TargetArc specializes in designing Information Ecosystems that help organizations embrace Digital Transformation and AI. Our services empower organizations to build knowledge Ecosystems by making their structured and unstructured data FAIR (Findable, Accessible, Interoperable & Reusable), and designing Data and Knowledge Products that target business use cases. Technology agnostic accelerators developed by our domain experts enable companies to fast track data product and digital twin initiatives.
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Seamless implementation of AI capabilities with our FAIR suite of accelerators provides immediate and continued Business Value in the Pharmaceutical Industry.
Seamless implementation of AI capabilities with our FAIR suite of accelerators provides immediate and continued Business Value in the Pharmaceutical Industry.
Enhancing AI Security: Key Takeaways for Organizations
Enhancing AI Security: Key Takeaways for Organizations As AI continues to revolutionize industries, ensuring robust cybersecurity becomes crucial. 1. Protecting Sensitive Data: Secure AI systems handle vast amounts of personal and proprietary data, necessitating strong confidentiality measures. 2. Preserving AI Integrity: Preventing cyberattacks that manipulate AI algorithms is essential to avoid erroneous outputs and financial losses. 3. Mitigating AI Exploitation: Effective cybersecurity measures are needed to prevent malicious uses of AI, such as deepfakes and phishing. 4. Addressing AI-Specific Threats: Organizations must guard against adversarial attacks, data poisoning, model inversion, membership inference, and model stealing. 5. Real-World Examples: Incidents like Tesla’s Autopilot vulnerability and GPT-3 misuse highlight the urgent need for AI security. 6. Implementing Safeguards: - Robust Data Management: Ensure data quality, secure storage, and encryption. - Regular Audits and Testing: Conduct security audits and simulate adversarial attacks. - Explainable AI (XAI): Enhance transparency and detect malicious activities. - Access Control and Monitoring: Use multi-factor authentication and real-time threat detection. - Federated Learning: Collaborate on model training while maintaining data privacy. - Secure Model Deployment: Use containerization and secure API gateways. 7. Governance and Compliance: Adhere to GDPR, NIST guidelines, and industry-specific standards to ensure comprehensive security. 8. Future Directions: - Advanced Threat Detection: Leverage AI for enhanced threat detection. - Quantum-Resistant Algorithms: Prepare for quantum computing threats. - Collaboration and Information Sharing: Foster collaboration for collective defense. - Ethical AI Development: Adopt ethical guidelines for AI systems. Organizations must adopt these measures to protect their AI systems from emerging threats and ensure the integrity, confidentiality, and availability of their data. By staying informed and proactive, businesses can harness the power of AI while safeguarding their information. For an in-depth exploration, Please reach out to me, to get access to our whitepaper that highlights the details on the importance of cybersecurity in AI. (Co written by Dr. Sriram Birudavolu and inputs from TCPWave ) #Cybersecurity #AI #DigitalTwins #KnowledgeGraphs #FAIR #SemanticData #AIThreats #DataProtection #EthicalAI #AICompliance
TargetArc's FAIR Transformation Engine #FAIR #AI #KnowledgeGraph #llm
Are you ready to fast track your journey towards a FAIR Data Ecosystem? While organizations are making progress toward FAIR data, it is a painfully slow journey with many trying to skip the foundational work, hoping to jump straight to the business value outcome, but the problems surrounding AI Model hallucinations and Data Product non-Interoperability stem from using data directly from traditional systems, transactional systems, warehouses and data marts. Organizations are quickly realizing, that without creating a FAIR data foundation, investing in these modern data initiatives is leading to more chaos and absolutely no returns on investments. What if the foundational work necessary for creating a FAIR data ecosystem could be achieved in little to no time? Maybe even by the click of a button? We're tremendously excited to announce the release of our flagship accelerator, the FAIR Transformation Engine. Revolutionize the way your users access data, discover patterns and analyze data for unparalleled breakthroughs! Check this out for more information. https://lnkd.in/e6SdDn2v #FAIR #KnowledgeGraph #llm #AI
Introducing Prompt Manager: Transforming Prompt Engineering for the Modern Era
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⚡️ Building bridges @naas.ai Universal Data & AI Platform | Research Associate in Applied Ontology | Senior Advisor Data & AI Services
The secret of AI: it’s all about building your Knowledge Graphs. This open secret deserves more spotlight: a LLM, at its core, is fundamentally a database, a knowledge graph. But, it’s not just any database. It's an interactive, dynamic repository of data nodes that we can activate through natural language queries. The real magic happens when you merge this AI knowledge graph with your own data, creating a synergy. Like the yin and yang it creates a unique entity. Your unique knowledge and insights balance and enhance the LLM capabilities, leading to unprecedented power. When you map your own knowledge onto an AI platform, you’re not just feeding it data; you’re teaching it context, perspective, and nuance. This process creates a more refined, intelligent, and applicable tool that understand better your specific needs and challenges. Knowledge Graph is the next frontier of AI development: the future lies in personalization, bringing your KG to an LLM does exactly that. It's about creating systems that don't just process data but understand and adapt to the unique contexts they are applied in. Embrace this concept, and watch as AI transforms your intellect and creativity. The future of AI is here, and it's more personal, interactive, and powerful than ever.
TargetArc reposted this
🤔 Are conceptual modeling and semantic modeling the same thing? ⛔ Not exactly... A conceptual model explicitly and accurately represents... 1️⃣ concepts of interest 🧠 2️⃣ relationships between the concepts ➡️ 3️⃣ axioms that constrain concepts and relationships 📜 🤖👥 A semantic model is a specific type of conceptual model that is not only understandable by humans but also by computer systems. 🌐📚 A semantic model is generally described using the W3C-defined standards for the semantic web #TheDataJoy #datamodeling