Sid Reddy

You know there's some momentum happening if Marty Cagan decides to write about a product topic you've been at work trying to enable for years. Love to see it, and BIG shout out to Stacey Langer for providing her expertise in this article! 🧘‍♀️ Here are a few other pros that came to mind for the product model: 1. The government product model makes product (public) value and risk management possible. Product risks—such as value, desirability, viability, and feasibility—are not risks that traditional project management models can effectively manage. Government agencies interact with the public through the services they offer, but government digital experiences are not always inherently valuable. It’s what these digital services do for users that create individual and public value. Building tech alone ≠ value. 2. Evidence-driven decision-making uses discovery, iterative delivery, and testing with actual end users, where you continuously learn. This enables many things, mainly building with your users while validating the solution direction. It can also enable an incremental funding option specifically for products/services (traditionally called ‘projects’) that are especially complex, high priority, and/or contain many unknowns. 3. Driving technology from a place of vision and strategy measured in outcomes that can align and even influence policy changes. 🤸‍♀️ Some realities in trying to adopt a product model in gov: 1. The word ‘product’ often can be confusing, especially when it doesn’t fully represent what is being built and managed. A lot, if not most, of the technology work happening in government often involves solving service design challenges. So, in gov products, you focus on solving for the end-to-end experience, which can mean processes and non-tech things. 2. Conway’s Law and government systems replicate the organizational structures in agencies, which are often siloed and isolated. This is often represented as 'legacy tech.' Much of the work in 'product' in government is modernizing legacy systems, which requires the same principles as the product model but often a different emphasis on its varied approaches. Not all products or digital services start from the same place; many are not greenfield. 3. Traditional ATO structures can stop the product operating model in its tracks. To overcome this, it is important to integrate the ATO into the product model, utilizing a ‘continuous ATO’ operating model. But see point 2 above. At the end of the day, the product model isn’t a process you learn to implement and then just implement. It’s not just ‘Agile’ or Scrum or DevOps. Those methods can (or sometimes can’t) enable the product model. It's a mentality and discipline for continuously using new inputs to deliver value throughout a product's LIFE. It's a cultural change as much as an operating change, and there is no 'single way' to do it. However, operating from product thinking principles can help us start from the right place.

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From where I sit in the SF Bay Area, cradle of innovation for at least the last 30 years, I'm a true believer in the promise of the #AI revolution. That said, I find it fascinating to consider views on the opposing side. I'm sharing below two opinion pieces from the last two weeks suggesting that the AI revolution is fundamentally overhyped and a bubble ripe for bursting, and that AI tools are actually not useful or transformative. Contrast this with venture capitalist Kai-Fu Lee's prognostication that 50% of white collar jobs will be displaced by #AI within three years, and that OpenAI and the other cutting edge movers in the space are headed to a trillion+ dollar valuation. Which side will win out? Where will we be by 2030 in the AI race? I'd love to hear your thoughts in the comments.

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In the race to advance AI, we often focus on algorithms and data. But what if the most fundamental bottleneck lies in the design of GPUs? I recently spoke with Sean Lie, co-founder and CTO of Cerebras Systems, about their audacious approach to AI computing. Instead of iterating on existing chip designs, they're reimagining the entire system from the ground up. This end-to-end approach is pushing Cerebras to innovate in unexpected areas. Take server design, a field long thought to be settled. When you're working with a chip the size of a dinner plate—57x larger than NVIDIA’s H100—even servers need an overhaul. It's a reminder that breakthrough innovation in AI will not come from algorithms alone, but from rethinking every layer of the stack, from bare metal to high-level software. Our full conversation here: https://lnkd.in/gWZCi4zE

