AUiX’s Post

Falcon2 11B is now available! BIG BUT! For those considering Falcon2 11B for commercial purposes, a word of caution: while the model is available for commercial use, I advise a careful review of the licensing terms. The Acceptable Use Policy is subject to change and may be updated periodically without notice. It's crucial to stay informed by monitoring the policy at its hosted web address to ensure compliance with any changes. #falcon211b #opensource #acceptableusepolicy Advanced Innovation

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Third-party APIs for LLMs and generative AI are risky because when they go down, there's not much you can do- and that's why we're building self-hosted solutions to run generative AI deployments on your own managed cloud. Check out our demo to see how we enable this at high-performance!

GPU memory is a scarce resource. Models are getting bigger. Say you had a 24GB vRAM GPU, and four ~8B models each taking 16GB of space. What do you do? Check out our demo that lets you swap between the models in <2 seconds.

If you didn’t make it to #InfoComm2024, listen to this episode as we dive into the highlights. Brian Galante shares his insights on the rapid development of #AI in #contentcreation, the push for #sustainablesolutions and the future of #hybridmeetingspaces with USB-C connectivity. Discover how these technological innovations are shaping the pro #AVindustry and what they mean for the future. #avtweeps https://lnkd.in/gYKv4aU3

Overall, PFLB is a robust solution for load testing with a focus on scalability and performance optimization. ADVANTAGES The platform offers a no-code editor, making it easy for users to create test scripts without needing extensive technical skills. DISADVANTAGES Cost Considerations: Pricing could be a challenge for smaller teams or those with limited budgets. Dependence on Internet Connectivity: Requires consistent internet access due to reliance on cloud-based services and integrations.

Promising reading list from AIxDesign on AIxDesign:

Your Daily AI Implementation Tip for Business Leaders 📌 Tip: Prioritize Data Quality ⚠️ Problem: Many organizations rush into AI projects without ensuring the quality and integrity of their data. Poor data quality results in unreliable AI insights and can even lead to misguided business decisions. 💡 Solution: Invest in a robust data governance framework before deploying AI. This includes cleaning up existing data, setting up protocols for data entry, and ensuring regular audits. Good data governance will amplify the accuracy and effectiveness of your AI applications, leading to more reliable and actionable insights. #AIforLeaders #GetSkillsAI #DataQuality #BusinessGrowth

Algorithm Audit did a briliiant job in comparing 10 existing #FRIA frameworks. Their key findings are: - Fundamental Rights Impact Assessments (FRIAs) review AI-systems mainly by posing questions that are meant to stimulate self-reflection. FRIAs do not provide answers or concrete guidelines how to realize ethical #algorithms. - Given the inherent tension between fundamental rights and between the ethical risks and benefits of #AIsystems, it is essential to provide a clear rationale for why a reasonable balance on these issues has been struck when deploying an AI-system. Many FRIAs fall short in adequately addressing the normative considerations underlying this balancing process. - Existing FRIAs often include common statistical measures to evaluate AI systems, such as false positive and false negative rates. However, they often fall short in connecting these metrics to assessing the conceptual soundness of the statistical methodology of the AI-system, such as (hyper)parameter sensitivity testing for machine learning and deep learning methods, as well as statistical hypothesis testing for AI-driven risk assessment of individuals. - The technocratic approach taken by most FRIAs does not empower citizens to meaningfully participate in shaping the technologies that govern them. - Digital tools and platforms should be developed to fill in lengthy FRIAs, preventing red taping and time-consuming paperwork. As per Article 27 of the #AIact, deployers that are bodies governed by public law, or are private entities providing public services, and deployers of high-risk AI systems shall perform an assessment of the impact on fundamental rights that the use of such system may produce. If any of the obligations laid down in this Article is already met through the #DPIA conducted pursuant to Article 35 of Regulation (EU) 2016/679 or Article 27 of Directive (EU) 2016/680, the fundamental rights impact assessment referred to in paragraph 1 of this Article shall complement that data protection impact assessment. It is now on #AIoffice to develop the questionnaire and the automated tool for this, hoping that they would take into consideration the gaps in the existing 10 FRIA frameworks. Lucilla Sioli #generativeai @ai #genai #GPAI https://lnkd.in/du9HiXAd

Get out-of-the-box metrics like latency, tokens and more, using #MLflow 2.8 with LLM-as-a-judge! ✅ Discover how it can save you time and money and best practices for #LLM evaluation in RAG applications. ➡ https://lnkd.in/e_mH6Wtp #opensource #llm #llmops #llms