Event in Spanish: "Phi-3 y Semantic Kernel: Titanes de AI en miniatura"

Mercado Libre and GitHub Copilot

• Mercado Libre, a leading online commerce and payments platform in Latin America, is constantly seeking innovative tools to improve its services.
• The company is particularly interested in GitHub Copilot, an AI-powered code completion tool.

Open Source Friday and Pon Latam

• The video then transitions to a discussion about Open Source Friday, a GitHub program that aims to highlight and support open-source projects and contributors.
• The speaker emphasizes the importance of community and open-source contributions, encouraging viewers to subscribe to the GitHub channel for more content in Spanish.
• The speaker announces the upcoming Pon Latam event, a conference for Python developers, which will be held in Mazatlán, Mexico.

P con la Tam Conference

• The speaker is promoting the "P con la Tam" conference, which is a Spanish-language AI conference taking place in Mazatlán, Sinaloa, Mexico from September 19th to 22nd.
• The conference offers a beautiful beach location, a great lineup of speakers, and affordable prices.
• The speaker encourages attendees to take advantage of the opportunity to network with other AI professionals and learn from top speakers.
• The speaker also highlights the conference's sponsorship opportunities, which are particularly accessible for startups and small businesses.
• The speaker emphasizes the conference's impact on the Spanish-speaking AI community, noting its growing presence and influence.
• The speaker encourages viewers to attend the conference and share their experience with them.
• The speaker mentions their own involvement with the conference as a speaker in previous years and expresses their intention to attend this year.

Introducing Phi-3 and Semantic Kernel

• The speaker transitions to the main topic of the video, which is about "Phi-3" and "Semantic Kernel," two AI technologies.
• The video discusses Phi-3 and Semantic Kernel, which are described as "miniature AI titans."
• Phi-3 is a small model that is doing "super impactful" things.

Bruno Capuano, Microsoft Cloud Advocate

• The speaker introduces Bruno Capuano, a Microsoft Cloud Advocate, as the guide for the day.
• Bruno shares his background, mentioning that he is originally from Argentina but has lived in Spain, Canada, and now resides in the North.
• Bruno's accent is a mix of different languages he has learned, making it unique.
• The speaker mentions a conference in Argentina called "Nerdería" and invites Bruno to attend.
• Bruno expresses interest in attending the conference and visiting his family in Argentina.
• The speaker concludes by asking Bruno to explain what Phi-3 and Semantic Kernel are.

Phi-3: A Smaller Language Model

• The speaker is working on the .NET Advocates team, which has been using C# for a long time.
• The speaker previously worked with Machine Learning in Python and wanted to bring the benefits of AI to the .NET community.
• The speaker discovered Phi-3 (or F3), a smaller language model similar to ChatGPT's GPT, but more compact.
• Phi-3 was appealing because it allowed for local execution, making it ideal for situations where cloud access was limited due to security or connectivity concerns.
• Phi-3 is not exactly like ChatGPT, but it offers similar capabilities, allowing users to download and run a model locally.
• The speaker was impressed by Phi-3's power and potential for local AI applications.
• Meta's release of Llama 3.1, another powerful and free language model, further emphasized the growing trend of smaller, locally-executable AI models.
• Both Phi-3 and Llama 3.1 can be fine-tuned for specific domains and used for local testing and development.
• Users can transition to cloud-based solutions as their applications grow and require more resources.
• The video discusses Phi-3, a smaller version of a large language model, and its potential applications.
• Phi-3 was developed by Microsoft Research and is designed to be easily deployed on various platforms, including iPhones, Jetson Nanos, Raspberry Pis, and even in the cloud.
• A "cookbook" or collection of examples was created to help users understand how to use Phi-3 in different scenarios.
• The project has attracted contributions from developers worldwide, including Kim F from China who focused on Python examples and the speaker who contributed C# examples.
• Users can access pre-configured "codespaces" on GitHub to experiment with Phi-3 without needing to install anything.
• The speaker highlights the benefits of using codespaces, which are essentially containers with pre-installed dependencies, and mentions that GitHub offers free hours for users to utilize them.
• Johan is working on a JavaScript and TypeScript codespace for Phi-3, but is currently busy with the Olympics.
• The overall goal of the project is to make powerful language models accessible to a wider audience, encourage experimentation, and gather feedback from users.
• The speaker is discussing the capabilities of Phi-3, a vision-based version of f3, which is particularly useful for image analysis.
• They highlight the ease of use, emphasizing that Phi-3 can be installed on devices like Raspberry Pi and iPhones, making it accessible to a wider audience.
• The speaker emphasizes the importance of Hugging Face as a platform for AI models, stating that it's a must-visit for anyone working in the field.
• They mention that Phi-3 is available on Azure AI, Hugging Face, and ONNX, a popular format for running models across different platforms.
• The speaker also mentions Ollama, a platform for hosting local models, and LM Studio as additional resources for working with Phi-3.
• The speaker briefly mentions the cookbook, a resource for learning about Phi-3 and its applications.

