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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

AvBytes: Key Developments and Challenges in Generative AI

Aayush1
Aayush1 26 Aug, 2024
5 min read

Introduction

Hey there, AI enthusiasts!

Welcome to The AV Bytes, your friendly neighborhood source for all things AI. Buckle up, because this week has been a wild ride in the world of AI! We’ve got some mind-blowing stuff to share with you.

Remember when we thought search engines couldn’t get any better? Well, OpenAI just raised the bar with their new SearchGPT. And Meta? They’ve taken things to a whole new level with Llama 3.1. Not to be outdone, Mistral AI joined the party with their impressive Large 2 model.

But that’s not all! We’ve got AIs acing math olympiads and giving doctors a run for their money in diagnostics. It’s like science fiction is becoming science fact right before our eyes! And trust us, we’re just getting started – this week has been absolutely packed with AI goodness.

So, let’s get started!

Highlights

  • Google’s Gemini AI Integration: Google has introduced its new AI assistant, Gemini, integrated into Android and Pixel 9 devices, enhancing user experience with advanced multimodal features and photo editing capabilities.
  • Anthropic’s API Enhancements: Anthropic has rolled out prompt caching in their API, dramatically reducing costs and latency, and improving the efficiency of AI applications like coding assistants.
  • xAI’s Grok-2 Release: xAI launched Grok-2, a new AI model rivaling top competitors, but it has sparked controversy over its lack of content restrictions and ethical concerns.
  • OpenAI and Claude 3.5 Sonnet Updates: OpenAI’s latest update, GPT-4o, improves image generation, while Claude 3.5 Sonnet outperforms GPT-4 in key areas, indicating a trend towards more specialized AI models.
  • AI Tools and Applications: Innovations like the Dora AI plugin for Figma and Box AI’s document processing API are enhancing productivity in design and document management.

Major AI Model Releases and Updates

Google’s Gemini AI and Pixel 9 Integration

Google has launched its new AI assistant, Gemini, integrated into Android devices and the Pixel 9 series. This integration enhances the user experience with advanced AI-driven features like multimodal capabilities, which combine text and images for more intuitive interactions, and sophisticated photo editing options. Gemini aims to make everyday tasks more seamless and efficient, positioning itself as a leading AI tool in consumer electronics.

Anthropic API Enhancements

Anthropic has introduced prompt caching in their API, a feature that reduces input costs by up to 90% and latency by up to 80%. This significant improvement allows the reuse of large amounts of contextual data across multiple API requests, enhancing applications such as coding assistants and document processing tools. Anthropic has also moved 8,192 token outputs from beta to general availability for the Claude 3.5 Sonnet model. These updates highlight Anthropic’s commitment to providing efficient and cost-effective AI solutions.

xAI’s Grok-2 Release and Controversy

xAI, founded by Elon Musk, has released Grok-2, an AI model that rivals top models like Claude 3.5 Sonnet and GPT-4-Turbo. Grok-2 supports both vision and text inputs and integrates external models for image generation, placing it among the leaders on the LMSYS leaderboard. However, the lack of content restrictions has led to ethical and legal concerns, drawing criticism from various stakeholders about responsible AI use.

OpenAI’s ChatGPT Update

OpenAI has rolled out an update to its ChatGPT model, GPT-4o, focusing on improving image generation quality and efficiency. This update, driven by user feedback, aims to provide more accurate and visually appealing outputs, enhancing the overall experience for users across various applications.

Claude 3.5 Sonnet’s Superior Performance

The Claude 3.5 Sonnet model has been reported to outperform GPT-4 in critical areas like coding and reasoning, suggesting a shift towards more specialized and efficient AI models. This development is indicative of a broader trend towards refining AI models for specific tasks to achieve better performance outcomes.

AI Tools and Applications

Dora AI Plugin for Figma

The Dora AI plugin for Figma is revolutionizing design automation by enabling users to generate complete landing pages in under 60 seconds. This tool exemplifies the potential of AI to enhance design efficiency, making professional web development teams significantly more productive.

Box AI API for Document Processing

Box has introduced a beta version of its AI API that allows users to interact with stored documents through AI-driven features such as data extraction, content summarization, and the generation of derived content. This development streamlines document management processes, showcasing AI’s ability to improve organizational efficiency.

Salesforce DEI Framework

Salesforce has launched DEI (Diversity Empowered Intelligence), an open AI software engineering agents framework that demonstrates a 55% resolve rate on SWE-Bench Lite. This framework surpasses the performance of individual agents, highlighting the potential for collaborative AI systems in complex software engineering tasks.

A U.S. court has allowed copyright infringement claims against Stability AI to proceed, based on allegations of unauthorized use of copyrighted materials in training models. This legal battle underscores the critical importance of adhering to intellectual property laws in AI development, emphasizing the need for transparency and ethical practices.

The Dutch copyright enforcement group BREIN has successfully taken down an unauthorized dataset used for AI training, highlighting the increasing scrutiny and enforcement of copyright laws within the AI industry. This action reflects the growing awareness and legal challenges surrounding the use of data in AI model training.

Hollywood’s AI Voice Replication Deal

In a groundbreaking move, SAG-AFTRA, the Hollywood actors’ union, has reached an agreement that allows actors to license their digital voice replicas for advertising. This deal sets a new standard for ethical AI use in the entertainment industry, ensuring that artists are compensated and retain control over their digital likenesses.

Expansion and Accessibility of AI Technologies

Samsung’s AI Expansion

Samsung has extended its advanced AI tools, like “Circle to Search,” to mid-range Galaxy A devices, democratizing access to sophisticated AI technologies. This expansion makes cutting-edge AI tools more accessible to a broader audience, reflecting a trend towards inclusive technological advancements.

Growth of AI-Enabled PCs

AI-enabled PCs, equipped with neural processing units for local AI tasks, now make up 14% of quarterly PC shipments. This growth, led by companies like Apple, demonstrates the increasing demand for devices that support advanced AI capabilities, marking a shift towards more powerful and versatile computing solutions.

AI in Education and Workforce Development

Nvidia and California’s AI Education Partnership

Nvidia has partnered with the state of California to enhance AI training resources in community colleges. This initiative aims to equip students and educators with the skills needed for future AI careers, focusing on generative AI training, new curriculums, certifications, and AI labs. This partnership represents a significant investment in the future workforce and the importance of AI education.

AI Safety and Regulation

California’s SB 1047 Amendment

California’s SB 1047, aimed at preventing AI-related disasters, has passed the Appropriations Committee with amendments that shift the focus from stringent safety certifications to public statements on safety practices. This change reflects the evolving discourse on balancing innovation with safety in AI development.

Our Say

The AI landscape is rapidly evolving, with significant advancements in model performance, tool integration, and research methodologies. At the same time, legal and ethical challenges are becoming more pronounced, highlighting the need for responsible development and use of AI technologies. As companies continue to innovate and integrate AI into various aspects of daily life, it is crucial to address these challenges and ensure that AI’s potential is harnessed for societal benefit. Stay tuned for more updates as we continue to explore the exciting world of artificial intelligence.

Aayush1
Aayush1 26 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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