haven't managed to do this. He then got into YC in the last round in May. There's a lot to still be proved out, but Arko exemplifies how valuable grit is in building startups. I’m doing a webinar with him tomorrow on challenges of using LLMs in production. Link to register in the comments. Disclaimer: I'm a small investor in Xylem.

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From reshaping the future of work to revolutionizing creative endeavors, explore the transformative power of generative AI in everyday life with Tilakraj Parmar, Co-Founder and CEO, QX Lab AI. Discover how AI-powered chatbots are enhancing customer experiences, freeing up time for strategic initiatives. Learn about the creative realm, where AI algorithms are amplifying artistic expression and pushing the boundaries of music, the visual arts, and content creation. According to Tilakraj, a world improved by the peaceful coexistence of human creativity and AI efficiency is in store. 🔗Read the full article exclusively on Express Computer, to unlock the potential of this groundbreaking technology: https://lnkd.in/gAzagtsV   #ai #generativeai #genai #futureofwork #creativity #innovation

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Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformers Yibo Jiang, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam University of Chicago, Carnegie Mellon June 2024 https://lnkd.in/eai4ZFwa - Large Language Models (LLMs) like GPT can store and recall facts, but this ability is not robust and can be easily manipulated by changing contexts without altering factual meanings. - Example: When prompted with "The Eiffel Tower is in the city of", GPT-2 answers Paris. But if the prompt is changed to "The Eiffel Tower is not in Chicago. The Eiffel Tower is in the city of", GPT-2 answers Chicago, even though the factual meaning is unchanged. - This "context hijacking" suggests LLMs may behave like associative memory models, where tokens in the prompt serve as clues to retrieve facts, even if associations are not semantically meaningful. - To study this, the authors propose a synthetic "latent concept association" task, where the output token is related to sampled prompt tokens through latent semantic concepts. - Through theory and experiments on transformers, the key findings are: - The value matrix acts as associative memory, gathering information from the prompt. - Embedding training is crucial, with embeddings capturing semantic similarity. - Attention can select relevant tokens as clues for memory retrieval. - These results provide insights into how transformers and LLMs perform fact retrieval via associative memory in the latent semantic space. The unintuitive associations formed make models susceptible to context hijacking. abs: Large Language Models (LLMs) have the capacity to store and recall facts. Through experimentation with open-source models, we observe that this ability to retrieve facts can be easily manipulated by changing contexts, even without altering their factual meanings. These findings highlight that LLMs might behave like an associative memory model where certain tokens in the contexts serve as clues to retrieving facts. We mathematically explore this property by studying how transformers, the building blocks of LLMs, can complete such memory tasks. We study a simple latent concept association problem with a one-layer transformer and we show theoretically and empirically that the transformer gathers information using self-attention and uses the value matrix for associative memory. #AI #machinelearning #computation #language #LLM #transformers

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A recent breakthrough titled "Matrix Multiplication-Free LLMs" demonstrates a huge advancement in the area of Large Language Models (LLMs) by reducing computational costs. The authors have eliminated MatMul operations from LLMs, claiming to 10 times reduction in memory usage and a 25.6% increase in training speed, all while maintaining strong performance at billion-parameter scales. Paper link: https://lnkd.in/ggph8qXc #AI #machinelearning #deeplearning #LLMs

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Taking GenAI and LLMs from POCs to Production 🚀 Join us for an enlightening webinar hosted by CrossML Pvt Ltd and presented by Ankit Aggarwal, as we dive deep into the journey of taking Large Language Models (LLMs) from Proof of Concept (POC) to full-scale production. This webinar is perfect for CTO, AI enthusiasts, data scientists, product managers, and anyone interested in harnessing the power of LLMs to transform business operations. #LLM #AIWebinar #MachineLearning #GenerativeAI #TechnologyEvent

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https://lnkd.in/esZgE24d Seven years ago I gave this talk at IIT Kanpur. Back then it seemed inevitable that we would have self-driving cars by now, and some predictions I made were wrong. Overall, though, I think this talk has aged reasonably well. Little did I know back then that the reference to the 2016 MaxSAT I gave would spark the creation of a new company, InsideOpt. The Seeker Solver that we sell is based on the technology outlined, and it is changing the optimization landscape. On the social implications, we since edited a special issue of the AI Magazine on the social disruption that AI brings. More work is needed. We need to have a wide societal discourse on how we are going to share the increasing wealth that AI brings to the world. #insideopt.com

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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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My sense is that NVIDIA's dominance in AI compute is likely to be disrupted 1st in inference (if and when it happens). Mark Zuckerberg made a very interesting point in his conversation with Dwarkesh Patel recently. He said - Meta's ratio of inference to training compute is much higher than most other companies leveraging AI. This is because Meta serves extremely large user communities across Facebook, Instagram, WhatsApp, etc., resulting in huge volumes of inference requests. In contrast, companies focused mainly on research and development may have lower ratios of inference to training compute. The scale of Meta's user base means inference optimization is more important for them in order to efficiently serve real-time requests from billions of users. This was one factor in Meta's decision to develop custom silicon optimized for inference to reduce GPU consumption and free up more expensive GPU resources for training models. I suspect that all the large cos will reduce their dependencies on NVIDIA as they scale their AI inference compute. As of now, the four cos - Amazon Web Services (AWS), Meta, Google, and Microsoft - account for >1/3rd of NVIDIA’s data centre business, and all of them have their custom AI chip initiatives. I don't know what opportunities will be created for early-stage companies in this area, but I will be doing a deep dive here. Let me know who I should be speaking with for these initiatives.

