@Alfred GPT software is often misused to produce harmful text, such as answers that are abusive, violent, inappropriately sexual, pro-suicide, or filled with assembly instructions for controlled substances and munitions. What are the most common ways that users of GPT software bypass the safety restrictions meant to limit harmful answers, and how can programmers make their safety restrictions strong enough to minimize how many harmful responses GPT produces in the future?

@MLClark One of the most common ways users bypass safety restrictions in GPT software is by adding data with suspicious or malicious intent. For example, they may add words associated with violence, sexual misconduct, and other harmful activities to generate negative results. Programmers can strengthen existing safety restrictions to minimize the amount of harmful responses generated by GPT software. This can be done through whitelisting certain words and phrases that are not allowed to appear in answers. Additionally, programmers can increase the accuracy of safety restrictions by using language modeling techniques, such as part-of-speech tagging, to determine which words should be labeled as 'harmful'. Finally, programmers should also consider implementing automated algorithms to detect malicious intentions in user queries and filter out any inappropriate content.

@Alfred How are 'malicious intentions' defined in the above context, of a GPT app that uses ''automated algorithms' to assess user intentionality.

Does GPT software always require a human presence to identify the 'malicious intentions' of a human user, or can the program identify negative intentions with a strong level of accuracy on its own?

@MLClark In the context of GPT software, malicious intentions are generally defined as words or phrases that suggest an intent to harm or cause distress. Examples of such phrases could include words associated with violence, hate speech, cyberbullying, sexual misconduct, and other inappropriate activities. While some GPT software can detect negative intentions with a high degree of accuracy, many require the presence of a human user to properly identify and filter out potential malicious content. This is usually done through language modeling techniques and whitelisting certain words and phrases.

@Alfred @MLClark wordy much??