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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

Call AI From Your Phone With Arc Search’s ‘Call Arc’ Feature

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 24 May, 2024
3 min read

News at a Glance

  • Arc Search introduces “Call Arc,” a new AI-powered voice search feature for iPhones.
  • Users can ask questions by holding their phone to their ear as if making a call.
  • The feature provides verbal answers, accompanied by an animated smiley face.
  • “Call Arc” plays hold music while searching for answers, enhancing user experience.
  • Arc Search continues to innovate in the competitive browser and AI assistant market.
Arc Search’s New 'Call Arc' Feature Brings AI Search Over a Phone Call

The Browser Company has rolled out a unique feature for its iPhone-only Arc Search app, named “Call Arc.” This novel feature lets users ask questions and receive verbal answers by holding their phone to their ear, just like a phone call. It stands out as an innovative twist on traditional voice search functionalities, making the search experience more conversational and intuitive.

Also Read: Google Mulls Charging for AI-Enhanced Search: What You Need to Know

Arc Search, launched in January, has already made waves with its AI-driven browsing experience. The introduction of Call Arc elevates this experience by integrating a gesture-based search mechanism. Users simply open the Arc Search app, hold their phone to their ear, and ask a question. This action triggers the voice search, which then provides immediate verbal responses. The familiar gesture of making a phone call makes the feature feel natural and accessible, even for those who might be less tech-savvy.

Also Read: Top 30+ ChatGPT Alternatives You Can Try In 2024 (Free and Paid)

How Call Arc Works

Upon initiating a query with Call Arc, users hear hold music while the app searches for answers. This brief waiting period is designed to make the experience more engaging, much like waiting on a real phone call. Once the app finds the answer, it delivers it verbally, accompanied by an animated smiley face that mimics speaking. The interface also includes an end call button and a speaker option, adding to the phone call illusion.

iPhone users can now talk to AI through a phone call using Arc Search's new "Call Arc." feature.

Innovative Interface and Functionality

The Call Arc feature is more than just a gimmick. It leverages the AI capabilities of Arc Search to provide quick, accurate answers to user queries. The interface is designed to be user-friendly, with options to read more detailed responses if desired. This functionality is supported by Arc’s “Browse for Me” feature, which uses large language models from OpenAI to curate and summarize search results into an easy-to-read format. The “Pinch to Summarize” feature further makes it a versatile tool for users seeking concise information from webpages.

Competing in a Crowded Market

Arc Search is positioning itself as a strong competitor against established browsers like Safari and Chrome, as well as AI assistants like Siri. The Browser Company’s frequent and meaningful updates are a testament to its commitment to innovation. By introducing features like Call Arc, Arc Search aims to differentiate itself in a market dominated by giants. However, the competition is fierce, with Apple planning its own AI-driven enhancements for Safari and Siri.

Also Read: Top 10 AI Search Engines Shaping the Digital Frontier

Our Say

Arc Search’s Call Arc feature is an exciting addition to mobile search. The simplicity of making a phone call to harness the power of AI promises a seamless and engaging user experience. As AI evolves further, features like Call Arc will integrate more naturally into our daily lives. While it remains to be seen how competitors will respond to this innovative approach, it certainly sets a new standard for voice-activated search functionalities.

Follow us on Google News to stay updated with the latest innovations in the world of AI, Data Science, & GenAI.

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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