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1 What Is Generative AI? Over the last decade, deep learning has evolved massively to process and generate unstructured data like text, images, and video. These advanced AI models have gained popularity in various industries, and include large language models (LLMs ). There is currently a signi昀椀cant level of fanfare in both the media and the industry surrounding AI, and there's a fair case to be made that Arti昀椀cial Intelligence (AI), with these advancements, is about to have a wide-ranging and major impact on businesses, societies, and individuals alike. This is driven by numerous factors, including advancements in technology, high-pro昀椀le applications, and the potential for transfor-mative impacts across multiple sectors. In this chapter, we'll explore generative models and their application. We'll provide an overview of the technical concepts and training approaches that power these models' ability to produce novel content. While we won't be diving deep into generative models for sound or video, we aim to convey a high-level understanding of how techniq ues like neural networks, large datasets, and computational scale enable generative models to reach new capabilities in text and image generation. The goal is to demystify the underlying magic that allows these models to generate remarkably human-like content across various domains. With this foundation, readers will be better prepared to consider both the opportunities and challenges posed by this rapidly advanc-ing technology. We'll follow this structure: Introducing generative AI Understanding LLMs What are text-to-image models? What can AI do in other domains? Let's start from the beginning-by introducing the terminology!
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What Is Generative AI? 2 Introducing generative AI In the media, there is substantial coverage of AI-related breakthroughs and their potential impli-cations. These range from advancements in Natural Language Processing (NLP ) and computer vision to the development of sophisticated language models like GPT-4. Particularly, generative models have received a lot of attention due to their ability to generate text, images, and other creative content that is often indistinguishable fr om human-generated content. These same models also provide wide functionality including semantic search, content manipulation, and classi昀椀cation. This allows cost savings with automation and al lows humans to leverage their creativity to an unprecedented level. Benchmarks capturing task performance in different domains have been major drivers of the de-velopment of these models. The following graph, inspired by a blog post titled GPT-4 Predictions by Stephen Mc Aleese on Less Wrong, shows the improvements of LLMs in the Massive Multitask Language Understanding (MMLU ) benchmark, which was designed to quantify knowledg e and problem-solving ability in elementary mathematics, US history, computer science, law, and more: Figure 1. 1: Average performance on the MMLU benchmark of LLMs Generative AI refers to algorithms that can generate novel content, as opposed to analyzing or acting on existing data like more traditional, predictive machine learn-ing or AI systems.
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Chapter 1 3 Y ou can see signi昀椀cant improvements in recent years in this benchmark. Particularl y, it highlights the progress of the models provided through a public user interface by Open AI, especially the improvements between releases, from GTP-2 to GPT-3 and GPT-3. 5 to GPT-4, although the results should be taken with a grain of salt, since they are self-reported and are obtained either by 5-shot or zero-shot conditioning. Zero-shot means the models were prompted with the question, while in 5-shot settings, models were additionally given 5 question-answer examples. These added examples could naively account for about 20% of performance according to Measuring Massive Multitask Language Understanding (Hendrycks and colleagues, revised 2023). There are a few differences between these models and their training that can account for these boosts in performance, such as scale, instruction-tuning, a tweak to the attention mechanisms, and more and different training data. First and foremost, the massive scaling in parameters from 1. 5 billion (GPT-2) to 175 billion (GPT-3) to more than a trillion (GPT-4) enables models to learn more complex patterns; however, another major chang e in early 2022 was the post-training 昀椀ne-tuning of models based on human instructions, which teaches the model how to perform a task by providing demonstrations and feedback. Across benchmarks, a few models have recently starte d to perform better than an average human rater, but generally still haven't reached the perfo rmance of a human expert. These achievements of human engineering are impressive; however, it should be noted that the performance of these models depends on the 昀椀eld; most models are still performing poorly on the GSM8K benchmark of grade school math word problems. Generative Pre-trained Transformer (GPT ) models, like Open AI's GPT-4, are prime examples of AI advancements in the sphere of LLMs. Chat GPT has been widely adopted by the general pub-lic, showing greatly improved chatbot capabilities enabled by being much bigger than previous models. These AI-based chatbots can generate human-like responses as real-time feedback to customers and can be applied to a wide range of use cases, from software development to writing poetry and business communications. As AI models like Open AI's GPT continue to improve, they could become indispensable assets to teams in need of diverse knowledge and skills. Please note that while most benchmark results come from 5-shot, a few, like the GPT-2, Pa LM, and Pa LM-2 results, refer to zero-shot conditioning.
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What Is Generative AI? 4 For example, GPT-4 could be considered a polymath that works tirelessly without demanding compensation (beyond subscription or API fees), pro viding competent assistance in subjects like mathematics and statistics, macroeconomics, biology, and law (the model performs well on the Uniform Bar Exam). As these AI models become more pro昀椀cient and easily accessible, they are likely to play a signi昀椀cant role in shaping the future of work and learning. By making knowledge more accessible and adaptable, these models have the potential to level the playing 昀椀eld and create new opportunities for people from all walks of life. These mode ls have shown potential in areas that require higher levels of reasoning and understanding, although progress varies depending on the complexity of the tasks involved. As for generative models with images, they have pushed the boundaries in their capabilities to assist in creating visual content, and their performance in computer vision tasks such as object detection, segmentation, captioning, and much more. Let's clear up the terminology a bit and explain in more detail what is meant by generative model, arti昀椀cial intelligence, deep learning, and machine learning. What are generative models? In popular media, the term arti昀椀cial intelligence is used a lot when referring to these new models. In theoretical and applied research circles, it is often joked that AI is just a fancy word for ML, or AI is ML in a suit, as illustrated in this image:Open AI is a US AI research company that aims to promote an d develop friendly AI. It was established in 2015 with the support of several in昀氀uential 昀椀gures and companies, who pledged over $1 billion to the venture. The organization initially committed to being non-pro昀椀t, collaborating with other institutions and researchers by making its patents and research open to the public. In 2018, Elon Musk resigned from the board citing a potential con昀氀ict of interest with his role at Tesla. In 2019, Open AI transitioned to become a for-pro昀椀t organization, and subsequently Micros oft made signi昀椀cant investments in Open AI, leading to the integration of Open AI systems with Microsoft's Azure-based supercomputing platfor m and the Bing search engine. The most signi昀椀cant achievements of the company include Open AI Gym for training reinforcement algorithms, and-more recently-the GPT-n models and the DALL-E generative models, which generate images from text.
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Chapter 1 5 Figure 1. 2: ML in a suit. Generated by a model on r eplicate. com, Diffusers Stable Diffusion v2. 1 It's worth distinguishing more clearly between the terms generative model, arti昀椀cial intelligence, machine learning, deep learning, and language model: Arti昀椀cial Intelligence (AI) is a broad 昀椀eld of computer science focused on creating intel-ligent agents that can reason, learn, and act autonomously. Machine Learning (ML) is a subset of AI focused on developing algorithm s that can learn from data. Deep Learning (DL) uses deep neural networks, which have many layers, as a mechanism for ML algorithms to learn complex patterns from data. Generative Models are a type of ML model that can generate new data based on patterns learned from input data. Language Models (LMs ) are statistical models used to predict words in a sequence of natural language. Some language models utilize deep learning and are trained on massive datasets, becoming large language models (LLMs ).
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