Ahmed Rebai

𝑼𝒏𝒍𝒐𝒄𝒌𝒊𝒏𝒈 𝑹𝒐𝒃𝒖𝒔𝒕 𝑹𝒆𝒑𝒓𝒆𝒔𝒆𝒏𝒕𝒂𝒕𝒊𝒐𝒏𝒔: 𝑻𝒉𝒆 𝑷𝒐𝒘𝒆𝒓 𝒐𝒇 𝑪𝒐𝒏𝒕𝒓𝒂𝒔𝒕𝒊𝒗𝒆 𝑳𝒆𝒂𝒓𝒏𝒊𝒏𝒈 𝒊𝒏 𝑴𝒂𝒄𝒉𝒊𝒏𝒆 𝑳𝒆𝒂𝒓𝒏𝒊𝒏𝒈 #ContrastiveLearning is a technique used in machine learning and deep learning to learn representations by contrasting similar and dissimilar pairs of data. Here are the key points: 𝐎𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞: The main goal of contrastive learning is to pull together representations of similar instances (positive pairs) while pushing apart representations of dissimilar instances (negative pairs). 𝐏𝐚𝐢𝐫𝐬: It involves creating pairs of data samples where: 𝐏𝐨𝐬𝐢𝐭𝐢𝐯𝐞 𝐩𝐚𝐢𝐫𝐬: Data samples that are similar (e.g., different augmentations of the same image or similar sentences). 𝐍𝐞𝐠𝐚𝐭𝐢𝐯𝐞 𝐩𝐚𝐢𝐫𝐬: Data samples that are dissimilar (e.g., different images or unrelated sentences). 𝐋𝐨𝐬𝐬 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧: Typically, contrastive learning uses a loss function that encourages the model to minimize the distance (or similarity measure) between representations of positive pairs and maximize the distance between representations of negative pairs. 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: Contrastive learning has been successfully applied in various domains, including computer vision (e.g., learning image representations), natural language processing (e.g., learning sentence embeddings), and reinforcement learning (e.g., learning state representations). 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬: It leverages the structure and relationships within the data to learn more meaningful and transferable representations, often leading to improved performance on downstream tasks compared to traditional self-supervised or unsupervised learning methods. Overall, contrastive learning has gained significant attention due to its ability to learn robust and semantically meaningful representations directly from unlabeled data through effective utilization of both positive and negative examples. #MachineLearning #DeepLearning #ContrastiveLearning #AI #ArtificialIntelligence #DataScience #RepresentationLearning #SelfSupervisedLearning #UnsupervisedLearning #NeuralNetworks #TechInnovation #DataAnalysis #AIResearch #MLTechniques #InnovationInAI

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Here's a link to the recording In case you missed our last session about the principles of classification in machine learning with Eng Abdel-hamid Mohamed-cheikh

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Fine-tuning Doesn't Have to Break the Bank it's essential to recognize that enhancing machine learning models doesn't always require immense resources. Here are some concepts you should know. 1. Parameter-Efficient Fine-Tuning 🎯: This strategy updates only a specific subset of a model's parameters, perfectly tailoring it to specific tasks without the need to overhaul the entire model. It's an excellent way to save on computational resources while achieving targeted results. 2. Frozen Parameters ❄️: In the realm of machine learning, 'Frozen Parameters' refer to those parts of the model that remain unchanged during training or fine-tuning. This approach is key to maintaining stability in certain areas of the model while others evolve and adapt. 3. Low-Rank Adaptation (LoRA) 🔍: LoRA tackles complexity by approximating a large matrix with two smaller matrices. This reduction in parameters needed for fine-tuning allows for faster and more efficient model adaptations. 4. Adapters ⚙️: These small yet powerful components are inserted into various layers of a model. By training only the parameters of these adapters—rather than the entire model—we can pinpoint improvements and streamline the fine-tuning process. Leveraging these techniques can dramatically reduce the costs and computational demands of deploying advanced machine learning models, especially in resource-limited scenarios. #MachineLearning #AI #CostEfficiency #Innovation #TechTrends #DataScience #LLMs #LargeLanguageModels #GPT

