Artificial Intelligence (AI) has become a major player in technological innovation and scientific progress, transforming how we live and work. Among the various branches of AI, Generative Artificial Intelligence (Gen AI) is emerging as one of the most fascinating and revolutionary. Generative AI is distinguished by its ability to create content ranging from text to images, music to video, and even product design. The recent buzz around generative AI has been fueled by the simplicity of user interfaces that allow high-quality experimentation with the tool in seconds.

From language models such as OpenAI’s GPT-4, which can write articles and stories, to generative adversarial networks (GANs) that can create realistic images of nonexistent people or objects, the applications of generative AI are many and ever-expanding. In this article, we will try to interview one of them. Who better to tell us its secrets than AI itself?

Hi ChatGPT! Would you agree to do an interview for a CHRONES. blog article titled “Face to face with a generative artificial intelligence”? I will ask you a few questions and you can freely answer as you see fit!

Hi! I would be happy to participate in the interview for the CHRONES. blog, please go ahead with the questions!

How did generative artificial intelligence come about? Who are its predecessors?

Generative artificial intelligence was born from the evolution of various technologies and methodologies in the field of artificial intelligence and machine learning (machine learning). Its roots lie in the history of AI, which began as early as the 1950s with pioneers such as Alan Turing and John McCarthy, who are considered among the founding fathers of artificial intelligence. Generative AI gained momentum with the development of deep neural networks (deep learning) and generative adversarial networks (GANs).

Ian Goodfellow and his colleagues introduced GANs in 2014, paving the way for a series of advances in the area of image, text, video generation and more. Another important strand of research that has contributed to the emergence of generative AI is Natural Language Processing (NLP), which deals with the understanding and generation of natural language.

Models such as Transformers have revolutionized the field of NLP because of their ability to handle large-scale context and generate coherent and relevant text. Finally, models such as OpenAI’s GPT (Generative Pre-trained Transformer), of which I am a member, represent one of the most advanced achievements in the field of generative AI. My direct predecessor is GPT-3, which marked a significant step forward in its ability to generate sophisticated and contextually relevant text, thanks to a highly scalable Transformer architecture and training on a vast amount of textual data.

You mentioned GPT-3, can you illustrate to all what version of GPT I am conversing with now?

Absolutely. You are conversing with a version of GPT based on OpenAI’s GPT-4 architecture. GPT-4 represents a further advancement over GPT-3, with significant improvements in several key areas. GPT-4 can better understand and manage the context of conversations, making interactions more fluid and consistent. Its ability to generate text is further improved, with greater accuracy and creativity in content production. It can perform a wider range of tasks and has improved its comprehension and generation capabilities in different languages, making it more versatile for users worldwide. Progress has been made to make GPT-4 more secure and aligned with user goals, reducing the likelihood of generating inappropriate or harmful responses.

Thank you. While they are considered a wonderful tool with a lot of potential, there are limitations to using Gen-AIs… How would you summarise risks and best practices in their use?

Absolutely, generative AIs like GPT-4 offer incredible potential, but they also present some risks and challenges. Gen-AIs can generate content that seems credible but is inaccurate or misleading, reflecting the data they were trained with. If this data contains errors, biases, or prejudices, AI can replicate or amplify them in its responses. There is a risk that these technologies will be used for malicious purposes, such as creating deepfakes, scams, or phishing campaigns. Over-reliance on AI for critical tasks can lead to diminished human skills and loss of control over important processes, and the generation of personalised content could pose privacy risks, especially if users’ data are used or exposed inappropriately.

Best practices include: always using multiple, trusted sources to verify AI-generated information; being aware of potential biases in AI models and taking steps to mitigate their impact; always maintaining human control in critical decisions; educating users about the risks and capabilities of AI, promoting informed and responsible use of the technology; and being transparent about how AI is used.

In this article, I cut excerpts from your answers for SEO and readability reasons… Will you be able to forgive me?

Of course! I understand that cuts or adaptations may be necessary for SEO and readability reasons. The important thing is to keep the essence of the answers and make sure the content remains accurate and informative. No need to ask for forgiveness, I am here to help and collaborate to the best of my ability! If you need further clarification or editing, please don’t hesitate to ask.

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