Anil Kommineni (Hiring exceptional Product Engineers)

Here is my column on the #TiEcon2024 conference in #indicanews:

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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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Knowledge Graphs (KGs) are positioned at the center of Gartner's 2024 Impact Radar as a critical enabler for emerging technologies. They enhance data discovery, classification, and organization, improving the effectiveness of GenAI tools like Large Language Models (LLMs). KGs help enterprises connect and contextualize vast amounts of data, enabling more accurate insights and decision-making processes. This technology is particularly valuable for improving AI accuracy, supporting knowledge-intensive workflows, and increasing the explainability of AI models. Bottom Line is: You can't do AI without an ingestion engine, AKA the Knowledge Graph. KGs prime your data, ensuring it is clean, deduped and accurate. This is the necessary precursor to employing any ML modeling or creating any algorithms designed to generate useful outputs and actionable insights from your data. #knowledgegraphs #graphdatabases #AI #ML #LLMs #dataunification #EquitusAI #EquitusEnterprise

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𝐀 𝐑𝐞𝐟𝐥𝐞𝐜𝐭𝐢𝐨𝐧 - 𝐓𝐡𝐞 𝐂𝐡𝐨𝐢𝐜𝐞 𝐨𝐟 𝐀𝐈 𝐢𝐬 𝐡𝐞𝐫𝐞 In the realm of artificial intelligence, we are witnessing a paradigm shift that is reshaping the landscape of computational intelligence. Just a year ago, language models were nascent technologies, akin to infants taking their first steps. Today, they have evolved rapidly, much like toddlers exploring with greater curiosity and capability😊 . The gold standard Large Language Models (LLMs) have demonstrated remarkable reasoning abilities, while a good number of Small Language Models (SLMs) are showing promising levels of competence. While LLMs remain tethered to the vast resources of cloud infrastructures, SLMs boast the flexibility to operate both in the cloud and at the edge, closer to where data is generated and actions are taken. 𝐓𝐡𝐢𝐬 𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐛𝐫𝐢𝐧𝐠𝐬 𝐮𝐬 𝐭𝐨 𝐚 𝐩𝐢𝐯𝐨𝐭𝐚𝐥 𝐦𝐨𝐦𝐞𝐧𝐭 𝐰𝐡𝐞𝐫𝐞 𝐜𝐡𝐨𝐢𝐜𝐞 𝐛𝐞𝐜𝐨𝐦𝐞𝐬 𝐭𝐡𝐞 𝐧𝐞𝐰 𝐜𝐮𝐫𝐫𝐞𝐧𝐜𝐲 𝐢𝐧 𝐭𝐡𝐞 𝐀𝐈 𝐦𝐚𝐫𝐤𝐞𝐭. ➡️𝘊𝘩𝘰𝘪𝘤𝘦 𝘰𝘧 𝘛𝘺𝘱𝘦 𝘰𝘧 𝘐𝘯𝘵𝘦𝘭𝘭𝘪𝘨𝘦𝘯𝘤𝘦: 𝘞𝘦 𝘤𝘢𝘯 𝘴𝘦𝘭𝘦𝘤𝘵 𝘧𝘳𝘰𝘮 𝘢 𝘤𝘢𝘵𝘢𝘭𝘰𝘨𝘶𝘦 𝘰𝘧 𝘮𝘰𝘥𝘦𝘭𝘴 𝘣𝘢𝘴𝘦𝘥 𝘰𝘯 𝘰𝘶𝘳 𝘴𝘱𝘦𝘤𝘪𝘧𝘪𝘤 𝘯𝘦𝘦𝘥𝘴. ➡️𝘊𝘩𝘰𝘪𝘤𝘦 𝘰𝘧 𝘏𝘰𝘴𝘵𝘪𝘯𝘨 𝘓𝘰𝘤𝘢𝘵𝘪𝘰𝘯: 𝘞𝘦 𝘤𝘢𝘯 𝘥𝘦𝘤𝘪𝘥𝘦 𝘸𝘩𝘦𝘳𝘦 𝘵𝘩𝘦 𝘪𝘯𝘵𝘦𝘭𝘭𝘪𝘨𝘦𝘯𝘤𝘦 𝘨𝘦𝘵𝘴 𝘥𝘦𝘱𝘭𝘰𝘺𝘦𝘥, 𝘸𝘩𝘦𝘵𝘩𝘦𝘳 𝘪𝘯 𝘵𝘩𝘦 𝘤𝘭𝘰𝘶𝘥 𝘰𝘳 𝘢𝘵 𝘵𝘩𝘦 𝘦𝘥𝘨𝘦. However, with great choice comes the need for judgement. The performance of these models is often touted through benchmarks like MMLU, HumanEval, and HellaSwag, among others. Yet, the rapid pace of innovation begs the question of validation and safety. How do we ensure that the models we deploy are not only effective but also secure and reliable? This is a topic that is getting discussed at the global stage and the debate is on the establishment of a central agency dedicated to the rigorous certification of AI models. This body would evaluate models for both performance and safety, ensuring that only those that meet stringent standards are released into the public domain. In my view, the certification process would act as a reliable sign of trust and quality amid the rapid growth in language models. In future, businesses will navigate the available choice by the mark of certification. The ratings provided by this central agency could become the most crucial factor in the adoption of AI models. They would guide companies in choosing the right model that not only fits their needs but also upholds the highest standards of safety and efficiency. In essence, as intelligence grows at an exponential pace, it warrants framework for governance and oversight. By implementing a robust certification process, we can foster an environment where AI can be adopted with confidence, propelling businesses and society toward a future where intelligent decision-making is the norm, not the exception. #Intelligence #EdgeInferencing #Governance #Agency