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Hey everyone 👋 Just shared two new blog posts on key topics in AI and data extraction: 1️⃣ Simple Techniques for Extracting Table Data from PDFs (part one)📤📊 Medium : https://lnkd.in/efWknuPz My webbook : https://lnkd.in/evGxTYAv 2️⃣ Serving Large models (part two): Ollama and TGI 🦙🚀 Medium : https://lnkd.in/ebABt_f7 My Webbook : https://lnkd.in/ecEsHy8b #Ollama #TGI #RAG #LLMs #AI

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Principal Component Analysis (PCA) is a valuable technique for dimensionality reduction, aiming to capture most of the variance with just a few principal components. However, in practice, real-world data often spreads variance across many components, making interpretation challenging. While PCA can be a powerful tool, it’s important to recognize that it isn’t suited for every type of data set. Thank you, Dr. Alexander Krannich, for sharing these insights. #DataScience #Statistics #PCA

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𝐃𝐞𝐜𝐨𝐝𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 𝐟𝐨𝐫 𝐌𝐋 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥𝐬💡 Model evaluation techniques are essential methods used to assess the performance of machine learning models. These techniques help determine how well a model predicts outcomes and generalizes to new, unseen data. Let's dive in: 1. 𝐌𝐞𝐚𝐧 𝐄𝐫𝐫𝐨𝐫 (𝐌𝐄): - The average of the differences between predicted values and actual values. • 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞: Useful for identifying bias in model predictions, especially when understanding if predictions systematically overestimate or underestimate the actual values. 2. 𝐌𝐞𝐚𝐧 𝐀𝐛𝐬𝐨𝐥𝐮𝐭𝐞 𝐄𝐫𝐫𝐨𝐫 (𝐌𝐀𝐄): - The average of the absolute differences between predicted values and actual values. • 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞: Ideal for measuring the magnitude of errors without considering their direction, commonly used in fields like weather forecasting. 3. 𝐌𝐞𝐚𝐧 𝐒𝐪𝐮𝐚𝐫𝐞𝐝 𝐄𝐫𝐫𝐨𝐫 (𝐌𝐒𝐄): - The average of the squares of the differences between predicted values and actual values. • 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞: Emphasizes larger errors, making it suitable for applications where significant mistakes need to be highlighted, such as financial forecasting. 4. 𝐑𝐨𝐨𝐭 𝐌𝐞𝐚𝐧 𝐒𝐪𝐮𝐚𝐫𝐞𝐝 𝐄𝐫𝐫𝐨𝐫 (𝐑𝐌𝐒𝐄): - The square root of the mean squared error. • 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞: Provides error magnitude in the same units as the target variable, making it more interpretable, useful in real-world scenarios like housing price predictions. 5. 𝐌𝐞𝐚𝐧 𝐏𝐞𝐫𝐜𝐞𝐧𝐭𝐚𝐠𝐞 𝐄𝐫𝐫𝐨𝐫 (𝐌𝐏𝐄): - The average of the percentage errors between predicted values and actual values. • 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞: Useful for understanding the relative size of errors in percentage terms, particularly in sales and financial forecasting. 6. 𝐌𝐞𝐚𝐧 𝐀𝐛𝐬𝐨𝐥𝐮𝐭𝐞 𝐏𝐞𝐫𝐜𝐞𝐧𝐭𝐚𝐠𝐞 𝐄𝐫𝐫𝐨𝐫 (𝐌𝐀𝐏𝐄): - The average of the absolute percentage errors between predicted values and actual values. • 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞: Preferred for its clear interpretability in percentage terms, valuable in business and economic forecasting. 7. 𝐌𝐞𝐚𝐧 𝐀𝐛𝐬𝐨𝐥𝐮𝐭𝐞 𝐒𝐜𝐚𝐥𝐞𝐝 𝐄𝐫𝐫𝐨𝐫 (𝐌𝐀𝐒𝐄): - The average absolute error scaled by a benchmark error, often the in-sample MAE of a naive forecast. • 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞: Beneficial for comparing the accuracy of different forecasting models, especially across datasets with different scales. #python #coding #data #science

