Large Language Models (LLMs) like OpenAI’s GPT have become more prevalent, enhanced by Generative AI for human-like textual responses. Techniques such as Retrieval Augmented Generation (RAG) and fine-tuning improve responses’ precision and contextuality. RAG uses external data for accurate, up-to-date answers, while fine-tuning adapts pre-trained models for specific tasks. RAG excels at dynamic data environments…

Google introduces Gemini, a versatile AI model family capable of processing text, images, audio, and video. Gemini will integrate into Google products like search, Maps, and Chrome. Its performance surpasses GPT-4 in benchmarks, with versions for Android, AI services, and data centers. Google highlights Gemini’s efficiency, speed, and ethical commitment, offering developer access through AI…

AI advancements aim to improve accessibility and usefulness across various communities, ensuring it addresses diverse needs and offers solutions that enhance daily life for all individuals.

ETH Zurich researchers developed an approach using Fast Feedforward Networks (FFF) to increase the speed of Large Language Models (LLM). By engaging only a small fraction of neurons for individual inferences, their UltraFastBERT model could potentially run 341x faster, although a software workaround currently yields a 78x improvement.

Elon Musk’s AI startup, X.AI, is seeking to raise $1 billion through an equity offering after securing $135 million in funding since July. The company aims to advance AI and compete with major players like OpenAI and Google. Their unique chatbot Grok features a distinct personality, drawing on talent from AI leaders for development.

Noah Gift switched his Duke University coding class from Python to the more challenging Rust language, leveraging GitHub’s AI tool Copilot to assist students. Copilot, developed from OpenAI’s GPT-3.5 and GPT-4 models, offers real-time coding assistance. While it’s transforming coding practices and enabling faster code production, there are concerns over IP security and potential quality…

This article details the integration of Large Language Models (LLMs), specifically the “Flan T5” model, with Apache Spark for text data transformations such as sentiment analysis. It provides instructions on setting up Apache Spark and Python, installing necessary libraries, and writing code to create a Spark User-Defined Function (UDF) for sentiment analysis on a dataset.…

This tutorial provides an end-to-end guide on implementing object detection using KerasCV, specifically RetinaNet, to identify healthy and diseased plant leaves. The process involves inspecting and preprocessing data, setting up RetinaNet with a YOLOv8 backbone, training the model with focal loss and smooth L1 loss, and making predictions, considering class imbalance with focal loss. It…

This article explores various methods of matrix multiplication on the M2 MacBook using Go and Metal, including cgo and Metal Shading Language, concluding that GPU-based methods and Metal Performance Shaders are remarkably faster than CPU-based implementations. Benchmarks and GPU usage data support the performance advantages of these GPU-accelerated approaches over Go and OpenBLAS.

University of Geneva researchers have developed Graph Neural Networks (GNN) to predict healthcare-associated infections, outperforming traditional models in early detection of multidrug-resistant Enterobacteriaceae colonization with over 88% accuracy. The GNN model utilizes patient and healthcare worker network data to significantly enhance infection prevention techniques in healthcare settings.