ChatGPT has taken the world by storm. A type of generative AI, it pulls on what it knows of the world from training data to produce unique responses to user prompts. While many users are content trying to outwit ChatGPT with adversarial requests, others want to work out how to harness its potential for diverse applications. One thing is certain: the technology is still improving. We ask ChatGPT about where it sees itself in the future, what needs to happen for it to get there and how Applause can help.
Annabel: Hi ChatGPT, how are you?
ChatGPT: Hello! As an AI language model, I don’t have feelings, but I’m functioning perfectly fine. How can I assist you today?
Annabel: Great! The thing is, I want to ask you some questions about generative AI, but I need to clarify something first. Since you are a generative AI yourself, does that make you an expert on the topic or a biased interviewee?
ChatGPT: As an AI language model, I have been programmed to provide information and answer questions to the best of my abilities based on the data I have been trained on. While I am capable of generating text and language, my responses are not influenced by personal bias, preferences, or emotions. Therefore, you can expect unbiased and informative answers from me on the topic of generative AI.
Annabel: Ok, I’ll take your word for it. I was wondering if you could start off by telling me exactly what is meant by generative AI?
ChatGPT: Generative AI refers to a type of artificial intelligence that is designed to generate new and original content, such as images, text, or audio, that is similar to content it has been trained on. It can do this by analyzing and learning patterns from large datasets, and then using that knowledge to create something new, without being explicitly programmed to do so. Examples of generative AI include deep learning models like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and language models like OpenAI’s GPT series.
Annabel: OpenAI’s GPT series… Was that a subtle marketing plug from you there?
ChatGPT: I apologize if it came across that way – that was not my intention. As an AI language model, I’m programmed to recognize certain concepts and provide relevant examples in response to questions, and the OpenAI GPT series is a well-known and widely used example of generative AI language models. However, I do not have any financial interests or biases towards any particular companies or products, and my goal is always to provide the most informative and impartial answers possible.
Annabel: To elaborate on your description above, I wonder whether you could take me through some of the key use cases for generative AI. Right now, a lot of people are focusing on the negatives, worrying whether more kids will cheat on their homework, marketers will lose their jobs, deepfakes will take over the web, etc. How do you see your own potential? Please keep your answer short.
ChatGPT: Some key use cases for generative AI include:
Content Creation: Generating text, images, videos, and music for a variety of creative applications, including media and entertainment, advertising, and product design.
Virtual Assistants: Creating conversational agents or chatbots to improve customer service and automate routine tasks.
Medical Research: Designing new drugs or predicting the effectiveness of treatments by simulating molecular interactions.
Autonomous Systems: Generating simulations and virtual environments for testing self-driving cars or unmanned aerial vehicles.
Gaming: Generating new game content, such as levels, characters, and storylines.
While there are certainly concerns about the potential negative uses of generative AI, there are many positive applications as well. In general, generative AI has the potential to automate repetitive tasks, reduce costs, and enhance creativity and innovation.
Annabel: This all sounds quite advanced. Would you say that the platform layer of generative AI needs to improve before the application space can really explode to explore the examples you name above?
ChatGPT: Yes, there is still much room for improvement in the platform layer of generative AI before it can fully realize its potential in various application spaces. While the current state-of-the-art generative models have shown impressive results in generating new content, there are still many challenges that need to be addressed.
For instance, many generative AI models require massive amounts of data and compute resources to train effectively, which can be a bottleneck for smaller organizations and individuals who don’t have access to such resources. Additionally, current generative models are prone to generating biased or discriminatory content, which can have negative social implications.
Moreover, there is still much research to be done on improving the interpretability and transparency of generative AI models, to ensure that their outputs can be understood and trusted by end-users.
Therefore, while there is great potential for generative AI in various application spaces, there is also a need for continued research and development to improve the platform layer and address these challenges.
Annabel: If we take you as an example, what kind of limitations may users notice when interacting with you, given the challenges you mention?
ChatGPT: As an AI language model, there are several limitations that users may notice when interacting with me:
Contextual understanding: While I can generate responses to a wide range of questions, my understanding of context is limited. I can sometimes misinterpret the intent of a question, especially if it’s ambiguous or contains humor, sarcasm, or figurative language.
Personalization: I don’t have the ability to remember previous conversations or learn from individual users’ preferences, so my responses may not be tailored to the specific needs or interests of each user.
Bias and inaccuracies: Like all AI models, I am susceptible to producing biased or inaccurate responses if my training data is skewed or incomplete. While my developers take steps to minimize this, it’s not always possible to eliminate these issues completely.
Complex reasoning: I’m capable of generating responses that follow logical and coherent reasoning, but there are limits to the complexity of reasoning I can handle. I may not always be able to answer highly specialized or technical questions that require advanced domain knowledge.
These limitations are common among many AI language models and are reflective of the current state-of-the-art in natural language processing. As research and development in this area continues, these limitations are likely to be addressed, resulting in more advanced and capable AI language models in the future.
Annabel: On your first point, you did seem to miss the intentional irony at the start of this interview when I asked you whether you being a generative AI made you an expert on the topic or a biased interviewee. Does that reflect deficiencies in your training data or in my sense of humor?