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Some Key Exploratory Data Analysis (EDA) Questions for Interview Preparation 👇 ✅ How do you identify and handle missing data during EDA? ✅ What statistical measures do you use to summarize the dataset during EDA? ✅ How do you check for outliers, and what are common techniques to handle them? ✅ Explain the difference between univariate, bivariate, and multivariate analysis. ✅ How do you assess data quality during EDA? ✅ What steps do you take to analyze the distribution of numerical variables? ✅ How do you handle categorical variables in a dataset during EDA? ✅ Explain how you would analyze the relationship between two variables (numerical or categorical). ✅ What are common visualizations used for EDA, and when would you use each? ✅ Which Python libraries are commonly used for EDA, and why? ✅ How would you use Python to generate a summary of missing values in a dataset? ✅ If you identify multiple outliers in a dataset, how do you decide whether to remove or retain them? ✅ How would you validate insights derived from EDA to ensure they are meaningful? ✅ How do you identify and address multicollinearity in a dataset? Why is it important? 🚨 We at Grow Data Skills bringing Live Classroom Bootcamp of "Data Analyst 4.0 Mastery With Business Intelligence" really soon and will be teaching everything from Basic To Advance👩‍💻👩‍💻 ✅ Fill the below form to reserve your spot before the launch - https://lnkd.in/dt3m2std #DataAnalysis #Bootcamp #GrowWithInsights #CareerReady Shashank Mishra 🇮🇳 SHAILJA MISHRA🟢 Sahil Choudhary Shubhankit Sirvaiya Aman Kumar

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𝐌𝐞𝐝𝐢𝐜𝐚𝐥 𝐈𝐦𝐚𝐠𝐢𝐧𝐠 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 ✍🏻💯 𝐒𝐭𝐞𝐩 𝟏: 𝐋𝐨𝐚𝐝𝐢𝐧𝐠 𝐈𝐦𝐚𝐠𝐞 𝐃𝐚𝐭𝐚 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 Instructions on how to upload and load images using Google Colab. Explanation of how images are formed (pixels and intensities). Code example of loading an image using PIL and displaying it with matplotlib. 𝐒𝐭𝐞𝐩 𝟐: 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞/𝐅𝐨𝐫𝐦𝐚𝐭 𝐨𝐟 𝐚𝐧 𝐈𝐦𝐚𝐠𝐞 Explanation of how images are stored in memory as arrays. Introduction to numpy arrays for image operations. Conversion between PIL images and numpy arrays. 𝐒𝐭𝐞𝐩 𝟑: 𝐏𝐥𝐨𝐭𝐭𝐢𝐧𝐠 𝐭𝐡𝐞 𝐇𝐢𝐬𝐭𝐨𝐠𝐫𝐚𝐦 𝐨𝐟 𝐈𝐧𝐭𝐞𝐧𝐬𝐢𝐭𝐢𝐞𝐬 Explanation of how pixel intensities can be treated as random variables. The histogram of intensities represents an approximation of the Probability Density Function (PDF) of the image pixels. 𝐒𝐭𝐞𝐩 𝟒: 𝐒𝐞𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐛𝐚𝐬𝐞𝐝 𝐨𝐧 𝐇𝐢𝐬𝐭𝐨𝐠𝐫𝐚𝐦 * Image Segmentation based on the Thresholding Technique 𝐒𝐭𝐞𝐩 𝟓: 𝐆𝐚𝐮𝐬𝐬𝐢𝐚𝐧 𝐌𝐢𝐱𝐭𝐮𝐫𝐞 𝐌𝐨𝐝𝐞𝐥𝐥𝐢𝐧𝐠 & 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧.

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Great visualization for what “AI” can mean

