Auxane explains 'Responsible AI' as an AI conforming to cultural, regulatory, social, and environmental norms, while 'AI Governance' is the actionable guide towards achieving it.
@whats_ai
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AI Accountability: Responsibility Through Practice, Not Legal Liability
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AI, not being an individual or legal entity, can't be liable. The focus should be on adopting responsible AI practices and decisions, involving analysis of rights to understand Responsible AI, often culture-dependent.
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Responsible AI: Insights from Ethics Research Discussion
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Understanding the ideals of Responsible AI is vital within the realm of modern technology. Here are some insights from my discussion with Auxane Boch, Research Associate and Doctorate Candidate at the Institute for Ethics in AI.
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PhD Journey: From Academia to Building Towards AI and Educational Content
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My Ph.D. highlights were working on building @towards_AI and making educational videos and podcasts with amazing guests… I didn't quit academia out of discontent, but to pursue something I simply personally enjoyed even more.
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7 Tips to Mitigate Hallucinations in Large Language Models
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Looking for more practical advice on overcoming the challenges with LLMs? Check out the newsletter at https://
louisbouchard.substack.com/p/7-tips-to-mi
tigate-hallucinations
… Subscribe to the newsletter for more AI insights! -
Cost-Effective Output Refinement Through Parameter Adjustment and Prompt Engineering
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Refining outputs is cost-effective through adjusting parameters like temperature and penalties. Implementing prompt engineering can control hallucinations and biases, promoting cautious, correct outputs rather than forced errors.
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Combat AI Hallucinations with RAG and Deep Memory Solutions
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Facing persistent hallucinations? Try Retrieval Augmented Generation or Deep Memory by @activeloop . It embeds input data, cross-references with documents and only answers when it matches, limiting biases.
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LLMs Hallucinations: Data Quality and Source Diversity Solutions
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LLMs often make up answers, intending to complete the text instead of recognizing their flaws. Mitigating these biases and illusory tendencies requires careful data cleaning and the use of varied, reliable sources.
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AI Model Hallucinations and Biases: Major LLM Challenges
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Is your AI model giving you unexpected results? Hallucinations and biases are the biggest challenges we face with large language models (LLMs) like ChatGPT. The gravity of the problem intensifies, especially when these models power commercial applications.
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Prompt Engineering: Diversity, Collaboration, and AI Innovation
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Prompt Engineering values diversity, encouraging learning and collaboration regardless of background. It's about gaining experience, integrating skills, and pioneering in the AI field.