Cleanlab

One of the largest financial institutions in the world, BBVA, uses Cleanlab to improve their categorization of all financial transactions. Results achieved *without having to change their current model*: ➡️ Reduced labeling effort by 98% ➡️ Improved model accuracy by 28% This is the power of #DataCentricAI tools that provide automation to improve your data: Your existing (and future) models improve immediately with better data! Start practicing automated data improvement: https://cleanlab.ai/studio

Product Announcement: Introducing Cleanlab Studio's Auto-Labeling Agent! Is your team bogged down by the tedious task of manual data labeling? Are your algorithms struggling with limited examples? Cleanlab Studio’s Auto-Labeling Agent is designed to alleviate these challenges by efficiently suggesting accurate new labels to complete your dataset effortlessly. 🔍 Why It Matters: Fully automated annotation can overlook important nuances, while fully manual annotation is both error-prone and labor-intensive. By blending human and automated efforts, our Auto-Labeling Agent enhances the accuracy and efficiency of data annotation, making the process seamless and significantly quicker. These AI-suggested labels mean humans only need to focus on the rows where their manual efforts have the highest ROI. ⚙️ How It Works: Simply import a dataset with less than 50% labels, and our Auto-Labeling Agent will automatically provide high-confidence suggestions for the remaining rows. This approach allows for streamlined and rapid iterations while keeping you in full control. Our pilot users have experienced an 80% reduction in time spent on labeling and iterations. Ready to put your annotation on cruise control? 🏎️💨 Read our blog for more details and sign up for Cleanlab Studio today – it’s free to try, with no code required. Learn more: https://lnkd.in/gfQqtjm9 Sign up for Cleanlab Studio: https://app.cleanlab.ai/

Product Announcement: Introducing Cleanlab Studio's Auto-Labeling Agent! Is your team bogged down by the tedious task of manual data labeling? Are your algorithms struggling with limited examples? Cleanlab Studio’s Auto-Labeling Agent is designed to alleviate these challenges by efficiently suggesting accurate new labels to complete your dataset effortlessly. 🔍 Why It Matters: Fully automated annotation can overlook important nuances, while fully manual annotation is both error-prone and labor-intensive. By blending human and automated efforts, our Auto-Labeling Agent enhances the accuracy and efficiency of data annotation, making the process seamless and significantly quicker. These AI-suggested labels mean humans only need to focus on the rows where their manual efforts have the highest ROI. ⚙️ How It Works: Simply import a dataset with less than 50% labels, and our Auto-Labeling Agent will automatically provide high-confidence suggestions for the remaining rows. This approach allows for streamlined and rapid iterations while keeping you in full control. Our pilot users have experienced an 80% reduction in time spent on labeling and iterations. Ready to put your annotation on cruise control? 🏎️💨 Read our blog for more details and sign up for Cleanlab Studio today – it’s free to try, with no code required. Learn more: https://lnkd.in/gfQqtjm9 Sign up for Cleanlab Studio: https://app.cleanlab.ai/

Despite rapid recent advances in Foundation models, most enterprises still struggle to deliver value with AI. Models became cheaper/faster, but remain fundamentally unreliable and prone to hallucination. Developed through years of hard research, Cleanlab software tackles this challenge head on. Today we're honored to be featured amongst the Top 5 AI Hallucination Detection Solutions https://lnkd.in/gHAJPpYR Cleanlab adds trust to every input and output of your GenAI solutions, so you can finally achieve reliable AI ✨

