Kai Ebert’s Post

Abstract in Architecture #day15 🌟 Unleashing the Future of Creativity with AI! 🌟 Discover how AI software is revolutionizing the world of creativity and discovery. From turning imaginative ideas into stunning realities to exploring uncharted territories, AI is the key to unlocking endless possibilities. Join us on this exciting journey and see the magic unfold! 🚀✨ #FutureOfCreativity #AIPower #InnovationInTech #AIRevolution #TechMagic P.S. How do you see AI impacting creativity in the future? Share your thoughts below! (Feel free to repost this and follow for more insights into the tech revolution.) ♻️

‘#Gen AI is challenging to implement quickly and efficiently. Join #NVIDIA at #HPEDiscover Las Vegas 2024 to learn how to avoid the pitfalls of inefficient and costly architecture choices and discover which approach works the best based on an organization's AI journey.’

‘#Gen AI is challenging to implement quickly and efficiently. Join #NVIDIA at #HPEDiscover Las Vegas 2024 to learn how to avoid the pitfalls of inefficient and costly architecture choices and discover which approach works the best based on an organization's AI journey.’

Text-generative AI is advancing rapidly, with models like google/gemini by Hugging Face boasting an impressive 2 billion parameters. Leveraging the Transformer architecture, this technology generates high-quality, coherent, and contextually relevant text. Its versatility highlights vast potential across diverse applications. Here's the link to my project: https://lnkd.in/gDDCi-FG #AI #Innovation

Mixture of A Million Experts 🚀🤩We interesting new paper by Google DeepMind! The paper "Mixture of A Million Experts" by Xu Owen introduces the Parameter Efficient Expert Retrieval (PEER) architecture, a groundbreaking development aimed at overcoming the scaling limitations of traditional MoE models. PEER revolutionizes transformer architectures by decoupling model size from computational cost, enabling efficient scaling to a vast number of experts. What is Mixture of Experts (MoE)? The Mixture of Experts (MoE) model improves efficiency by using specialized sub-networks and selecting only a few experts to process each input. However, scaling up the number of experts is challenging due to complex computations, limiting performance gains as the model size increases. This makes it hard to grow MoE models efficiently beyond a certain point. 🔍 Key Insights: 📚Traditional transformers face scaling challenges due to computational costs, especially in feedforward layers. 📚PEER introduces an innovative approach using product key retrieval, efficiently managing inputs across over a million single-neuron experts. 📚This allows for massive model scaling without proportional increases in computational costs, decoupling size from expense. If this is recreated in production, this will be groundbreaking architecture that could redefine how we scale and deploy transformer models, driving further advancements in AI. #ai #largelanguagemodels #machinelearning #deeplearning

"The breakneck speed of AI advancements imposes not just technological pressures on architects but demands a fundamental reevaluation of their approach to design processes. "This shift poses a formidable challenge for seasoned professionals, urging them to transition from an intuitive, hands-on approach to a more data-driven process." #AI #architecture #generativedesign Cas Esbach contemplates the massive opportunities - and potential pitfalls - presented by AI. https://lnkd.in/eiN644vG

Can a simple API call be called Agentic? How intelligent should an AI, be to term as “Agent”? How does Cognitive Architecture relate to Agents? Lot of good discussion points emerging in recent conversations. Here is my PoV - link in comment #1. #agents #cognitive #architecture #generativeAI #llm #intelligence

Jada Ai received the following questions.. Q: You mentioned that building #artificialgeneralintelligence is both an intellectual challenge as it is also a substantial economic effort, what are you doing to overcome the #ai hardware problem and who is backing you in this venture? Me: We previously mentioned that in order to ensure the positive economic viability of the project, together with running our proprietary General #artificialintelligence architecture, the design of new hardware was necessary. Whatever is currently commercially available is not enough to run our AI model, for that reason we began designing and planning for the production of custom-made hardware that would be better in-tune with our architecture, thus ensuring that it operates at peak efficiency with minimal energy usage. We cannot wait to share more about our AI’s development, but for now.. below is a picture of my cats performing #QA on a newly arrived shipment of hardware that made it to our laboratory all the way from the factory, made possible thanks to our wonderful team of scientists, collaborators and investors. As for who is backing us, that is a post for another day 🤝 Happy Sunday everyone 🌱

#GenAi had two prominent issues as a call out ⁉️ #Bias (+) #Hallucination But what about costs... computation and Complexity (3 Cs) We are all wanting solutions and there's which I've been researching about is Ｍｉｘｔｕｒｅ ｏｆ Ｅｘｐｅｒｔｓ － ＧｅｎＡi 💡 There's this article shared below and there's more here 🔗 https://lnkd.in/g7iEcGgG Summary While MoE involves decomposing a problem into specialized sub-components, it does not necessarily imply the kind of autonomous agency that characterizes agentic architectures. MoE is more focused on improving the efficiency and scalability of large AI models, while 'Agentic architecture' is about imbuing AI systems with a greater degree of independence and self-directed behavior... How do you see this unfolding 💬 #AgenticArchitecture #MOE

While high-tech industries have been at the forefront of AI adoption due to their technical capabilities and resources, low-tech industries are constantly exploring the potential benefits of integrating AI. However, the process is not easy. Confronting some of the following challenges , the low tech industry needs to tackle in their arduous journey towards adopting and embracing AI 1. Lack of clean and labeled data 2. Limited computational power 3. Integration with legacy systems 4. Skills gap 5. Regulatory compliance 6. Ethical considerations 7. Change resistance 8. Costs vs benefits analysis What are you’re thoughts on this ? What other challenges are you facing in your industry ? #construction #legacy #design #artificialintelligence #technology #innovation #architecture #sustainability