Introduction

Machine intelligence (MI, [ 72 , 71 ]) focuses on the creation of models, evolutionary dynamics, and algorithms that enable machines or software to co-learn from data and improve their performance over time. Machine intelligence is therefore an advanced computer science that allows a machine, device, software, program, code, or algorithm to interact intelligently with its environment, which means it can collect data, make decisions, perform actions, and develop strategies to maximize its chances of successfully achieving its preferences and objectives [ 42 ]. A neural network is a computational model inspired by the structure and function of the neural units, comprised of interconnected nodes or machine/artificial neural units that process and transform information to solve various machine learning and pattern recognition tasks. A large language model is a type of machine intelligence model that uses a vast amount of data to understand and generate human-like text or language responses. The methodology has been extended to large learning models that go beyond texts. A large learning model typically refers to a learning model, often a neural network, that is trained on a substantial amount of data to perform various tasks or make risk-aware predictions with high accuracy. In recent years, both deep learning and game theory have emerged as powerful tools in various fields, from machine intelligence to engineering to economics. We provide the intersection of these two domains, offering insights into how they complement and enhance one another in solving societal problems: Machine learning is part of machine intelligence. Deep learning is part of machine learning. Generative pretrained transformer is part of deep learning. Generative pretrained transformer is a specific large learning model. Deep learning is a game. Generative pretrained transformer outcomes are Nash equilibria of non-zero sum hierarchical game. Pre-training generative transformer is a game (coalitional, cooperative, non-cooperative depending on the implementation). Generative models focus on the joint distribution of input-output. There are many other generative machine intelligence tools that differ from pre-trained generative transformers. It includes Long Short-Term Memory, Gated Recurrent Units, Convolutional Neural Networks, Variational Encoders, Generative Adversarial Networks, Neural ODEs, Diffusion Models, Sub/Super Diffusion Models, Sparse Mixture-of Experts, Gaussian Mixture Models, Hidden Markov Models, Distributional Reinforcement Learning, etc. There are also other machine intelligence methods used in discriminative tasks such as classification, identification, etc. Classifying methods strictly as generative or discriminative can be technically misleading because many methods have dual capabilities. A movie model trained on predicting the next image/scene (discriminative) can generate videos by iteratively applying this discriminative task. Some tasks require both generation and discrimination. Q-reinforcement learning uses discriminative aspects from estimated rewards and generation from the transition to the next state. Generative adversarial networks use co-training from a generator and discriminator. Joint Embedding Predictive Architecture uses self-supervised learning, and combine both discriminative and generative tasks. Coalitional training of transformers is a game. Federated training is a game. Diffusion-Transformer is a Mean-Field-Type Game. Self-Attention Mechanism is a Mean-field-Type Game (see Figure 0.1 ). Figure 0.1 Connection Between Mean-Field-Type Game Theory and Transformers The conceptual progression of machine intelligence is given and shown as a vertical sequence. The stages listed are mean field type game theory, strategic competitive intelligence, strategic machine intelligence, machine intelligence, generative machine intelligence, generative large learning models, and generative mean field type transformers. https://www.w3.org/1999/xlink" xlink:href=" https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9781003664581/a7394b32-42dd-4484-9e6c-075ba7e2659a/content/fig0_1.tif "/>