MIT OpenCourseWare: How to Do Anything with AI (PDF file)

How to useAI"Do Anything" is a cutting-edge course offered by MIT, exploring in depth...artificialintelligentTechnology and its applications in various real-world data modalities. The course focuses on modern...Deep learningAnd basic models, covering everything from language to vision, audio, sensors, medical data, etc.MultimodalContent. Through lectures, reading, discussions, and research projects, students will develop critical thinking skills and gain an understanding of... AI ofup to dateTechnological achievements, and masteryMultimodal AI The course emphasizes theoretical learning, practical application, and encourages students to innovate and explore in multiple fields. AI The infinite possibilities.

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Course Introduction

The course is taught by Professor Paul Liang from MIT and aims to cultivate students'...MultimodalintelligentandartificialintelligentResearch and application skills in the field. The course content is divided into four modules:AIBase,MultimodalAIBasic, large-scale models and modernAIInteractiveAIEach module covers multiple topics, such as data structures,MultimodalThe course incorporates elements such as fusion, large-scale language models, and human-computer interaction. Students are required to complete reading assignments, participate in discussions, and conduct a high-quality research project. Learning outcomes are assessed through proposals, mid-term and final reports, and presentations.

The course assessment criteria include reading assignments (40%) and research projects (60%). Students are required to take on different roles in weekly discussions, such as reading leaders and discussion summarizers, to enhance their critical and creative thinking skills. The course provides a variety of role-playing tasks to help students understand and apply the knowledge from different perspectives.AItechnology.

How to proceedAIResearch

This course guides students on how to generate research ideas, read papers, execute research plans, and write research papers. It details each stage of the research process, including bottom-up discovery and top-down design methodologies, emphasizing the importance of scientific questions and hypotheses, and providing examples of various research directions, such as...MultimodalAISensor data processingAIReasoning, Interactionintelligentbody, incarnationAI,societyintelligentAIThe course covers topics such as human-computer interaction, ethics, and safety. It introduces methods for literature reviews, steps for testing research ideas, and the structure and key points of writing a research paper.

The course provides abundant resource links and tool suggestions to help students obtain support during the research process. The document also outlines this week's assignments, including the submission of a project preference form and a preview of next week's course topics. The course provides students with comprehensive...AIThis introductory guide to research covers all aspects, from theory to practice.

Data, Structure and Learning

This course primarily introduces the characteristics, structures, and common learning objectives of different data modalities (such as visual, verbal, audio, sensor data, tabular data, graph data, and ensemble data). The course delves into the data representation, distribution, granularity, structure, information content, noise, and correlations for each modality, and discusses various learning paradigms such as supervised learning, unsupervised learning, and reinforcement learning, as well as their applications.MultimodalApplications in multi-task learning. The course emphasizes the importance of data preprocessing, visualization, and model selection, as well as how to evaluate a model's generalization ability using training, validation, and testing data.

The course concluded with a summary of this week's assignments, including submitting the project preference form, preparing and presenting project proposals, and outlining next week's tasks.Machine LearningOptional tutorials for the tools. Providing students with data processing and...Machine LearningBasic knowledge to help them inAIThe research aims to better understand and apply data from different modalities.

practicalAItool

The course primarily focuses on PyTorch and Hugging Face tools, providing students with the skills to use these tools for...AIDevelopment and debuggingpracticalGuide. This course introduces the main features of Hugging Face, including the transformers and datasets libraries, tools that integrate seamlessly with PyTorch, and provides...powerfulThe course covers pre-trained models and data loading capabilities. It mentions the bitsandbytes and flash-attn libraries, commonly used with PyTorch, which can optimize model performance and memory usage. The course explains...AIDebugging techniques during development include how to establish a close connection with the data, set up an end-to-end skeleton model, diagnose errors through overfitting, improve generalization ability through regularization, adjust hyperparameters, and extract the final performance improvement.

The course provides information on how to design new data.Machine LearningThe model's recommendations emphasize the following:SimpleThe method for gradually increasing model complexity is described. Key steps in model debugging are summarized, such as checking the correctness of input data and labels, observing training loss, and handling overfitting and underfitting. This can help students...AIAvoid common pitfalls during development to improve the reliability and performance of the model.

