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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

o1: OpenAI’s New Model That ‘Thinks’ Before Answering Tough Problems

Himanshi
Himanshi 16 Sep, 2024
5 min read

Have you heard the big news? OpenAI just rolled out preview of a new series of AI models –  OpenAI o1 (also known as Project Strawberry/Q*). These models are special because they spend more time “thinking” before they give you an answer. That means they’re better at tackling really tough problems in areas like science, coding, and math compared to earlier models.

OpenAI is taking the motto “Think Before You Speak” to heart with the o1 series!

Overview

  • OpenAI’s new o1 model series excels in reasoning through tough problems in math, science, and coding, outshining previous versions.
  • The o1-preview model tackles advanced tasks, solving 93% of AIME math problems and surpassing human experts in scientific benchmarks.
  • OpenAI’s o1-mini offers powerful coding capabilities at 80% of the cost, making it an accessible tool for developers.
  • With improved safety measures, the o1 models ensure responsible AI use while providing enhanced problem-solving for researchers, developers, and educators.

Table of contents

What’s the Big Deal?

The o1-preview models are trained to take a step back and really think things through, much like a human would when faced with a tough problem. They consider different approaches, refine their thoughts, and even catch their own mistakes along the way. This deeper level of thinking allows them to solve problems that older models couldn’t handle.

Use Cases of OpenAI o1

Coding with OpenAI o1

Writing Puzzles with OpenAI o1

HTML Snake with OpenAI o1

Impressive Test Results

To see how much better o1 is compared to the earlier GPT-4o model, OpenAI put them through a series of tough tests, including human exams and machine learning benchmarks. And guess what? o1 outperformed GPT-4o on most of these reasoning-heavy tasks!

Let’s break down some of the results:

Advanced Math Competitions

They tested the models on the AIME (American Invitational Mathematics Examination), which is a super challenging math exam for top high school students in the U.S.

  • GPT-4o: Solved about 12% of the problems (roughly 1.8 out of 15 questions).
  • o1: Solved 74% with just one attempt per problem (around 11.1 out of 15). When they let the model try multiple times and took the most common answer, it scored 83%. Using even more advanced methods, it reached 93%, solving about 13.9 out of 15 problems!

To put that into perspective, a score of 13.9 would place o1 among the top 500 students nationally and above the cutoff for the USA Mathematical Olympiad. That’s some serious brainpower!

Science Expertise

They also evaluated o1 on GPQA-diamond, a tough benchmark that tests knowledge in chemistry, physics, and biology. OpenAI even brought in experts with PhDs to answer these questions.

  • Result: o1 outperformed these human experts, becoming the first AI model to do so on this benchmark! This shows that o1 can solve complex scientific problems at a very high level.

Coding

In coding competitions like Codeforces, the new models reached the 89th percentile, showing they can generate and debug complex code with ease.

Science Expertise of 4o1

Other Benchmarks and Visual Understanding

But that’s not all! The o1 model also showed significant improvements in other areas:

Understanding Visual Information (Vision Perception)

The o1 model can now interpret and understand images—a capability known as vision perception. This means it can analyze visual data and answer questions about it, which is a big step forward for AI.

Medical Imaging Test (MMMU Benchmark)

OpenAI tested o1 on a challenging benchmark called MMMU (which stands for Multimodal Medical Machine Understanding). This test evaluates how well an AI can understand medical images and make accurate assessments, similar to tasks performed by medical professionals.

Result: o1 scored 78.2% on this test, making it the first AI model to perform at a level comparable to human experts in medical imaging. This is huge because understanding and interpreting medical images requires deep knowledge and precision.

Wide Range of Knowledge (MMLU Benchmark)

The o1 model was also tested on the MMLU (Massive Multitask Language Understanding) benchmark, which covers 57 different subjects ranging from history and literature to mathematics and computer science.

Result: o1 outperformed GPT-4o in 54 out of 57 subjects! This shows that o1 isn’t just specialized in one area—it’s demonstrating improved understanding across a broad spectrum of topics.

GPT 4o vs o1

In simpler terms, o1’s ability to understand both text and images means it’s becoming more versatile and capable. Whether it’s analyzing complex medical images, solving advanced math problems, or answering questions across various subjects, o1 is setting new standards for what AI can do.

Meet o1-mini

OpenAI has also introduced o1-mini, a smaller, faster, and more affordable version of the o1-preview model that’s especially good at coding tasks. It’s 80% cheaper, making it a great option for developers who need powerful reasoning abilities without breaking the bank.

Math with o1 Mini

Also Read: OpenAI’s o1-mini: A Game-Changing Model for STEM with Cost-Efficient Reasoning

Who can use o1-preview?

These new models are a game-changer for anyone dealing with complex problems:

  • Researchers and Scientists: They can help annotate cell sequencing data or generate complex formulas needed in fields like quantum physics.
  • Developers: Building and executing multi-step workflows becomes easier and more efficient.
  • Students and Educators: They offer a new way to explore challenging concepts in math and science.

How to access o1-Preview?

ChatGPT Plus and Team Users: You can access the o1-preview and o1-mini models in ChatGPT starting today. Just select them from the model picker. There are weekly message limits for now (30 messages for o1-preview and 50 for o1-mini), but OpenAI is working to increase these limits soon.

How to access ChatGPT o1-Preview?
  • ChatGPT Enterprise and Edu Users: You’ll get access to both models starting next week.
  • Developers: If you’re in API usage tier 5, you can start experimenting with these models through the API today. Some features like function calling and streaming aren’t available yet, but they’re on the way.
  • ChatGPT Free Users: Great news! OpenAI plans to make o1-mini available to all free users soon.

Safety Also Matters

OpenAI has also stepped up the safety features with these models. They’ve been trained to better understand and follow safety guidelines by reasoning about the rules during conversations. This means they’re less likely to be tricked into doing something they shouldn’t (you might have heard of “jailbreaking” AI models).

In tough safety tests, the o1-preview model scored 84 out of 100, compared to GPT-4o’s score of 22. That’s a significant improvement, showing they’re much better at staying within safe and appropriate boundaries.

OpenAI is working closely with safety organizations in the U.S. and U.K. They’ve even given these institutes early access to the models to help with research and ensure everything is up to par.

What’s Next?

This is just the beginning. OpenAI is planning regular updates and improvements to these models. They’re looking to add features like browsing the web, uploading files and images, and more to make them even more helpful.

They’re also continuing to develop models in the GPT series alongside this new o1 series, so there’s a lot to look forward to.

Final Thoughts

The launch of the o1-preview and o1-mini models is a big deal in the AI world. They represent a significant step forward in how AI can reason through complex problems. With better performance and enhanced safety measures, these models are set to be game-changers for many people working on challenging tasks.

Stay tuned to Analytics Vidhya blog to know more about the uses of o1 and o1 mini!

Generative AInewso1o1 MiniOpenAIOpenAI o1
Himanshi
Himanshi 16 Sep, 2024

I am a data lover and I love to extract and understand the hidden patterns in the data. I want to learn and grow in the field of Machine Learning and Generative AI.

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