Uday Subbarayan

AI leaders have a crucial chance to address risks now and strengthen governance practices before playing catch-up to regulations, notes Kjell Carlsson, Ph.D. of Domino Data Lab. In a recent VentureBeat article, he shares his perspective on California's AI bill veto and why proactive governance is key to the future of AI innovation. Learn more about this addressing this opportunity: https://gag.gl/4WCTkP #AI #AIGovernance #AIInnovation #DataScience

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Some freshness in the LLMs arena! LLM can be accurate if you follow SOTA practices!

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If you are working with Docker... I highly recommend looking at the Slim toolkit. https://lnkd.in/gX_Gb9Kf Why? Slim allows developers to inspect, optimize, and debug their containers using its xray, lint, build, debug, run, images, merge, registry, vulnerability (and other) commands. It simplifies and improves your developer experience building, customizing and using containers. It makes your containers better, smaller and more secure while providing advanced visibility and improved usability working with the original and minified containers. Don't change anything in your container image; minify it by up to 30x to make it secure, too! Optimizing images isn't the only thing it can do, though. It can also help you understand and author better container images. Keep doing what you are doing. No need to change anything. Use the base image you want. Use the package manager you want. Don't worry about hand-optimizing your Dockerfile. You shouldn't have to throw away your tools and your workflow to have small container images. Node.js application images: from ubuntu:14.04 - 432MB => 14MB (minified by 30.85X) from debian:jessie - 406MB => 25.1MB (minified by 16.21X) from node:alpine - 66.7MB => 34.7MB (minified by 1.92X) from node:distroless - 72.7MB => 39.7MB (minified by 1.83X) Go application images: from golang:latest - 700MB => 1.56MB (minified by 448.76X) from ubuntu:14.04 - 531MB => 1.87MB (minified by 284.10X) from golang:alpine - 258MB => 1.56MB (minified by 165.61X) from centos:7 - 615MB => 1.87MB (minified by 329.14X) Rust application images: from rust:1.31 - 2GB => 14MB (minified by 147.16X) Java application images: from ubuntu:14.04 - 743.6 MB => 100.3 MB

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Ai : Hunanity's greatest creation for Good and Bad. To secure ourself #zerotrustai In the journey, MS brings up VASA-1 will AI model which essentially spins a selfie image and turn it into a talking clip of you. All you have to do is upload a photo along with a voice note and let the AI model do the talking for you.

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🚀 Ramki, Prime Focus Technologies 🚀Excited to share the latest episode of 'The Vitrina Podcast', where I had the privilege of interviewing my good friend and industry visionary, Ramki Sankaranarayanan, CEO of Prime Focus Technologies. 🏆 Ramki and his team are true trailblazers in the media supply chain, driving profound and lasting changes that are shaping the future of the industry. Their work epitomizes innovation, efficiency, and the seamless integration of cutting-edge technologies and data to deliver their software solutions and services. 🌎 In our conversation, Ramki delves into the incredible journey of Prime Focus Technologies, a pivotal part of the Prime Focus and DNEG family. They are committed to ensuring premium content reaches every corner of the globe. Their end-to-end solutions span content localization, mastering, compliance, packaging, fulfillment, and marketing, all powered by their revolutionary CLEAR platform. 💡 CLEAR is a game-changer, offering a multi-vendor supply chain software that enhances task management, collaboration, and trackability along with their pioneering use of AI to streamline workflows, create content highlights, and automate video creation. 👉 We discussed their major bets and investments in AI, the necessity of productivity and efficiency tools, and the boundless potential of generative AI. Despite the challenges faced by the industry, Ramki’s optimism for the future is truly inspiring. ☢ Tune in to hear about these incredible innovations and gain insights from one of the industry's leading minds. #Leadership #Innovation #MediaTechnology #AI #Efficiency #Inspiration #globalsupplychains #filmproduction #tvproduction #postproduction #localization Listen here:

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A great and detailed article by Nukri Basharuli (CEO of Super Protocol) about "Trustless Execution Environments" (a play on "Trusted Execution Environments"): https://lnkd.in/ebTs4685. This echoes my views on in my book Trust in Computer Systems and the Cloud on "explicit trust" (the term I use rather than "trustless"). I really like the approach here: it's definitely worth a read (https://lnkd.in/eYtN4tGT) Super Protocol Confidential Computing Consortium #confidentialcomputing

