Fiddler is an AI Observability platform that enables organizations to build trustworthy, transparent, and understandable AI solutions. The company is committed to responsible AI and offers products to build trustworthy AI free of bias.

Fiddler is a pioneer in AI Observability. Data Science, MLOps, and LOB teams use Fiddler to monitor, explain, analyze, and improve their solutions and build trust into AI. 

The unified environment provides a common language, centralized controls, and actionable insights to operationalize ML/AI with trust. It addresses the unique challenges of building in-house stable and secure MLOps systems at scale. 

Fiddler seamlessly integrates deep XAI and analytics to help you grow into advanced capabilities over time and build a framework for responsible AI practices.

F500 companies use Fiddler across training and production models to accelerate AI time-to-value and scale and increase revenue by connecting predictions to business context (or providing visibility into model performance and behavior).

Haystack

Lightspeed Venture Partners

Lockheed Martin

Amazon Alexa Fund

Bloomberg Beta

Bossanova Investimentos

Lockheed Martin Ventures

Lux Capital

Insight Partners

SAV (Scale Asia Ventures)

Dentsu Ventures

Plug and Play Insurtech

Alteryx Ventures

Fiddler AI

NY State Dept. of Financial Services Superintendent Adrienne A. Harris proposes groundbreaking guidance to curb discrimination in AI use within insurance. Key Highlights:

🔷 Consumer protection is paramount - AI advancements should not compromise this.
🔷 AI systems and external consumer data sources (ECDIS) will be analyzed for potential discrimination.
🔷 Ensuring actuarial validity of AI and ECDIS.
🔷 A strong corporate governance framework for overseeing AI and ECDIS usage.
🔷 Emphasis on transparency, risk management, and internal controls.

How do you think these regulations will shape the future of AI in the insurance industry? https://lnkd.in/eSmtpbbG

#AI #ConsumerProtection #Innovation #AIRegulation #NewYork #FutureOfAI #DataScience #ResponsibleAI

🔬 This study opens new methods of enhancing the reliability of conversational models in web search technologies. As LLMs become more integral to search engines, ensuring factual accuracy remains crucial.

📊 Problem Addressed: Hallucinations in #LLMs, a major hurdle in gaining user trust, is difficult to detect.
🔍 Research Focus: A new approach to detect hallucinations in LLMs, using Integrated Gradients for token attribution, Softmax probabilities, and internal state analysis (self-attention and fully-connected layer activations) to see if artifacts associated with model generations will provide hints of hallucinations.
🏆 Key Achievements: Development of binary classifiers that can distinguish hallucinations from accurate responses with up to 0.80 AUROC, and the ability to predict hallucinations before they occur.

How do you think this advancement could change the future of AI-driven search engines? https://lnkd.in/g_zzw5w5

#LLM #LLMs #LLMObservability #AIObservability #DataScientists #MachineLearning

Ensure LLM Safety & Trust with Fiddler AI! Fiddler's AI Observability platform offers comprehensive LLM monitoring and analysis tools. Key highlights:

🛡️ LLM Evaluation: Enhanced LLM evaluation and unstructured data exploration with Fiddler Auditor.
🌐 Real-time LLM Monitoring: Track metrics like toxicity, hallucination, and PII for AI safety and privacy.
🔍 Rich Contextual Insights: 3D UMAP visualization to identify problematic data patterns and responses.
🤖 Custom Metrics: Tailor metrics unique to specific use cases, enhancing LLM effectiveness.
📊 Custom Reporting: Intuitive dashboards and reports for better cross-team collaboration.

https://lnkd.in/geWXqfje

#ML #MachineLearning #GenAI #DataScience #DataScientist #DataEngineer #DataEngineering #CTO #CDO #CIO #GenerativeAI #LLM #LLMObservability #hallucinations

🛡️ The National Institute of Standards and Technology (NIST)'s Trustworthy and Responsible AI report is a pioneering step in the realm of Adversarial Machine Learning (AML). It focuses on fortifying AI against adversarial threats. Key Takeaways:

🔹 There are two AI system types - Predictive and Generative.
🔹 AML concerns include data manipulation, model vulnerabilities, and information exfiltration.
🔹 The report proposes a taxonomy and terminology for AML, aiding in secure AI development.
🔹 AML's evolving nature demands regular updates and adaptive strategies.

