Joas is a machine learning (ML) and artificial intelligence (AI) enthusiast passionate about using these technologies to solve real-world problems. He believes that ML and AI have the power to transform industries and improve people’s lives and is always exploring new ways to apply these technologies. As a lifelong learner, Joas is constantly seeking out new tools and techniques to expand his skillset and keep up with the latest developments in the field.
Language models have shown impressive capabilities. But that doesn’t mean they’re without faults, as anyone who has witnessed a ChatGPT “hallucination” can attest. In this article, Joas Pambou diagnoses the symptoms that cause hallucinations and explains not only what RAG is but also different approaches for using it to solve language model limitations.
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Discuss the concept of large language models (LLMs) and how they are implemented with a set of data to develop an application. Joas compares a collection of no-code and low-code apps designed to help you get a feel for not only how the concept works but also to get a sense of what types of models are available to train AI on different skill sets.
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In this article, Joas Pambou builds the tool to provide a sentiment score in real-time with enhanced user experience by providing multilingual support. You will use an OpenAI library called Whisper that transcribes audio files into text and detects the language, and Gradio, a UI framework, to establish the interface.
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In this article, we’ll explore how to build a chat summarizer using the Cohere API and deploy it as a web application using Gradio. Cohere is an AI platform that provides state-of-the-art natural language processing models for a variety of tasks, including summarization. We’ll cover the steps involved in training the summarizer using sample chat conversations, interacting with the Cohere API to generate summaries, and creating a user-friendly interface using Gradio.
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Dive into an article where you will build an app that evaluates audio files for positive and negative sentiments. The idea is that you will create an interface for uploading an audio file, then transcribe the contents into text before analyzing the text and assigning it a positive or negative score for how the tone is perceived.
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