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

Generative AI, specifically CHATGPT, has the ability to generate sophisticated code based on user input and has the potential to revolutionize various fields, but its capabilities also raise concerns about its potential impact and ethical implications

 Questions to inspire discussion 

• What is Generative AI?

  —Generative AI is a computer program that performs tasks typically done by humans, such as creating new content in various forms like audio, computer code, images, and text.

• How does Generative AI work?

  —Generative AI uses language modeling and neural networks to predict and generate the most likely continuation of a given context, allowing it to create text based on prompts and user input.

• What are the potential risks of Generative AI?

  —Generative AI models like GPT consume a significant amount of energy during training and deployment, leading to potential job losses in repetitive text writing and the creation of fake content.

• Can Generative AI be used for harmful purposes?

  —While AI may pose a threat, regulation is necessary for risky technologies like AI, and it is important for society to mitigate the risks rather than preventing their existence altogether.

• What are the ethical implications of Generative AI?

  —Regulating the content people are exposed to is difficult due to historical biases and the potential for undesirable behavior, and it is important to consider the ethical implications of using Generative AI for various tasks. 

Key Insights

Training and Architecture of Generative AI

• 💻 CHATGPT, a generative AI tool, has the capability to generate JavaScript code based on user input, making it more sophisticated than previous AI tools like Google Translate and Siri.
• 🌐 Generative AI requires a large amount of data, including sources like Wikipedia, Stack Overflow, social media, GitHub, and Reddit, to create a ginormous Corpus for training.
• 🤯 The Transformer model consists of multiple blocks stacked on top of each other, each containing mini neural networks, resulting in a complex system for generating AI.
• 🌍 The pre-trained model of GPT, which stands for generative pre-trained Transformer, is built by feeding it with vast amounts of data from the world, enabling it to generate new content.
• 🌐 GPT models start as a baseline and become great through pre-training on a vast amount of text data, gradually improving their language generation capabilities.
• 💡 Scaling language models with more parameters allows them to perform a wider range of tasks, from summarization to translation, showcasing the potential of generative AI.

Applications and Impact of Generative AI

• 🖥️ Generative AI has the potential to create new content in various forms such as audio, computer code, images, text, and videos.
• 📱 Generative AI has been around for a while and is already a part of our daily lives through applications like Google Translate, Siri, and Amazon Alexa.
• 💡 The capabilities of GPT-4 extend beyond exams, as it can also excel in various other fields, showcasing its versatility and potential impact.
• 🤔 The sophistication of generative AI prompts can surpass human capabilities, as it can generate arguments that humans may struggle to come up with.
• 💡 According to Tim Berners-Lee, the inventor of the Internet, super intelligent AI is likely to exist in the form of multiple intelligent AIs like GPT, which can be both beneficial and potentially harmful.

