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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Google and Stanford Develop AI Housemaid

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 04 Jan, 2024
3 min read

Stanford University has introduced Mobile ALOHA, a robotic system that advances the capabilities of bimanual mobile manipulation through low-cost whole-body teleoperation. This innovation, which builds upon Google DeepMind’s existing ALOHA system, brings mobility and dexterity to the forefront of robotic learning. The project developed in collaboration with Berkeley University and Meta has many features, which we will explore in this article. We will also delve into its real-life uses ranging from cooking to navigating complex environments, which make it a potential AI-powered housemaid of the future!

Also Read: Samsung to Unveil AI-Powered Kitchen at CES 2024

Expanding ALOHA with Mobility

Mobile ALOHA extends the capabilities of Google’s ALOHA system by incorporating a mobile base and a whole-body teleoperation interface. This evolution allows the system to imitate complex mobile manipulation tasks. Thereby, addressing the limitations of traditional imitation learning that often focuses on tabletop scenarios. The primary purpose of Mobile ALOHA is data collection, laying the foundation for learning and replicating various bimanual activities.

Google and Stanford's Mobile ALOHA Bimanual Mobile Manipulation model.

Learning from Human Demonstrations

The heart of Mobile ALOHA lies in its ability to co-train with existing static ALOHA datasets. This is what sets it apart from conventional robotic systems. By leveraging supervised behavior cloning and 50 demonstrations for each task, the system achieves remarkable success rates, boosting performance on mobile manipulation tasks by up to 90%. This breakthrough empowers the robot to autonomously handle complex scenarios, from sautéing shrimp to opening wall cabinets.

Also Read: Google DeepMind RoboCat: A Self-Learning Robotic AI Model

Real-World Applications

Mobile ALOHA’s capabilities extend beyond the confines of traditional robotics, showcasing its potential for real-world applications. The system excels in tasks such as calling and entering elevators, storing heavy cooking pots, and rinsing used pans. The cost-effectiveness of the robot makes it a practical solution, opening doors for a new era in robotics where machines can perform a wide range of mobile manipulation tasks with precision and adaptability. Hopefully, Google and Standford will soon be able to develop Mobile ALOHA into a fully functional AI housemaid.

Also Read: Samsung’s AI Powered Vaccuum Cleaner & Mop Can Remove the Toughest Stains

Mobile ALOHA real life use | AI housemaid

Advancements in Robotics

The introduction of Mobile ALOHA aligns with notable advancements in the field of robotics in 2023. From Boston Dynamics upgrading Atlas for intricate construction tasks to Elon Musk’s Tesla working on the humanoid robot Optimus, the robotics landscape has evolved rapidly. Stanford’s Mobile ALOHA joins this wave of innovation, providing a cost-effective solution that enhances efficiency in kitchen tasks and navigates complex environments.

Our Say

Mobile ALOHA’s integration of mobility and dexterity into bimanual mobile manipulation represents a significant stride in robotics. Stanford’s innovative approach to Google’s base model combines low-cost hardware with a novel imitation learning algorithm. This sets Mobile ALOHA apart in the realm of robotic systems. As the robotics field continues to push boundaries, Mobile ALOHA stands as a testament to the potential of accessible and reproducible solutions for fine manipulation tasks. This breakthrough not only enhances the capabilities of robots in various domains but also paves the way for a future where robotics seamlessly integrates into our daily lives.

AIAI housemaidApplicationsArtificial IntelligenceFeaturesgooglehouse assistantMobile ALOHamobile manipulationnewsRoboticsStanford University
K.C. Sabreena Basheer
K.C. Sabreena Basheer

Sabreena Basheer is an architect-turned-writer who's passionate about documenting anything that interests her. She's currently exploring the world of AI and Data Science as a Content Manager at Analytics Vidhya.

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