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Exciting announcement today - Introducing Moveworks Enterprise Search The initial promise of RAG, leveraging LLMs for a superior search experience, was quickly overshadowed by its intrinsic challenges—accuracy and hallucinations. These issues, compounded by the vast scale of data, diverse formats, and the complex web of permissions within organizations, have limited the widespread adoption of RAG for enterprise search. To address these shortcomings, Moveworks introduces Agentic RAG, which combines the latest in LLM advancements with the unique demands of enterprises - data accuracy, scale, various integrations, and business context. Moveworks Enterprise Search leverages Agentic RAG to deliver not just search results, but accurate, reliable, and contextually relevant insights personalized to the user's intent, all within our existing Agentic AI assistant as well as a deeper search mode. Want to contribute building Agentic RAG? My team is hiring an experienced PM to build out the platform that brings content for search - Job: https://lnkd.in/gctjfmBH - Learn more: https://lnkd.in/g_ZBVH_N

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Three years ago, I pitched and received funding for an idea that focused on preventing safety and compliance incidents in target geographies. This was before GenAI was all the craze. We leveraged an ensemble of ML models (ex: CV, NER) that scanned millions of tunnel images in fulfillment centers, and a human-in-loop system that closes the gap when inference confidence was low. Today, I rejoice when I witness other teams working on reducing product damages under Project P.I., which uses a combination of GenAI and Computer Vision to scan 100’s of millions of products, improving the customer experience. I can’t wait to witness what teams across Amazon achieve with GenAI as they focus on solving real-life, papable challenges on behalf of customers. This is the type of GenAI ROI that I believe in! Kudos to them all. You can check more about Project P.I. in the article below: https://lnkd.in/g_SnqhgA cc: Amazon Science #genai #technology #artificialintelligence Set the 🔔 notification on my page, don’t miss a post!

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AI memory is fundamental for conversational AI and AI agents. It is critical for building personalized experiences and improving reasoning. Check out Letta’s course to understand how to use AI memory to enhance the customization and quality of your AI app.

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I'm happy to share a blog post on our newest Amazon Just Walk Out Technology, which offers a frictionless, checkpoint-free shopping experience. This new system is powered by a store domain-focused foundation model. It is end-to-end learned, transforming raw sensing data directly into receipts. One of our foundation model's key strengths is its ability to perform a diverse range of tasks across multiple modalities (e.g. videos, images, sensor timeseries, 3D), all within a single neural network. This enables knowledge transfer, allowing the tasks and modalities to mutually enhance each other. Read this blog for more details: https://lnkd.in/gpC9um5P

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Everyone knows Moore’s law, the idea that CPU performance doubles roughly every two years. But remarkably, driven by GPU performance and AI model breakthroughs, we’ve seen a very different law emerge with respect to the rate at which AI model performance improves — in many cases improving by orders of magnitude in the past decade. This introduces massive implications in a world of AI Agents. For the past five plus decades, in any domain that computers touch, we’ve generally seen massive efficiency gains that bring more tech to more people. Yet the same has not been possible for essentially any non-digital services. Historically, anytime you want to solve non-computerized business problems (i.e. most knowledge work), we have seen the cost to solve a problem — say from healthcare services to legal work — tend to only go up over time. Due to a mix of inflation, specialization in certain fields, regulation, decreased competition in markets, or other added costs, we’ve seen the price of most services generally monotonically increase over time. This also puts a burden on driving economic activity in general, especially when creating new ideas from scratch, or starting and running a new business. Yet, in a world of AI Agents, anyone — from a small business or large enterprise — has access to automated work that can let them address problems with variable capacity. When you decrease the cost of entry for getting knowledge work done, you dramatically increase the use cases for that work. That new sales program can be tested instantly instead of waiting weeks or months or never happening at all; you can test a broader surface area of your product for bugs or security issues; legal work and reviews becomes affordable on the seemingly insignificant transactions that otherwise produce risk; or you can launch a new marketing campaign in markets you didn’t otherwise serve. Even in a world of AI costs remaining stable this is a super compelling proposition, but in a world of constantly improving technology the implications are vast. Just as Moore’s law steadily lowered the cost of software, increased its sophistication, and made it more ubiquitous, we can imagine a world where knowledge work regularly gets more affordable, sophisticated, and ubiquitous at a constant rate. This means AI Agents present the ability for any unit of work to continuously drop overtime, enabling us to solve greater and greater problems for a wider set of people and businesses. Of course, a large portion of these efficiency gains will quickly get “eaten up” by solving increasingly more sophisticated problems, but those problems will be solved at the cost of a previously simpler problem. The implications of this are enormously interesting, and it’s going to create incredible opportunities all around.

