Brad Green

Having worked at Cruise and Waymo on self-driving taught me many things. I hope to bring some of these learnings to healthcare so it too can soon have the magical, "no-driver-at-the-wheel" moment. Here's a short conversation I had with Louie Cai, MD on this topic!

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Check out our blog comparing SingleStore vector search performance to Milvus, pgvector. https://lnkd.in/gfS82kmd We're on par with Milvus and way faster than pgvector, and we're getting better fast -- 2x to 4x perf gains or more in near term. And our index build times are much better.

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I will be attending this session May 9th. #AIagents #AI

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as a hearing impaired, hearing-aid augmented person, @MikeKuniavsky nails it on the topic of distributed cognition. In some aspects technology can add to your inputs, but in others like noise-cancellation it can subtract so you can focus. With hearing-aids going OTC there is a ripe landscape for AI developers to lean in.

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AI news roundup: 1. Pope Francis raises some alarms about AI. https://lnkd.in/g5gxxCM6 I'd argue that we're already well down the rabbit hole of decisions being made absent a human touch or ethical concerns and that's long before the rise of "AI" as we're using the term today. But he has a good point. 2. Interesting take on Apple and what they're doing to address AI or "Apple Intelligence" https://lnkd.in/gHEUppwz I like that Apple just named it "Apple Intelligence" and then packaged up a bunch of commodity items inherent in any of the last few generations of foundational AI models like summarization, text generation, revamping Siri, and being able to call up ChatGPT to ask questions. 3. Although this is the actual plot of a Black Mirror episode, this is a great, humanistic use case for AI -- making a digital clone of yourself for your loved ones to talk to after you're gone. https://lnkd.in/g2-KyYdP 4. AI-powered candidate for local election: https://lnkd.in/g_TXPkUz Not sure how you'd actually vet that it's AI/ML recommending things or making suggestions. This reminds me of the Net Party in Argentina (letting voters make decisions directly via an app): https://lnkd.in/gujR_-WZ

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This is a quick way (Bill came in below his 4 minute limit!) to learn more about what we're building with Prak.

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CS Leaders based out of the SF Bay Area: Join us in Menlo Park next Friday (6/13) for our inaugural "Application of AI in CS" monthly luncheon. Our featured guest, Ashvin Vaidyanathan (VP of Customer Success and Insights at LinkedIn), will kick us off with how the LinkedIn CS team is embracing AI to increase customer value and efficiency. We will follow that with a roundtable discussion on how AI can help with your current priorities, how are you leveraging AI today, are there any adoption hurdles that you are running in to? https://lu.ma/g9u8mu8q

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The afternoon Keynote @ Stripe Sessions was Fireside Chat between Patrick Collison (CEO of Stripe) and Jensen Huang (CEO of NVIDIA). Patrick was an excellent moderator — asked Jensen really probing, nuanced questions that Jensen answered with his usual sharp wit and unique (and impressively well rationalized) perspective. A couple of quotes among many that stuck with me: - “Current CPU installed market is $1 trillion of hardware, all of which will be upgraded to GPUs over the next 5 years. And I would estimate about another $1 trillion will be spent on top of that during the same time period as companies try to do more with AI. Add those together — and I think at least $2 trillion will be spent on GPUs in the next 5 years.” - “I like to go after Zero-Billion Dollar Markets — nonexistent markets that I envision & can rationalize well, but will be very hard, expensive & time-consuming to achieve.”

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My cofounder Saumil goes on the Stack Overflow Podcast to deep dive on AI Agents! He goes into detail how we leverage AI agents to power automated workflows to accomplish the most complex tasks. Including agents firing off tasks to other agents to tackle the details. Need an agent to verify your work? No problem. Give it a listen and let us know what you think!

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Meta Introduces Chameleon: A State-of-the-Art Multimodal Model 🦎 Meta has unveiled Chameleon, a groundbreaking multimodal model that pushes the boundaries of AI capabilities. Chameleon seamlessly integrates vision and language, enabling it to understand and generate images and text with remarkable accuracy. Key Takeaways: 🎨 Chameleon excels in both image captioning and text-to-image generation, showcasing its versatility across multiple tasks. 🧠 The model leverages a novel architecture called Mixture-of-Attention (MoA), which efficiently processes and combines visual and textual information. 🌐 Chameleon outperforms existing state-of-the-art models in zero-shot image captioning and text-to-image generation on various benchmarks. 🔍 The model's ability to handle complex, compositional prompts sets it apart from other multimodal models. 🔒 Meta has open-sourced Chameleon's code and trained weights, fostering transparency and enabling further research in the field. Chameleon represents a significant milestone in the development of multimodal AI systems. Its ability to understand and generate both images and text opens up exciting possibilities for applications in fields such as creative content generation, assistive technologies, and more. Read the full article to learn more about Chameleon and its implications for the future of AI: https://lnkd.in/gk_ZTZdR #chameleon #multimodalai #artificialintelligence #ai #machinelearning #ml

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A colleague referred me to this. Great use case in how using humans to perfect, train and scale an AI system. The difference between self driving mode of Tesla vs. Waymo. Waymo is way ahead because of using humans to train the models faster for edge cases. In driving, as we now there are too many edge cases. Similarly, a colleagued complained to me why Speechify does not work well to read legal briefs (for example the solution reads outs headers and footers along with text, it does not disregard table of contents). I told her the solution will become better. Right now we are in first or second grade. Key lesson: solutions will get better and faster for now with humans in the loop.

