• Data Pragmatist
  • Posts
  • 🧠 Learn about Pearson R Correlation; Top 5 trending AI podcasts

🧠 Learn about Pearson R Correlation; Top 5 trending AI podcasts

FTC is probing Reddit’s AI licensing deals

Author

Ishaan Mahajan
March 18, 2024

Sponsored by

Welcome to learning edition of the Data Pragmatist, your dose of all things data science and AI.

đź“– Estimated Reading Time: 4 minutes. Missed our previous editions?

How do you stay up-to-date with the insane pace of AI? Join The Rundown – the world’s fastest-growing AI newsletter with over 500,000+ readers learning how to become more productive using AI every morning.

1. Our team spends all day researching and talking with industry experts.

2. We send you updates on the latest AI news and how to apply it in 5 minutes a day.

3. You learn how to become 2x more productive by leveraging AI.

Top 5 trending AI podcasts

  1. Future of Data and AI Podcast Hosted by Data Science Dojo

  2. The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) Hosted by Sam Charrington

  3. The AI Podcast Hosted by NVIDIA

  4. DataFramed Hosted by DataCamp

  5. Data Skeptic Hosted by Kyle Polich

🧠 Pearson R Correlation

Correlation is a bi-variate analysis technique used to quantify the strength and direction of the relationship between two variables. The correlation coefficient, denoted by r, ranges from +1 to -1. A value of ±1 indicates a perfect association, while values closer to 0 signify weaker relationships. The sign of the coefficient (+ or -) indicates the direction of the relationship.

Types of Correlation

  1. Pearson Correlation: Measures linear association between two continuous variables.

  2. Kendall Rank Correlation: Evaluates association based on ranks rather than actual values.

  3. Spearman Correlation: Similar to Kendall but uses a different method to calculate correlation.

  4. Point-Biserial Correlation: Measures association between a continuous variable and a binary variable.

Pearson R Correlation: Key Features

  • Pearson correlation coefficient (r) quantifies linear association.

  • It answers questions regarding relationships between variables, such as age and height, temperature and ice cream sales, or job satisfaction, productivity, and income.

  • Assumptions include normal distribution, absence of significant outliers, linearity, paired observations, and homoscedasticity.

Assumptions and Considerations

  1. Normal Distribution: Variables should follow a normal distribution, assessed through tests like the Shapiro-Wilk Test.

  2. Outliers: Significant outliers can distort correlation results, necessitating their identification and handling.

  3. Continuous Variables: Both variables under analysis should be continuous (interval or ratio).

  4. Linear Relationships: Pearson correlation assumes a linear relationship between variables, which can be verified through scatter plots.

  5. Paired Observations: Each observation of the independent variable must correspond to an observation of the dependent variable.

  6. Homoscedasticity: Error terms should be consistent across all values of the independent variable, as indicated by equally dispersed points around the line of best fit in a scatter plot.

Understanding correlation and its nuances is crucial for accurate data analysis and interpretation. Adhering to assumptions and considering the nature of variables ensures reliable insights into relationships between phenomena.

🔍 FTC is probing Reddit’s AI licensing deals LINK

  • Reddit is under investigation by the FTC for its data licensing practices concerning user-generated content being used to train AI models.

  • The investigation focuses on Reddit's engagement in selling, licensing, or sharing data with third parties for AI training.

  • Reddit anticipates generating approximately USD 60 million in 2024 from a data licensing agreement with Google, aiming to leverage its platform data for training LLMs.

đź’» New jailbreak uses ASCII art to elicit harmful responses from leading LLMs LINK

  • Researchers identified a new vulnerability in leading AI language models, named ArtPrompt, which uses ASCII art to exploit the models' security mechanisms.

  • ArtPrompt masks security-sensitive words with ASCII art, fooling language models like GPT-3.5, GPT-4, Gemini, Claude, and Llama2 into performing actions they would otherwise block, such as giving instructions for making a bomb.

  • The study underscores the need for enhanced defensive measures for language models, as ArtPrompt, by leveraging a mix of text-based and image-based inputs, can effectively bypass current security protocols.

How did you like today's email?

Login or Subscribe to participate in polls.

If you are interested in contributing to the newsletter, respond to this email. We are looking for contributions from you — our readers to keep the community alive and going.