Comment l'IA générative transforme la formation en ligne ? (360Learning)

This subject revolves around the innovative approach taken by Marie and Benoît of 360 Learning to accelerate content creation for daily learning. They have leveraged AI techniques, particularly machine learning, to create a dynamic solution that can adapt to various content themes such as recommendation systems and generative models.

360 Learning is a learning management system designed for enterprises to facilitate employee training internally. The platform offers courses on various subjects including project management, marketing, and communication. It also features an author tool that allows anyone within the enterprise to create content, thereby fostering collaborative learning.

This subject explores the specific use cases where AI has been applied within 360 Learning. These include generating questions using open-source models, creating courses from PDF documents, and developing a course based solely on inputs from the author.

The first developed feature of the platform, focusing on creating questions from various text content to test the knowledge of learners. This tool allows authors to create different cheat sheets with appropriate texts and suggest questions based on them.

The use of fine-tuned NLP models, such as T5 model, for question generation. These models were trained on Wikipedia text to generate questions and have been utilized to power the generation of questions within the platform.

Integration of ChatGPT API for enhancing question generation capabilities. The platform makes use of Azure OpenAI to ensure responsible handling of sensitive data while sending course content to open ai or chatpt.

An innovative feature that allows users to generate courses from PDF documents. The system uses a generative model to automatically create titles, descriptions, slides with illustrations, questions, and key points for the generated course.

The underlying technology used in the automated course generation feature, utilizing Prompt Engine (Long Chain) and GPT-3.5 models for efficient and cost-effective content creation in multiple languages.

A technique used to improve the efficiency of generating content from large documents, by breaking them into smaller chunks and using embedding models to create a dense representation of each part.

Learn about a scalable technique for generating structured content using ChatGPT, and how it can be applied to various use cases. The technique allows users to create courses with minimal input by utilizing context-aware content generation from an existing database of courses.

Discover the differences between semantic and keyword search in Elasticsearch, and learn about their respective advantages and use cases. Semantic search vectorizes content to achieve a more meaningful search, while keyword search is optimized for finding documents containing specific terms.

Explore the potential benefits of using automated tools to create engaging courses, while still allowing experts to edit and refine the content. The goal is to make it easier for non-educators to create high-quality courses that are both informative and interactive.

These models can generate questions, create courses from documents, or simply from a title and audience. They are versatile and can perform various tasks such as language generation, clustering, skill extraction, and content classification. They have applications in a wide range of fields.

A platform that leverages generative AI models to perform tasks such as question generation, document summarization, and course creation. It uses techniques like asynchronous processing, division and conquest strategy, and caching strategies for efficient operation.

A popular model within the Longchain platform. It can generate responses in multiple languages and perform tasks like skill extraction, content classification, and document summarization. However, it requires significant computational resources and is subject to availability and stability issues.

Utilization of generative AI for accelerating employee competency within companies. Focus on creating questions and generating entire courses using tools like HeightAI to share knowledge effectively.

Improving resilience of services by using retry mechanisms, timeouts, and load balancing across multiple regions and providers like Azure. Includes monitoring, error handling, and demand forecasting.

Challenges in evaluating the quality and performance of generative AI models. Discussion on techniques like manual testing, A/B testing, user feedback, and model comparison to ensure readiness for production.

This subject discusses the measures taken to ensure data segregation between different enterprises using a multi-tenant SaaS platform, focusing on Azure Open Enterprise guarantees and filtering mechanisms within Elastic Search for maintaining user visibility.

This subject explains the process of splitting long documents into chunks for course creation, highlighting the factors considered when deciding how to split the document, such as token limits and embedding models.

This subject discusses the trade-off between generating slides with global context versus independent slides, and how this affects the quality of generated content.

Identifying and addressing the issue of redundancy by adding layers to presentations, improving content quality.

Use of AI to identify repetitive patterns in presentations for the purpose of improving and streamlining content.

Enhancing the overall quality of presentations by focusing on user experience, ensuring information is presented effectively and efficiently.