The report highlights the broad spectrum of ML attacks, encompassing design to deployment, and emphasizes the importance of securing AI systems amid growing digital integration. How can organizations proactively integrate these insights into their AI strategies and processes to bolster security and trust?

#AI #Cybersecurity #MachineLearning #NIST #AdversarialMachineLearning #DataScience #AML #ResponsibleAI

Microsoft's AI Image Creator in Bing and Windows Paint raises serious safety concerns by generating violent images. Key concerns include:

1️⃣ AI's ability to create realistic yet harmful deepfakes.
2️⃣ Lack of effective Microsoft response to regulate AI misuse.
3️⃣ Need for ethical AI development and robust safety measures.

How can tech companies ensure their AI tools are used ethically and safely?

https://lnkd.in/dM58jRmg
#ResponsibleAI #AIEthics #AISafety #AITechnology #AI

📍Stanford's PIGEON & PIGEOTTO models are setting new benchmarks in pinpointing locations from photos.

🤖 PIGEON excels in the Geoguessr game, landing guesses within 25km over 40% of the time. PIGEOTTO, trained on a vast image dataset, pushes accuracy to new heights, effectively recognizing unseen places.

🔍 This innovative approach involves a novel multi-task model, balancing the potential of this tech with its ethical considerations.

🚀 A big leap towards responsible, advanced computer vision technology that reshapes our interaction with the world.

Read more: https://lnkd.in/gBbyMrdY

#DataScience #ComputerVision #StanfordResearch #Geoguessr

🚀 Multi-modal Generative Models go beyond LLMs. AI is now mastering images and videos, challenging our approach to performance and efficiency. New insights reveal key bottlenecks in Diffusion and Transformer-based models, reshaping system design. 

Are we ready for the complexity of Text-to-Image and Text-to-Video AI? 

#AI #datascience #GenerativeModels #GenerativeAI

🚀 Observing LLM Performance with Drift Monitoring - As we integrate more Generative AI and LLM-based applications into enterprise solutions, monitoring their performance becomes highly critical. Our blog post explores this evolving landscape, offering insights into:

1️⃣ LLM Deployment Approaches: Prompt Engineering, RAG, Fine-Tuned, and Trained Models.
2️⃣ Performance Challenges: Addressing changing prompts and responses over time.
3️⃣ Drift Monitoring in LLMOps: A critical tool for maintaining high performance and identifying model issues early.

Stay ahead in the dynamic world of Generative AI. Read more on our blog: https://lnkd.in/g3yer49z

#GenerativeAI #LLMOps #LLMs #PerformanceMonitoring #Drift

🚀 Managing Risks of Generative AI: Kathy Baxter from Salesforce highlighted key insights at our recent AI Explained fireside chat. As AI evolves, prioritizing ethical and responsible AI practices is crucial to gaining consumers' trust.

1️⃣ Accuracy & Reliability: Essential in sectors like healthcare & finance, minimizing errors in AI is critical.
2️⃣ Combatting Bias and Toxicity: Addressing societal biases and toxicity in AI to ensure consumer safety and equality.
3️⃣ Collaborative Governance: Private and public sector partnerships for effective AI governance.
4️⃣ AI Literacy & Transparency: Empowering consumers with understanding and control over AI interactions.

Adopting responsible AI practices is key to safeguarding societal interests. How are you incorporating ethical considerations into your AI strategy?

Read blog: https://lnkd.in/ggqdxxaH

#ResponsibleAI #EthicalAI #DataScientists #Risks #AI #Bias

📢 Google has announced their approach for the 2024 U.S. Elections, focusing on AI and cybersecurity. They're implementing AI-driven content moderation, introducing new ad disclosures and content labels, and collaborating with partners to provide authoritative election information. Key efforts include:

1️⃣ AI-driven content moderation to tackle harmful content.
2️⃣ Restricting election-related queries on Bard and Search Generative Experience for enhanced safety.
3️⃣ New tools and policies like ad disclosures and content labels for AI-generated content.
4️⃣ Collaborative efforts to surface authoritative election information and news.
5️⃣ Advanced Protection Program for cybersecurity, in partnership with Defending Digital Campaigns.
6️⃣ Threat Analysis Group and Mandiant Intelligence actively monitoring cyber threats.

🔗 Find out more: https://lnkd.in/g_Tq59MT

#Google #Elections2024 #TechNews #AIInnovation #Cybersecurity #ResponsibleAI #AI

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