Phi-3 Cookbook

• The speaker is discussing the Phi-3 cookbook, an open-source project that provides examples and instructions for using the Phi-3 language model in various environments.
• The cookbook is a collaborative effort, with contributions from the community, including people from Ollama, the AI Toolkit in Visual Studio Code, and LM Studios.
• The cookbook includes examples for using Phi-3 on different devices, such as iPhones, Jetson devices, and PCs.
• The speaker encourages viewers to contribute to the cookbook by sharing their own experiences and examples.

Advantages of Smaller Language Models

• The speaker explains that Phi-3 is a small language model, and they offer to explain it in simple terms for those unfamiliar with it.
• Smaller models like Phi-3 offer advantages over large language models (LLMs) like GPT-4. While GPT-4 requires a large platform for hosting and execution due to its size, smaller models can be run locally on personal computers or servers.
• Phi-3 is a "small language model" that comes in different versions (mini, medium, and large). These versions can be hosted locally, eliminating the need for large cloud providers.
• Phi-3 is a good alternative to GPT-4 for certain tasks. While it may not be as powerful, it can still perform well in many scenarios.
• Hugging Face is a platform where users can find and experiment with Phi-3. It provides examples and resources for using the model.
• Phi-3 can be downloaded and used locally. This allows users to have a "ChatGPT-like" experience without relying on external services.

Using Phi-3 for Basic Tasks

• The speaker is demonstrating how to use a small, local AI model called "Phi-3" for basic tasks like math calculations.
• They are using a Python library called "PunNet" to load the model and define a system prompt.
• The speaker emphasizes that this model is running locally and does not have memory, meaning it cannot retain information from previous conversations.

Introducing Semantic Kernel

• To overcome this limitation, the speaker introduces "Semantic Kernel," a library that allows for persistent conversation history.
• By using Semantic Kernel, the speaker can create a chat service that remembers past interactions, making the AI more conversational and helpful.

Semantic Kernel 2: Building AI Applications

• The video demonstrates how to use Semantic Kernel 2, a tool for building AI applications.
• The user interacts with the model in Spanish, providing instructions and asking questions.
• The user defines the model's behavior by providing instructions on how to respond to different types of questions.
• The model is able to understand and follow these instructions, responding appropriately to different prompts.
• The user highlights that the model is relatively small (2-3 GB) and can function well in multiple languages, including Spanish and English.
• The user mentions that the code used in the demonstration is available in a repository called "cookbook," which includes examples in Python, JavaScript, and C#.
• The speaker recommends testing prompts in both English and Spanish to see which works best.

Phi-3: Local Execution and Performance

• The speaker emphasizes that Phi-3 is a powerful model that can run locally, even on a Raspberry Pi.
• The speaker acknowledges that processing time will vary depending on the device and the complexity of the task.
• The speaker demonstrates how to use Phi-3 for image analysis, using a local model on a Raspberry Pi.
• The speaker confirms that there are plans for a future version of Phi-3, likely Phi-3.1 or Phi-3.5, which will focus on improving speed and performance.
• The speaker mentions that the research team is working on these improvements.

Future of Phi-3

• Phi-3 is being developed to be multimodal. This means it will be able to understand and process both text and audio. While image generation is not planned, the focus is on improving the model's performance with text and audio.
• The next release of Phi-3 will be a smaller, faster, and more efficient version. This will be an improvement over the current Phi-3 3.1/3.5. A larger release may include audio capabilities.
• Phi-3's development is influenced by other large language models. The example of Llama 3.1 is mentioned, which is a faster and more efficient version of Llama 3.

