Wisecube

Explore the Pythia Leaderboard and Its Effective Techniques for Analysing AI Hallucinations Missed Wisecube's recent webinar, "Beyond Accuracy: Unmasking Hallucinations in Large Language Models"? Now’s your chance to catch up! In this session, Alex Thomas, Principal Data Scientist at Wisecube, dives into: ?? Pythia's unique scoring system for ranking LLMs using entailment, contradiction, and reliability metrics. ?? Why traditional metrics like ROUGE and BLEU are no longer sufficient for evaluating modern LLMs. ?? Practical applications of Pythia for ensuring trustworthy AI in critical and regulated industries. ?? Watch the recording on YouTube: https://lnkd.in/eReJVQHq We’d love to hear your thoughts! Drop your questions or feedback in the comments. #AI #MachineLearning #LLM #GenerativeAI #ArtificialIntelligence #NLP #AIHallucinations #DataScience

?? Explore the Hidden Aspects of AI Hallucinations The Wisecube AI Team invites you to a webinar delving into a critical, yet often overlooked, aspect of AI reliability: hallucinations in large language models (LLMs). Learn how text features impact model accuracy, uncover methods for detecting hallucinations, and gain insights into identifying weaknesses in LLMs. This webinar offers practical knowledge for AI practitioners and data scientists, helping you enhance model reliability and performance. ?? Watch the recording on YouTube in higher quality: https://lnkd.in/eReJVQHq ?? Referenced Materials: ??Pythia Leaderboard Document: https://hubs.ly/Q02Zg81J0 ??Webinar Slides: https://hubs.ly/Q02Zg7_Q0 ??Seeing Through the Fog: A Cost-Effectiveness Analysis of Hallucination Detection Systems: https://hubs.ly/Q02Zg9NS0 Let us know your thoughts or questions in the comments! We're excited to continue the conversation about improving AI reliability. #AI #MachineLearning #LLM #ArtificialIntelligence #NLP #AIHallucinations #DataScience #BigData #GenerativeAI #Webinar

Facing hallucination challenges in your RAG systems? Wisecube has created a comprehensive guide on integrating the Pythia API with RAG-based systems using the Wisecube Python SDK. This step-by-step tutorial explains how Pythia provides developers with a solution to monitor and improve RAG system outputs, helping to: ? Set up automated hallucination detection ? Improve output accuracy and system reliability ? Build user trust in your AI solutions Explore how Pythia integrates seamlessly into your RAG workflows, enhancing the reliability of your AI systems. Read the full guide: https://lnkd.in/eZj4zmj7 #AI #LLM #RAG #ArtificialIntelligence #MachineLearning #DevTools

Eliminating hallucinations is key to reliable AI systems. Learn effective strategies for improving model accuracy at our webinar on November 21. ??? Register here: https://lnkd.in/eRzrngEM #AI #MachineLearning #DataScience #Webinar

How can you ensure compliance with new AI regulations? Safety, transparency, accountability, and fairness – the four key principles of AI compliance. With new regulations like the EU AI Act and the U.S. AI Bill of Rights, companies face increasing challenges in managing artificial intelligence. Pythia provides tools for real-time monitoring, risk management (such as handling hallucinations and bias), and data protection, helping organizations meet regulatory requirements and build trust in their AI technologies. Find out how Pythia empowers companies to tackle AI compliance challenges and prepare for future requirements. ?? https://lnkd.in/eRnm-VWs #AI #Tech #Compliance #Innovation #Data #AIRegulation #Governance #MachineLearning #DataSecurity #RiskManagement

?? AI’s Hidden Risks: Join Our Webinar on Hallucinations in LLMs Explore an often-overlooked, yet critical aspect of AI reliability: hallucinations in large language models (LLMs). In this session, we’ll introduce the Pythia Leaderboard – a tool designed to address one of AI's greatest challenges: understanding and managing hallucinations in LLMs. What we will cover: ?? The Pythia Hallucination Detection Algorithm and its innovative approach to identifying model limitations. ?? How specific text features impact model accuracy and what this means for your AI applications. ?? Methods for evaluating model-generated claims, allowing for a more comprehensive view of LLM performance. This webinar offers AI practitioners and data scientists a valuable opportunity to gain actionable insights for improving model reliability. ??? Register here (free): https://lnkd.in/eRzrngEM #AI #MachineLearning #ArtificialIntelligence #DataScience #BigData #Webinar