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Tinkering fuels innovation and curiosity. It allows us to truly understand the tools and systems we work with at a granular level. Recently, I've been tinkering with Rust, a language gaining popularity for building reliable, safe, and sustainable systems at scale. The best way to learn is by doing. So, I turned our simple summarization proof of concept, Distill, into a CLI leveraging Rust's performance and memory safety advantages. As builders, it's our responsibility to continuously look for optimizations in everything we do. To ask questions and challenge the status quo. To learn and be curious. I encourage you to give Distill CLI a try, tinker with it, and share your feedback. And as always – now, go build! #AWS #tinkering https://lnkd.in/e7HUQwEt

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"Since 2018, we have increasingly leveraged Rust for critical workloads across various [AWS] services like S3, EC2, DynamoDB, Lambda, Fargate, and Nitro, especially in scenarios where hardware costs are expected to dominate over time." We've seen a lot of success with Rust at AWS, including our work on Firecracker, on Lambda's container dataplane, and on many other systems we've built. I love the fact that we can get memory safety without compromising performance or efficiency. I also enjoy seeing how much less time my teams spend tuning runtimes or debugging memory issues. I also really appreciate this sentiment from Werner's post: "... this is not a call to rewrite everything in Rust. ... It’s about using the right tool for the right job. This means questioning the status quo, and continuously looking for ways to incrementally optimize your systems – to tinker with things and measure what happens." Make decisions about programming languages the same way you make all critical engineering decisions: with data!

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This right here. These are the moments you live for as an Engineer. Domo.AI is a game-changer. Our customers are amazed at what they can do. Raw data to critical business insights... in seconds. Bring on the #ai #revolution

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*Mixture of Agents (MoA)* is an innovative way to boost Large Language Models’ performance by leveraging the strengths of multiple agents as a team. Layered Architecture: MoA arranges many LLMs in layered architecture, each layer composed of numerous individual agents(LLMs). Collaboration: These agents generate responses that are based on outputs from the previous layer’s agents. They repeatedly refine and enhance the last output. Diverse Insights: By integrating different capabilities and insights from various models, MoA achieves a more powerful combined model that is secure against diverse risks. Performance Boost: MoA outperforms standalone LLMs by a wide margin. For instance, it set a new benchmark with 65.1% on AlpacaEval 2.0 as against GPT-4 Omni at 57.5%, using only open-source models. In short, if we combine language modelers’ strengths then it results in increased efficiency and versatility. Here is a nice implementation of MOA by Together AI Github source: https://lnkd.in/dMCVRS3D #AI #LLM #LargeLanguageModels #MachineLearning

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"Each functional department – legal, marketing, coding, finance, etc. will most likely have a different approach to get the most out of AI for their discipline." Well said Neil Cohen! The governance framework that an organization adopts has to give room for this. "One size fits all" controls across the company may be a good place to start. However in order to bring out the inner Kungfu Panda from each team, a flexible approach to governance based on monitoring and visibility must take its place.

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// A Beautiful Mess of Metaphors (Voice UI) // Just a few hours down the road from me, Michael Twidale and Smit Desai at UIUC go deep on the ways metaphors can be used (with a focus on Voice UI), answering questions like: ✦ Which metaphors do designers, end-users, manufacturers, and researchers use to describe their interactions with VUIs? ✦ What do metaphors reveal about people’s perceptions of voice user interfaces? ✦ How can metaphor analysis be used in design, analysis, and research? Catch the team presenting this work at #chi2024: https://lnkd.in/g7wpsCBp #hci #interactiondesign #voiceUI #conversationalUI #dialoguesystems #Metaphor

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Today, we launched Salt AI Beta (v0.5), a significant step in making AI workflows accessible for developers. Our team's hard work streamlined creation and deployment, simplifying AI development so builders can focus on delivering value. This launch is just the beginning. 💅Congrats Caitlin Sowers Charlie Basil Jim Benedetto Chris DeWolfe Aber Whitcomb Chase Richter Anabel Krishnan Philip Sahyoun Alex Duffy Marie Paoli Ruby Malamed Ryan Wang Emil Zakirov Polina Lunina Nick Darabi Josh Brooks @earl Artemiy Shirokov Tyler Marques Katie Baker Learn more and get started: https://lnkd.in/gwAnFNu8 #SaltAI #AIWorkflows #DeveloperTools #ComfyUI #BuildTheFuture

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