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

The Royal Institution・31 minutes read

Generative AI creates new content using computer programs, with a focus on text generation through natural language processing. OpenAI's GPT-4 can outperform humans in exams and tasks, reaching 100 million users in just two months, but challenges like inaccuracies and environmental impact remain.

Insights

  • Generative artificial intelligence, like GPT-4, can create diverse content types, from text to audio and images, based on user prompts, showcasing its versatility in various tasks.
  • Language modeling using neural networks, like in ChatGPT, involves predicting words based on context, with layers abstracting input data and connections determining network size, highlighting the intricate process behind AI text generation.

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Recent questions

  • What is generative artificial intelligence?

    Generative artificial intelligence involves using computer programs to create new content that the computer has not seen before, such as audio, computer code, images, text, or videos.

  • How does GPT-4 function in text generation?

    GPT-4 can generate text based on prompts provided by users, like writing essays or creating web pages, and it can outperform humans in various exams and tasks, showcasing its advanced capabilities in natural language processing.

  • What is the core technology behind ChatGPT?

    Language modeling is the core technology behind ChatGPT and other AI models, predicting the next word based on context, which is essential for generating coherent and contextually relevant text.

  • How are neural networks used in language modeling?

    Neural networks used in language modeling consist of input nodes, output nodes, and hidden layers that abstract the input data, processing vectors to predict missing words in sentences and identify patterns for accurate text generation.

  • What are the challenges associated with GPT?

    Challenges with GPT include inaccuracies, high energy consumption, and concerns about job loss due to AI advancements, highlighting the need for further research and regulation to address potential risks and ethical considerations in AI development.

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Summary

00:00

"Generative AI: Text Creation and Language Modeling"

  • Generative artificial intelligence involves using computer programs to create new content that the computer has not seen before.
  • The new content can include audio, computer code, images, text, or videos.
  • The focus of the lecture is on text generation through natural language processing.
  • Generative AI is not a new concept and has been utilized in tools like Google Translate and Siri for years.
  • OpenAI announced GPT-4 in 2023, claiming it can outperform humans in various exams and tasks.
  • GPT-4 can generate text based on prompts provided by users, such as writing essays or creating web pages.
  • ChatGPT reached 100 million users in just two months, showcasing its rapid growth compared to other tools like Google Translate.
  • Language modeling is the core technology behind ChatGPT and other AI models, predicting the next word based on context.
  • Building a language model involves collecting a large corpus of data, training a neural network to predict missing words in sentences, and adjusting based on feedback.
  • The neural network used in language modeling consists of input nodes, output nodes, and hidden layers that abstract the input data.

15:52

Neural Networks: Layers, Parameters, and Transformers

  • Neural networks use layers to generalize input and identify patterns.
  • Nodes in the network process vectors, not words.
  • The network consists of input, middle layers, and output connected by weights.
  • The number of connections determines the network's size.
  • A toy neural network has 99 trainable parameters.
  • Transformers, like ChatGPT, are built using blocks of neural networks.
  • Transformers process input, predict continuations, and use self-supervised learning.
  • Pre-trained models like GPT require fine-tuning for specific tasks.
  • Model size and cost increase with the number of parameters.
  • Fine-tuning involves human preferences to ensure helpful, honest, and harmless behavior.

32:22

"AI GPT: Creative, Controversial, and Uncertain Future"

  • GPT is used for questions and answers, providing detailed responses based on its last knowledge update.
  • It can generate poems and even haikus upon request, showcasing its creative abilities.
  • GPT's responses can vary in length and humor, with examples like jokes about men and women.
  • The model can also provide information on historical figures like Alan Turing and engage in songwriting.
  • Challenges with GPT include inaccuracies, as seen with Google's Bard model, leading to significant financial losses.
  • GPT's energy consumption is high, with larger models emitting more CO and potentially impacting the environment.
  • Concerns exist about job loss due to AI advancements, particularly in repetitive tasks and the creation of fake content.
  • The future of AI remains uncertain, with discussions on regulating AI technologies to mitigate potential risks.
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