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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: New Models, Research Advances, and Regulatory Debates

Aayush1
Aayush1 01 Sep, 2024
4 min read

Introduction

This week, the AI field saw significant updates as top companies unveiled new models and tools. AI21 Labs launched Jamba 1.5, AnthropicAI improved Claude 3, and Bindu Reddy introduced Dracarys, a coding-focused model. Researchers also made strides in prompt optimization and hybrid architectures, highlighting ongoing advancements that are set to transform AI capabilities and applications.

Overview

  • New Model Releases: AI21 Labs launched Jamba 1.5, a scaled-up model with faster inference speeds and superior performance in long-context processing, outperforming models like Llama 3.1 70B.
  • Model Enhancements: AnthropicAI updated Claude 3 with LaTeX rendering and prompt caching, improving mathematical capabilities and query efficiency. Bindu Reddy introduced Dracarys, a leading open-source model for coding tasks.
  • Research Advancements: Significant progress in prompt optimization and hybrid architectures, enhancing AI’s ability to handle complex tasks and long contexts.
  • AI Tools and Applications: New tools like Spellbook Associate for legal work and MLX Hub for model management have been introduced, expanding AI’s practical applications.
  • AI Industry Challenges: Highlighted the difficulties in achieving high accuracy in multi-step workflows and the debate between open-source and closed-source model performance.
  • Regulation and Safety: Ongoing discussions on AI safety and regulation, particularly around California’s SB 1047 and Anthropic’s stance on regulating open-source models.

AI Model Releases and Developments

Jamba 1.5 Launch by AI21 Labs

AI21 Labs has released Jamba 1.5, a scaled-up version of their original Jamba model. This new model excels in long-context processing and offers up to 2.5x faster inference speeds. It has shown impressive performance in benchmarks, outperforming larger models like Llama 3.1 70B.

  • Jamba 1.5 is a hybrid SSM-Transformer MoE model available in Mini (52B – 12B active) and Large (398B – 94B active) versions.
  • Key features include a 256K context window, multilingual support, and optimized performance for long-context tasks.
  • The model demonstrates superior performance, achieving a score of 65.4 on the Arena Hard benchmark, outperforming larger models like Llama 3.1 70B.

Claude 3 Updates by AnthropicAI

Claude 3 has received updates including LaTeX rendering support, enhancing its ability to display mathematical equations and expressions. Prompt caching is now available for Claude 3 Opus, improving efficiency in handling repeated queries.

Dracarys Release by Bindu Reddy

Bindu Reddy announced Dracarys, claiming it to be the best open-source 70B class model for coding. It surpasses Llama 3.1 70B and other models in benchmarks and is available on Hugging Face. The model shows significant improvements in coding performance compared to other open-source models.

Mistral Nemo Minitron 8B

This model demonstrates superior performance to Llama 3.1 8B and Mistral 7B on the Hugging Face Open LLM Leaderboard. The success suggests the potential benefits of pruning and distilling larger models.

Phi-3.5 and Flexora

Microsoft’s Phi-3.5 model has been praised for its safety and performance. Flexora introduces a new approach to LoRA fine-tuning, yielding superior results and reducing training parameters by up to 50%. The technique involves adaptive layer selection for LoRA.

AI Research and Techniques

Prompt Optimization

The challenges of prompt optimization are highlighted, emphasizing the complexity of finding optimal prompts in vast search spaces. Simple algorithms like AutoPrompt/GCG have shown surprising effectiveness in this area.

Hybrid Architectures

Hybrid Mamba/Transformer architectures are noted for their effectiveness, especially for long context and fast inference tasks.

AI Applications and Tools

Spellbook Associate

Spellbook Associate is an AI agent for legal work capable of breaking down projects, executing tasks, and adapting plans.

LlamaIndex 0.11

The latest version of llamaindex includes new features such as Workflows replacing Query Pipelines and a 42% smaller core package.

MLX Hub

MLX Hub, a new command-line tool for searching, downloading, and managing MLX models from the Hugging Face Hub has been introduced.

AI Development and Industry Trends

Challenges in AI Agents

Achieving high accuracy across multi-step workflows in AI agents is highlighted as a significant challenge, akin to the last-mile problem in self-driving cars.

Open-Source vs. Closed-Source Models

Most open-source fine-tunes tend to deteriorate overall performance while improving on narrow dimensions. Dracarys is noted for improving overall performance.

AI Regulation

A letter to Governor Newsom discusses the costs and benefits of California’s proposed AI regulation bill, SB 1047.

AI Hardware

The potential of combining resources from multiple devices for home AI workloads is discussed, highlighting the importance of efficient hardware utilization.

AI Safety and Legislation

California’s SB 1047

This bill aims to regulate AI applications for safety. Entities like Stanford and Anthropic have expressed mixed views. While some see it as a necessary step to mitigate AI risks, others worry it might stifle innovation.

Anthropic’s Stance on AI Regulation

Anthropic appears to be taking a more aggressive stance against open-source LLMs, potentially suggesting legislation to Senator Wienner. This has sparked a debate about the balance between AI safety and innovation.

Our Say

In the past week, the AI field has seen a wave of exciting developments and critical discussions. From AI21 Labs’ Jamba 1.5 setting new benchmarks in long-context processing to AnthropicAI’s updates on Claude 3, and Bindu Reddy’s Dracarys excelling in coding tasks, innovation continues to drive the industry forward. Meanwhile, research in prompt optimization and hybrid architectures is reshaping AI capabilities, and debates around AI safety and regulation highlight the growing need for responsible AI practices. As the field rapidly evolves, balancing technological advancement with ethical considerations will be key to ensuring that AI benefits all of society.

Stay tuned for more insights and updates in next week’s edition of The AI Chronicle.

av bytes
Aayush1
Aayush1 01 Sep, 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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