Automate detecting document tampering and fraud at scale using AWS AI and machine learning services for mortgage underwriting. Develop a deep learning-based computer vision model to detect and highlight forged images in mortgage underwriting using Amazon SageMaker.
The article explores the math behind the Adam optimizer, explaining why it is the most popular optimizer in deep learning. It delves into the mechanics of Adam, highlighting its adaptive learning rates and its ability to adjust its step size based on the complexity of the data.
Generative AI models like Claude 2 and Llama 2 can perform tasks on text data, but what about multimodal data? This article presents a solution using Amazon Titan Multimodal Embeddings model and LLaVA 1.5 to perform generative tasks on text and visual elements, including tables and graphs, in slide decks.
A data science associate used NLP techniques to analyze Reddit discussions on depression, exploring gender-related taboos around mental health. They found that zero-shot classification can easily produce similar results to traditional sentiment analysis, simplifying the process and eliminating the need for a training dataset.
Product developer Matt Webb has launched a Kickstarter for the "Poem/1" e-paper clock, which tells time using AI-generated rhyming poetry. Powered by ChatGPT, the clock occasionally lies about the time or makes up words to create rhymes.
This article provides a hands-on tutorial using Facebook Prophet for time series analysis, aimed at removing entry barriers. Prophet is an open-source tool by Facebook that produces accurate time series forecasts with ease, making it ideal for business applications.
NVIDIA Studio features artist Brandon Tieh's whimsical scene Magic Valley, inspired by video games and anime, in their latest Studio Standouts video. The new GeForce RTX 4080 SUPER, equipped with more cores and faster GDDR6X video memory, accelerates content creation by up to 70% in 3D apps and 30% in AI effects.
The article in Microsoft Visual Studio Magazine discusses the implementation of matrix inverse using the Householder version of the QR algorithm in C#. The demo includes a small matrix example and verifies the result by computing the inverse multiplied by the original matrix.
ChatGPT is leaking private conversations, including login credentials and personal details, as revealed by screenshots. The leaked information involves usernames and passwords linked to a pharmacy prescription drug portal's support system, highlighting serious security concerns.
Learn how to create and style inset axes in matplotlib with this tutorial, which covers 4 methods for creating insets and 2 ways to style zoom insets using leader lines or color-coded overlays. The tutorial also introduces the outset library for multi-scale data visualization.
OpenAI and Common Sense Media have joined forces to create AI guidelines and educational materials for parents, educators, and teens, including family-friendly GPTs in OpenAI's GPT store. Common Sense Media aims to help teens and families safely utilize AI, expanding from their reviews of films and TV shows to AI assistants.
The article discusses the challenges of implementing matrix inversion code and presents a demo of four different C# functions using various algorithms. The author emphasizes the complexity and flexibility of the LUP, QR, and SVD algorithms, as well as the specific use case of the Cholesky algorithm.
Unlocking Performance: Benchmarking and Optimizing Endpoint Deployment in Amazon SageMaker JumpStart
This article explores the complex relationship between latency and throughput when deploying large language models (LLMs) using Amazon SageMaker JumpStart. Benchmarking of LLMs like Llama 2, Falcon, and Mistral variants reveals the impact of model architecture, serving configurations, instance type hardware, and concurrent requests on performance.
This article explores methods for creating fine-tuning datasets to generate Cypher queries from text, utilizing large language models (LLMs) and a predefined graph schema. The author also mentions an ongoing project that aims to develop a comprehensive fine-tuning dataset using a human-in-the-loop approach.
MIT researchers have developed an automated interpretability agent (AIA) that uses AI models to explain the behavior of neural networks, offering intuitive descriptions and code reproductions. The AIA actively participates in hypothesis formation, experimental testing, and iterative learning, refining its understanding of other systems in real time.