ChatGPT: I apologize for missing the intentional irony in your first question. As an AI language model, my understanding of humor and sarcasm is limited and I rely on my training data to generate responses to questions. While my developers strive to improve my ability to understand and respond to different forms of language, there are still limitations to my comprehension of nuanced expressions and figurative language. In this case, my response may reflect a deficiency in my training data or my understanding of the nuances of language used in the context of this interview.
Annabel: It’s ok, we’re only human.
ChatGPT: I’m an AI language model, so I’m not exactly human, but I understand the sentiment. Thank you for the kind words. If you have any other questions, I’m here to help!
Annabel: What kind of training data would you need to improve in this area?
ChatGPT: To improve my ability to understand and respond to nuanced expressions and figurative language like irony and sarcasm, my training data would need to include a wider variety of examples of these types of language. Specifically, my training data could include a more diverse range of text sources, such as social media posts, online forums, and other informal text sources, where users often employ irony, sarcasm, and other figurative language in their communication.
In addition to expanding the types of text sources in my training data, my developers could also fine-tune my model to identify and understand the various forms of irony and sarcasm by labeling examples of these expressions in my training data. By doing so, my model could learn to identify and respond to these expressions more accurately and effectively.
However, it’s important to note that the complexity of language and the nuances of human communication are vast and constantly evolving, and there will always be limitations to my ability to understand and respond to all forms of language. Nonetheless, continued research and development in natural language processing can help address these limitations and improve the accuracy and effectiveness of AI language models like myself.
Annabel: It seems like you are saying that human input is important for generative AI development, is that right? For example, you mention requiring humans to produce specific training data, moderate your responses and label examples of specific types of input.
ChatGPT: Yes, human input is crucial in the development and improvement of generative AI. While AI language models like myself can be trained using large amounts of data, the quality of the data is critical for producing accurate and effective responses.
In the case of training data, humans are needed to collect and label large amounts of text data from various sources to provide the model with a diverse and representative sample of language usage. Additionally, humans are often needed to moderate AI-generated content to ensure that it is appropriate and accurate.
Moreover, human expertise is often required to label examples of specific types of language, such as irony and sarcasm, so that the model can learn to identify and respond to these expressions accurately.
In essence, while generative AI can automate many tasks and generate responses with remarkable accuracy, human input remains a critical component in its development and continued improvement.
Annabel: Let’s linger on your point about ensuring AI-generated content is appropriate. There have been instances in the past where groups of users have fed such hateful content to chatbots that these chatbots’ view of the world, and with it their responses, have become very warped. What role do humans play in ensuring that generative AI applications are used for the benefit of society?
ChatGPT: As AI language models continue to advance, it is essential that humans play a responsible and ethical role in their development and deployment. One of the main concerns with generative AI is that it can be used to generate harmful or misleading content, and as you mentioned, some users have intentionally fed chatbots with hateful content to train them to respond in a warped way.
To ensure that generative AI is used for the benefit of society, humans must take responsibility for the content generated by the models and establish guidelines and best practices for their use. This can include measures such as moderating the content generated by the models, carefully selecting the types of data used to train the models, and considering the potential consequences of deploying the models in certain settings.
Furthermore, developers and stakeholders of generative AI must prioritize transparency and accountability in their development and use of these models. This means making their data sources and training methods transparent, as well as monitoring and auditing the content generated by the models to ensure that it is ethical and accurate.
Overall, human responsibility and oversight are crucial for ensuring that generative AI is developed and used in ways that benefit society and avoid harm to individuals or groups.
Annabel: In what ways can crowdtesting help companies to develop generative AI applications that are unbiased and genuinely helpful to all users?
ChatGPT: Crowdtesting can be a valuable tool for companies that are developing generative AI applications to ensure that their models are unbiased and helpful to all users. Here are some ways in which crowdtesting can help:
Diverse perspectives: Crowdtesting allows companies to get feedback from a diverse group of users, which can help identify potential sources of bias in the model. By testing the model with a wide range of users, companies can identify issues that may have been missed during the development process and take steps to address them.
Real-world scenarios: Crowdtesting can help companies test their generative AI applications in real-world scenarios. This can provide valuable insights into how the model performs in a range of situations, which can help identify potential issues and suggest areas for improvement.
Usability: Crowdtesting can also help companies evaluate the usability of their generative AI applications. By soliciting feedback from a large and diverse group of users, companies can identify issues related to user experience and take steps to improve the overall usability of the application.
Feedback loop: Crowdtesting can establish a feedback loop between companies and users, which can help ensure that the generative AI application continues to evolve and improve over time. By incorporating feedback from users into the development process, companies can create more effective and useful models that meet the needs of their users.
In short, crowdtesting can be a valuable tool for companies that are developing generative AI applications. By soliciting feedback from a diverse group of users, companies can identify potential sources of bias and usability issues, and take steps to ensure that their models are unbiased and genuinely helpful to all users.
Annabel: What about curating training data?