#AI #GenerativeAI

Clips 

• 00:00 🤖 Generative AI is a computer program that can create new content, particularly in text form, and has been used in applications like Google Translate and Siri, with a focus on the advancements made by Open AI in 2023.
  • Generative AI is a computer program that performs tasks typically done by humans.
  • Generative AI can create new content in various forms such as audio, computer code, images, and text, with a focus on text in this lecture, debunking myths and explaining how the technology works.
  • Generative AI is not a new concept and the lecture discusses the past, present, and future of AI.
  • Generative AI, such as Google Translate and Siri, has been around for years and is used in various applications like completing sentences and predicting search terms, but the focus is on what happened in 2023 with the company Open AI.
• 05:19 🤖 Generative AI, like gp4 and CH GPT, can pass standardized tests, write text based on prompts, create programs and websites, and use language modeling to predict the next word based on context.
  • Generative AI, specifically gp4, has the ability to achieve high scores on standardized tests such as the SAT, law and medical exams, demonstrating its capability to pass exams.
  • Generative AI can be used to write text based on prompts, such as providing arguments in favor of using mobile phones while driving.
  • Generative AI, such as CH GPT, can be used to create programs and websites based on user input, and it has shown rapid growth in user adoption compared to other tools like Google Translate and TikTok.
  • The lecture discusses the development of CH GPT, its core technology, potential risks, and provides a glimpse into the future of generative AI.
  • Language modeling is a principle used in generative AI where a sequence of words is used to predict the next word based on the context, and neural networks are now used to learn and predict these continuations in a more sophisticated way.
  • Jpt and GPT variants use context to predict what comes next, and language models are trained to generate the most likely continuation based on the given context.
• 12:34 🧠 Collect data from various sources, use a neural network language model to predict missing words, adjust and iterate until convergence, and generalize patterns to generate AI-generated text.
  • Collect a large corpus of data from various sources, such as Wikipedia, Stack Overflow, social media, GitHub, and Reddit, ensuring legal permissions, to use for generative AI.
  • The language model can predict the next word by removing sentences from the corpus and having the model generate those sentences.
  • The speaker explains the process of using a neural network to calculate the probability of a missing word, adjusting and feeding back to the model until it converges and provides the correct answer.
  • A neural network language model uses input nodes, layers, and output nodes connected by weights to learn and generalize patterns, with the number of weights determining the size of the network.
• 17:26 🤖 Generative AI uses neural networks called Transformers to predict sentence continuations by inputting context and using an end of sentence marker.
  • Generative AI uses neural networks to make predictions and correct them, with the size of the model determined by the number of parameters, which can range from small networks with 99 parameters to larger networks with billions of parameters.
  • Generative AI uses neural networks called Transformers, which are made up of blocks stacked on top of each other, to process input and generate output.
  • The task is to predict the continuation of a sentence, such as "the chicken walked across the road," by inputting the context and using an end of sentence marker to inform the neural network that the sentence is complete.
• 20:34 🤖 The Transformer architecture, specifically the generative pre-trained Transformer (GPT), is widely used in generative AI, allowing for specialized applications and improving language models over time through pre-training and fine-tuning, although they are still far from the complexity of the human brain and require careful engineering to avoid costly mistakes.
  • The Transformer is a widely used architecture in generative AI, specifically in the form of the generative pre-trained Transformer (GPT), which utilizes self-supervised learning to predict probabilities based on given data.
  • Generative AI involves fine-tuning a pre-trained model for specific tasks, allowing for specialized applications, and the quality of a language model improves over time through pre-training and fine-tuning.
  • Model sizes have significantly increased since 2018, emphasizing the importance of scale in generative AI.
  • Generative AI models like GPT have a large number of parameters and need to process a significant amount of text, but they are still far from reaching the complexity of the human brain, although they can generate text and be retrained with it.
  • GPT-4 costs $100 million to develop, so the training process must be carefully engineered to avoid costly mistakes.
• 27:01 📚 Fine-tuning language models like GPT allows them to perform a wide range of tasks, but aligning them with human desires and preferences is a challenge that requires collecting instructions and preferences from humans.
  • Scaling language models with more parameters allows them to perform a wider range of tasks, but fine-tuning is necessary for the models to behave as desired for specific user tasks.
  • Collecting instructions and fine-tuning a language model allows for generalization to unseen tasks, but the challenge lies in creating an agent that aligns with human desires and reliably utilizes desired skills.
  • GPT should be helpful, accurate, and able to follow instructions, perform tasks, provide answers, ask relevant questions, and clarify.
  • The model is fine-tuned by asking humans for preferences, making the process more expensive but necessary for accuracy.
• 32:16 🤖 Generative AI, like GPT, can answer questions and provide information, but it may lack up-to-date knowledge and struggle with accuracy and helpfulness.
  • Generative AI, specifically GPT, is capable of answering questions and providing information based on its knowledge, but it may not always have the most up-to-date information and may not be aware of specific individuals or their roles.
  • The speaker discusses generating AI and its ability to create poems, answer questions, and tell jokes, showcasing its understanding of language and wordplay.
  • Generative AI can produce outputs based on input instructions, but it may not always follow instructions accurately or be helpful.
• 37:59 🤖 Generative AI models like GPT have the potential for both positive and negative impacts, including job losses, fake content creation, and deep fakes, but it is important for society to regulate and mitigate the risks rather than preventing their existence altogether.
  • Regulating the content people are exposed to is difficult due to historical biases and the potential for undesirable behavior.
  • Google's model Bard made a mistake about the first image of a planet outside our solar system, causing a significant financial loss, and it also tells jokes about men but refuses to do so about women.
  • Generative AI models like GPT consume a significant amount of energy during training and deployment, leading to potential job losses in repetitive text writing and the creation of fake content.
  • A college student fooled people by using generative AI to create a fake song collaboration between Drake and The Weeknd, and the same technology can also be used to create deep fakes like a video showing Trump being arrested.
  • We cannot predict the future, but it is likely that there will be many intelligent AI systems, some of which may be used for harm, so it is important for society to mitigate the risks rather than preventing their existence altogether.
  • Chat GPT 4 cannot replicate itself or acquire resources autonomously, and while AI may pose a threat, climate change is a bigger concern, and regulation is necessary for risky technologies like AI.

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