In this article, we will explore various aspects of BERT, including the landscape at the time of its creation, a detailed breakdown of the model architecture, and writing a task-agnostic fine-tuning pipeline, which we demonstrated using sentiment analysis. Despite being one of the earliest LLMs, BERT has remained relevant even today, and continues to find applications in both research and industry.
Google has launched Model Explorer, an open-source tool designed to help users navigate and understand complex neural networks. The tool aims to provide a hierarchical approach to AI model visualization, enabling smooth navigation even for massive models. Model Explorer has already proved valuable in the deployment of large models to resource-constrained platforms and is part of Google's broader ‘AI on the Edge’ initiative.
Google's Gemini Pro model in NotebookLM can now create study guides, FAQs, quizzes, and even spoken dialogue discussions. This new feature allows students to learn in an interactive and personalized way by connecting physics and basketball through AI-generated examples.
Stay informed about the latest artificial intelligence (AI) terminology with this comprehensive glossary. From algorithm and AI ethics to generative AI and overfitting, learn the essential AI terms that will help you sound smart over drinks or impress in a job interview.
Google is introducing updates to the Gemini family of models, including a new lighter-weight model called 1.5 Flash, improvements to 1.5 Pro, and a look at the future of AI assistants with Project Astra.
Benchmarking long-form factuality in large language models. Original code for our paper "Long-form factuality in large language models".
Key concept: Setting mental models can help users understand how to interact with products that adapt over time. This chapter covers:
Identifying existing mental models
Onboarding in stages
Planning for co-learning
Accounting for user expectations of human-like interaction
Key concept: To build effective mental models of AI-powered products, consider what you want people to know about your product before their first use, how to explain its features, and when it will need feedback from them to improve.
Identifying existing mental models
Onboarding in stages
Planning for co-learning
Accounting for user expectations of human-like interaction
Key concept: To build effective mental models of AI-powered products, consider what you want people to know about your product before their first use, how to explain its features, and when it will need feedback from them to improve.
llm-tool provides a command-line utility for running large language models locally. It includes scripts for pulling models from the internet, starting them, and managing them using various commands such as 'run', 'ps', 'kill', 'rm', and 'pull'. Additionally, it offers a Python script named 'querylocal.py' for querying these models. The repository also come
