The article discusses the launch of ChatGPT and the rise in popularity of generative AI. It highlights the creation of a web UI called Chat Studio to interact with foundation models in Amazon SageMaker JumpStart, including Llama 2 and Stable Diffusion. This solution allows users to quickly experience conversational AI and enhance the user experience with media integration.
Data projects often fail to deliver real-life impact due to macro-elements such as data availability, skillset, timeframe, organizational readiness, and political environment. The availability and accessibility of relevant data are fundamental, and if data is unattainable, the feasibility of the project should be reconsidered.
Summarization is essential in our data-driven world, saving time and improving decision-making. It has various applications, including news aggregation, legal document summarization, and financial analysis. With advancements in NLP and AI, techniques like extractive and abstractive summarization are becoming more accessible and effective.
Large language models (LLMs) like GPT NeoX and Pythia are gaining popularity, with billions of parameters and impressive performance. Training these models on AWS Trainium is cost-effective and efficient, thanks to optimizations like rotational positional embedding (ROPE) and partial rotation techniques.
Spectral clustering is a complex machine learning technique that uncovers patterns in data. Implementing it involves computing affinity and Laplacian matrices, eigenvector embeddings, and performing k-means clustering.
Conversational AI has evolved with generative AI and large language models, but lacks specialized knowledge for accurate answers. Retrieval Augmented Generation (RAG) connects generic models to internal knowledge bases, enabling domain-specific AI assistants. Amazon Kendra and OpenSearch Service offer mature vector search solutions for implementing RAG, but analytical reasoning questions requir...
MLOps is essential for integrating machine learning models into existing systems, and Amazon SageMaker offers features like Pipelines and Model Registry to simplify the process. This article provides a step-by-step implementation for creating custom project templates that integrate with GitHub and GitHub Actions, allowing for efficient collaboration and deployment of ML models.
Vodafone is transforming into a TechCo by 2025, with plans to have 50% of its workforce involved in software development and deliver 60% of digital services in-house. To support this transition, Vodafone has partnered with Accenture and AWS to build a cloud platform and engaged in an AWS DeepRacer challenge to enhance their machine learning skills.
LM Studio is a tool that allows local machine usage of large language models like GPT-x, LLaMA-x, and Orca-x, offering a clean and intuitive UI for exploring models and conducting reasoning tasks. However, its creator and potential connections with other companies remain unclear.
LLMs like Llama 2, Flan T5, and Bloom are essential for conversational AI use cases, but updating their knowledge requires retraining, which is time-consuming and expensive. However, with Retrieval Augmented Generation (RAG) using Amazon Sagemaker JumpStart and Pinecone vector database, LLMs can be deployed and kept up to date with relevant information to prevent AI Hallucination.
Mistral AI announces Mixtral 8x7B, an AI language model that matches OpenAI's GPT-3.5 in performance, bringing us closer to having a ChatGPT-3.5-level AI assistant that can run locally. Mistral's models have open weights and fewer restrictions than those from OpenAI, Anthropic, or Google.
GeForce NOW adds 17 new games, including The Day Before and Avatar: Frontiers of Pandora, with over 500 games now supporting RTX ON. Ultimate members can experience cinematic ray tracing and stream at up to 4K resolution, while Priority members can build and survive at 1080p and 60fps.
Getir, the ultrafast grocery delivery pioneer, has implemented an end-to-end workforce management system using Amazon Forecast and AWS Step Functions, resulting in a 70% reduction in modelling time and a 90% improvement in prediction accuracy. This comprehensive project calculates courier requirements and solves the shift assignment problem, optimizing shift schedules and minimizing missed orders.