- Pascal's Chatbot Q&As
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- Page 59
Archive
The paper "Google Tag Manager: Hidden Data Leaks and its Potential Violations under EU Data Protection Law" examines Google Tag Manager (GTM) and its compliance with EU data protection laws
GPT-4: The research uncovers that certain data collectors do not disclose what data they collect. This lack of transparency raises concerns about data collection practices in GTM​​
Bard: Even though you're training your LLM on out-of-copyright works, the resulting outputs could be considered derivative works if they share substantial similarities with the copyrighted versions
Bard: Copyright law protects against unauthorized derivative works, meaning you'd need permission from the copyright holders of the modern iterations (e.g., comic book publishers, movie studios)
GPT-4: This method enhances the quality of text embeddings significantly by using LLMs and diverse synthetic data, setting new records in the field
The efficiency improvements in training large language models (LLMs) for text embeddings, as described in the paper, can have significant implications for both costs and environmental impact
GPT-4: The paper details the development and optimization of strategies for training extremely large AI models effectively on one of the world's most powerful supercomputers
This advancement could facilitate more sustainable and accessible AI development, particularly for large-scale models that require substantial computational resources
About the 'Mango vs VEGAP' case. GPT-4: The court's interpretation of transformation may not align with the U.S. Fair Use doctrine's emphasis on adding new expression or meaning
GPT-4: From a strictly U.S. Fair Use perspective, the creation of an NFT from an existing artwork, without substantial alteration or addition of new expression, may not be considered transformative
GPT-4: There are several important topics that could enhance the scope and depth of the white paper "Navigating 2024: Unveiling Generative AI Trends in Finance and Private Equity"
Incorporating these topics would provide a more holistic view of the implications of AI and GenAI in the finance and private equity sectors, considering the broader societal implications
GPT-4: The current absence of prospective configuration in AI learning models, relying predominantly on backpropagation and other traditional learning methods, presents several limitations...
...that this new method could potentially address: Efficiency in Learning, Understanding Biological Learning, Learning in Dynamic and Complex Environments, Transfer and Multitask Learning...
MS Copilot analyzes the LLM and AI report from the House of Lords in the UK: "The recommendations made by the House of Lords could be considered by other countries..."
...as they are based on principles of responsible AI development and use. These principles include promoting innovation, ensuring fairness and privacy, managing risks, and respecting IP rights"
Asking AI: Do you see the paradox so far? Increased technical sophistication versus decreasing human understanding? What will society look like when these suggested solutions completely fail?
Hyper-advanced AI and autonomous systems rapidly accelerate, AI makes most major decisions, humans adopt tech readily, surveillance is pervasive, widening power asymmetry, "Truth" is obscured
Asking AI: If Big Tech was a child and global regulators were a parent, would you say the regulators are truly educating the child or only warning the child now and then or in fact spoiling the child?
ChatGPT-4: Global regulators are primarily "warning the child now and then." Copilot: Spoiling. Claude: Spoiling. Bard: I lean towards a cautious "warning"