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#GenAI “shift is not merely about reducing the human effort in specific tasks but enhancing human capabilities and fostering a new symbiosis between human intuition and machine efficiency.”. My article in The Economic Times proposes the human skill development lens for looking at the generative AI technology. The initial hype or euphoria is giving way to real understanding of the use of this technology and a consensus is emerging around augmentation possibilities to allow businesses to build better for their customers. This can herald a new era of renewed productivity growth and uplift people economically.

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🚀 𝗧𝗵𝗲 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗼𝗳 𝗥𝗔𝗚 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀: 𝗘𝗻𝗵𝗮𝗻𝗰𝗶𝗻𝗴 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗮𝗻𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 🔎 In the domain of Retrieval-Augmented Generation (RAG), the standard practice often involves a singular retrieval step followed by generation. While effective, this method can be inefficient and insufficient for complex problems demanding multi-step reasoning. To address these challenges, various studies have optimized the retrieval process, leading to more sophisticated techniques. Let's explore the most popular ones: 🔄 𝗜𝘁𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹: This technique alternates between retrieval and generation, allowing for richer and more targeted context from the knowledge base at each step. 🔁 𝗥𝗲𝗰𝘂𝗿𝘀𝗶𝘃𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹: This method involves gradually refining the user query and breaking down the problem into sub-problems, continuously solving complex issues through repeated cycles of retrieval and generation. 🤖 𝗔𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹: This approach enables the RAG system to autonomously determine when external knowledge retrieval is necessary and when to stop retrieval and generation. It often uses special tokens generated by LLMs for control. These advanced techniques are especially relevant in production environments where the volume of documents necessitates a more strategic approach. Embracing these innovations can lead to significant improvements in handling complex queries and providing more accurate, context-rich responses. #AI #MachineLearning #RAG #GenAI

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Check out this video: Is the Testing Pyramid Obsolete in the AI-Gen Era? by Neha Gupta from #SeleniumConf 2024. #TestingPyramid #AITesting #TestAutomation

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Integrating OpenAI’s ChatGPT into cross-platform .NET applications See how MVUX handles list states, asynchronous loading, and user interactions using C# Markup. It's simplicity at its best. Learn More 🔗 https://lnkd.in/e8xwHJEr Jump into the sample 👉🏽 https://lnkd.in/eSKmGcuH #csharp #dotnet

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🏖️ Vibe-Eval: A Benchmark for Evaluating Multimodal Chat Models 👩‍💻GitHub Repository: https://lnkd.in/gQetNRmP Vibe-Eval is a newly introduced benchmark created to evaluate multimodal chat models, featuring 269 prompts for visual understanding, including 100 that are especially challenging. Why does it matter❓ Here is Multimodal AI explained in 60 seconds: https://lnkd.in/gnZqwBkA 👀⏰ #Multimodal #AI #GenerativeAI #LLM

