Mindaou Gu

Last week, in the 100+ years’ old Palace of Fine Arts, gorgeous in the early summer California sun, over 30,000 people and some robots showed up for the GenAI summit https://genaisummit.ai/#/. The somewhat disorganized conference that attracted attendees from all over the world is a testimony of the current state of AI: everyone wants to get in, a LOT is happening, no one really has the paved path, but there is no doubt the future is here. As a student of AI, my key takeaways: ▶ Development of base models will be done by a few players who have the money to burn. One presenter quoted up to $100M for a single model. Though the architecture of the base models may be similar, each player has their secret sauce. And the winner of one model is unlikely to take all. All players are rapidly developing and revolutionizing. 🔹 Model “aggregators” fit domain-specific data to base models. They are not “picky” about which model to use. They add value by fine-tuning the models for specific domains. From prompt engineering to RAG to fine-tuning, SMEs play a key role in “landing” LLMs well. There is a boom of startups in this space. ⬆ Data is still foundational. It’s the era of unstructured data. New data storage and processing technologies are the backbone. Is there “enough” data for AI? It depends. Synthetic data is widely used. While “connected” data on the web is relatively easy for the models to access, “protected” data in enterprises requires jumping many hoops, including expert domain knowledge, governance and “imagining” the business problems they can solve. Data about the real world is fast growing, collected from sensors and fed into robotics. 💲 GPUs are expensive and power hungry. Optimization is the path today. Alternative hardware can take time. The bigness of AI meets the smallness of nuclear power. Mini nuclear power plant https://lnkd.in/giJkjTiG. 🛠 While the base model developers are relatively a small elite group, often with deep academia roots, applying the models to real world problems needs everyone in their current job working with AI. AI certainly created new job titles, but also makes it increasingly easier for the public to use it as a new “tool”, instead of a complete career change. 🐬 The most optimistic prediction of AGI is 3 years. The honest answer is no one really knows. How far will AI go from data, information, knowledge, intelligence to wisdom? Opinions differ. When the answer is 42, how would we know 😉 ? 

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For the builders and cloud developers, you don't want to miss this ... perhaps you want to impress your boss 😉 Would you be interested in starting leveraging AI with confidence? https://eqix.it/3UpVGLM JJ Asghar is hosting a hashtag #DemoDay session to help you engage in AI from an hashtag #OpenSource Standpoint. Jump in to set your reminder so you don't miss out. 🤩 For more info ⤴

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As AI continues to evolve, so does its lexicon. Our next-level breakdown of #AI terms will get you ready to tackle any conversation about artificial intelligence. Stay ahead of the game with our expert insights! #MicrosoftAdvocate

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🧠🧠🧠 Let's not forget that daily model updates does not mean marketers need to leverage their MMM every single day. It means that our data is ready when YOU need it, not when we feel like running the model. Does that cost more for us? Sure it does. But maybe doing things just a bit differently is what our industry needs. Reach out if you have questions 🤙🏼

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AI can be an incredibly powerful technology for creating valuable products. However the golden rule – “Do not delegate thinking” –becomes most critical than ever. AI can seriously hallucinate, lack the right context, and become a source of quality and originality regression. Great article by Itamar Gilad

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BIG product update here at Forwrd.ai! 🔥 Improved business logic editor 🔥 Enhanced usability for data modeling. [link in the comments] Our north star product metric is TIME TO VALUE ⏳ Achieving faster time to value often requires re-writing core product modules. These engineering tasks are complex and can be time-consuming. It’s natural to delay these crucial updates, pushing them to “later” until they become unmanageable. My recommendation: Incorporate these vital tasks into your backlog on a monthly basis. Trust me, the payoff is worth it. Regularly addressing core updates not only enhances performance but also ensures a seamless user experience. #Startup #ProductManagement 

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If you know Anil Jain you'll want to read our recent interview where we talk about Gen AI, social's impact on media and a few other things #Google Cloud (#GCP) is involved with. https://lnkd.in/gGQsm8nk

