Diego Oppenheimer

#ObsessivelyEnthusiasticInvestor #UnreasonablyEnthusiasticInvestor #IrrationallyEnthusiasticInvestor at REFASHIOND Ventures: The Industrial Transformation Fund. We look for the people we believe are doing the best work in Industrial Transformation & Supply Chain Technology; Attempting the impossible. Then we go all-in on them. With obsessive and unreasonable enthusiasm. Join us. #IndustrialTransformation #SupplyChain #Innovation #Technology #Startups #VentureCapital #DemandTheImpossible #UnreasonableEnthusiasm #ObsessiveEnthusiasm #IrrationalEnthusiasm 

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Exciting news! From the White House: "Microsoft will invest $3.3 billion in a new datacenter. Microsoft is committed to creating good-paying jobs with growth pathways and comprehensive benefits such as paid family leave. Microsoft will pair its datacenter investment with a commitment to investing in innovation and workforce in Racine and statewide. Microsoft will partner with Gateway Technical College to develop a Datacenter Academy that trains 1,000 Wisconsinites for datacenter and STEM roles by 2030, and will employ up to 2,000 people in permanent roles at its Racine facility. And, Microsoft will build a Co-Innovation Lab in southeast Wisconsin, as well as team up with the startup accelerator Gener8tor to train 1,000 business leaders to adopt AI in their operations, so that Wisconsin manufacturers can seize AI’s promise. Together, these investments will position the industrial heartland to lead the way in industries of the future."

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💥 New day, new model 💥 We ran our performance benchmarks on Anthropic Claude 3.5 Sonnet v/s OpenAI's GPT-4o & the results weren't what you'd expect ⏬ Anthropic says Claude 3.5 Sonnet outperforms all competitor models on key evaluations, at twice the speed of Claude 3 Opus and one-fifth the cost. In our benchmarks 3.5 Sonnet & 4o came out neck & neck. Ultimately, you need to run evaluations on your task at hand and measure the quality yourself. 🔍 Data Extraction: Both models identified 60-80% of data correctly, but neither excelled in this task. 📊 Classification: Claude 3.5 Sonnet (72%) outperformed GPT-4o (65%) in mean accuracy. However, GPT-4o led in precision (86.21%), crucial for accurately classifying customer tickets. 🧠 Verbal Reasoning: GPT-4o outperformed Claude 3.5 Sonnet with 69% accuracy, versus 44%. Both models did well on analogy and relationship questions. 📜 Context window: Claude 3.5 Sonnet boasts a larger context window (200K) compared to GPT-4o's 128K 🌪 Latency: Claude 3.5 Sonnet is 2x faster than Claude 3 Opus, but it’s still lags behind GPT-4o when it comes to latency (50% faster Time to First Token) Price and throughput for these models was very similar Want to learn more about how these models stack up? Check out our full analysis on the Vellum blog: https://lnkd.in/dwEcQJe4 Oh and did I say, Claude 3.5 Sonnet is already in Vellum? Sign up for a demo to run your evals & start building!

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GoodData's vision for the next-gen Analytics Infrastructure fundamentally differs from traditional #BI and #visualization tools. While building monolithic and closed architectures worked well for Tableau, PowerBI, and Qlik, today's VUCA (Volatility, Uncertainty, Complexity, Ambiguity) environment requires a very different, more agile, composable, and open approach with minimal vendor lock-in and an accelerated rate of innovation. Adding fast-evolving #GenAI technologies to the GoodData Cloud is a good example. What worked six months ago is now replaced by new #LLMs, more sophisticated vector databases, and #RAG (Retrieval-Augmented Generation) techniques for enhancing the accuracy and reliability of generative #AI models:

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🥁 With the latest #MD101 milestone, we launched a 𝐍𝐞𝐰 𝐒𝐞𝐫𝐢𝐞𝐬 that targets the root of the 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐂𝐨𝐧𝐟𝐮𝐬𝐢𝐨𝐧: How to redirect what I have to enable Data Products? In this series, we want to highlight the ease of leveraging your existing stack to get started with Data Products. This piece is ideally for data leaders who want to adopt the data product approach while staying rooted in big investments like Snowflake, Dbt, Databricks, or Tableau. 🤓 Dive in here: https://lnkd.in/guSJHTcn It was a spectacular experience penning this with a long-time supporter and expert in all things Data and AI: Dr. Balaji Muthusubramanian! The team and I highly appreciate his expertise and recommendations in the space. By the end of this piece, you'll have clarity on: 💡 Motivation for Data Products: Why repurpose existing tools? 🔦 How Snowflake is realigned to support Data Products? 💡 How cost efficiencies for Snowflake are achieved? 🔦 A step-wise breakdown with some YAML & SQL samples for connectivity and Data Product Activation While this piece focuses on cost savings, in the upcoming parts, we’ll cover the 𝘳𝘦𝘷𝘦𝘯𝘶𝘦 and 𝘦𝘹𝘱𝘦𝘳𝘪𝘦𝘯𝘤𝘦 angle in greater detail before diving into other tools! 🔑 Access the entire piece here: https://lnkd.in/guSJHTcn We hope this perspective is helpful, and we are always open to new feedback that improves these actionable resources! #datastrategy #dataproducts #dataengineering

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Teragonia operates at the bleeding edge of leveraging AI to accelerate enterprise value growth for private equity backed companies. Here’s the first of our “What’s new in AI” series. More exciting updates to come in the coming weeks, stay tuned! #Teragonia #ValueCreation #AnalyticsEngineering #PrivateEquity #PEBacked #PortfolioCompany #GenAI #GenerativeAI #Innovation #ArtificialIntelligence #DataScience #FinancialSponsor #MachineLearning #BusinessIntelligence #360CustomerJourney #DataModel #ScalableSystems #ArtificialIntelligence #GlobalData #IntellectualHumility #mergers #acquisitions #divestitures #investmentbanking #mergersandacquisitions #restructuring #capitalmarkets #dataintegration #dataanalytics #analytics #datawarehouse #datawarehousing #sellside #exitpreparation #exitplanning

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Excellent report from General Dynamics Information Technology John P. Curran regarding adoption and trends of AI in the Govt. I used Meta to generate summary below and included link to original article. Main AI use cases are chat and creating/updating computer code. Note also the 23% failure rate of AI projects due to user expectation misalignment. This can be overcome with thorough protoyping and establishing use case metrics. - Successful AI project completion timelines are narrowing, with an average of 14 months from pilot to production. - Future demand for AI projects is building around chat and coding-related functions. - 54% of surveyed AI experts said projects currently under development involve improving computer code. - 59% of projects involve "research and understanding", which may relate to creating chat functions. - 23% of AI projects fail due to misalignment between AI solutions and user expectations. - Partnership efforts are required for successful AI projects. Data diversity is crucial, and incorporating diverse data sources can lead to better AI outcomes. Trends: - AI adoption is increasing across the Federal government, with a growing focus on chat and coding-related functions. - Agencies are leveraging cloud-native services, tooling, and other capabilities to accelerate AI project development. - There is a growing interest in translating older code to newer languages, such as Python or cloud-based analytic tools. - Domain-specific chat functionality is becoming a priority, with many agencies planning to implement chat services in the next two years. Statistics: - 325 AI experts surveyed across the Federal government - 14 months: average time from pilot to production for AI projects - 54%: percentage of projects currently under development that involve improving computer code - 59%: percentage of projects that involve "research and understanding" - 23%: percentage of AI projects that fail due to misalignment between AI solutions and user expectations. Bill Casola John Grim Leslie Anne McIndoe Kim Giffin Michael S. Freeman III Tobias Quaranta https://lnkd.in/eqvmAVFG

