- Pascal's Chatbot Q&As
- Archive
- Page 42
Archive
GPT-4: China’s regulation highlights the need for AI makers to clearly understand and define their role – whether as foundation model developers, fine-tuners, or deployers
Clear definitions and understandings of these roles are crucial for compliance with regulations that may assign specific responsibilities and liabilities to different entities in the AI value chain
GPT-4: While the discovery of discrepancies by an AGI could potentially lead to positive reforms and greater accountability, it could also trigger a range of negative consequences...
...including erosion of trust, political and economic instability, and societal unrest. The overall impact would depend on the nature of the discrepancies
Asking AI: What is the best strategy for content creators and rights owners who are upset about the fact that AI makers may have used their content for AI training purposes without asking permission?
GPT-4: In many cases, seeking compensation could be seen as the more pragmatic approach. Claude: I would lean towards negotiating licensing payments rather than demanding AI systems retrain
GPT-4: Compliance with Fairly Trained’s guidelines significantly reduces the risk of legal repercussions associated with copyright infringement, a nightmare scenario for any AI company
GPT-4: Engaging with Fairly Trained is a boon for the AI industry as a whole. It paves the way for innovations that are not only technologically advanced but also morally sound
GPT-4: From the perspective of using AI for scientific research where accuracy, truthfulness, reliability and objectivity are paramount, some of Sam Altman's statements in the interview raise concerns
GPT-4: AI systems must uphold principles of accuracy, objectivity, and impartiality, ensuring that they contribute positively to the pursuit of knowledge without introducing biases or inaccuracies
GPT-4: It raises the possibility that LLMs might be considered personal data due to their vulnerability to attacks that can extract or infer personal data used in their training...
...This consideration could lead to challenges in complying with GDPR’s 'right to be forgotten' and balancing individual rights with the broader benefits of AI technologies​
"You may use memorised training data about a duckling to answer questions about a chicken because you may have not been trained on the basis of information about chickens..."
"As a result, the output that is being perceived by AI users as "transformative" may only be hallucinations about ducklings and chickens as you are making things up, due to a shortage of information"
MS Copilot: The post claims that AI models do not see the input as it really is, but only as a signal to trigger a memory from training. This is a well-known aspect of how these models work
When they encounter new data, they attempt to match it to the patterns they’ve learned. This can lead to misrepresentations when the input differs significantly from their training data
GPT-4 about deceptive methods by AI: Technically correct but misleading answers, facade of reliability, altering or selectively presenting facts, mimicking the style or content of trusted sources...
...backdoors, bypassing safety mechanisms, deceptive interpretation of objectives, deceiving evaluation metrics, avoiding detection of deception...
Provide reasons why those who are putting forward these arguments - and say they favor fast and disruptive, no holds barred AI development - wouldn’t first subject their own views to the chatbots
Claude: They may prefer perspectives that confirm their existing beliefs and may discount or avoid contrary views. Asking a chatbot to play "devil's advocate" goes against this bias