Superb AI Inc.’s Post

From the increasing complexity of generative AI strategies to a greater focus on centralized governance and cost-control, DataRobot Chief Technology Officer Michael Schmidt explores some of the current trends shaping how enterprises are leveraging the power of generative AI. Read his full thoughts here ⤵️ https://lnkd.in/gBQgZTEC #generativeAI

Generative AI is all the rage in business now. Here, we outline a number of can't-miss sessions coming to the Gen Ai X Summit, co-located with ODSC West. #datascience #AI #artificialintelligence https://hubs.li/Q02V1Hqk0 If you enjoyed this post, follow me for more insights on AI and data science!

Generative AI has been recently the talk of the town! It's not only attracting interest from tech explorers but also from organizations for actual use cases in realms of content and productivity enhancements. The potential applications of Generative AI are plenty, and it's exciting to see how it can shape up transformation in several work streams. The latest MLPerf Inference Benchmark Results have highlighted the rapid growth of Generative AI models. When training and inference benchmarking comes with constraints and specific perspectives, it provides valuable indicators for real-world tasks. Check out the results here: https://lnkd.in/gpK5b78m #GenerativeAI #AI #Technology #Innovation Consult Red

LoRA make AI models easier to customize and serve at scale. Like custom bits for a multi-head screwdriver, LoRAs can be swapped in and out of the base model to give it specialized capabilities. Interesting paper by your friend Rickard Brüel Gabrielsson. (https://lnkd.in/eA3UP_i5).

It's time to go beyond #algorithims and embrace #DataCentricAI, where the quality and relevance of #data -- and not necessarily just algorithms -- are key to improving AI systems. TechTarget offers an explainer here: 💯 https://hubs.ly/Q02z25540 #DataScience #ThirdPartyData

Is AI really ready to understand our world? I recently shared my thoughts on CreativeBloq about why Spatial AI—not just traditional 2D AI—is essential to making this happen. As AI stands today, it lacks the depth to truly interpret our three-dimensional world. Spatial intelligence is the missing piece, and it's set to redefine everything from e-commerce to healthcare. At Nfinite, we're building the high-quality 3D data needed to push AI beyond flat perception and into a world of real spatial awareness. This is more than evolution—it's a transformation. Read the full article on CreativeBloq to see where we’re headed. https://lnkd.in/eTScV_Ab

Imagine a world where everyone has their own highly tailored AI models. This excellent article from IBM explores our research on serving customized foundation AI models at scale using LoRAs. Parameter-efficient adapters like LoRAs will be key to building a resilient model ecosystem and democratizing AI. Check it out: https://lnkd.in/estqZ63W

🔍 Tabular Data's Evolutionary Leap into AI Explore how tabular data is revolutionizing AI applications, bridging the gap between traditional data structures and advanced AI capabilities. 🌟 Key Highlights: 1. The transformative role of tabular data in AI development 2. Cutting-edge resources driving innovation 3. Real-world use cases across industries Stay ahead of the curve with insights into this game-changing trend! 🔗 https://lnkd.in/gTkZXC9s #AI #TabularData #Innovation #Quantzig

Generative AI continues to gain strong momentum, with use cases multiplying across industries. Because data analytics and business intelligence are data-heavy processes, generative AI has a strong potential to transform the way we consume and interpret information. https://lnkd.in/g2KAdfv6

Move over Model-Centric AI – enter Data-Centric AI! This approach flips the script, focusing on enhancing data rather than tweaking the model for robust algorithms. The traditional model-centric path posed challenges like accessibility to large datasets, high costs, environmental concerns, and privacy risks. Now, the spotlight is on refining data quality for more potent AI outcomes. 🎯 3 key practices for data-centric AI success: 1️⃣ Leverage MLOps Practices: Streamline ML lifecycle management through automated pipelines and unified frameworks. 2️⃣ Enhance Data Quality: Ensure accurate labels, complete & representative datasets, and mitigate biases. 3️⃣ Involve Domain Expertise: Collaborate with industry experts to enrich datasets with domain-specific insights. How can your organization harness the power of Data-Centric AI? #AI #innovation #datascience #datacentricai