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I am so proud of the work that is being delivered by our team in Search that was showcased at I/O this week. Highlights were... ⚡Plan Ahead with Generative AI in Search ⚡ Thanks to a new Gemini model customized for Google Search, we are able to do SO MUCH more. With planning capabilities *directly* in Search, you can get help creating plans for whatever you need, starting with meals and holidays. Search for something like “create a 3 day meal plan for a group that’s easy to prepare,” and you’ll get a starting point with a wide range of recipes from across the web. And if you don’t like the suggestions, Search will continue working on your behalf. So if you want to swap the dinner suggestion for a vegetarian option, just like that, Search will customize your meal plan. On top of this, there are lots more updates in Search so — for the curious  — have a read of the blog by Head of Search Elizabeth Reid below 🔽 https://lnkd.in/en6_7fvX #GoogleIO #GenAI #GoogleSearch

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Scientifically, the best you'll see in Product Analytics today! 😁 Watch the expert sessions highlights from the recent Product Analytics Leader’s Meetup in San Francisco, featuring Casey Winters, Olga Berezovsky, Ken Rudin, Snehal Karanjkar, and myself. Great product analytics content, from renown industry experts, in an easy-to-digest format. 🚀 Check out each of these short presentations from our experts: 🔥 Casey Winters, drawing on his extensive experience with 15 renown companies, on how the most successful analytics teams building learning machines. 🔥 Olga Berezovsky on precisely what NOT to do when defining key metrics. 🔥 Snehal Karanjkar zeroing in on how to cut preparation time and increase the quality of weekly, monthly, and quarterly business reviews. 🔥 Ken Rudin on the three things that the best Product Analysts do to ensure their actionable insights drive real impact. 🔥 And, me on how to deal with the correlation / causation problem by leveraging causal inference models. See the set now: 👉 https://lnkd.in/dKEYKM-f

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Stay ahead of the competition with an AI center of excellence. Learn how to gain the right #AI resources, skills, and strategies: https://lnkd.in/eArUqqgd

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🚨 Fullstory Announcement - Data Direct Release 🚨 Our new product release Data Direct syncs clean, indexed, and structured behavioral data to your destination of choice helping to fuel your predictive models, feed your LLM with essential context, and drive your GenAI initiatives. Read more about Data Direct below 🔥🔥

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Last week I got to spend two whole days in our beautiful Chicago office, where we hosted ~20 customers in our Product Advisory Council. Its always energizing and enlightening to interact with customers. This engagement also reinforced and remined me a few things as a product manager. 1 We have so many avenues to gather customer feedback, but nothing beats directly listening to them. 2 Customers certainly expect their solution providers to explore how cutting edge technologies like AI can be leveraged. However, enterprise customers are very discerning when it comes to investing in these solutions. They expect that the new tech can actually solve a meaningful problem, and can do so in a significantly better way. Otherwise not many would not want to invest in that tech without a real ROI. Of course, security, compliance etc also play a role. The bar for adoption is much higher than consumer facing apps where anyone would play around with ChatGPT, Midjourney etc. #ProductManagement #CustomerSuccess #AI #EnterpriseSoftware