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Ali Azhar covers our news from #CCSummit: "The Confidential AI platform was developed at the Berkeley RISELab, a world-renowned lab known for developing technologies such as Apache Spark and Databricks. It was at this lab where the breakthrough MC2 (Multiparty Collaboration and Competition) platform was created, incubated, and open-sourced. In 2021, this platform served as the foundation to build the Opaque platform. Building on Opaque’s existing services that facilitate secure collaboration with cryptographic verification of privacy, the new platform allows organizations to unlock business insights securely and efficiently from sensitive data that wasn’t fully utilized before. Last year, Opaque announced key innovations to the platform including broader support for confidential AI use cases and new safeguards for ML and AI models from exposure to unauthorized parties. “Opaque offers a breakthrough for organizations struggling with the tension between innovation and security. By embedding privacy and security into every step of the ML pipeline, we enable enterprises to accelerate AI adoption confidently,” said Chester Leung, co-founder and Head of Platform Architecture at Opaque.  “Our confidential AI platform uniquely enables the processing of encrypted data without a noticeable performance hit at cloud scale. With Opaque securing entire data workloads, companies can unlock new business opportunities and manage risks effectively, all while maintaining absolute control and privacy of their data.” Use cases for the new platform span across various industries. In the high-tech sector, Confidential AI can be used to secure data pipelines for analytics and ML workloads and enable dynamic model training on encrypted data.   Users in the manufacturing sector can use the platform as a confidential control plane to enforce data governance rules. Financial services can also benefit from the platform through secure data sharing and collaboration across business units.  Human resources professionals can harness the power of the platform to securely share and analyze employee data across multiple data silos and ensure enterprise compliance with data privacy regulations. With the launch of the new platform, enterprises could finally have a solution to eliminate the tradeoff between innovation and security. As more organizations use the new platform, we will have a better understanding of the performance of Confidential AI in terms of integration with existing ecosystems and scalability."

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Congratulations Sridhar, and Jensen, on your partnership to integrate NeMo into Cortex AI. This is a huge step to build custom AI apps. We have integrated #denodo platform with Snowflake #Arctic and #Cortex for building powerful #RAG applications, and are demoing this at our booth in the #datacloudsummit. As you know, relevant corporate #data is essential for #enterprise AI. To that effect, today, we proudly announced integrating NVIDIA NIM Interface Microservices with Denodo to accelerate and optimize #ai capabilities for enterprises. This integration will provide enterprise customers with faster, more secure, and fully scalable AI capabilities. https://lnkd.in/gqgwZ5Dh Looking forward to our partnership with Snowflake and NVIDIA to further the #ai innovation for our enterprise customers! #datavirtualization #logicaldatamanagement

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Databricks disclosed that they are expecting to cross $2.4B in revenue run-rate growing ~65% Y/Y by H1’25 (Jan FYE). This was alongside top-tier metrics, including 80%+ GMs, 140%+ NDR, and a multi-product story where ~50% of customers use 6+ products (incl. their Data Warehouse product which generated $400M in revenue up 4.0x Y/Y). Given their accelerated growth, Databricks is among the few scaled software companies, alongside hyperscalers, capitalizing on the AI boom. Equally noteworthy is their market share gain against competitors. Considering their growth trajectory, we explored various scenarios to determine when Databricks will surpass Snowflake in scale. The question is not if, but when, and we believe it is sooner than most anticipate.

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Anthropic’s newly released model, Claude 3.5 Sonnet, is now generally available on Vertex AI. Claude 3.5 Sonnet outperforms Anthropic’s previous most intelligent model, Claude 3 Opus, on a wide range of Anthropic’s evaluations. With this release, enterprises will now have an even broader choice of high-performing and cost-effective models on Google Cloud. Customers can begin experimenting with or deploying in production Claude 3.5 Sonnet on Google Cloud. Google Cloud offers an enterprise-grade platform for customers to build AI agents and applications, with an open and integrated technology stack, including models, application development tooling, and important capabilities such as grounding. Claude 3.5 Sonnet joins the Claude 3 model family on Vertex AI Model Garden, further extending Google Cloud’s open platform for AI and giving customers more tools to experiment, customize, and deploy AI applications tailored to their unique requirements.

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The Information has a great write-up about the intense battle for foundational data storage companies namely Snowflake and Databricks and who really has the pole position. It is a great read for a part of the technology stack that frequently does not get the headlines they deserve. What do you think? #mangement #venturecapital #privateequity #cloudcomputing #capitalmarkets

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#AIforBusiness —> Right Model for the right use-case running on the right inferencing infrastructure to ensure unit marginal value > unit marginal cost. The probability of successfully moving from POV to scale deployment grows with —> (1) Trust in the data used to train the model… (2) Efficiency and accuracy in model performance and (3) flexability of deployment options. The IBM #Granite model family stands out for it’s performance attributes in these dimensions. See how it stacks up and how this is achieved…. Https://lnkd.in/eMkuX2an