Want to improve LLM trustworthiness? Check out this innovative approach! 🌟 In the evolving AI landscape, ensuring language model reliability is crucial. One promising method is agent self-reflection and correction, explored using Cleanlab Trust LLM with LlamaIndex introspective agent framework. What is agent self-reflection and correction? 🤔 AI agents critically evaluate and refine their outputs to meet trustworthiness thresholds, ensuring more accurate information. Why is this important? 🌟 - Mitigating Hallucination: Reduces factually incorrect outputs. - Enhancing Trustworthiness: Improves output reliability, crucial for healthcare, finance, and legal fields. - Iterative Improvement: Promotes continuous learning and robustness. - Transparency: Ensures clear criteria for corrections and accuracy. Practical Example 🛠️ Using Cleanlab and Llama Index, I developed a tool-interactive reflection agent. It effectively reduces errors, as demonstrated by correcting misleading statements about nutrition. Find implementation details and code in my GitHub repository and read the research paper "CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing." Looking Ahead 🚀 Integrating self-reflection in LLMs is a major AI advancement. As we refine these techniques, expect more reliable and trustworthy AI systems. Check out the notebook! https://lnkd.in/ehEWBJh3 #AI #MachineLearning #DataScience #LLM #ArtificialIntelligence #TrustworthyAI #Innovation #Cleanlab #LlamaIndex

Tomorrow! Join us for a Sake Social in SF with Cleanlab and @_odsc! 🍶✨ Let's toast to AI with a data curation keynote from @cgnorthcutt and enjoy some delicious sake. Don't miss out on this opportunity to network and learn from industry experts. RSVP: https://bit.ly/3RQlPmw

In our latest blog, we walk through the benefits of reframing time-series data as a classification problem. This approach simultaneously leverages enhanced performance, flexibility, and interpretability while access to a variety of models like random forests and neural networks can capture complex patterns more effectively. Using a popular energy consumption dataset, we benchmark Cleanlab Studio AutoML alongside Prophet and Gradient Boosting - Cleanlab Studio's AutoML reached 94.61% accuracy, far outperforming other methods. Not only does Cleanlab Studio AutoML achieve superior results with minimal effort through automated modeling training, hyperparameter tuning and predictor selection, it simplifies deployment for production-level models, enabling real results in minutes. Accelerate development time and enhance forecast accuracy with Cleanlab Studio. Read the full blog and sign up today to see the difference it can make in your projects. https://lnkd.in/gKptsgBN

Give it a shot - sign up for Cleanlab Studio today. https://app.cleanlab.ai/

The problem is that most companies are investing 80% of their AI budget into models and only 20% into data. Here’s what needs to change to unlock AI value. Shift investment from gathering data to curating data. Curation builds a dataset for model consumers vs. human consumers. The cost of model training drops because reliable use case support is delivered with less data and less complex models. Shift investment from engineering data pipelines to engineering data-generating processes. Moving data from one place to another creates no value, while each new dataset makes the business more valuable. Data creates more AI opportunities. Unique datasets are the primary competitive advantage and AI moat. Models are only best-in-class for a few months. GPT-4o was upstaged by Gemini 1.5, which was just surpassed by Claude 3.5. The investment required to win on massive models is much too high for enterprise business models to support. Follow me here and click the link under my name to learn more about how to deliver value-centric AI. #AI #Data #AIStrategy #DataQuality

Is “data-driven with a disclaimer” an acceptable future for AI applications? Tomasz Tunguz posed the same question in one of his latest newsletters highlighting the implicit bias consumers feel toward their AI products. Tomasz asserts that the totally reasonable expectations we have for SaaS products to be both safe and accurate for enterprise doesn’t apply to the AI era—at least not yet. With every AI software sneaking disclaimers into their products (“Gemini may display inaccurate info…double-check its responses” or, “ChatGPT can make mistakes…check important info.”), we’ve all but accepted the reality that we can’t totally trust AI applications. And if we can’t trust them, we can’t fully embrace them. “We suffer from a cognitive bias: work performed by a human is likely more trustworthy because we understand the biases & the limitations. AIs are a Schrodinger’s cat stuffed in a black box. We don’t comprehend how the box works (yet), nor can we believe our eyes if the feline is dead or alive when we see it," says Tomasz. The more important our work is, the more confident we all need to be. Even human error rates are too much for the most financially—and socially—critical data use cases. Self-driving cars. Navigation systems. Insurance claims. News summaries. AI trust requires trustworthy AI. And trustworthy AI requires trustworthy data. How is your team going beyond the status quo to meet the real-time data quality demands of generative applications? Let me know in the comments!