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١ تعليق واحد

#Post -1 - Summary of 𝗟𝗟𝗠 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 𝗛𝗮𝗻𝗱𝗯𝗼𝗼𝗸 📚 by Maxime Labonne and Paul Iusztin, with forewords by Julien Chaumond (Cofounder & CTO Hugging Face) and Hamza Tahir (Cofounder & CTO ZenML). GitHub Repo to access the content in detail 👉 : https://lnkd.in/dd6Ad__6 𝟭. 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗟𝗟𝗠 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: Exploring the LLM Twin concept and the FTI (Features, Training, Inference) architecture for scalable ML systems. 𝟮. 𝗧𝗼𝗼𝗹𝗶𝗻𝗴 𝗮𝗻𝗱 𝗜𝗻𝘀𝘁𝗮𝗹𝗹𝗮𝘁𝗶𝗼𝗻: Setting up essential tools like #Python, #ZenML for pipeline orchestration, #CometML for experiment tracking, #Opik for prompt monitoring, #MongoDB for storing unstructured text data, #Qdrant vector DB for embeddings, #AWS CLI and #SageMaker for cloud ML lifecycle, and CI/CD with GitHub Actions. 𝟯. 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: Collecting and storing raw data from dynamic sources like Medium, GitHub, and LinkedIn Posts using tools like BeautifulSoup, Scrapy, and various APIs. 𝟰. 𝗥𝗔𝗚 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲: Building Retrieval-Augmented Generation pipelines with SentenceTransformers and Qdrant DB, exploring both vanilla and advanced RAG frameworks. 𝟱. 𝗦𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴: Refining pre-trained models using Hugging Face Transformers, LoRA/QLoRA techniques with the PEFT library, working with models like Llama 3.1 8B, and annotating data with Label Studio. 𝟲. 𝗣𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗔𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁: Aligning models with human preferences using methods like Direct Preference Optimization (DPO) and tools like Unsloth, focusing on models like TwinLlama-3.1-8B. 𝟳. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗻𝗴 𝗟𝗟𝗠𝘀: Assessing model performance using techniques like Multi-Choice-Question-Answering, LLM-as-a-judge, and conducting evaluations on models, RAG systems, and specifically TwinLlama-3.1-8B. 𝟴. 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Discussing strategies like speculative decoding, model optimization, model parallelism, and model quantization to improve inference speed and efficiency. 𝟵. 𝗥𝗔𝗚 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲: Implementing advanced RAG techniques and designing a custom retrieval module. Also query expansion, self-querying, filtered vector search, collecting results, reranking, building prompts, and generating answers. 𝟭𝟬. 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁: Deploying models to AWS SageMaker, building RESTful APIs with FastAPI, containerizing with Docker and AWS ECR, and setting up API Gateways with AWS API Gateway. 𝟭𝟭. 𝗠𝗟𝗢𝗽𝘀 𝗮𝗻𝗱 𝗟𝗟𝗠𝗢𝗽𝘀: Deploying ML pipelines to the cloud, setting up CI/CD/CT pipelines with GitHub Actions, using Docker and AWS ECR for containerization, and leveraging Git for version control. Let's learn and grow together! 🚀 #MLOps #LLMOps #GenAI #AIInDubai #AICommunity #learnandgrowtogether

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🚀 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐎𝐯𝐞𝐫𝐟𝐢𝐭𝐭𝐢𝐧𝐠 𝐯𝐬. 𝐔𝐧𝐝𝐞𝐫𝐟𝐢𝐭𝐭𝐢𝐧𝐠 𝐢𝐧 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 📊 Hello 👋, When building machine learning models, achieving the right balance between overfitting and underfitting is crucial for creating robust and reliable models. Here's a quick overview of these concepts: 𝘖𝘷𝘦𝘳𝘧𝘪𝘵𝘵𝘪𝘯𝘨: When the model performs well on training data but poorly on test data due to high variance and low bias. The model is too complex and captures noise along with the underlying patterns. 𝘜𝘯𝘥𝘦𝘳𝘧𝘪𝘵𝘵𝘪𝘯𝘨: When the model performs poorly on both training and test data due to high bias and low variance. The model is too simple and fails to capture the underlying patterns in the data. 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 𝐭𝐨 𝐀𝐝𝐝𝐫𝐞𝐬𝐬 𝐓𝐡𝐞𝐬𝐞 𝐈𝐬𝐬𝐮𝐞𝐬: 1. 𝘙𝘦𝘥𝘶𝘤𝘦 𝘖𝘷𝘦𝘳𝘧𝘪𝘵𝘵𝘪𝘯𝘨: Simplify the model, increase training data, use regularization techniques, and apply early stopping. 2. 𝘙𝘦𝘥𝘶𝘤𝘦 𝘜𝘯𝘥𝘦𝘳𝘧𝘪𝘵𝘵𝘪𝘯𝘨: Increase model complexity, enhance feature representation, and ensure adequate training data. ✍🏻Lekhansh #data #science #model #over #under

