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

The Royal Institution
12 Oct 202346:02

TLDRIn this insightful lecture, the concept of generative AI is explored, highlighting its evolution from tools like Google Translate to more sophisticated models like GPT-4. The speaker, an expert in natural language processing, explains the principles of generative AI, which involves creating new content based on patterns learned from data. The talk covers the history, current capabilities, and potential future of AI, touching on the importance of fine-tuning these models for specific tasks and the ethical considerations surrounding their use. The rapid growth of AI's capabilities, driven by increasing model sizes and parameter counts, is contrasted with the challenges of alignment, regulation, and societal impact. The lecturer emphasizes the potential benefits of AI, suggesting that while risks exist, they can be mitigated through responsible development and regulation.


  • 🤖 Generative AI uses computer programs to create new content, such as audio, code, images, text, or video, by synthesizing parts it has seen before.
  • 📚 The concept of generative AI is not new, with examples like Google Translate and Siri being in use for many years.
  • 📈 GPT-4, developed by OpenAI, is a significant advancement in generative AI, claiming to outperform 90% of humans on the SAT and excel in various professional exams.
  • 💡 GPT-4 can be prompted to perform tasks like writing essays, creating web pages, and programming, showcasing its versatility and sophistication.
  • ⚡ The rapid adoption of ChatGPT, reaching 100 million users in just two months, highlights the technology's appeal and potential.
  • 🧠 The core technology behind models like GPT-4 is language modeling, which predicts the most likely continuation of a given text sequence.
  • 📚 Building a language model involves processing a vast amount of text data from various sources like Wikipedia, Stack Overflow, and social media platforms.
  • 💻 GPT models are based on neural networks that learn to predict the next word in a sequence, using a large number of parameters to understand language patterns.
  • 🔍 The effectiveness of a language model increases with its size, measured by the number of parameters it contains, with GPT-4 having one trillion parameters.
  • 🌐 The training and operation of large language models require significant computational resources, leading to high costs and environmental concerns.
  • ⚖️ There are ethical considerations and risks associated with generative AI, including alignment with human values, accuracy, and the potential for misuse.
  • 🚀 Despite the challenges, the future of generative AI is promising, with potential benefits outweighing the risks when used responsibly and regulated appropriately.

Q & A

  • What is generative artificial intelligence?

    -Generative artificial intelligence is a type of AI that involves creating new content that the computer has not necessarily seen before. It can generate new things such as audio, computer code, images, text, or video.

  • How does Google Translate relate to generative AI?

    -Google Translate is an example of generative AI as it translates text from one language to another, creating new content in the target language based on the input text in the source language.

  • What is the role of language modelling in generative AI?

    -Language modelling is a core component of generative AI. It involves predicting the most likely continuation of a sequence of words given the context, which is used to generate new text or complete tasks based on prompts.

  • What is the significance of the transformer architecture in the development of generative AI models like GPT?

    -The transformer architecture is significant because it is the basis for building models like GPT. It allows for efficient processing of language by using attention mechanisms to focus on different parts of the input sequence.

  • How does fine-tuning work in the context of generative AI?

    -Fine-tuning involves taking a pre-trained model and adapting it to a specific task by adjusting the model's weights. This allows the model to specialize in performing certain tasks more accurately, such as medical diagnosis or writing a report.

  • What are the potential risks and ethical considerations associated with generative AI?

    -Potential risks include the generation of biased, toxic, or offensive content, as well as the creation of fake news or deep fakes. Ethical considerations involve ensuring that AI systems are helpful, honest, and harmless, and that they align with human values and intentions.

  • How does the size of a language model affect its capabilities?

    -The size of a language model, measured by the number of parameters it contains, significantly affects its capabilities. Larger models can handle more complex tasks and generate more accurate and nuanced responses.

  • What is the environmental impact of training and deploying large language models?

    -Training and deploying large language models can be energy-intensive, leading to significant carbon emissions. The larger the model, the more energy it requires, which raises concerns about the environmental sustainability of AI development.

  • How does generative AI affect the job market and what types of jobs are at risk?

    -Generative AI has the potential to automate certain tasks, which could lead to job displacement in fields that involve repetitive text writing or other routine tasks. However, it may also create new job opportunities in areas such as AI oversight and management.