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The term “AI Hallucination” describes instances where LLMs generate incorrect, misleading, or nonsensical results. However, recent discussions in psychology suggest that “confabulation” is a more accurate term (https://lnkd.in/gvYwBGCk). While hallucination implies a false sensory experience, confabulation refers to the creation of false memories—more akin to what AI does. Understanding these nuances helps us harness AI’s potential while mitigating its shortcomings. At GumGum, we thoughtfully evaluate state-of-the-art technology with rigorous quantitative testing and meticulous manual spot-checking before incorporating it into our existing workflows. This ensures our datasets are curated to remove anomalies and yield top-performing models.

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Collaboration boosts execution 10x Earlier in my career, I had been on teams where members worked mainly on their C:// drives and information was sparingly shared via mail attachments. This slowed execution and impeded learning and development of junior members - particularly for people, who are eager to learn and develop rapidly. So, in 2014, when I was interviewing for a role in PwC’s Corporate Finance team, one of the questions I asked the interviewing manager Gbolahan Ashagbe was: “does the team actively use a shared work drive?” It was crucial for me. And when I founded in Leonine in 2017, one of the 1st expenses I incurred was Microsoft 365 for Business licenses for myself and Yinka Ojikutu, CFA because, amongst other things, I wanted to set a solid foundation for organization and collaboration – a lesson I had learnt the hard way. I believe having a culture of collaboration, where information and communication are open to the relevant people, boosts execution many folds. This is one of the practices that has served Leonine (and Aster-BI) over the years and helped us thrive as a remote-first company.

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YugabyteDB ❤️ Apache Airflow: check out this talk at the Airflow Summit by Amit Chauhan.

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Beyond single-score measurement and rankings this article provides more comprehensive approach to LFM (Large Fundamental Model) evaluation from end user perspective. https://lnkd.in/gSZbrqEb

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Sanjay Jha is moderating an intriguing panel discussion at GSA (Global Semiconductor Alliance ) executive forum now Rene Haas : insatiable demand driven by AI speed up our roadmap. Software does not die, AI is an additional demands for hardware. Life science and drug development will be the next big thing. Anirudh Devgan : AI is in the phase 1 of infrastructure and phase 2 of AI in existing application, 3rd phase is AI new application. Cadence is engaging phase 1 by enabling AI Chips, and phase 2 which drive design automation. 3DIC is natural extension of Moore's law. Optimistic about power, as new Innovation along the way will bend the straight line projection usually seen in the early inning of technology like AI. Real value realization will mostly be vertical based, not the horizontal LLM. AI will move from infrastructure AI, physical AI(automotive, robotics) to the horizon 3, that is likely AI biggest apps such as AI for science, particularly life science Sandra Rivera : AI impact is multi facets. First it is a TAM expansion for FPGA, which is needed as co-accelerator. AI is also embedded in placement tool and in even in basic fabrics, which will be making FPGA better. Power is the single Largest limiter for AI deployment. Also, Should AI be better regulated than what happened in social media, leading to the data security and privacy. Tien Wu : AI has driven ASE to increase capex to 4B This year from 2B last year due to systematic complexity, not just technology but the more players in ecosystem, which breaks the traditional pricing. AI has made us seeing the first time that Hardware is the bottleneck in term of improvement and return in profit. US has the data and economic scale to deploy AI across all sectors in term of efficiency improvement, given US also has been a highly "wasteful" nation.

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Thrilled to support 💪 Perplexity return citations param - https://lnkd.in/g7kkztxX (+4 more updates 👇) ⚡️ Azure - return response headers ✅ Streaming - break out of infinite model loops 🎉 Instructor - fix completion_cost calc 👀 [PR] logging by API key - https://lnkd.in/gAG5YKQa

