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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

Experience Augmented Reality (AR) Directly With Your Own Eyes Using AI

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 12 Jul, 2023
4 min read

In a technological breakthrough, Brilliant Labs has disrupted the Augmented Reality market with its cutting-edge open-source AR lens, Monocle. This innovative wearable starkly contrasts Apple‘s pricey and bulky Vision Pro, offering a more accessible and user-friendly alternative. With Monocle, users can clip the pocket-sized AR lens onto any eyewear or hold it up to their eye, opening up a world of augmented reality experiences. Launched in March, Monocle has captivated tech enthusiasts with its affordability, priced at just $349. Let’s find out more about this fascinating AI-powered invention.

Brilliant Labs Introduces Monocle - an AI-Powered Augmented Reality (AR) Lens

Thinking Outside the Box: Brilliant Labs’ Vision for AR

While other AR and VR headsets tend to confine users to their home environments, Brilliant Labs is breaking the mold. The company’s CEO and co-founder, Bobak Tavangar, emphasized their mission to explore uncharted territories. In an interview with TechCrunch, Tavangar stated, “We are running in the exact opposite direction.”

Also Read: WWDC Highlights: Apple’s Practical A.I. Solutions Revealed

The Fusion of AI and AR: Brilliant Labs’ Ambitious Goal

To seamlessly integrate the AR lens into users’ daily lives, Brilliant Labs aims to embed generative AI into its compact wearable. With this ambitious vision in mind, the Singapore-based startup has secured $3 million in seed funding. The funding round was led by notable industry veterans, including Brendan Iribe (co-founder of Oculus), Adam Cheyer (co-founder of Siri), and Eric Migicovsky (founder of Pebble and former Y Combinator partner), among others.

Brilliant Labs' Monocle is an innovative AI-powered AR lens that users can hold up to their eye and experience a world of augmented realities

Monocle’s Open-Source Community Sparks Innovation

The open-source nature of Monocle has sparked a dedicated following among developers and enthusiasts. The AR lens gained widespread attention when a group of Stanford students transformed it into a display for GPT-4. Leveraging the power of generative AI, Monocle can listen to real-time conversations through the user’s smartphone microphone and offer personalized suggestions. This unique functionality has garnered significant interest and showcases the immense potential of fusing AI and AR.

Also Read: Meta Open-Sources All their Promising Projects | Find Out Why

Seamless Connectivity: How Monocle Transmits Data

Monocle relies on a seamless connection between the user’s smartphone and OpenAI‘s server. The device transmits data via Bluetooth, enabling users to receive responses projected onto the AR lens in front of their eye. This streamlined process ensures a fluid and immersive augmented reality experience.

Brilliant Lab's Monocle architecture block diagram | AI-powered AR lens

Empowering Developers: arGPT Solution and Its Potential

Brilliant Labs has also developed an in-house application called arGPT, designed to enhance the functionality of Monocle. The arGPT solution facilitates low-latency dialogue with GPT-4 by leveraging Monocle’s microphone and other sensors. Currently, Monocle has primarily attracted developers, hackers, hobbyists, and researchers who are eager to explore the possibilities of this palm-sized device. This collaborative approach encourages innovation and unlocks the full potential of generative AI and AR.

Also Read: Google Adds AI Coding Bot Codey to Google Colaboratory

Miniature Marvel: The Technology Behind Monocle

Despite its compact size, Monocle packs a punch with its array of processors. Equipped with five different processors, including a hackable field-programmable gate array (FPGA) accelerator chip, Monocle handles data from the device’s camera, microphone, and capacitive touch sensor. The use of low-power electronics and miniaturized electronic parts allows the lens to weigh a mere 15 grams, ensuring comfort and portability for users.

Parts of the AI-powered AR lens Monocle

A Paradigm Shift in AR: Brilliant Labs’ Market Approach

Brilliant Labs has disrupted the market by eschewing the traditional bulky and expensive headset approach. Instead, the company focuses on delivering an affordable and efficient solution. Tavangar shared insights, stating, “Every other device company was doing bulky, expensive headsets. They weren’t making money out of them.” Brilliant Labs’ focus on unit economics and operational discipline positions them for scalability and profitability.

Also Read: US Stock Market Finds Boost From Artificial Intelligence (AI)

A Glimpse into the Future: Monocle’s Evolution

While Monocle currently relies on smartphones as a relay point, Brilliant Labs envisions a future where the hardware directly connects to the cloud. This eliminates the need for a smartphone host and paves the way for running deep learning models directly on the tiny AR device. This advancement promises even lower latency and functionality, ensuring a seamless experience even in areas with limited internet connectivity.

Brilliant Labs' Monocle is an innovative AI-powered AR lens | Smallest AR device

Beyond Hardware: Expanding Services and Charging for AI-powered Features

Brilliant Labs plans to expand its services beyond hardware sales. By integrating large language models (LLMs), the company envisions offering additional AI-pOpenAI and Meta Sued for Copyright Infringementowered services. While Brilliant Labs initially integrated with GPT due to its well-documented API, the team seeks compressed LLMs to improve efficiency and explore new possibilities. The founder envisions a future where the hardware becomes more powerful while maintaining low power consumption.

Also Read: How To Use ChatGPT API In Python?

Our Say

While exact sales figures remain undisclosed, Brilliant Labs boasts strong profit margins and thrives solely on hardware sales. The company is poised for further success with a foundation built on operational discipline and unit economics. As Brilliant Labs expands its offerings and explores the vast potential of AI and AR, it promises to shape the future of augmented reality experiences.

Image Source: brilliant.xyz

AIAI technologyARAR lensArtificial Intelligenceaugmented realityGPThardwarelensesnews
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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