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🚀 Introducing 𝗔𝘂𝘁𝗼𝗚𝗚𝗨𝗙: An Automated Graphical Interface for GGUF Model Quantization 🎉 AutoGGUF, a new graphical user interface (PyQt6) app developed in Python to streamline the quantization process of GGUF models using the llama.cpp library. 🎳 𝗞𝗲𝘆 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: - Automated Download & Management: Seamlessly manage llama.cpp backends, including CUDA. - Simple Model Selection & Quantization: Easily choose and quantize models. - Configurable Parameters: Tailor your quantization settings. - Resource Monitoring: Monitor system resources during operations. - Parallel Tasks: Enjoy the benefits of threaded execution. - Preset Saving: Save your quantization presets for future use. - iMatrix Generation: Effortlessly generate iMatrix. - Extensive Logging: Access detailed logs for better tracking and troubleshooting. 🍦 𝗖𝗼𝗺𝗽𝗮𝘁𝗶𝗯𝗶𝗹𝗶𝘁𝘆 & 𝗔𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆: - Cross-Platform: Compatible with multiple platforms. - Open Source: Licensed under Apache-2.0. - Language Support: Available in 28 languages. - Executable Releases: Windows and Ubuntu users can download the latest release executable built with PyInstaller for enhanced performance, while other platforms can run it from source. 🕸️ 𝗦𝗶𝗺𝗽𝗹𝗶𝗳𝗶𝗲𝗱 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄: AutoGGUF removes the need for command line operations, automates directory creation, and offers extensive customization options. It addresses common pain points in the quantization workflow, making it easier to work with GGUF models. 👁️ 𝗚𝗶𝘁𝗛𝘂𝗯 𝗥𝗲𝗽𝗼𝘀𝗶𝘁𝗼𝗿𝘆: https://lnkd.in/dqTzAShC 🧠 𝗞𝗻𝗼𝘄𝗻 𝗜𝘀𝘀𝘂𝗲𝘀: - Preset Saving During Quantization: May cause UI thread crashes. - Task Deletion During Processing: You must cancel the task first to avoid crashes. 🤖𝗨𝗽𝗰𝗼𝗺𝗶𝗻𝗴 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: - Custom command line parameters - Additional iMatrix generation parameters - Perplexity testing - Conversion of HF safetensors to GGUF - Enhanced progress tracking Below a screenshot of the app to give you a glimpse:

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One particularly powerful thing about open-source LLMs with Ollama and Timescale's pgai for #Postgres is that you can now host your own models without needing to pay or share private data with closed-sourced providers like OpenAI.

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Integrating Habits and Goals in AI with Variational Bayesian Methods Follow me for more technical data :https://lnkd.in/evN3jc94 I want to share my insights on a cutting-edge theoretical framework recently published in " Nature Communications " . This framework, developed by scientists from the Okinawa Institute of Science and Technology (OIST) and Microsoft Research Asia, revolutionizes how AI integrates habitual and goal-directed behaviors using variational Bayesian methods. - Key Technical Insights: - Behavioral Synergy: By leveraging variational Bayesian theory, this framework combines habitual (fast but inflexible) and goal-directed (flexible but slower) behaviors. Habitual behavior is based on the prior distribution of intention derived from sensory context, while goal-directed behavior relies on a goal-conditioned posterior distribution, inferred through variational free energy minimization. - Unified Model for AI Decision-Making: This approach creates a model that allows AI agents to balance dynamically between fast, inflexible actions and slower, flexible planning, addressing the need for both efficiency and adaptability. - Advanced Sensorimotor Learning: Applied in vision-based sensorimotor tasks, this method has demonstrated the ability to simulate a robot navigating through complex mazes. Our simulations show that AI can rapidly adapt to new environments by synergizing habitual and goal-directed intentions, mimicking human and animal behaviors observed in similar tasks. - Reinforcement Learning with Active Inference: This framework integrates reinforcement learning with active inference, improving decision-making by utilizing learned habitual behaviors to streamline planning, thereby reducing computational costs. - Efficient Planning: The model's internal representation of the environment supports goal planning without explicit training for every goal, facilitating quicker, adaptive responses. - Insights into Neural Mechanisms: This work offers new perspectives on the neural mechanisms behind habitual and goal-directed behaviors, shedding light on decision-making processes and providing valuable implications for understanding neurological disorders like ADHD, OCD, and Parkinson's disease. - Implications: This framework represents a significant advancement in AI, allowing systems to mimic the complex, adaptive behaviors seen in biological entities. By merging reinforcement learning with variational Bayesian methods, we can develop AI that is more efficient, flexible, and capable of human-like decision-making. This research also enhances our understanding of the computational principles underlying human and animal behavior. #ArtificialIntelligence  #MachineLearning 

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