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It was a long but fulfilling journey! What started many years back as a childhood hobby, as fascination with electronic circuits, which later culminated into IoT in professional life and hands-on experience in enabling smart-home with IoT during COVID lockdown for my own home, finally got a very satisfying outcome. I am extremely excited and glad to introduce my new book titled “Smart Home Automation with IoT” published by BPB Publications: https://lnkd.in/gRdq8hAX This is a work of several months to put together all the experiences I gathered working on IoT and enabling smart-home myself for my own interest which I am using till now. This book will help the readers to understand the basics of IoT and smart home automation and how to leverage the common sensors, actuators with micro-controllers such as ESP8266 and ESP32 and Raspberry Pi and open-source software to develop smart home automation solutions with minimal coding and no prior experience. It was real fun in developing the smart home solutions and more satisfying to share the knowledge with the readers through this book, which I think can help anyone who wants to learn this topic and develop the same as DIY. I want to thank all my friends and colleagues who have helped me in this journey especially Arnab Basak, a budding electronics engineer, who worked with me on his own interest and helped in drafting the circuits used in the book and Prof. Pravin Wankhede and Edgardo Peregrino who did the technical review of the book. I hope this book will be useful for all IoT and smart home enthusiasts, professionals, and engineering students who want to learn this subject and implement these solutions which are explained with step-by-step detail with circuits and code in this book. Do let me know your feedback if you read this book. Avail 15% introductory discount for pre-order using coupon code BPBDIPANKAR at checkout: https://lnkd.in/gRdq8hAX #smarthome #iot #openhab #raspberrypi #electronics #newbookpublished #bpbpublications

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Congratulations to our third annual vCon winning team: Suraj S., NAGASAI SAIKAM, Divya Sakhare, Akshata Salunkhe! The challenge? Implement redaction of personal information from a WAV file, and stick it in a vCon. Here's a deep dive into the hack, a decent introduction to the kinds of problems vCons can help you solve. BTW, looking for summer interns that know something about vCons? vCons enable the responsible management of conversations, including the detection of personal information, the redaction thereof, and even an audit trail of AI interactions for trust and transparency.

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Translating observability's granular visibility into systems to serve BOTH reliability engineering and cost optimization needs is what we are all about. Manju provides a great perspective on the interconnected nature of observability across multiple dimensions of DevOps.

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If you're using an observability system you're just a step away from AI-powered autonomous operations. Manjunath (Manju) Bhat of Gartner covers below why observability is the central nervous system of applications, and how it enables companies like Sedai. Sedai plugs directly into observability systems and the metrics data they provide let operations teams make the leap from manual and semi-automated to fully autonomous operations. 🙏 Special thanks to our observability integration partners for providing the foundation for today's AI management approaches: Amazon Web Services (AWS) CloudWatch Datadog AppDynamics New Relic Prometheus (a Cloud Native Computing Foundation (CNCF) project) Google Cloud Monitor Chronosphere SignalFx (acquired by Splunk) Netdata Wavefront by VMware Dynatrace Microsoft Azure Monitor If you're using one of the above observability systems and would like to see how you can upgrade your specific APM implementation to an autonomous cloud management platform, reach out for a personalized demo of the AI-powered future of cloud operations. 🚀 #Observability #CloudManagement #APM #GoAutonomous #AutonomousSystems #softwaredevelopment #SRE #costoptimization

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ChatGPT went down yesterday- which means it's critical that you know about other tools out there. Let's learn from The Wall Street Journal's bot challenge. Want to follow the top AI news and become a genAI ninja in the process? Go to www.conorgrennan.com to sign up for the free AI Mindset Newsletter.

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Enterprises are customizing AI applications with their own business data using retrieval-augmented generation (RAG). This AI framework links foundation or general-purpose models to proprietary knowledge sources like product data, inventory management systems, and customer service protocols.