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#AI is both over-hyped and under-hyped. Over-hyped in the sense that I don't believe artificial general intelligence (AGI) is imminent. Yann LeCun had a great interview in Wired where he outlined why (and dropped a great quote - "Machine Learning Sucks"). https://lnkd.in/exv35XGT Under-hyped in the sense that ROI is already hiding within existing business processes - someone just needs to do the work of re-thinking, re-designing and re-building with an AI first mindset. Friday, April 19th at 11:30am, join me at the River Club (https://lnkd.in/evs8XsYB) with the First Coast Tiger Bay Club for lunch, a short presentation and some q&a. To RSVP, email fctigerbay@gmail.com. The event is open to everyone, and $35 for lunch for non-members. Socrates Rivers

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The Rise of Agentic-Architectures in Generative AI: Old wine in a new bottle?      Tldr; As generative AI evolves, developers are increasingly recognizing the need for sophisticated orchestration of Large Language Models, Retrieval Augmented Generation, and multi-agent architectures to handle complex queries, prompting us to better leverage established multi-agent systems and AI planning techniques in the field of Computer Science to build more capable and efficient agents.     ——— As developers gain a deeper understanding of the challenges in building and deploying generative AI applications such as chatbots and copilots with Large Language Models (LLMs), Retrieval Augmented Generation (RAGs), and agents, they are recognizing the limitations of solely relying on prompt engineering and basic agent architectures. To address complex, real-world challenges, sophisticated orchestration of prompts, LLMs, and specific task execution tools is required.      For example, a generative AI solution with simple and single agent architecture even with a powerful LLM is unlikely to address complex multi-part queries such as ‘compare the revenue of company XYZ from Q1 through Q4 of FY2024 and provide an analytical commentary on the key contributing factors that led to the changes in revenues during this time’.  These kinds of use cases are driving increasing interest towards multi-agent architectures capable of complex task decomposition and more.        So, naturally, these days, we are increasingly noticing discussions, and papers about agentic architectures for generative AI solutions. Interestingly, many of the design patterns that are gaining traction in agent architectures are reminiscent of earlier research in Multi-agent Systems (MAS) and AI planning in the field of Computer science. Old wine in a new bottle? Well, time will tell. In any case, some noteworthy agent patterns from previous works include:     * Layered Architectures: Enable task decomposition through hierarchical agent structuring.   * Blackboard Architectures: Facilitate independent agent work with a communal 'blackboard' for sharing results, ideal for diverse expertise collaboration.   * Belief-Desire-Intention (BDI) Models: Support planning and decision-making with agents driven by their beliefs, desires, and intentions.   * Contract Net Protocol (CNP): Uses a decentralized protocol for task allocation where agents bid on tasks based on their capabilities.   * Market-Based Architectures: Inspired by economic models, these architectures use auctions and currency systems for efficient resource allocation.   * Hybrid Architectures: Combine various frameworks to leverage the strengths of different systems, creating robust and flexible solutions.     As we dig deeper into the world of agentic architectures for generative AI, the wisdom from decades of research in MAS, and AI planning domains seem increasingly relevant. So, let's build on prior work and not reinvent the wheel.  #genAI

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“Meta AI chief says large language models will not reach human intelligence.” Financial Times (link in comments). I rarely step into the AGI debate - a lack of definition and too many unknowns - but this makes sense to me. Yann LeCun said to the FT: “[LLMs have] very limited understanding of logic . . . do not understand the physical world, do not have persistent memory, cannot reason in any reasonable definition of the term and cannot plan . . . hierarchically”. The area I’m interested in - culture creation - is socially constructed. To make it, you need to be living in the real world and having human relationships. So, the same reason we don’t (yet) have AGI, may be the reason we still need a human in the loop to make any meaningful art with AI. #agi #ArtificialGeneralIntelligence #CreativeReality