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A Marriage Made in Heaven - LLM's and DADA-X by Decision-Zone The integration of task-oriented Large Language Models (LLMs) with the dynamic capabilities of DADA-X is indeed a transformative development in the realm of distributed systems management. These advanced LLMs, with their profound ability to simulate intricate Operational Technology (OT) systems, are revolutionizing the way mission-critical processes are conceived and executed. When these processes are fed into DADA-X, they are ingeniously converted into causal event patterns, which act as the backbone for autonomous applications operating in the real world. This convergence is unlocking unprecedented levels of real-time control and management. Organizations can deploy these sophisticated event patterns within DADA-X, enabling them to fully exploit the capabilities of their distributed systems. The result is a paradigm shift in operational efficiency, with DADA-X autonomously orchestrating complex missions, optimizing performance, and proactively mitigating risks. The emergence of the Artificial General Intelligence (AGI) market is a direct consequence of the synergy between LLMs and DADA-X. This market is poised to offer organizations the ability to construct intelligent systems that not only comprehend but also autonomously execute complex tasks with remarkable precision and efficiency. The potential here is immense, with implications across various sectors and industries. As we move forward, the traditional approaches of manual intervention and centralized control are becoming obsolete. The combination of LLMs and DADAX equips organizations to confidently step into the future of distributed systems management. This transition heralds the end of inefficiencies and the beginning of an era characterized by autonomy, efficiency, and reliability. The opportunity presented by this technological evolution is significant. Organizations that act swiftly can transform their distributed systems into agile, intelligent networks capable of managing the complexities of contemporary operations. The power of LLMs and DADA-X is not just theoretical but is a tangible force driving the AGI market forward. To be at the forefront of this revolution is to be a pioneer in the AGI market. It's an invitation for organizations to empower themselves with the necessary tools and technologies to succeed in an increasingly intricate and dynamic landscape. The future of distributed systems management has arrived, and it is powered by the innovative fusion of LLMs and DADA-X. The call to join this revolution is clear, and it promises a journey towards a smarter, more autonomous world.

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Interesting paper developed earlier in the year showing the potential in using LLMs to decode human intent and P4/eBPF-based APIs to programmatically update SDN devices. This isn't (yet) at the level of intelligent infrastructure, but it does demonstrate some of the necessary underlying capability to achieve it. See Instructing network devices via large language models (https://lnkd.in/gbgpBjZb).  

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Come check out this talk if you want to learn how we built distributed queries for Iceberg + Python!

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I just witnessed a VIP demo of Blitzy. Mind Blowing 🤯 If you are building enterprise software and want to get 1000X better, you should reach out to Brian/Pardeshi Link Ventures

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I am incredibly excited to announce Arch gateway https://lnkd.in/ePAf-VK8, an open source project for developers to build genai apps faster. It is engineered with purpose-built LLMs, Arch handles the critical but undifferentiated tasks related to the handling and processing of prompts, including detecting and rejecting jailbreak attempts, intelligently calling “backend” APIs to fulfill the user’s request represented in a prompt, routing to and offering disaster recovery between upstream LLMs, and managing the observability of prompts and LLM interactions in a centralized way - all outside business logic. Here are some additional key details of the project, * Built on top of Envoy and is written in rust. It runs alongside application servers, and uses Envoy's proven HTTP management and scalability features to handle traffic related to prompts and LLMs. * Function calling for fast agentic and RAG apps. Engineered with purpose-built fast LLMs to handle fast, cost-effective, and accurate prompt-based tasks like function/API calling, and parameter extraction from prompts. * Prompt guardrails to prevent jailbreak attempts and ensure safe user interactions without writing a single line of code. * Manages LLM calls, offering smart retries, automatic cutover, and resilient upstream connections for continuous availability. * Uses the W3C Trace Context standard to enable complete request tracing across applications, ensuring compatibility with observability tools, and provides metrics to monitor latency, token usage, and error rates, helping optimize AI application performance. This is our first release, and would love to build alongside the community. We are just getting started on reinventing what we could do at the networking layer for prompts. Do check it out on GitHub at https://lnkd.in/ePAf-VK8 Please leave a comment or feedback here or in GH and I will be happy to answer. #gateway #envoy #rust #genai #developers #ai cc Salman Paracha | Muhammad Ikramul Haq | Syed Anas Hashmi | Fahim ul Haq | Farheen Omar | Umar Farooq | Arslan Ahmad | Abdul Rafay Abbasi |

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AI next frontier is possibly developing and scaling the reasoning layer, where AI systems possess hard problem-solving and higher order cognitive intelligence. Came across this good article from Sequoia.

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