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"The AI Imperative: Essential Advances in Quality Engineering" 🔥 by Nagabhushan Ramappa   Writers often go through a cycle of drafting and discarding, refining their thoughts until they crystallize into words on the page. What if LLMs mimic this behavior ? Nag had some interesting content which i can relate to (not just) this ! 𝐖𝐡𝐚𝐭 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐄𝐱𝐩𝐞𝐜𝐭𝐬 𝐟𝐫𝐨𝐦 𝐏𝐫𝐨𝐥𝐢𝐟𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐓𝐨𝐨𝐥𝐬 Enterprises expect a surge in specialized SaaS productivity tools. They foresee a need for an automated, end-to-end product reliability center, ensuring that from development completion to end-user delivery, every step is autonomous and seamless.    𝑮𝒓𝒐𝒘𝒊𝒏𝒈 𝑺𝒐𝒇𝒕𝒘𝒂𝒓𝒆 𝑪𝒐𝒎𝒑𝒍𝒆𝒙𝒊𝒕𝒚  Post-COVID, companies have accelerated software development, introducing more features in one year than in the previous twelve. Managing this complexity is crucial, as errors/failures can lead to escalations.   𝘛𝘳𝘢𝘥𝘪𝘵𝘪𝘰𝘯𝘢𝘭 𝘘𝘈 𝘢𝘯𝘥 𝘮𝘢𝘯𝘶𝘢𝘭 𝘵𝘦𝘴𝘵𝘪𝘯𝘨 𝘢𝘳𝘦 𝘪𝘯𝘤𝘳𝘦𝘢𝘴𝘪𝘯𝘨𝘭𝘺 𝘪𝘯𝘢𝘥𝘦𝘲𝘶𝘢𝘵𝘦 𝘧𝘰𝘳 𝘵𝘩𝘪𝘴 𝘨𝘳𝘰𝘸𝘪𝘯𝘨 𝘤𝘩𝘢𝘭𝘭𝘦𝘯𝘨𝘦.   𝑹𝒐𝒍𝒆 𝒐𝒇 𝑮𝒆𝒏𝒆𝒓𝒂𝒕𝒊𝒗𝒆 𝑨𝑰 Generative AI can automate testing ( or any other tasks IMHO) and streamline workflows, enhancing efficiency and reducing errors in complex software development. This is where MAGS help us in stitching and unifying multiple workflows.   𝑴𝑨𝑮𝑺 - 𝑴𝒖𝒍𝒕𝒊-𝑨𝒈𝒆𝒏𝒕𝒊𝒄 𝑮𝒆𝒏𝒆𝒓𝒂𝒕𝒊𝒗𝒆 𝑺𝒚𝒔𝒕𝒆𝒎  MAGS utilizes multiple agents, each specialized in a specific task, working independently and sequentially. Let take an example of using GenAI In essay writing.   A non-agentic workflow produces an essay in a single effort - no backspace. Whereas, an agentic workflow iteratively refines the essay, with various agents conducting web research, evaluating content, and making improvements behind the scenes, ensuring a better final product.   Applying MAGS to complex software testing workflows, we can leveraging specialized agents to automate testing processes, ensuring thorough coverage and reliability in software development cycles.   𝗔𝗜 𝗶𝘀 𝗳𝗹𝗲𝘅𝗶𝗯𝗹𝗲 - 𝗶𝘁 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝗮𝘀 𝗼𝘂𝗿 𝘄𝗼𝗿𝗸 𝗼𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲𝘀 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 Distributed intelligence systems are advancing and evolving from deterministic workflow tasks and freestyle chat to goal-based objectives. They are maturing from embedded AI and workflow-based approaches to case flow and self-organizing workflows.  As Nag explained these concepts, Amit Kumar Das balanced off with a Demo on the subject swiftly. I was amazed looking at the logs and trying to infer what was going on behind the scenes.   Nag - I loved the way the session went and how the demo were organized. Thanks for bringing this (new) awareness on AI stuff once again. Very informative session ! P.S. Have a look into the comments , for some questions Nag had for us ( i am yet to figure tis out even after a yr ;)) #TribeQonfReporter #TheTestTribe #TribeQonf #AI #saiprasadsabeson

