Iteration Studio Ai’s Post

On Artificial Intelligence for Data Intelligence (AI for DI) I'm often asked why I'm not diving head long and knee deep, into the magical, mystical, magnificent world of Generative AI - and gathering up as many specializations and certifications and nano-degrees and trailhead badges to add to my portfolio. Since, isn't that the world of the future...(or future of the world, I forget which)? Won't everything be powered, driven, processed, built, designed, defined, predicted, analyzed and solutioned for you by Generative AI apps that leverage Gen AI models, running on AI enabled data platforms and delivered on AI enhanced smart devices?" To which I typically reply - "I'd love to. But then, when will we do the real work?" 😉 😀

🖼️ Elevate your image understanding with our latest video on using Gemini multimodal AI to ask insightful questions based on an image! 🤖 🎥 Tap into AI's potential to delve deeper into visual data—ideal for creators and innovators! 🌟

🖼️ Elevate your image understanding with our latest video on using Gemini multimodal AI to ask insightful questions based on an image! 🤖 🎥 Tap into AI's potential to delve deeper into visual data—ideal for creators and innovators! 🌟

This was my first project working on image generation, and I'm honored to have had the pleasure to have Anton Jonathan Goorin as a lead on that project, and my senior in that sense. We also did lots of text generation work here. If you are working with story generation, including images and text (more so, if your product is targeting children), definitely save this post and video by Anton and watch it when you have time. I can almost guarantee it will bring value to your team and might save you months of work. STORI was featured on Google for Developers Conference in San Francisco in 2023: https://lnkd.in/d3PdZsKs I hope you learn something from our lessons ✨ #storygeneration #aistories #textgeneration #narrativeai #commercialimagegeneration #imagegeneration

🌟 Exciting Journey in AI: SAWiT.AI Learnathon! 🌟 The final challenge of this Learnathon was to build a RAG (Retrieval-Augmented Generation) application, and I decided to add a magical twist by basing my project on the Harry Potter universe! ⚡📚 Building a RAG model was a great opportunity to dive deeper into integrating retrieval mechanisms with generative models, and I couldn't be more excited to have brought the Wizarding World to life in the AI space. Looking forward to applying these new skills in future projects.

What these researchers did is fascinating we can “travel” through the weight space It's like having 60,000 artists who each learned to paint in the style of a different individual. Imagine that instead of just feeding a model a noise vector to generate an image (like in typical GAN or diffusion approaches), we treat the entire set of model weights itself as something we can explore and manipulate. In this work, each “point” in the weight space corresponds to a fully personalized diffusion model  for example, one that knows how to paint a specific person’s face in countless styles. By gathering tens of thousands of these personalized models (each fine-tuned for a different person), the authors discover that the resulting model-weight manifold has fascinating linear properties. In practical terms, this means we can “travel” through the weight space to: 1️⃣ Sample new, never-before-seen identities (imagine generating a completely new face), 2️⃣ Edit existing identities (like adding a beard or altering facial features), and 3️⃣ Invert a single input image to create a brand-new model that consistently generates variations of that image’s subject. This “weights2weights” (w2w) space effectively works as a meta-latent space that controls not just how one image is generated, but how entire models are created or modified. It opens up a whole new dimension of possibilities for customizing and creatively exploring generative AI.

🚀 Day 2 of the 90-Day Generative AI Challenge 🚀 Welcome back, fellow explorers! Today, we're delving deep into the heart of AI revolution: the power of O(1) attention in Transformer models. 🧠💥 Now, I know what you're thinking. What's all this talk about O(1), right? Well, buckle up, because we're about to uncover the magic behind this seemingly simple notation. 🎩✨ In Day 2, aptly titled "Getting Started with the Architecture of the Transformer Model," we're diving headfirst into the inner workings of these transformative models. But before we do, let's take a moment to appreciate the significance of O(1) in the grand scheme of things. 📈 Imagine, if you will, a world where complexity is no longer a barrier. That's the power of O(1), my friends! It represents constant time complexity, meaning operations are executed in a snap, regardless of the size of the input. 🕒💡 But how does this relate to AI, you ask? Ah, that's where it gets interesting! 🤔 In the realm of Transformers, O(1) attention is the secret sauce that fuels their unparalleled performance. It's like having a supercharged engine under the hood of your favorite sports car. 🏎️💨 You see, while traditional methods like recurrent layers may chug along at O(n) complexity, Transformers breeze through computations with O(1) efficiency. It's like upgrading from a horse-drawn carriage to a sleek, futuristic hovercraft! 🚀🐎 But don't just take my word for it. Let's break it down, shall we? Picture an attention layer in a Transformer model as a master conductor, orchestrating relationships between every word in a sequence. 🎼📝 Each word, or token, is like a musical note in a symphony, harmonizing with every other note in perfect synchronization. And thanks to O(1) magic, this symphony plays out effortlessly, without skipping a beat. 🎵✨ So, as we embark on this journey through the intricacies of Transformer architectures, let's remember the power of O(1) and the boundless possibilities it brings. Together, we'll unravel the mysteries of AI and pave the way for a future limited only by our imagination. 🌟💻 Stay tuned for Day 3 as we dive deeper into the wonders of self-attention and its transformative impact on the world of AI! 🌐🔍 #transformers #GenerativeAI #NLP

Can’t see the forest for the trees? Black Forest Labs’ generative visual models can. Black Forest Labs’ open-source suite of models, FLUX.1, defines the frontier of text-to-image synthesis and expands the boundaries of creativity, efficiency, and diversity. We’re deeply aligned with their commitment to collaboration and believe their technical prowess and prescient vision can take the future of generative media to the next level, benefiting a wide range of industries. “We believe that generative AI will be a fundamental building block of all future technologies. By making our models available to a wide audience, we want to bring its benefits to everyone, educate the public and enhance trust in the safety of these models. We are determined to build the industry standard for generative media.” -BFL team We’re proud to invest in their seed round and welcome Robin, Patrick, Andreas, and the entire team to the GC family. More from the BFL team & links to the models in comments ↓

🤖 I used generative AI to write thousands of poems inspired by art. They’re mostly garbage, but the process of building these systems has opened my eyes to all kinds of possibilities. 🤓 This week I’m exploring two different approaches building off the same single idea. 💡

Let’s talk about the elephant in the room: AI and product design. It’s that love-hate relationship we all have mixed feelings about. I’m here to break it down for you, from one designer to another. We’re fascinated by what AI can do for us, yet terrified it might one day steal our jobs. Let’s unpack this together, diving deep into the current state of AI in product design, its pros and cons, and the future it holds for us. AI and Product Design: A Love-Hate Relationship by Tomer Gilat Read the full article: https://lnkd.in/dXQAZRbw