Madrona Venture Labs’ Post

This in-depth analysis of AI models' comparative transparency presents crucial data in a digestible format: https://lnkd.in/gcnUk3zr

Can #AI predict whether you will read this article? No, lol. Prediction in AI has a very different meaning than how most of us know it. A very quick read on this key concept! #LLM #generativeai

Navigating the AI Landscape: A Practical Guide to Model Selection https://buff.ly/3Rot0Cb by @madronavl choosing a LLM requires a fairly nuanced understanding of your project's specific needs and landscape of available AI models. Guideline from investor’s perspective! #rafAIgual

What does "prediction" really mean in the AI framework? Our experts, Lucy Tancredi and Ruggero Scorcioni, PhD, demystify these concepts for you. Learn about Machine Learning, and the role of predictive models, and get a sneak peek into FactSet's predictive Signals. Dive deeper into the intriguing world of AI with our latest article: https://bit.ly/3snCiF1 #GenAI #ArtificialIntelligence #LLM

In a world where foundation models reshape the tech landscape and the economic race in AI is won by those with the deepest pockets, the #StanfordAIReport paints a picture of industry dominance. This report highlights the surge in open-source models, but with closed models still leading the performance charts, what does this mean for the future of #AIinnovation? As AI benchmarks evolve to challenge machines with even more complex tasks, let's ponder if we're witnessing a paradigm shift where AI not only complements but also competes with human intelligence. #StateOfAI #FutureOfWork #AIBenchmarks 🤖💡🌐

"State of AI in 13 Charts" from the Stanford Institute for Human-Centered Artificial Intelligence (HAI) provides a visual snapshot of: - biggest players in AI model development (including a comparison of corporate vs. academic sectors and open vs. closed-source models) - AI investment and anxiety levels by country - How businesses are using AI (e.g., contact center automation, new customer acquisition, new and existing product development, etc.) #ainews #aistats #aitrends https://lnkd.in/eEHja2Ey

Can AI solve your problem? Three simple heuristics for recognising AI eligible project ideas by Daniel Bakkelund

Noelle Saldana and I wrote about what we've learned from our collaboration over the past 10 years at the intersection of design and AI to help you identify how to apply AI to your products and services purposefully. https://lnkd.in/g8ph4J6g

3 Tips for Business Owners Who Want to Use Anthropic's New Flagship AI Model, Claude 3 - https://lnkd.in/gevF73i2

Hot take: We spend a disproportionate amount of time pursuing AI dream ideas and not enough time refining the questions and meaningful problems we want to solve. Data scientists are also guilty of doing this; we are often overly eager and hyper focused on getting into the data and building models before evaluating if we’re even asking the right questions. It is critically important to consider the broader context when it comes to AI. Working with Martina Hodges-Schell over the last 10 years, we discovered that applying best practices in Design to build products with AI helps ensure you’re not just building demoware. More details in part 4 of 6 in our blog series: https://lnkd.in/gt5ZN-JY