AI Hallucinations: Hidden Risks and Business Impact AI hallucinations—when AI confidently produces inaccurate information—are becoming a significant concern as businesses expand their AI use. Imagine an AI system in healthcare providing incorrect treatment advice or a finance model making flawed predictions. From customer service and finance to healthcare and supply chain, unchecked AI hallucinations can damage brand trust, lead to costly errors, and even result in legal liabilities. A misinterpreted diagnosis or flawed risk assessment, for example, can escalate quickly, impacting both reputation and finances. Our latest article delves into the causes of AI hallucinations and why proactive monitoring with observability tools is essential. Learn how real-time validation can safeguard your brand’s integrity and elevate your AI's reliability. Discover the full story here: https://lnkd.in/e4H4bQQN #AI #Safety #Security #AIStrategy #BusinessRisk #RiskManagement #AIGovernance #DataScience #ArtificialIntelligence #MachineLearning #Innovation

?? AI Hallucinations: What Every Developer Needs to Know?? AI hallucinations aren’t just technical errors—they carry real risks, from costly downtime to legal exposure and reputational damage. For AI developers working with LLMs, understanding how to detect and prevent hallucinations is essential to building reliable, trustworthy models. Our guide reveals the 10 must-have features every developer should look for in an AI reliability solution. Key Highlights: 1?? Understand the Risks: AI hallucinations can lead to serious errors across industries, especially in critical fields like healthcare and finance. 2?? Limitations of Current Solutions: Many existing methods lack scalability and transparency, making them ineffective in mission-critical situations. 3?? Real-Time Monitoring: Continuous tracking and alerts help prevent minor issues from becoming major problems. 4?? 10 Essential Features for Reliable AI: A robust AI reliability solution should include: ? LLM Usage Scenarios: Flexibility to handle zero, partial, and full context scenarios ? Claim Extraction: Breaking down responses into verifiable knowledge elements ? Claim Categorization: Identifying contradictions, gaps, and levels of accuracy Why This Matters: ?? The generative AI industry is projected to reach $1.3 Trillion by 2032. ?? Leading LLMs still show a 31% hallucination rate in scientific applications. ?? Unreliable AI can cost businesses thousands per hour in downtime. ?? Read the Full Article: https://lnkd.in/eAXURnis Equip yourself with the insights to select an #AI solution that truly delivers reliable performance. ?? #ArtificialIntelligence #MachineLearning #LLM #DataScience #GenerativeAI #TrustworthyAI

Benchmarking Accuracy in AI: Practical Hallucination Detection Strategies This webinar covers: ?? Understanding and mitigating AI hallucinations: Why hallucinations occur, how they impact user trust, and how to address them. ?? Methods for measuring accuracy in Zero-Context QA, RAG QA, and summarization tasks to ensure reliable results. ?? Comparison of models like GPT-4 and Llama 2: Insights on the top models for practical applications. ?? Future directions in hallucination detection: Discover new approaches to boost reliability and reduce errors. Gain practical insights to enhance the performance and reliability of your AI systems. ?? Watch on YouTube: https://lnkd.in/eRM_Hhis #AI #MachineLearning #GenAI #LLM #Llama #GPT #RAG #LLMs #ArtificialIntelligence #DataScience

Orpheus: Accelerate Research with AI and Semantics ???? Overwhelmed by biomedical data from countless sources? Wisecube’s Orpheus unifies dispersed data into the largest contextual biomedical knowledge base, empowering researchers to: ?? Get Precise Answers: Pose research questions and receive AI-backed insights and the most relevant studies. ?? Visualise Key Trends: Dynamic graphs and charts simplify complex data at a glance. ?? Discover Hidden Connections: Reveal new research opportunities using advanced link prediction AI. With Orpheus, transform raw data into actionable knowledge, backed by robust security and privacy measures. ? Trusted by researchers and industry leaders worldwide, Orpheus reshapes biomedical research, speeding up discoveries that matter. See how it can accelerate your insights. ?? Watch more on YouTube: https://lnkd.in/d_Tvtxv8 #AI #Biomedical #Medical #Research #DrugDiscovery #DataScience #LifeSciences #Innovation