Model Architecture

This course introduces the design principles and methods for model architectures of different data modalities (such as sequence data, spatial data, ensemble data, and graph data). The course content includes how to select an appropriate model architecture based on the characteristics of the data, such as the cyclical nature of time series data.Neural Networks(RNN), ConvolutionNeural Networks(CNN) is used for spatial data processing and graphs.Neural Networks(GNN) is used for graph data processing. This paper discusses how to achieve data invariance and equivariance through parameter sharing and information aggregation, and how to design models based on the semantic information, granularity, structure, information content, noise, and correlation of the data.

The course concluded with a summary of the week's assignments, including the submission of project proposals and preparation for reading and discussion. The course provided students with systematic guidance on model architecture design, helping them in...AIThe research aims to better understand and apply different types of model architectures.

MultimodalAIAlignment

In-depth discussion of the courseMultimodalAIThe core concepts include data heterogeneity, intermodal connectivity, and interactivity. The course content covers...MultimodalThe historical stages of research, from the behavioral era to...Deep learningFrom the early days of the basic model to the present day, this section introduces...MultimodalThe diversity of tasks, such as the combination of language and vision, sentiment analysis, and video event recognition, will be explored.MultimodalAlignment challenges include methods for discrete and continuous alignment, and how to achieve intermodal alignment through contrastive learning.

The course provides a detailed introductionMultimodalAIThe course covers six core challenges: representation learning, alignment, reasoning, generation, transfer, and quantization. It specifically mentions the application of the CLIP model in language and vision tasks, and how to learn shared and unique modal information through contrastive learning. Finally, it summarizes this week's assignments, including reading discussions and feedback on project progress. The course provides students with…MultimodalAIA comprehensive overview of the field helps them understandMultimodalThe complexity of data and how to address the challenges through model design and learning methods.

MultimodalFusion

In-depth discussion of the courseMultimodalThe core concepts and techniques of fusion include early-stage, mid-stage, and late-stage fusion, as well as various methods such as additive fusion, multiplicative fusion, tensor fusion, low-rank fusion, and gated fusion. The course content covers a wide range of topics, from...SimpleFrom linear fusion to complex nonlinear fusion techniques, and how to optimize through dynamic fusion strategies.MultimodalThe data representation and learning process was discussed.MultimodalOptimization challenges in fusion include balancing overfitting and generalization ability across different modalities, and how to achieve this through comparative learning.MultimodalIt means learning to solve problems in practical tasks.

Course IntroductionMultimodalThe course addresses the challenges of fusion in practical applications, such as handling modal heterogeneity, avoiding the impact of single-modal biases on the fusion results, and improving model performance through dynamic fusion and architecture search. The course concludes with a summary of the week's assignments, including readings, discussions, and feedback on project progress, to help students better understand and apply the concepts.MultimodalFusion technology.

Cross-modal learning

The course delves into the fundamental concepts and techniques of cross-modal learning, including knowledge transfer between modalities through fusion, alignment, and translation. The course content covers...MultimodalVarious transfer strategies in learning are discussed, such as transfer from pre-trained models, co-learning, and model induction. High-modal learning is specifically mentioned.MultimodalThe HighMMT (High Modal Transformer) model can perform knowledge transfer between partially observable modes, and is suitable for multi-task learning and transfer learning of various modalities and tasks.

The course discusses open challenges in cross-modal learning, such as learning in low-resource modalities, applications beyond language and visual modalities, challenges of training complex data and models, and model interpretability. The document concludes with a summary of this week's assignments, including readings, discussions, and feedback on project progress, to help students better understand and apply cross-modal learning techniques.

Large base model

The course provides a detailed introduction to large-scale language models (LLMThe development history, pre-training methods, architecture types, instruction fine-tuning, and preference adjustment of s), andHigh efficiencyTraining and reasoning techniques. Course content covers everything from loops...Neural NetworksThe evolution from RNNs to Transformer architecture is discussed, along with how to pre-train on large-scale text data using unsupervised learning. The paper explores how to optimize model performance through instruction fine-tuning and preference adjustments, and how to improve training efficiency and inference speed through techniques such as LoRA (low-rank adaptation) and quantization.

The course discusses the future development direction of large-scale language models, including how to teach models to reason and how to scale them.MultimodalLLMs to cover moreMultimodalThe course covers how to evaluate and deploy these models in real-world applications. It concludes with assignments for the week, including submitting a mid-term report and completing a project resource request form, to help students better understand and apply the techniques of large-scale foundational models.

