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.
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.
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.
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.
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.
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.
Researchers at MIT and IBM have developed a new method called "physics-enhanced deep surrogate" (PEDS) that combines a low-fidelity physics simulator with a neural network generator to create data-driven surrogate models for complex physical systems. The PEDS method is affordable, efficient, and reduces the training data needed by at least a factor of 100 while achieving a target error of 5 per...
Developers of open world video games and analytics managers both face the challenge of balancing exploration and exploitation. To solve this tension, they can build alternative paths, offer knowledge management systems, foster online communities, and make continuous improvements. Salespeople, like gamers, have main quests in the form of specific metrics they need to track, so creating simple an...
MIT neuroscientists have discovered that sentences with unusual grammar or unexpected meaning generate stronger responses in the brain's language processing centers, while straightforward sentences barely engage these regions. The researchers used an artificial language network to predict the brain's response to different sentences.
MIT's Improbable AI Lab has developed a multimodal framework called HiP, which uses three different foundation models to help robots create detailed plans for complex tasks. Unlike other models, HiP does not require access to paired vision, language, and action data, making it more cost-effective and transparent.
MIT PhD students are using game theory to improve the accuracy and dependability of natural language models, aiming to align the model's confidence with its accuracy. By recasting language generation as a two-player game, they have developed a system that encourages truthful and reliable answers while reducing hallucinations.
Atacama Biomaterials, a startup combining architecture, machine learning, and chemical engineering, develops eco-friendly materials with multiple applications. Their technology allows for the creation of data and material libraries using AI and ML, producing regionally sourced, compostable plastics and packaging.
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.