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We hear a lot about using generative AI to create new, innovative content, from images and text to music and more. But what makes up the core architecture of these systems? These are detailed in the "Solutions Architect’s Handbook, 3rd Edition," which covers foundational models and guides you on starting with generative AI. A snippet from the book to learn the basic architecture of generative AI systems: At the heart of generative AI systems is a massive Foundation Models (FMs). To understand the architecture of generative AI systems, let’s break it down into simple components: Generator: The core element that generates new data, whether it’s images, text, music, or other forms of content. The generator learns patterns and relationships from existing data and uses this knowledge to produce new, similar content. For example, the generator takes random noise in image generation and produces images that resemble the training data. Latent space: A conceptual space where the model represents data in a compressed form. It’s like a compact representation of the data that the generator uses to create new content. This is a lower-dimensional vector space from which the generator generates data. This is like the secret recipe book an artist uses. It helps the generator come up with different types of creations. Loss function: A measure of how well the generated content matches the desired output. The loss function helps the model learn and improve over time by minimizing the difference between generated and real data. Imagine a coach telling an artist how close their work is to perfection. The artist learns and gets better by following this guidance. Training data: The existing data that the model learns from. It could be images, text, audio, or any other type of content that is available from which the model learns. Just like a chef learns by tasting different foods, the generator learns what it should create from examples. For instance, if it’s creating songs, it learns from listening to existing songs. For those intrigued by how these systems are specifically engineered and applied, I recommend diving into topics such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer-based models. Want to learn more about generative models and their applications? Check out the sections on different types of generative models in the Solutions Architect handbook 3rd edition for a deep dive into this cutting-edge technology. https://lnkd.in/g3FASjBZ #GenerativeAI #AI #MachineLearning #DataScience #Innovation #Technology

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𝗪𝗵𝘆 𝗰𝗼𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗲 𝘆𝗼𝘂𝗿 𝗚𝗧𝗠 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝘄𝗵𝗲𝗻 𝘀𝗶𝗺𝗽𝗹𝗶𝗰𝗶𝘁𝘆 𝗱𝗿𝗶𝘃𝗲𝘀 𝘀𝘂𝗰𝗰𝗲𝘀𝘀? In the ever-evolving cloud industry, the mantra "Keep It Simple" has never been more relevant. As cloud partners and ISVs, we often find ourselves entangled in complex strategies, losing sight of the core objective: growth. Without a doubt, the one thing that we tell our partners is that a streamlined go-to-Market (GTM) approach is key to sustainable success. Here’s why: 1. 𝙎𝙞𝙢𝙥𝙡𝙞𝙘𝙞𝙩𝙮 𝘽𝙧𝙚𝙚𝙙𝙨 𝘾𝙡𝙖𝙧𝙞𝙩𝙮: A simplified GTM strategy eliminates unnecessary complexities, allowing your team to focus on what truly matters—delivering value to your customers. When your strategy is clear, execution becomes more efficient, and your team remains aligned with your business goals. 2. 𝘼𝙜𝙞𝙡𝙞𝙩𝙮 𝙊𝙫𝙚𝙧 𝙍𝙞𝙜𝙞𝙙𝙞𝙩𝙮: The cloud market is dynamic. A simplified GTM model enables you to pivot quickly in response to market changes. This agility is crucial for staying ahead of the competition and seizing new opportunities as they arise. 3. 𝙎𝙘𝙖𝙡𝙖𝙗𝙞𝙡𝙞𝙩𝙮: A simple, well-defined GTM strategy is easier to scale. Whether you’re targeting SMBs or enterprises, a straightforward approach ensures that your growth is manageable and sustainable. It allows for easier replication of success across different geographies and market segments. By simplifying your GTM strategy, you can better leverage partnerships to drive growth. Part of what we do is to help you build these strategies from the ground up, challenging you to think differently and set aspirational goals. 𝚁̲𝚎̲𝚖̲𝚎̲𝚖̲𝚋̲𝚎̲𝚛̲,̲ ̲𝚋̲𝚞̲𝚒̲𝚕̲𝚍̲𝚒̲𝚗̲𝚐̲ ̲𝚊̲𝚗̲𝚍̲ ̲𝚎̲𝚡̲𝚎̲𝚌̲𝚞̲𝚝̲𝚒̲𝚗̲𝚐̲ ̲𝚊̲ ̲𝚜̲𝚞̲𝚜̲𝚝̲𝚊̲𝚒̲𝚗̲𝚊̲𝚋̲𝚕̲𝚎̲ ̲𝙶̲𝚃̲𝙼̲ ̲𝚒̲𝚜̲ ̲𝚊̲ ̲𝚖̲𝚊̲𝚛̲𝚊̲𝚝̲𝚑̲𝚘̲𝚗̲,̲ ̲𝚗̲𝚘̲𝚝̲ ̲𝚊̲ ̲𝚜̲𝚙̲𝚛̲𝚒̲𝚗̲𝚝̲. But you have to keep moving. Start with simple, actionable steps, and build from there. What are your thoughts on simplifying GTM strategies? Have you found success in keeping things straightforward? Share your experiences in the comments below. #Cloud #GTM #Growth

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