LargeMultimodalModel

The course provides a detailed introduction to large-scale...MultimodalBasic knowledge of models, pre-training methods, and how to integrate large language models (LLMs) AdaptationMultimodalLLMs, and from text toMultimodalgeneratedup to dateProgress. Course content coversMultimodalBasic model representation methodsMultimodalTransformer architecture, cross-modal attention mechanism, and how to optimize through instruction fine-tuning and preference adjustment.MultimodalModel performance. The course explores how to achieve this through prefix adjustment and adapter layers.MultimodalConditional generation, and how to use large-scale pre-trained datasets andMultimodalInstructions for fine-tuning the dataset improve the model's generalization ability.

The course discussedMultimodalThe future development direction of the model, including nativeMultimodalModel design,MultimodalApplications of Hybrid Expert Models (MoE), and how to integrate them.MultimodalThe model is applied in real-world scenarios such as time series data. The course concludes with assignments for the week, including reading, discussion, and feedback on project progress, to help students better understand and apply large-scale data.MultimodalThe technology of the model.

Modern generativeAI

The course provides a detailed introduction to generative programming.AIThe course covers core concepts, current technological advancements, conditional generation methods, model architectures, and training techniques for these models. The content encompasses various generative models, ranging from variational autoencoders (VAEs) to diffusion and flow matching models, exploring training objectives, noise handling, sampling speeds, and their advantages and disadvantages. The course also introduces how to optimize the generation process using conditional vector fields and loss functions, and how to improve model performance through specific architectural designs.

The course also discussed generative programming.AIEvaluation metrics, such as Fréchet Inception Distance (FID), CLIP Score, precision/recall, and aesthetic rating, help assess the quality and diversity of generated content. The course concludes with this week's assignments, including reading tasks and feedback on project progress, to help students better understand and apply generative methods.AIThe technology.

Reinforcement learning and interaction

The course provides a detailed introduction to the fundamentals of reinforcement learning and its application in large-scale language models.LLMs) Applications in alignment and reasoning, and interactive...LLMThe course covers agent design and implementation. It includes fundamental concepts of reinforcement learning, such as Markov Decision Processes (MDPs), policy learning, a comparison of model-based and policy-based methods, and how to optimize policies using policy gradient methods (such as REINFORCE and PPO). The course also explores reinforcement learning through human feedback, including training reward models and preference optimization techniques.

The course delves into the challenges of applying reinforcement learning in practice, such as reward function design, balancing model exploration and exploitation, and how to improve model performance and efficiency through methods like Direct Preference Optimization (DPO) and Group Policy Optimization (GRPO). The course concludes with assignments for the week, including submitting a final project report and preparing a project presentation, helping students better understand and apply reinforcement learning techniques.

up to dateDevelopment direction

The course exploredMultimodalreasoning,AICutting-edge fields such as agency, human-computer interaction, and ethics and security. Course content includes...MultimodalThe representation methods of the basic model and how to adapt large language models to...MultimodalThe discussion covers text generation models, how to achieve text and image generation, and how to improve the reliability and security of these models through human-computer interaction. The "Interactive Sketchpad" system, designed for collaboration and visual problem-solving, is specifically mentioned.MultimodalTutoring systems can enhance the effectiveness of problem-solving through visual reasoning and code execution.

The course discussedMultimodalThe model's application in education, such as using visual reasoning to help students understand abstract geometric concepts. (Introduction)MultimodalThe course covers the safety and ethical challenges of the models, including how to quantify model limitations, predict and control model behavior, and address biases and unfairness within the models. The course concludes with a summary of the week's assignments, including the submission of the final project report and the collection of course feedback.

Course project address

• Project official website: https://mit-mi.github.io/how2ai-course/spring2025/
• GitHub repositoryhttps://github.com/MIT-MI/how2ai-course

Application scenarios of the course

• Healthcare:based onAIProcess medical images and medical records to develop auxiliary diagnostic systems and improve the accuracy and efficiency of disease detection and treatment.
• intelligenttransportation:based onAITechnical analysis of traffic camera video and sensor data to achieveautomaticDriver assistance systems and traffic flow optimization.
• Art and Creative Design: with the help ofAIGenerate creative content such as music and paintings, transforming verbal descriptions into visual or audio works, and providing creative tools for artists and designers.
• intelligenteducateDevelopmentAIDriven by personalized learning systems, this system provides customized learning paths and teaching content by analyzing student behavior and feedback.
• Environmental protection:useAIIt processes satellite imagery and environmental sensor data to monitor environmental changes, predict natural disasters, and support ecological protection and sustainable development.

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