AI development assistant

Meta-Prompting is an effective scaffolding technique designed to enhance the functionality of language models (LM). This method transforms a single LM into a multi-faceted commander, adept at managing and integrating multiple independent LM queries. By using high-level instructions, meta-cues guide LM to decompose complex tasks into smaller, more manageable subtasks. These subtasks are then handled by different "expert" instances of the same LM, each operating according to specific customized instructions. At the heart of this process is the LM itself, which, as the conductor, ensures seamless communication and effective integration between the outputs of these expert models. It also leverages its inherent critical thinking and robust validation processes to refine and validate the final results. This collaborative prompting approach enables a single LM to simultaneously act as a comprehensive commander and a diverse team of experts, significantly improving its performance in a variety of tasks. The zero-shot, task-agnostic nature of meta-cues greatly simplifies user interaction, eliminating the need for detailed task-specific instructions. Furthermore, our research shows that external tools, such as the Python interpreter, can be seamlessly integrated with the meta-hint framework, thereby broadening its applicability and utility. Through rigorous experiments with GPT-4, we demonstrate that meta-cueing outperforms traditional scaffolding methods: averaged across all tasks, including the 24-point game, One Move General, and Python programming puzzles, meta-cueing using the Python interpreter feature outperforms standard prompts by 17.1%, is 17.3% better than expert (dynamic) prompts, and is 15.2% better than multi-personality prompts.

IBM Watson Studio is a collaborative platform that enables data scientists, developers and analysts to build, train and deploy machine learning models. It supports a variety of data sources, enabling teams to streamline their workflows. With advanced features such as automated machine learning and model monitoring, Watson Studio users can manage their models throughout the development and deployment lifecycle.

Amazon SageMaker is a fully managed machine learning service that helps developers and data scientists quickly and cost-effectively build, train, and deploy high-quality machine learning models. It provides a complete development environment, including visual interface, Jupyter notebook, automatic machine learning, model training and deployment and other functions. Users can build end-to-end machine learning solutions through SageMaker without managing any infrastructure.

StableCode is the first programming-oriented generative AI product released by Stable AI. It uses three different models to help developers improve programming efficiency. The base model was first trained on BigCode’s stack-dataset (v1.2) and further trained on popular programming languages ​​such as Python, Go, Java, Javascript, C, markdown, and C++. In total, we trained on 560B code tokens on a high-performance computing cluster. Subsequently, by tuning the basic model, approximately 120,000 code instruction/response pairs were trained to solve complex programming tasks. StableCode is an ideal building block for learning programming, and the long text environment window model provides users with single-line and multi-line autocomplete suggestions. The model can process more code at once (2-4x more code than previously released open source models, with a context window of 16,000 tokens), enabling users to view or edit the equivalent of five average-sized Python files simultaneously, making it an ideal learning tool for beginners to take on larger challenges.

AWS HealthScribe is a HIPAA-compliant service that helps healthcare software vendors build clinical applications by analyzing patient-clinician conversations to automatically generate clinical notes. Features: - Enhance clinical productivity with AI-generated notes that are easier to reference, edit and complete. - Use AI responsibly in clinical settings, providing traceable transcript references for every AI-generated note. - Unified, integrated conversational and generative AI services across applications to simplify implementation. - Protect patient privacy with HIPAA-compliant services for telemedicine and in-clinic consultations. Pricing: Pay as you go, no upfront fees. Usage scenarios: - Reduce documentation writing time - Improve the work efficiency of medical recordkeepers - Provide patient-friendly consultation summary Tags: clinical notes, artificial intelligence, medical software

Intel AI and Deep Learning Solutions are a series of downloadable AI reference kits launched by Intel in partnership with Accenture to help enterprises accelerate their digital transformation journey. These kits are built on the AI ​​application tools Intel provides to data scientists and developers, and each kit includes model code, training data, instructions for machine learning pipelines, libraries, and Intel oneAPI components.