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Reading list

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
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 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

AV Bytes: AI Industry Shifts and Technological Breakthroughs

Aayush1
Aayush1 14 Aug, 2024
4 min read

Introduction

In this edition of AV Bytes, we delve into some of the most impactful developments in the AI industry over the past week. From Google’s strategic acquisition of Character.ai to the release of BitNet b1.58, the AI landscape is rapidly evolving with innovations that promise to reshape the future of technology. We also explore the latest advancements in AI infrastructure, tools, and domain-specific models, all of which are driving new capabilities and efficiencies across various sectors.

Join us as we break down these major milestones and what they mean for the future of AI.

Overview

  • Google acquired Character.ai and launched the Gemma 2 models, reinforcing its AI leadership.
  • BitNet b1.58 and domain-specific models highlight trends toward efficient and specialized AI.
  • Tools like PyTorch’s torchchat and CXL technology are boosting AI performance.
  • Multimodal and domain-specific AI are gaining importance in industry applications.
  • A new no-code tool for AI testing in healthcare emphasizes ethical AI use.

Major AI Model Developments and Industry Shifts

Google’s Acquisition of Character.ai

Character.ai, renowned for its innovative chatbot technology, has been acquired by Google, marking a significant expansion of Google’s AI capabilities. This deal includes the return of CEO Noam Shazeer to Google and reflects the broader trend of tech giants acquiring AI startups to strengthen their AI portfolios.

BitNet b1.58

BitNet b1.58, a 1-bit LLM where every parameter is ternary {-1, 0, 1}, has been introduced. This approach could potentially allow running large models on devices with limited memory, such as phones.

GitHub Model Hosting

GitHub has introduced a new feature that allows developers to host AI models directly on the platform, providing a seamless path to experiment with model inference code using Codespaces.

Gemma 2 and FLUX.1

Google’s new Gemma 2 models and Black Forest Labs’ FLUX.1 are pushing the boundaries of what AI can achieve. These models are setting new benchmarks in AI capabilities, demonstrating significant advancements in both efficiency and performance.

AI Infrastructure and Tools

torchchat by PyTorch

PyTorch has released torchchat, a versatile solution for running large language models (LLMs) locally on various devices. Supporting models like Llama 3.1, torchchat offers features for evaluation, quantization, and optimized deployment across different platforms.

LangGraph Studio by LangChain

LangChain introduced LangGraph Studio, an agent IDE designed for developing LLM applications. It provides visualization, interaction, and debugging tools for complex agentic applications, streamlining the development process.

CXL Technology in AI

Compute Express Link (CXL) technology is revolutionizing AI by enhancing memory bandwidth and capacity, addressing one of the most critical constraints in AI development. This technology is vital for creating more powerful and efficient AI models.

AI Research and Developments

PyTorch Distributed Shampoo

Distributed Shampoo has outperformed Nesterov Adam in deep learning optimization, marking a significant advancement in non-diagonal preconditioning.

MoMa Architecture by Meta

Meta introduced MoMa, a new sparse early-fusion architecture for mixed-modal language modeling that significantly improves pre-training efficiency. MoMa achieves approximately 3x efficiency gains in text training and 5x in image training.

Domain-Specific and Multimodal AI Innovations

Generative AI in Healthcare

John Snow Labs has launched a no-code tool for responsible AI testing in healthcare, enabling non-technical experts to evaluate custom language models. This tool is crucial for ensuring the safe and effective deployment of AI in healthcare settings.

Advancements in Multimodal AI

Multimodal AI, which integrates various data types into unified AI solutions, is gaining momentum. This approach is particularly beneficial in fields like healthcare and law, where diverse data types are common.

Domain-Specific AI Models

The rise of domain-specific AI models offers tailored solutions for industries like healthcare and law. These models are designed to meet the unique needs of specific domains, providing more accurate and relevant insights.

Apple’s AI Suite and Quantum AI

Quantum AI  

Quantum computing is poised to revolutionize AI by providing faster computation and more powerful algorithms. This technology opens new research and application avenues, potentially transforming fields that require complex computations.

Apple’s AI Suite

Apple has launched “Apple Intelligence,” a suite of AI features aimed at enhancing services like Siri and automating various tasks. This suite includes advanced machine learning models and natural language processing capabilities, positioning Apple as a significant player in the AI space.

AI Ethics and Policy Developments

NTIA’s Support for Open AI Models

The National Telecommunications and Information Administration (NTIA) issued a report advocating for the openness of AI models while recommending risk monitoring. This report, which directly influences White House policy, could shape future AI regulations in the United States.

Watermarking Debate in AI Trust

A debate emerged around the effectiveness of watermarking in solving trust issues in AI. Some argued that watermarking only works in institutional settings and cannot prevent misuse entirely. The discussion highlighted the need for better cultural norms and trust mechanisms to address the spread of deepfakes and misrepresented content.

Our Say

As AI continues to advance at an unprecedented pace, the developments highlighted in this edition of AV Bytes underscore the transformative impact these technologies are having across industries. From Google’s strategic moves to innovations in AI infrastructure and domain-specific applications, the progress made in just a week is a testament to the field’s dynamism. As we move forward, these advancements will not only reshape industries but also redefine the possibilities of what AI can achieve, paving the way for a future where technology and human ingenuity converge in new and exciting ways.

Stay tuned for more updates and insights in the world of artificial intelligence in the next edition of our AI News Blog!

AI newsav bytesav news
Aayush1
Aayush1 14 Aug, 2024

Data Analyst with over 2 years of experience in leveraging data insights to drive informed decisions. Passionate about solving complex problems and exploring new trends in analytics. When not diving deep into data, I enjoy playing chess, singing, and writing shayari.

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