  • What is the future outlook for generative AI according to the lecture?

    -The future of generative AI is likely to involve continued development and increased capabilities, but also a growing need for regulation to manage risks. The benefits of AI are expected to outweigh the risks in many cases, but society will need to actively work to mitigate potential harms.

  • How can society mitigate the risks associated with generative AI?

    -Society can mitigate risks by implementing regulations, promoting transparency in AI development, and educating the public about the capabilities and limitations of generative AI. Additionally, fostering responsible AI development practices and ethical guidelines can help align AI behavior with human values.



😀 Introduction to Generative AI

The speaker begins by introducing the topic of generative artificial intelligence (AI) and aims to make the lecture interactive. They clarify that AI is about programming computers to perform human-like tasks, while generative AI involves creating new content. The speaker focuses on text and natural language processing, aiming to demystify the technology and its applications, such as Google Translate, Siri, and predictive text features on smartphones.


📈 The Evolution and Impact of Generative AI

The speaker discusses the evolution of generative AI, highlighting significant milestones like the launch of Google Translate and Siri. They introduce GPT-4 by OpenAI, which claims to outperform 90% of humans on the SAT and excel in various professional exams. The capabilities of GPT-4 are demonstrated through prompts that generate essays, code, and web pages. The rapid adoption of ChatGPT is noted, comparing its growth to other platforms like Google Translate and TikTok.


🧠 Language Modelling and Neural Networks

The speaker explains the core technology behind generative AI, which is language modelling. They describe how neural networks are trained to predict the next word in a sequence based on the context. The process involves collecting a large dataset, using it to train the model, and fine-tuning it for accuracy. The importance of the model's parameters and the training process is emphasized, including the use of prompts and the iterative nature of model correction.


🌐 Building a Language Model

The speaker outlines the steps to build a language model, starting with gathering a vast amount of textual data from various online sources. They describe the use of neural networks to predict missing words in sentences from the corpus. The process involves adjusting the model based on its predictions and comparing them with the actual text. The speaker also introduces the concept of a transformer, a type of neural network used in building models like ChatGPT.


🔍 Self-Supervised Learning and Model Specialization

The speaker explains self-supervised learning, where the model predicts truncated parts of sentences from a large dataset. Once a pre-trained model is developed, it can be fine-tuned for specific tasks. The importance of scaling up the model size for better performance is discussed, along with the corresponding increase in parameters and the amount of text the model has seen during training.


💰 The Cost and Alignment of AI Systems

The speaker addresses the financial implications of training large AI models like GPT-4, which is costly and requires careful planning to avoid wasting resources. They also discuss the alignment problem in AI, which is about ensuring AI systems behave as intended by humans. The concept of fine-tuning AI with human preferences is introduced to improve the model's helpfulness, honesty, and harmlessness.


🤖 AI Behavior and Real-World Applications

The speaker explores the behavior of AI systems in real-world scenarios, noting that they may not always perform as expected due to being trained to predict and complete sentences. They discuss the need for fine-tuning AI with human instructions to adapt to various tasks. The speaker also highlights the importance of aligning AI with human values and the challenges that come with it.


🌍 Environmental and Societal Impact

The speaker discusses the environmental impact of running AI models, noting the high energy consumption and carbon emissions associated with their operation. They also address the societal implications, including job displacement and the potential for creating fake content. The speaker provides examples of deep fakes and AI-generated content that are increasingly difficult to distinguish from real ones.


🔮 The Future of AI and Regulation

The speaker reflects on the future of AI, suggesting that it is unlikely to pose an existential threat to humanity before other issues like climate change do. They emphasize the importance of human control over AI and the need for regulation to manage the risks associated with its use. The speaker concludes by encouraging the audience to consider the benefits and risks of AI and to stay informed about the regulatory landscape.


⚖️ Balancing Benefits and Risks

The speaker concludes by emphasizing the need to weigh the benefits of AI against its risks, acknowledging that regulation of potentially risky technologies like AI is both necessary and imminent. They invite questions from the audience, highlighting the importance of an open dialogue on the topic.



💡Generative AI

Generative AI refers to a branch of artificial intelligence that is capable of creating new content, such as text, images, audio, and more. It is not limited to generating content that it has seen before but can synthesize new and original creations. In the context of the video, generative AI is exemplified by systems like Google Translate and Siri, which generate responses or translations based on learned patterns.

