Generative AI solutions are transforming businesses, but they can produce factual inaccuracies. A Retrieval Augmented Generation (RAG) pipeline, using an AI-native technology stack, offers accurate, transparent, and secure generative AI applications by providing additional information from an external knowledge source.
Gemini, Google's new language model, aims to rival OpenAI's GPT-4 with its larger size and multi-modal capabilities. However, the article questions how Gemini truly compares to its competitor and highlights the need for further examination of benchmark test results.
An exploration of the taste differences between European and American M&Ms, with European chocolate being perceived as superior. The author conducts an experiment with other Americans in Denmark to compare the two varieties.
Czech playwright Karel Čapek invented the word "robot" in 1921, but was unhappy with its evolution to denote mechanical entities. In a newly translated article, Čapek expresses frustration with how his original vision for robots was being subverted, arguing that they should be based on science, not technology.
This article explores the hot topic of LLM hallucination in AI research, highlighting the significant repercussions of mistakes or lies produced by large language models. It discusses metrics for detecting and measuring hallucinations in question-answering workflows, with 90% accuracy for closed-domain and 70% accuracy for open-domain question-answering.
Amazon Textract is a ML service that extracts text and data from scanned documents with high accuracy, automating document processing for various purposes. It offers a solution for streamlining the verification of vaccination status, providing precise information from vaccination cards through Amazon Textract Queries.
This article explores the use of diffusion technology in creating groundbreaking AI tools for artists and producers. It delves into the distinction between AI-generated music and human originality, shedding light on the technical aspects without requiring an engineering background.
Optimize your data science workflow by automating matplotlib output with 1 line of code using the teeplot tool. teeplot simplifies work with data visualizations, handles output management, and saves plots with meaningful filenames.
Spectral clustering, a complex form of machine learning, transforms data into a reduced-dimension form and applies k-means clustering. Implementing spectral clustering from scratch in Python was a challenge, but the results were identical to the scikit-learn module, with the most difficult part being computing eigenvalues and eigenvectors of the normalized Laplacian matrix.
A neural network with one hidden layer using ReLU activation can represent any continuous nonlinear functions, making it a powerful function approximator. The network can approximate Continuous PieceWise Linear (CPWL) and Continuous Curve (CC) functions by adding new ReLU functions at transition points to increase or decrease the slope.
The rise of tools like AutoAI may diminish the importance of traditional machine learning skills, but a deep understanding of the underlying principles of ML will still be in demand. This article delves into the mathematical foundations of Recurrent Neural Networks (RNNs) and explores their use in capturing sequential patterns in time series data.
Recent advancements in artificial intelligence have enabled models to mimic human-like capabilities in handling images and text, but the lack of explainability poses risks and limits adoption. Critical domains like healthcare and finance heavily rely on tabular data, emphasizing the need for transparent decision-making models.
This article demonstrates how neural architecture search can be used to compress a fine-tuned BERT model, improving performance and reducing inference times. By applying structural pruning, the size and complexity of the model can be reduced, resulting in faster response times and improved resource efficiency.
The article explores the use of lightweight hierarchical vision transformers in autonomous robotics, highlighting the effectiveness of a shared trunk concept for multi-task learning. It also discusses the emergence of large multimodal models and their potential to create a unified architecture for end-to-end autonomous driving solutions.
This article discusses the implementation of a semantic layer that allows an LLM agent to interact with a knowledge graph, using tools such as an information tool, recommendation tool, and memory tool. These predefined functions enhance the robustness of the system and improve the overall user experience.