Large Language Models (LLMs) present a unique challenge when it comes to performance evaluation. Unlike traditional machine learning where outcomes are often binary, LLM outputs dwell in a spectrum of correctness. Also, while your base model may excel in broad metrics, general performance doesn’t guarantee optimal performance for your specific use cases. Therefore, a … [Read more...] about Beyond Metrics: A Hybrid Approach to LLM Performance Evaluation
Generated with Midjourney Enterprises in every industry and corner of the globe are rushing to integrate the power of large language models (LLMs) like OpenAI’s ChatGPT, Anthropic’s Claude, and AI12Lab’s Jurassic to boost performance in a wide range of business applications, such as market research, customer service, and content generation. However, building an LLM … [Read more...] about Step-By-Step LLM Product Development For Business Leaders
Figure 1: Representation of the Text2SQL flow As our world is getting more global and dynamic, businesses are more and more dependent on data for making informed, objective and timely decisions. However, as of now, unleashing the full potential of organisational data is often a privilege of a handful of data scientists and analysts. Most employees don’t master the … [Read more...] about Creating An Information Edge With Conversational Access To Data
ChatGPT is a GPT (Generative Pre-trained Transformer) machine learning (ML) tool that has surprised the world. Its breathtaking capabilities impress casual users, professionals, researchers, and even its own creators. Moreover, its capacity to be an ML model trained for general tasks and perform very well in domain-specific situations is impressive. I am a researcher, and its … [Read more...] about Can ChatGPT Compete With Domain-Specific Sentiment Analysis Machine Learning Models?
This article is based on the chapter from the Interpretable AI book by Ajay Thampi. Take 35% off Interpretable AI or any other product from Manning by entering bltopbots23 into the discount code box at checkout at manning.com. Diagnostics+ AI – Diabetes Progression Imagine that you’re developing an AI-powered app that will help you diagnose illnesses: Diagnostics+. The … [Read more...] about Using GAMs To Project Progression Of Diabetes
How to advance social capital in a world increasingly governed by surveillance capitalism? About a year ago, I read Atlas of AI by Kate Crawford, a brilliant analysis of the extractive processes that govern the field of machine learning, from environmental resource allocation to the harvesting of our political fabric to data privacy infringements. This article … [Read more...] about Data Capitalism: Innovation, Extraction, Social Conscience
Imagine that all people around the world could use voice AI systems such as Alexa in their native tongues. One promising approach to realizing this vision is massively multilingual natural-language understanding (MMNLU), a paradigm in which a single machine learning model can parse and understand inputs from many typologically diverse languages. By learning a shared data … [Read more...] about Amazon Releases 51-Language Dataset for Language Understanding
Evidence of AI Bias The primary purpose of Artificial Intelligence (AI) is to reduce manual labour by using a machine’s ability to scan large amounts of data to detect underlying patterns and anomalies in order to save time and raise efficiency. However, AI algorithms are not immune to bias. AI Bias has presented itself in several forms, with some examples highlighted … [Read more...] about Eliminating AI Bias
Why do we need variance reduction? When we do online experiments or A/B testing, we need to ensure our test has high statistical power so that we have a high probability to find the experimental effect if it does exist. What are the factors that might affect power? Sample sizes, sampling variance of the experiment metric, significance level alpha, and effect size. The … [Read more...] about Online Experiments Tricks – Variance Reduction
It’s an exciting time to be working on recommender systems. Not only are they more relevant than ever before, with Facebook recently investing in a 12 trillion parameter model and Amazon estimating that 35% of their purchases come from recommendations, but there is a wealth of powerful, cutting edge techniques with code available for anyone to try. So the … [Read more...] about How Can You Tell If Your Recommender System Is Any Good?