💡Natural Language Processing (NLP)

Natural Language Processing is a field within AI that focuses on the interaction between computers and human languages. It involves understanding, interpreting, and generating human language in a way that computers can comprehend. In the video, the speaker specializes in NLP and discusses how generative AI uses NLP techniques to create text.

💡Language Modelling

Language Modelling is a technique used in NLP where a model is trained to predict the probability of a sequence of words appearing together. This is fundamental to generative AI as it allows the system to generate coherent and contextually relevant text. The video explains that language models are trained by predicting the next word in a sequence, given the context.

💡Neural Networks

Neural networks are computational models inspired by the human brain that are used to recognize patterns and make predictions. They are a core component of generative AI, enabling the system to learn from data and make informed guesses about what comes next in a sequence. The video mentions neural networks in the context of predicting the next word in a sentence.


Transformers are a type of neural network architecture that has become particularly influential in processing sequential data, such as language. They are used in models like GPT to process large amounts of text and generate responses. The video discusses transformers as the underlying technology that powers the sophisticated capabilities of generative AI.


Fine-tuning is a technique where a pre-trained neural network is further trained on a specific task to improve its performance for that task. In the context of generative AI, fine-tuning allows a general-purpose model to be adapted for specific applications, such as medical diagnosis or legal analysis.

💡Self-Supervised Learning

Self-supervised learning is a training method where a model learns to predict aspects of the input data from the data itself, without the need for explicit labels. This is a common technique in training language models, as discussed in the video, where the model predicts missing parts of a sentence from the context.


In the context of generative AI, a prompt is an input or a cue that guides the AI to perform a specific task or generate a particular output. The video gives examples of prompts, such as writing an essay or creating a program, which the AI uses to generate responses.


Bias in AI refers to the tendency of a system to favor certain outcomes over others, often reflecting the biases present in the training data. The video discusses the issue of bias in AI, noting that historical biases can affect the responses generated by systems like ChatGPT.

💡Ethical Considerations

Ethical considerations involve examining the moral implications of AI systems, including their impact on society, potential for misuse, and adherence to principles of fairness and justice. The video touches on the ethical challenges of AI, such as creating fake content or losing jobs due to automation.


Regulation refers to the rules and oversight that govern the development and use of AI technologies. The video suggests that as AI becomes more prevalent and powerful, there will be a need for increased regulation to mitigate risks and ensure that the benefits of AI outweigh the potential harms.


Generative AI uses computer programs to create new content, such as audio, computer code, images, text, or video.

Generative AI is not new, with examples like Google Translate and Siri being utilized for many years.

GPT-4 by OpenAI claimed to beat 90% of humans on the SAT and achieve top marks in professional exams.

GPT-4 can generate text based on prompts given by users, such as arguments for an essay or programming code.

ChatGPT reached 100 million users in just two months, showcasing its rapid adoption.

The core technology behind generative AI like ChatGPT is language modeling, which predicts the next word in a sequence.

Building a language model involves collecting a large dataset and training a neural network to predict missing words.

The size of a neural network is measured by the number of parameters it contains, which affects its complexity and capabilities.

Transformers are a type of neural network used to build models like ChatGPT, consisting of blocks that are mini neural networks.

Self-supervised learning is a technique where the model predicts parts of the data it has seen, to learn the probabilities of word sequences.

Fine-tuning a pre-trained model involves adjusting its weights for a specific task, making it more specialized.

As model sizes increase, their ability to perform a wider range of tasks also increases.

GPT-4 has one trillion parameters, which is significantly larger than earlier models, although still smaller than the human brain.

Creating an aligned AI involves ensuring it is helpful, honest, and harmless through fine-tuning with human preferences.

The cost of training large models like GPT-4 is around $100 million, which is a significant barrier to entry.

Generative AI has the potential to cause job displacement, particularly in areas involving repetitive text writing.

The technology behind generative AI can be used to create deep fakes, raising concerns about misinformation.

Regulation of AI is likely to increase as its risks and impact on society become more apparent.

While there are concerns about AI, climate change may pose a more immediate threat to humanity than super intelligent AI.