What is generative AI and how does it work? – The Turing Lectures with Mirella Lapata

The speaker begins with a warm greeting to the audience, highlighting the intent to demystify generative artificial intelligence (AI). They explain that generative AI combines artificial intelligence with the capability to create new, unseen content, ranging from audio to text and images. Focusing on text generation through natural language processing, the speaker outlines the presentation to cover the past, present, and future of AI, emphasizing generative AI's roots in innovations like Google Translate and Siri, and its evolution to more sophisticated tools like ChatGPT.

The narrative progresses to the sudden prominence of generative AI, sparked by OpenAI's announcement of GPT-4 in 2023. The speaker discusses GPT-4's capabilities, surpassing human performance in standardized tests and generating complex outputs from simple prompts. Highlighting the rapid adoption of ChatGPT compared to predecessors like Google Translate and Siri, the speaker sets the stage for an exploration of the technology behind ChatGPT, including the shift from single-purpose systems to more advanced, multifunctional models.

The speaker dives into the technical foundation of generative AI, focusing on language modeling. They describe the process of training AI using large datasets from the web, involving prediction of next words in sentences. This segment demystifies the transition from simple counting methods to sophisticated neural networks, leading up to the introduction of transformers as the core technology behind ChatGPT and other generative models, explaining the concept of self-supervised learning.

This part elaborates on the structure and function of transformers, the latest in neural network architecture pivotal to the development of generative AI like ChatGPT. The speaker explains the transformer model's reliance on embeddings (vector representations of words) and its ability to predict text continuations. They also touch on the importance of self-supervised learning, where AI models are trained to predict text sequences, and the concept of pre-training followed by fine-tuning for specific tasks.

The speaker discusses the exponential growth in the capabilities of generative AI as models become larger, with billions to trillions of parameters. They question how good a language model can become through scaling and training on vast amounts of text. Despite the impressive progress, the speaker notes the environmental impact, the cost of development, and the diminishing returns of simply increasing model size without corresponding increases in training data.

Addressing the alignment of generative AI with human intentions, the speaker underscores the importance of fine-tuning models to be helpful, honest, and harmless. They delve into the process of fine-tuning with human feedback to train models to avoid toxic or biased outputs, and the financial and ethical implications of integrating human judgment into the AI training process. This segment highlights the ongoing challenge of creating AI that aligns with human values and preferences.

The speaker conducts live demonstrations of ChatGPT, asking it various questions to showcase its capabilities and limitations. The interactions reveal the AI's proficiency in generating poems, answering factual questions, and explaining jokes, but also its challenges with brevity and task specificity. These examples illustrate the practical application of generative AI and its potential for both impressive outputs and humorous or unexpected responses.

The speaker reflects on the broader impact of generative AI, from its potential to revolutionize access to information and automate tasks to its implications for job displacement and the propagation of misinformation. They highlight specific instances where AI-generated content, like songs or news, fooled the public or caused significant financial losses. The discussion extends to the environmental cost of running large AI models and the societal need to balance innovation with ethical considerations.

In concluding, the speaker contemplates the future of generative AI, weighing its benefits against its risks. They argue for the necessity of regulation, similar to other potentially risky technologies, to ensure that the development and application of AI are aligned with societal values and safety. The speaker encourages a thoughtful consideration of AI's role in our future, emphasizing the importance of addressing climate change and the ethical use of AI technology.