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Common Room's latest launch—RoomieAI™—is a big one. It’s not just one thing, it’s a suite of AI-powered tools built with modern go-to-market teams in mind. ✨ Ask RoomieAI Our AI agent will help you deep dive into account research—super fast and without the tab-hopping reps have to put up with today. Need to run some competitive analysis? RoomieAI will explain a target account’s market positioning and serve up their top competitors. Looking to extract insights from recent financial filings? RoomieAI will summarize the top takeaways from a 10-K so you can craft better outbound. All the intel you want is just a prompt away. Use our out-of-the-box options or create your own. ✍️ RoomieAI message snippets In private beta now (get in touch to request early access!), RoomieAI message snippets will help you go from blank page to impactful outreach lightning-fast. No more wasting time trying to piece together the right signals and turn them into personalized, relevant messages. RoomieAI can do the heavy lifting for you by turning the buyer actions and attributes captured in Common Room into ready-to-send outbound snippets. Add them to your sequencer tool and nail the right message right away. There are lots of reasons I’m excited about these new features, but the biggest one is this: It helps our customers tap into their ultimate differentiator, customer intelligence. Lots of tech vendors are racing to scale outbound using the same datasets every broker offers. But what’s the point of automating the same stale info? Using AI to personalize attributes like company size, role, etc., might be better than randomly dropping someone into a prospecting sequence, but it’s not going to help you stand out. Especially not if everyone else is doing the same thing. Common Room fixes that problem. How? Comprehensive signal capture across 1st-, 2nd-, and 3rd-party data sources and our proprietary identity resolution and enrichment engine, Person360™. When you capture more signals—and connect them to real people and real accounts—you have more and better data to train an AI model with. That means our new generative AI tools can build messaging with more context, more personalization, and more relevance. Proprietary data and the right set of signals is how you create truly differentiated outbound. But I’ll step down from my soapbox 😉 Check out the link in the comments for more info ⬇️

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Succeeding with #AI initiatives requires access to the high-quality, most appropriate data & AI models. Learn about the data & AI model marketplaces surfacing to meet this need: 

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Delivering Outcomes✨, Not Just Outputs! - Meet Your Future Assistant: 🤖 Imagine you have a super assistant you can ask to work on something and get back to you with outcomes instead of outputs. Seems sci-fi? Well, it isn’t with the advent of Agentic Architecture—this is possible. Recently, I went into the rabbit 🐇 hole of AI Agents and must say this is game-changing with huge potential. Thanks to the mini-course by crewAI’s CEO João (Joe) Moura on Andrew Ng’s DeepLearning.AI platform which helped me see how my world view of orchestrator based AI platforms getting manifested through #AI #Agents Coincidently, while exploring the Google Vertex platform, I came across function calling enabled agent builder, which is also doing something similar. This has the potential to empower us to reimagine every digital product we have today. While cost concerns and the need for deterministic responses in several cases are still to be solved at scale, this could be a great approach to realize what I have been evangelizing for a while: An orchestrator system that decodes the query and uses multiple AI tools, be it - NarrowAI models, - OR models, - #GenAI Small Language Models (#SLMs) - #LLMs - Image Gen Models - Video Gen - OCR - Computer vision - Speech-To-Text - Text-To-Speech - #API integration for CRUD operations, and deliver outcomes. Lets understand this with an Example of a AI Trip Agent Imagine planning a trip to Leh Ladhak. You provide tentative dates, the number of people, and any special occasions. The system comes back with: 1. Whether it’s the right season to visit. 2. Possible itinerary options with stay, commute, and food exploration suggestions. 3. Suggestions for flights, car rentals, and hotel bookings with prices. 4. Finally, it books everything for you once you give the go-ahead. All of this in one seamless conversation is no longer a dream—it's a reality. Wanna jam about AI agents and brainstorm? Feel free to reach out and follow me (Vellanki Sriharsha) for more such insights. #AgenticArchitecture PS: Co-Author - #ChatGPT https://lnkd.in/ggyMEhsT

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Expedia Group will be at WiDS Puget Sound! Meet Lucy Zou, a Machine Learning Scientist III at Expedia Group. Lucy shares with WiDS Puget Sound insights from her education and career in data science - here are 3 key takeaways: 🔑 Adaptability and continuous learning are crucial for navigating career transitions in tech. 🔑 Building a diverse network and seeking mentorship are invaluable for career development. 🔑 Embrace challenges, take calculated risks, and seize growth opportunities to thrive in the dynamic tech industry https://bit.ly/3ydfRVz

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We’re experiencing a massive platform shift to LLMs. Lightspeed's Michael Mignano and I discussed what this shift means for Grammarly, what the industry needs to do to ensure #genAI adoption and value creation, and where I predict true efficiency gains through AI will come from. Hint: it’s not about creating more content. Thanks for having me on, Michael! https://lnkd.in/gpruc99i

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