Optimizing Large Language Models: Techniques and Future Directions for Efficiency
Ever since OpenAI introduced ChatGPT UI in November 2022, the way we interact, process and consume information has changed drastically. It was science fiction to have a long and meaningful conversation with a machine until this point.
Making AI transparent- The What, Why and How of Explainable AI
In 2015, one of the largest e-commerce companies built an automated hiring tool to review and filter resumes based on skill sets and job descriptions. However, this project had to be halted after it was noticed that the hiring tool showed significant racial bias and systematically discriminated.
Insights from the Data Science Salon’s AI & Machine Learning Conference in San Francisco
Have you ever had one of those days where it feels like you time-traveled into the future? That’s exactly how I felt when I attended the Data Science Salon’s conference on “Using AI & Machine Learning in the Enterprise” at Google’s San Francisco headquarters.
Tackling burnout with Large Language Models
Industry burnout is interlinked with cultural, individual, physical, or emotional exhaustion, and social factors, the resolution of which requires the technology-driven trends in the workplace and the technologies such as work pattern monitoring and Artificial Intelligence that can deal with large.
Data Privacy Challenges with Large Language Models
Large Language Models (LLMs) have rapidly evolved, building on decades of research and advances in computing power and data availability. Most LLMs consist of billions of parameters; trained on vast amounts of diverse datasets. LLMs generate highly contextual and personalized outputs. This.
Introduction: From Simple to Multiple Linear Regression
In our previous post, we introduced Linear Regression, a fundamental technique used to predict outcomes based on a single factor—such as estimating house prices based on square footage. We also discussed essential performance metrics like Mean Squared Error (MSE) and R-squared, which help assess.
Exchanging Knowledge and Insights About AI and ML with DSS Community Events
Generative AI, synthetic data, language models in healthcare, AI in finance, and ML in the enterprise are among the most significant topics to be covered during the Data Science Salon community events.
Understanding Customer Concerns in Retail: A review of LLMs and Traditional ML-based methods for Topic Modeling and Multi-label Classification
Did you know that organizations risk losing an average of 8% of their revenue due to poor Customer Experience (CX)? This significant potential loss can be attributed to the fact that over 50% of consumers either decreased or stopped spending with a business after a bad experience.
Machine Learning in Production
In recent years, Machine Learning (ML) has propelled software systems into new realms of capability. From revolutionizing medical assistance and personalized recommendations to enabling chatbots and self-driving cars, ML has become a cornerstone of modern technology.
Selection Bias in product analytics and common pitfalls
Statistical Inference is centered on using random samples effectively. In order to draw a sample at random, one has to ensure that each observation is drawn independently from the same population. A random sample of observations is said to be independent, identically distributed (iid) and this.