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𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰: 𝐏𝐢𝐨𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 Recently I happened to see this nice video ( 𝘩𝘵𝘵𝘱𝘴://𝘸𝘸𝘸.𝘺𝘰𝘶𝘵𝘶𝘣𝘦.𝘤𝘰𝘮/𝘸𝘢𝘵𝘤𝘩?𝘷=𝘴𝘢𝘭78𝘈𝘊𝘵𝘎𝘛𝘤 ) by Andrew Ng in which he talks about Agentic workflows. In Language Model (LLM) development, we provide a zero-shot prompt and receive an answer. We may review it and add more prompts to get it to a desired shape. This is exactly what the agentic workflow does. Basically, 𝘢 𝒃𝒖𝒏𝒄𝒉 𝒐𝒇 𝑨𝑰 𝒂𝒈𝒆𝒏𝒕𝒔 𝒘𝒐𝒓𝒌 𝒕𝒐𝒈𝒆𝒕𝒉𝒆𝒓 𝒊𝒏 𝒂𝒏 𝒊𝒕𝒆𝒓𝒂𝒕𝒊𝒗𝒆 𝒘𝒂𝒚 𝒕𝒐 𝒑𝒓𝒐𝒅𝒖𝒄𝒆 𝒂 𝒅𝒆𝒔𝒊𝒓𝒆𝒅 𝒐𝒖𝒕𝒑𝒖𝒕. Moreover, he even claims the output produced by agentic workflow is much better than the output produced single LLM which might sound obvious but what surprised me was, as per the “𝑯𝒖𝒎𝒂𝒏𝑬𝒗𝒂𝒍” coding benchmark, agents-based approach beating plain LLM by quite a margin (see the picture below). The plain 𝐆𝐏𝐓 3.5 was at 48% but 𝑳𝑫𝑩+𝑹𝒆𝒇𝒍𝒆𝒄𝒕𝒊𝒐𝒏 (𝘈 𝘓𝘢𝘳𝘨𝘦 𝘓𝘢𝘯𝘨𝘶𝘢𝘨𝘦 𝘔𝘰𝘥𝘦𝘭 𝘋𝘦𝘣𝘶𝘨𝘨𝘦𝘳 𝘷𝘪𝘢 𝘝𝘦𝘳𝘪𝘧𝘺𝘪𝘯𝘨 𝘙𝘶𝘯𝘵𝘪𝘮𝘦 𝘌𝘹𝘦𝘤𝘶𝘵𝘪𝘰𝘯 𝘚𝘵𝘦𝘱-𝘣𝘺-𝘴𝘵𝘦𝘱) fared at impressive 95.1% with the same GPT 3.5. The plain 𝐆𝐏𝐓 4 was at 67% but 𝑨𝒈𝒆𝒏𝒕𝑪𝒐𝒅𝒆𝒓 (𝘔𝘶𝘭𝘵𝘪-𝘈𝘨𝘦𝘯𝘵-𝘣𝘢𝘴𝘦𝘥 𝘊𝘰𝘥𝘦 𝘎𝘦𝘯𝘦𝘳𝘢𝘵𝘪𝘰𝘯 𝘸𝘪𝘵𝘩 𝘐𝘵𝘦𝘳𝘢𝘵𝘪𝘷𝘦 𝘛𝘦𝘴𝘵𝘪𝘯𝘨 𝘢𝘯𝘥 𝘖𝘱𝘵𝘪𝘮𝘪𝘴𝘢𝘵𝘪𝘰𝘯) fared at whooping 96.3% with the same GPT 4.  You can get more information about the evaluation and the models at 𝘩𝘵𝘵𝘱𝘴://𝘱𝘢𝘱𝘦𝘳𝘴𝘸𝘪𝘵𝘩𝘤𝘰𝘥𝘦.𝘤𝘰𝘮/𝘴𝘰𝘵𝘢/𝘤𝘰𝘥𝘦-𝘨𝘦𝘯𝘦𝘳𝘢𝘵𝘪𝘰𝘯-𝘰𝘯-𝘩𝘶𝘮𝘢𝘯𝘦𝘷𝘢𝘭. He also mentioned the following are the 4 design patterns that are emerging in the agentic workflow. 1.    Reflection 2.    Tool use 3.    Planning 4.    Multi-agent collaboration If you are interested in learning more, instead of me explaining each of these patterns, I would encourage watching the video :) #ai #agent #agenticworkflow #gpt

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In just a couple of days, thanks to the work of Osma Suominen (and the Annif tool - suggested by Juho Inkinen), there are some very compelling results (> 0.95 recall for policy area, and F1 of nearly 0.74 for subject areas), demonstrating the power of open data on Hugging Face and the excellent international Library community! In follow-up discussions with Marci Harris and Leonard Park, my team at DreamProIT plans to add Committee referrals to the data set. This could allow a model to recommend committees, based on prior bill referrals... See https://lnkd.in/gww39QeG

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This is a very interesting take on economics of GenAI! It stands exactly inverse of cloud economics today! Semis are taking the highest share followed by infra providers and then apps! This stands exactly opposite in cloud where apps have the highest share. Author argues #GenAI will transition to same structure #cloud is today over next 10 years!

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