Introduction When it comes to data science, the spotlight is usually on statistics, machine learning, and data wrangling techniques. However, one of the most crucial yet often overlooked skills that distinguish great data scientists is their mastery of software engineering principles—particularly inheritance. Inheritance is a foundational concept in object-oriented programming
Veena is revolutionizing the way Indian languages are accessed and understood through technology. Powered by deep learning and artificial intelligence, Veena is an open-source text-to-speech (TTS) engine designed especially for Indian languages, filling a long-standing gap in the TTS ecosystem. In this post, we’ll explore what Veena is, its core
In the world of artificial intelligence, language models like GPT-4, Gemini, and others have drastically changed our understanding of how machines process and generate human language. Traditional views considered these models as advanced statistical predictors, but a more nuanced, and frankly fascinating, framework is emerging: Inference as Interference. This framework
Danny’s Eliza is a fascinating piece of computer history that marks one of the earliest milestones in the development of artificial intelligence chatbots. If you’ve ever engaged with a modern virtual assistant or chatbot, you owe a small thanks to projects like Eliza, and in particular, to the popular adaptation
In the realm of Retrieval-Augmented Generation (RAG) applications, the way data is divided and processed significantly impacts the quality of AI-generated responses. This process, known as chunking, involves breaking down large datasets into smaller, manageable pieces before converting them into embeddings and storing them in a vector database. These “chunks”
Reinforcement Learning from Human Feedback (RLHF) Reinforcement Learning from Human Feedback (RLHF) is a technique used to improve1 the performance and alignment of AI systems, particularly large language models (LLMs), with human preferences and values. It’s a method to fine-tune AI models using human feedback to ensure their outputs are
In the rapidly evolving landscape of artificial intelligence, Retrieval-Augmented Generation (RAG) has emerged as a pivotal technique, combining the strengths of information retrieval and text generation to produce more accurate and contextually relevant outputs. To further refine RAG systems, integrating user intent classification and emphasizing contextual understanding are essential. Additionally,
The rapid advancement of artificial intelligence (AI) has been significantly propelled by large language models (LLMs) like OpenAI’s GPT series. These models, with their billions of parameters, have demonstrated remarkable capabilities in understanding and generating human-like text. However, training such expansive neural networks demands immense computational resources and time. As
Introduction: The Dawn of AI-Powered Game Engines https://arxiv.org/pdf/2408.14837 The world of video games has always been at the forefront of technological innovation. From the pixelated classics of the 80s to the hyper-realistic virtual realities of today, game engines have evolved dramatically. Yet, the core loop has remained the same: gather