Plagiarism

Tim: Let's have a conversation about the problem of plagiarism as it relates to AI. Keep your answers to less than a paragraph and conversational and finish each answer with a question back to me.

ChatGPT: Plagiarism in AI, particularly in creative fields, is a complex issue. AI models, like the one we're using, can generate content based on a vast amount of existing data. However, distinguishing between inspiration and outright copying can be tricky. AI doesn't create with intent, but its outputs can sometimes mirror existing works too closely. This raises questions about originality and intellectual property. How do you think AI-generated content should be regulated to address plagiarism concerns?

Tim: Protections against plagiarism should be included in regulation and the policies and procedures of individual companies. The challenge is how end-users know they may have copied someone's work. How does ChatGPT for example protect against violating copyright or plagiarism?

ChatGPT: ChatGPT, like other AI models, is designed to reduce the risk of plagiarism by generating original responses based on a blend of information it has learned. It doesn't access or retrieve information from the internet in real-time, which helps mitigate direct copying. Moreover, OpenAI has implemented policies to respect copyright and intellectual property, though the system isn't foolproof. The model can occasionally produce content that resembles existing material, as it's trained on a vast corpus of text from diverse sources. Users are encouraged to use AI outputs responsibly and check for potential issues. How do you see the role of the user in managing and identifying potential plagiarism in AI-generated content?

Tim: The only thing the user controls directly is the prompts that they are providing and so the user can be aware of generating content that might mimic an original work too closely. If I request a poem to be written in the style of a live poet - does that violate a copyright law or cause another regulatory or legal issue?

ChatGPT: Requesting a poem in the style of a living poet is a nuanced issue. It's generally not a violation of copyright law to imitate a style, as copyright protects specific expressions of ideas, not styles or genres. However, there could be ethical and legal considerations, especially if the resulting work closely resembles the specific, copyrighted works of that poet. It's always a good practice to attribute inspiration and to avoid closely mimicking unique, identifiable elements of someone's work. The challenge lies in balancing inspiration with originality. In your opinion, how can AI tools like ChatGPT help promote original creative expression while respecting existing artists' rights?

Tim: The key is for users to be open and transparent about how their work has been created and attribute any original inspirations. I think we will see multiple legal cases in the near future that challenge creations that have been too closely inspired by original works. Music streaming platforms like Spotify are now full of AI-generated soundtracks and scores that have been generated suing an existing composer's body of work as a training model. They are attributed, but I think it devalues the original work and there seems to be a backlash from the Spotify community that these creations should not sit alongside traditionally created works. Do you think there is a suitable compromise to be found?

ChatGPT: Your point about transparency and attribution is vital. As for the compromise, it might lie in clearly distinguishing AI-generated works from human-created ones, perhaps through labeling or categorization. This can help in managing expectations and respecting the efforts of traditional artists. It's also about striking a balance between embracing technological advancements and preserving the value of human creativity. Platforms could implement guidelines or special sections for AI-generated content, ensuring they don't overshadow or undervalue traditional works. What are your thoughts on how platforms like Spotify can better manage this coexistence of AI-generated and human-created content?

Tim: I agree with your suggestions relating to labelling and classification. It reminds me of the use of performance-enhancing drugs in sports. Most sporting competitions ban the use completely, because they provide an unfair advantage. In the case of AI creations, I believe most people perceive these to be inferior to the "real thing" - but that could change in the near future as the technology improves further. It goes back to trust, people don't like to be misled or duped - particularly if money is changing hands. How about the problem of plagiarism in the academic world and the use of ChatGPT by students to pass courses and exams - how much of a problem do you think this is?

ChatGPT: The issue of using AI like ChatGPT in academics is indeed significant. It poses a challenge for educators in maintaining academic integrity. The ease with which students can generate essays or answers can lead to a rise in plagiarism if not monitored. However, this also presents an opportunity for educational institutions to adapt their assessment methods. Emphasizing project-based learning, in-person assessments, and oral exams could be more effective in this new landscape. Moreover, tools are being developed to detect AI-generated text, which could help in identifying work that wasn't produced independently. How do you think educational systems should adapt to maintain integrity in the face of these advanced AI tools?

Tim: Moving to live and oral assessments and project based work makes sense and is probably a fairer form of assessment anyway - although it does have implications for the cost of delivering education. The education field has to embrace AI as there is so much potential - I think the answer could be more transparency around the original sources and the ability to quote sources for academic work. Why does ChatGPT not quote the sources it is drawing information from?

ChatGPT: ChatGPT doesn't quote specific sources because it doesn't retrieve or reference information from external databases or the internet in real-time. Instead, it generates responses based on patterns and knowledge it learned during training from a diverse range of texts. This process doesn't involve accessing or storing individual sources, so it can't attribute specific quotes or facts to specific origins. The challenge here is ensuring users understand the nature of the information provided—it's often a synthesis of learned knowledge, not direct quotes from specific sources. Do you think there are ways AI can be more transparent about the nature of its knowledge base and limitations?

Tim: Yes, I think it would be useful for AI applications to be able to describe their limitations more clearly. I could see how a student could take information provided by say ChatGPT at face value and include mistakes in their work. There could be more serious implications for people training for a profession such as medicine or veterinary care. Are there AI tools available now that can cross-check AI responses and/or fact check them?

ChatGPT: Yes, there are tools and approaches designed to cross-check or fact-check information, including those generated by AI. Some of these tools are AI-based themselves, using advanced algorithms to verify the accuracy of information against trusted sources. However, they are not foolproof and often work best in conjunction with human oversight. In fields like medicine or law, where accuracy is crucial, these tools can be used as an additional layer of verification, but they cannot replace professional judgment and expertise. Additionally, educational and professional training programs increasingly emphasize critical thinking and source evaluation skills, which are essential in an era where information comes from various automated sources. How do you see the balance between leveraging AI's capabilities and maintaining human oversight, especially in critical fields like healthcare or law?

Tim: The common approach is to have "humans in the loop". A person or group of people responsible for quality and fact checking. The challenge is that these people can only check a sub-set of the information and so the system is not fool-proof. Crowd-sourcing could also be used (similar to the Wikipedia concept) to make spotting mistakes more likely. If a mistake is spotted, can ChatGPT update its own training data to rectify it quickly?

ChatGPT: The idea of "humans in the loop" and crowd-sourcing for quality checking is indeed valuable, but there are limitations to how quickly AI models like ChatGPT can update their training data. The process of training these models involves large datasets and substantial computational resources. They aren't updated in real-time or based on individual interactions. Instead, improvements and updates are made periodically when the model is retrained with new data, which can include corrections and more recent information.

However, direct user feedback can be valuable for future iterations. For instance, OpenAI collects anonymized data from interactions to understand how the model is being used and where it might need improvements. But in terms of rapid, real-time learning from specific user interactions, current AI models like ChatGPT don't have that capability. How do you think this process of updating and improving AI models could be made more efficient or responsive to user needs?

Tim: Hopefully, the training periods can be shortened over time and it would be good if faulty data could be marked in some way so that it is not used when generating responses. Let's leave it there for today - thanks for the conversation.

ChatGPT: You're welcome! It's been a great discussion, and your insights on how AI can evolve and improve are thought-provoking. If you ever have more questions or topics to discuss, feel free to reach out. Have a great day!