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I couldn't recommend this more - I was reading through the other day, and the arguments resonated quite strongly with the intuitions around why a purely preferentist (or alternatively, purely utilitarian) approach to AI alignment may be flawed. This is especially important in the coming era where we are likely to see advanced AI assistants more widely deployed than ever before - and where aligning them to human values and user well-being remains of utmost importance. https://lnkd.in/dyWqkTQm

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Lin Guan and Yifan Zhou have led a very interesting study investigating the effectiveness of Vision Language Models in critiquing robot behaviors in terms of the desirability of their "style." If you are wondering how our work on LLM's inability to verify correctness squares with this study on using them as style critics, see my earlier post https://lnkd.in/gBYAr5qq ! This is the reason why being a style critic is one of the roles LLMs can play in LLM-Modulo framework: https://lnkd.in/gyAyKx4E Check out the details of the work @ https://lnkd.in/gfVZ4EYW ( Lin Guan also has a nice twitter thread on the work here: https://lnkd.in/gnieU-qM )

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Alphabet committed another $5 billion recently to Waymo's self-driving cars. A renewed vow of confidence. Per my post yesterday, fast growing additional compute and better AI architectures are changing self driving cars rapidly. Notably, some traditional car manufacturers pulled out investments in full self-driving cars. It didn't think the tech was going to be ready for decades to come, if ever. I think it may be a mistake given AI and compute grow exponentially, not linearly. As an example, it may take 20-40 years to achieve 1% of progress, then just 5-10 years for the remaining 99% (instead of 1,000s of years per our intuition) link in comments #AI #Transformers #waymo

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"Have you ever thought about how you're not using your bed for two thirds of the day? Enter Napster 2.0: A platform where you can rent out your bed for micro-naps when you're not using it! Also we use generative AI."

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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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Jobs to Be Done. I first heard this phrase when I was working at Intuit. We were doing a HBR workshop designed by Clayton Christensen, a leading thinker on #innovation, who popularized the Jobs to Be Done (#JTBD) theory as part of his work on innovation and market disruption. According to #Christensen, Jobs to Be Done refers to the idea that people “hire” products or services to accomplish specific tasks or fulfill particular needs in their lives. The Jobs to Be Done (JTBD) framework can be highly valuable when applied to creating work opportunities for people with Intellectual and Developmental Disabilities (IDD). By focusing on the specific tasks that need to be accomplished within an organization, the JTBD approach helps in identifying work that can be structured, repetitive, and suitable for individuals with IDD, while also contributing meaningfully to business outcomes. The Jobs to Be Done (JTBD) framework can effectively create meaningful work opportunities for individuals with intellectual and developmental disabilities (IDD) by focusing on specific tasks and outcomes that match their inherent strengths. Task-Specific Roles: JTBD emphasizes identifying structured, repetitive tasks, that are also core and value add tasks and essential to delivering business outcomes, that people with IDD can be trained to perform, contributing to critical business operations. Matching Abilities to Business Needs: By aligning roles with the individual's strengths, companies can assign specific work responsibilities, ensuring that individuals with IDD can excel and add value. Customized Roles: JTBD encourages designing roles around specific outcomes rather than fitting people into predefined jobs. This allows businesses to break down larger tasks into manageable, repeatable steps. Emotional and Social Impact: Beyond functional jobs, the framework also helps fulfill emotional and social needs, such as fostering confidence and inclusion, through meaningful work. Business Value: Using JTBD ensures that roles created for individuals with IDD solve real business problems, moving from a charity mindset to one of strategic value. The Accidental Ally is a social enterprise on a mission to create meaningful career pathways for individuals with Intellectual and Developmental Disabilities (IDD). Through our work, we have uncovered numerous areas of ‘Jobs to be Done’ where people with IDD can make significant contributions to delivering impactful business outcomes. Here are some of them - - Digital Marketing - Executive Admin - Technology Showcase or Innovation Center - Finance Operations - Sales Operations This is by no means a conclusive list. If you thought people with IDD are only able to work at the ice cream shop or grocery store or give speeches and become disability advocates....think again. We are here to shatter the glass ceiling and it's happening now! #disability #inclusion #FutureOfWork #IDD #employment