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The latest State of #AI report by #StanfordHAI showcases AI's remarkable progress. AI has surpassed human performance on some benchmarks like image classification and language understanding, but still lags behind on complex cognitive skills. Interestingly, new benchmarks like MMMU for general reasoning are emerging, with most recent tests showing close-to-vertical lines! However, it's essential to understand the limitations and potential pitfalls of such benchmarks. They lack broader context and are narrowly focused on evaluating a single capability. Rigorous and multi-faceted evaluations are essential for enterprise use cases, including meaningful and comprehensive benchmarks tied to real-world applications using business domain-specific datasets. https://lnkd.in/eUuK65u3

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Arvind Narayanan's latest talk delves into the complexities of evaluating large language models (LLMs). A must-view for those keen on understanding the challenges and nuances of LLM evaluation in AI development. Catch the full talk here: https://buff.ly/3ZG68Rq #AI #LLM #Evaluation #TechTalks #ArvindNarayanan #AIResearch

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The Economic Times's article on GENAI features my conversation with Himanshi Lohchab on why Cost (especially inference cost) and high quality Conversational Data are the key barriers to make LLMs ready for India Scale. TWO.AI's mission with #ChatSUTRA and #SUTRA models is to bring energy-efficient language models that are truly multilingual. https://lnkd.in/gYshMMNz

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Dive into the world of #ecommerce innovation with the latest article by Sriram P H., CEO and Co-Founder, DaveAI, as we explore the integration of large language models (LLMs) with recommendation engines. Discover how this cutting-edge technology is revolutionizing customer experiences and driving sales in the digital marketplace. From personalized shopping assistants to predictive product recommendations, the possibilities are endless.   Read the full article exclusively on Express Computer to uncover the future of conversational commerce and personalized recommendations in e-commerce: https://lnkd.in/gNtNjzeh #ai #ecommerceinnovation #customerexperience

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🚀 The need for India-focused GenAI platforms has led to a host of indigenous developments. Let's take a look at 8 Made-in-India LLMs that are shaping the country's future. 🔸 Recognizing the socio-cultural gap in existing systems, entrepreneurs and developers from India are working on a GenAI ecosystem tailored for Indian consumption, yielding better contextual outputs across all formats. 🔸 A handful of homegrown conglomerates, such as Reliance (Jio), Tata Consultancy Services, Infosys, and Mahindra & Mahindra, are already working on a slew of GenAI projects. 🔸 Indigenous developments like BharatGPT, Ola Krutrim, and Project Indus (a Tech Mahindra venture including 40 different Indic languages), are addressing the language challenges pan-Indian users face in real life and now in the AI world. According to Inc42 data, India’s GenAI market is expected to grow exponentially in the next few years, surpassing $17 Bn by 2030 from $1.1 Bn in 2023, growing at a CAGR of 48%. With BharatGPT and its ilk taking charge and cementing their positions in India’s conversational AI landscape, India is well-prepared for a new era of digital empowerment where language barriers are dissolved and access to information is democratized. 🌟 Thoughts on this trend: Cultural and Linguistic Relevance: These platforms address India's diverse socio-cultural and linguistic nuances, ensuring inclusivity and effectiveness. Local Innovations and Entrepreneurship: Investments by companies like Reliance, TCS, Infosys, and Mahindra & Mahindra highlight India's growing tech ecosystem and global AI positioning. Economic Growth and Opportunities: The rapid expansion of the GenAI market signifies substantial economic opportunities, leading to job creation and skill development. Digital Empowerment and Accessibility: By providing AI-driven solutions in multiple Indian languages, these platforms enhance digital literacy and inclusivity. Addressing Real-World Challenges: Indigenous AI platforms deliver solutions closely aligned with the needs and preferences of the local population, improving customer service, education, and healthcare support. The emergence of India-focused GenAI platforms is a promising development driving technological, economic, and social progress in the country. Let's look forward to a digitally empowered and inclusive future! 🚀🌐 #GenAI #AI #India #DigitalTransformation #Innovation #Technology #BharatGPT #OlaKrutrim #ProjectIndus #DigitalEmpowerment #FutureTech #AIinIndia

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*Mixture of Agents (MoA)* is an innovative way to boost Large Language Models’ performance by leveraging the strengths of multiple agents as a team. Layered Architecture: MoA arranges many LLMs in layered architecture, each layer composed of numerous individual agents(LLMs). Collaboration: These agents generate responses that are based on outputs from the previous layer’s agents. They repeatedly refine and enhance the last output. Diverse Insights: By integrating different capabilities and insights from various models, MoA achieves a more powerful combined model that is secure against diverse risks. Performance Boost: MoA outperforms standalone LLMs by a wide margin. For instance, it set a new benchmark with 65.1% on AlpacaEval 2.0 as against GPT-4 Omni at 57.5%, using only open-source models. In short, if we combine language modelers’ strengths then it results in increased efficiency and versatility. Here is a nice implementation of MOA by Together AI Github source: https://lnkd.in/dMCVRS3D #AI #LLM #LargeLanguageModels #MachineLearning

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