Robert Nishihara

Check out our latest post on Clappy.ai, "Leverage Layout-Aware RAG for More Accurate Retrieval of Knowledge from Documents." Discover how to harness the power of Retrieval-Augmented Generation (RAG) for documents in PDF, Doc, and Markdown formats. Dive into the specifics of implementation and explore our reference repository to get started with advanced RAG for your knowledge base. Don't miss out on this essential read! Continue reading here for all the details: The Power of Hierarchical Indexing with RAG. (https://lnkd.in/gbiVHfcH)

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This is a great first step in understanding how scaling dynamics extend beyond language to other types of sequential data. Time is a universal axis, understanding how models learn optimally over data collected over time is a crucial step in the broader AI research agenda.

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New Mindset Needed for Large Language Models - https://buff.ly/3XbN4vp #AI #ML #LLMs #genAI

That's a good summary Manny Bernabe - Reflecting on an insightful article from O'Reilly about LLMs, I was struck by this point: "LLMs are also broadly accessible, allowing everyone, not just ML engineers and scientists, to build intelligence into their products. While the barrier to entry for building AI products has been lowered, creating those effective beyond a demo remains a deceptively difficult endeavor." This democratization reminds me of the printing press's impact on biblical texts, which expanded access but also introduced new interpretive challenges. As we navigate these advancements, it's crucial to balance accessibility with responsible use. At Ushur, we've been at the forefront of leveraging LLMs in our backend/knowledge work automation as well as carefully introducing them on the conversational side via our Chatbots and other modes. More will be coming out on that. Read more here: O'Reilly Article #AI #LLM #Technology #Innovation #History #Democratization #ResponsibleAI #Ushur

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Replacing an expensive generalist LLM like GPT-4 with a cheaper more specialized model like a fine-tuned Mistral 7B is a tried and true approach to really drive your GenAI expenses down. We've got a handy guide on how to start your fine LLM cost optimization journey with OpenPipe for fine tuning and OctoAI for model serving https://lnkd.in/e8udh8de

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💡 Why do we need so many evaluation tools for LLMs? As engineers, we build 'production-ready' LLM products using these metrics. But what happens next? How do we maintain control and ensure reliability? At Feedback Intelligence, we’ve crafted a cookbook to keep your LLMs reliable and aligned with user expectations. 🍲 📖 Give it a read and let’s chat!

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Daniel Wilson and Reza Rassool Thanks for the opportunity to join the conversation, and more importantly to contribute to the critical work of Kwaai Personal AI -- turning AI into a meaningful benefit versus a threat in people's lives. (My comments start about 30 min in, after Reza's introduction to Kwaai))

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Another great paper on In-Context Learning (ICL) for LLMs: https://lnkd.in/e4SP4K5d More and more, we're hearing from builders that ICL is an improvement over fine-tuning for dialing in domain/task specificity. Fine tuning is slow, expensive, and improves specialization at the cost of generalizability. On the other hand, an efficient ICL pipeline is quick and cheap to set up, boosts performance on specialized tasks without sacrificing on general ones, and can be iterated quickly as the project context/use case shifts. If any builders are still choosing fine-tuning over ICL, let's chat. I'd love to learn more!

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This new little product demo is awesome! Our new Rev AI Transcript Assistant will save you TONS of time. Easily pull out nuggets of info from your transcripts, making valuable content more accessible than ever. https://lnkd.in/gzzFpcDa #transcription #asr #accessibility #transcript #summarization #ai #assistant

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Quantization is a game changer for deploying large language models like Mistral 7B on consumer hardware. This course offers invaluable techniques to harness powerful AI models even with limited resources.

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Fascinated by the capabilities of agentic AI as discussed here! The example of an AI adapting during a live demo to overcome unexpected challenges showcases its potential for business applications, particularly in automating and enhancing decision-making processes. Excited to see how these advancements will transform operational efficiencies in various industries.

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OpenPipe Introduces a New Family of ‘Mixture of Agents’ MoA Models Optimized for Generating Synthetic Training Data: Outperform GPT-4 at 1/25th the Cost Quick read: https://lnkd.in/gfxdiDT5 OpenPipe’s MoA models have excelled in rigorous benchmarking tests, achieving notable scores on LMSYS’s Arena Hard Auto and AlpacaEval 2.0. The MoA model scored 84.8 on Arena Hard Auto and 68.4 on AlpacaEval 2.0, indicating its superior performance in generating high-quality synthetic data. These benchmarks are critical as they represent challenging user queries that test the robustness and adaptability of AI models. The MoA model has been benchmarked against various GPT-4 variants in real-world scenarios. Results showed that OpenPipe’s MoA model was preferred over GPT-4 in 59.5% of the tasks evaluated by Claude 3 Opus. This is a significant achievement, highlighting the model’s effectiveness and practical applicability in diverse tasks encountered by OpenPipe’s customers...... OpenPipe

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Day 3/3 GenAI Summit San Francisco Causal AI, causal grounded RAG - cRAG, scaling from POC to production in AWS, a deep dive into the evolution of Llama models and what's coming next, synthetic data in the age of genAI, PII, and domain randomization. I can't count how many times I've heard the words AI, genAI, data, cloud, RAG, LLM, and multimodal in the last 3 intense days. And when I finally went to have lunch at a restaurant, at the adjacent table, a fintech AI startup founder was pitching to a VC, while the people at the table behind me were talking about data engineering. It feels so good to be in SF, where at the airport’s security check-in a big Anthropic advertisement greets you! 😊 The last 3 days were amazing, filled with inspiration and thought-provoking new tech. I can't wait to get back to building DiffuseDrive and working with our customers!

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You're so right Vikram Sharma Mailthody... The most overlooked fact of this chart is 2 GPU Nodes out perform 200 CPU nodes at 10TB and 30TB. Not only are the GPU clusters cheaper per hour AND faster... it's significantly less data center space and energy consumption. When you have a tight SLA for workloads, and you have to scale out to meet those time constraints... CPUs get extremely expensive.

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Small models becoming smarter! Phi-3 reinforces the importance of data quality.

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A good read on building with LLMs https://applied-llms.org/

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KV-cache quantization is super useful when you have a large batch-size and/or longer context-windows. This requires on-the-fly quantization. Happy to see HQQ used in this context: https://lnkd.in/eyxyPnhP

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My cofounder Saumil goes on the Stack Overflow Podcast to deep dive on AI Agents! He goes into detail how we leverage AI agents to power automated workflows to accomplish the most complex tasks. Including agents firing off tasks to other agents to tackle the details. Need an agent to verify your work? No problem. Give it a listen and let us know what you think!

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