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Today, I found a fascinating way to think about “Tokens” in LLMs. It came from a conversation between Jensen Huang and Patrick Collison at a recent Stripe event. When asked about the future of compute capacity in five years, Jensen smartly dodged that question a bit, but then gave an excellent explanation and analogy about why tokens are the new forces that will power the next decades of humanity. He explained that we're now producing something unprecedented (and at scale): floating point numbers that possess “value”, which we now call tokens. These tokens are valuable because they encapsulate “intelligence”. People are now taking these tokens and transforming them into English, French, images, videos, chemicals, proteins, robotic movements, etc. And many are working hard to expand the range of concepts and ideas we can create with these tokens. He then goes on to made a compelling comparison between tokens and electricity. In the past industrial revolution, we successfully found a way to convert “atoms” into “electrons” (by boiling water to power electricity turbines). And now, we've discovered a way to convert "electrons" into "tokens" (by using energy to power data centers that train and run LLMs). When electricity was first introduced, few people understood its value. Today, paying for kilowatts is routine. The same will happen with tokens. Right now, only early adopters and builders are paying tokens. Soon, everyone will be paying for tokens on a daily basis to supercharge productivity and power new products and services. Many new industries will be born from and built on top of tokens. When I first heard about this way of thinking, I got goosebumps. Perhaps it’s because Jensen has a good storytelling skill that helps him sell what he builds, but I have to admit this way of thinking gave me a brief surge of pride. I'm proud of humanity's collective effort and creativity, which have taken us from living under rocks to building machines that can "think." That's nothing short but a miracle.

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While in San Francisco this week I had the opportunity to do a Waymo ride and see personally what the technology and AI can do in something as simple as an Uber type ride. After a 25 mins ride in the heart of San Francisco I can say as a techie how impactful the experience is. And as a Data and AI leader how incredible the implementation of this technology is to consume the information from all the sensors and process them in real time to deliver a flawless ride. From reacting to a light change from green to yellow , to a person opening a parked car door on the side of the road, navigating multiple stop signs and even yielding to pedestrians on a cross walk the algorithm showed quick and effective response to all events delivering a sometimes better than human ride. And these types of high performing models are just the beginning. The AI revolution, especially in the real physical world business applications, will enable us to marry information from thousands of physical sensors, allowing AI models to feel the reality of the physical world and act on it, delivering activities we today need to perform as humans. Autonomous driving is something that we have been working on for a long time now.But now that I experience it myself, I can personally see a future where we don't necessarily need to drive anywhere ourselves anymore. But this is just the beginning. - - Connect 🤝 Abdul Rahim Roni Follow 👉 Abdul Rahim Roni Notification: Press the 🔔 icon Follow Abdul Rahim Roni for more content like this. _________ If you like this and want to get more AI tools, tutorials and news, join Cool Tech Gadgets - my newsletter that helps you get smarter about Microsoft and AI and technology: https:👉 https://cooltech99.com/ - -  #innovation #digital #ai #AI #robot #robotics #squid #tech #robotforhuman #technology #innovattion #Robots #Robot #datascience #google #ibm #ai #generatieveai #programming #developer #chatgpt #aws #learning #repost #reshare #smartcities #sustainability #data #python #sql #dataanalytics #project #learning #machinelearning #datascience #AI #artificialintelligence #aiwarfare #aibattletested #python #jobs #work #sql #resume #powerbi #projects #dataanatana #technology #innovation #robotics #AI #ai #artificialintelligence #ChatGPT #AI #Microsoft #Google #excelskills #excel #dataanalysis #freelyanalysis #project #management #data #analysis

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