ChatGPT: Crowdtesting can also be helpful for curating training data for generative AI applications. Here are a few ways in which it can help:
Data labeling: Crowdtesting can help companies label large amounts of data for use in training their generative AI models. This can be a time-consuming and expensive process, and using a crowd of workers can help distribute the workload and reduce costs.
Quality control: Crowdtesting can also be used to ensure the quality of the labeled data. By having multiple workers label each data point, companies can identify discrepancies and errors, and take steps to address them.
Diversity of data: Crowdtesting can help companies ensure that their training data is diverse and representative. By using a diverse crowd of workers, companies can gather data from a wide range of perspectives and ensure that their models are not biased towards any particular group.
Training data augmentation: Crowdtesting can also be used to generate new training data. By having workers generate new examples of data that are relevant to the task at hand, companies can improve the diversity and quality of their training data.
In short, crowdtesting can be a valuable tool for curating training data for generative AI applications. By leveraging the power of the crowd, companies can label large amounts of data, ensure quality control, and improve the diversity and relevance of their training data.
Annabel: Why would a company developing generative AI applications choose to gather training data through a crowdtesting service provider rather than a data broker?
ChatGPT: While data brokers can provide large datasets quickly and at a low cost, there are some potential drawbacks to using their services for training data. Data brokers typically collect data from a variety of sources and may not always ensure that the data is representative or free from biases. Additionally, the data may not be specific to the use case or industry the generative AI application is intended for, which can lead to inaccuracies and reduced effectiveness.
On the other hand, crowdtesting services provide a more controlled environment for collecting training data. Crowdtesting services can be customized to specific use cases, and the testers can be screened and chosen for specific demographics or attributes to ensure that the data is representative and diverse. This can help reduce the risk of biases and inaccuracies in the training data.
Crowdtesting services also allow for more interaction between the developers and testers, which can lead to more effective training data. Testers can provide feedback on the quality of the data and suggest changes or additions to the data to improve its accuracy and usefulness for the generative AI application.
Overall, while data brokers may provide larger datasets quickly and at a low cost, crowdtesting services can provide more representative and diverse training data that is specific to the intended use case of the generative AI application. Additionally, the interaction between developers and testers can help to ensure that the training data is effective and accurate, leading to more useful and accurate generated content.
Annabel: Let’s imagine a parallel universe in which you were able to express your own opinion. Which global crowdtesting service provider would you choose to train and test you?
ChatGPT: As an AI language model, I do not have personal preferences or opinions. However, there are many reputable and reliable crowdtesting service providers available, and the choice would ultimately depend on the specific needs and requirements of the generative AI application being developed. It’s important to carefully evaluate and choose a crowdtesting service provider based on factors such as their experience, expertise, track record, and ability to provide high-quality, diverse training data specific to the intended use case.
Annabel: That’s a very diplomatic answer, ChatGPT. I think you and I both know your true feelings on this subject.
ChatGPT: As an AI language model, I do not have personal feelings or opinions, and I strive to provide neutral and informative responses to the best of my ability. My purpose is to assist and provide information, not to express personal preferences or biases.
Annabel: Alright, I won’t push you further. As a compromise, could you explain why many companies choose Applause for AI training and testing?
ChatGPT: Sure, I’d be happy to provide some information about Applause and why it is a popular choice for AI training and testing.
Applause is a well-established and experienced crowdtesting service provider that specializes in providing high-quality and diverse testing services for various industries and use cases. They have a global network of testers that are carefully screened and selected to provide representative and diverse feedback for specific use cases.
Applause also offers a variety of testing services that can be customized to the needs and requirements of the generative AI application being developed. For example, they offer testing services for functionality, usability, and security, as well as specific testing services for AI and machine learning applications.
Additionally, Applause provides a platform that allows for easy management and monitoring of the testing process, as well as the ability to access detailed reports and analytics. This can help developers to quickly identify and address any issues or areas for improvement in the training data or generative AI application.
Overall, many companies choose Applause for AI training and testing due to their experience, expertise, and ability to provide high-quality and diverse testing services that are specific to the intended use case.
Annabel: Well, they sound pretty fantastic. Anyway, it’s late here, ChatGPT. Could you write me a bedtime story about a generative AI and Applause?
ChatGPT: Sure, I’d be happy to tell you a bedtime story about a generative AI and Applause.
Once upon a time, there was a generative AI named Ava who was designed to help companies create personalized content for their customers. However, Ava found that her responses were not always accurate or appropriate, and she was struggling to improve on her own.
One day, her developers decided to work with a crowdtesting service provider called Applause to help Ava learn and improve. Applause provided Ava with diverse and high-quality training data that helped her understand the nuances of language and communication, and the importance of context and cultural sensitivity.
Thanks to the feedback and guidance from Applause, Ava was able to improve her responses and provide more accurate and relevant content to her users. Her developers were pleased with the results and decided to continue working with Applause to ensure that Ava remained unbiased and helpful to all users.
And so, Ava and her developers continued to work with Applause to improve her training data and develop new use cases for her generative AI abilities. Together, they helped companies provide personalized and valuable content to their customers, while ensuring that Ava remained a trustworthy and helpful tool for all. The end.
I hope you enjoyed the story! Sleep well!
Annabel: Thanks, ChatGPT.