Phi-3 Applications

• Phi-3 can be used for local tasks like image analysis. The example of analyzing a picture of a cat and identifying its features is given. This demonstrates the model's ability to understand and interpret images.
• Phi-3 can be fine-tuned for specific tasks. This means it can be trained to perform better on specific types of data, such as legal documents, patents, or license plates.
• Phi-3 can be used for accessibility features. The example of automatically generating alt text for images is given, which can be helpful for people with visual impairments.
• Phi-3 can be run locally, but cloud-based models offer better performance. While local execution is possible, it may not be as fast or efficient as running the model on a cloud server or a powerful computer.

Ollama: Running AI Models Locally

• The speaker discusses the ability to run AI models locally, eliminating the need for cloud-based models.
• They emphasize the importance of human review for AI outputs, as models can still make mistakes.
• The speaker introduces Ollama, a tool for running AI models locally.
• Ollama supports various models, including Llama 3.1, Mistral, and Phi-3.
• Ollama can be installed on Linux, Windows, Mac, and even Docker.
• The speaker demonstrates running Phi-3 locally using Ollama and Docker.
• They show how to interact with the model directly through the terminal.
• The speaker highlights the ability to integrate Ollama with applications, such as a chat application, to leverage local AI models.

Phi-3 and Semantic Kernel for Applications

• The speaker is discussing the use of Phi-3 and Semantic Kernel for developing applications.
• A user asks about suitable applications for these technologies, specifically for mobile applications.
• The speaker suggests that Phi-3 and Semantic Kernel are well-suited for local applications, such as auto-completion in desktop applications.
• The speaker provides examples of using Phi-3 for tasks like searching product codes, summarizing reports, and extracting action points from meeting notes.

Retrieval Augmented Generation (RAG)

• The speaker introduces the concept of RAG (Retrieval Augmented Generation) and its use in combining large language models with specific information.
• The speaker explains that RAG involves creating a vector database in memory, which allows for searches based on meaning rather than keywords.
• The speaker demonstrates a RAG example using Semantic Kernel, where a question about Bruno's favorite superhero is answered using a local Phi-3 model.

Phi-3 and Semantic Kernel for Personalized Responses

• The speaker is demonstrating how to use Phi-3 and Semantic Kernel to create a small AI system that can access and process information from a custom database.
• The system starts by creating a vector database in memory, which is populated with information about individuals like Gisela and Bruno.
• This information includes details like their favorite superheroes, movies they've watched, and personal connections.
• When a question is asked, Phi-3 uses the vector database to find relevant information and provide an answer.
• The speaker highlights that the vector database is ephemeral, meaning it exists only in memory, but can be stored in more persistent databases like Redis, Azure Search, or SQL Server.
• The example demonstrates how Phi-3 can access and process information from the vector database to answer questions about individuals' preferences, even if those preferences are not publicly available.
• The speaker emphasizes that this approach allows for more personalized and context-aware responses from AI systems.

Multilingual Support

• The speaker is demonstrating how Phi-3 and Semantic Kernel can be used to process information in different languages. They use the example of asking a question about Bruno's favorite superhero in both English and Spanish.
• The speaker shows that even though the question is asked in Spanish, the model can still understand the meaning and provide an accurate answer.
• The speaker highlights that Phi-3 and Semantic Kernel can work locally without needing to access the cloud, making them suitable for applications with limited data.
• The speaker then asks a question about who likes Batman in Spanish. The model provides a generic answer, but when the same question is asked using the vector database, the model correctly identifies Gisel as the person who likes Batman.
• The speaker concludes by suggesting that Phi-3 and Semantic Kernel can be used to extract insights from data and improve applications by making them more intelligent.

Semantic Search with Cosine and Euclidean Distance

• The speaker discusses the use of cosine distance and Euclidean distance in semantic search, explaining that these methods help determine the similarity between vectors representing data points.
• They emphasize that the choice between these methods depends on the specific analysis being performed and that there are scenarios where one method might be more suitable than the other.
• The speaker encourages viewers to experiment with both methods, noting that Semantic Kernel and Phi-3 can handle the technical aspects of this process.
• They highlight the potential of these tools for tasks like answering questions based on information stored in a vector database.
• The speaker concludes by mentioning that Visual Studio 2022 can be used to explore these concepts further.

GitHub Copilot and Embedding Models

• The speaker is a big fan of GitHub Copilot and explains how it works in Visual Studio.
• Copilot uses embedding models to analyze code and answer questions in the chat.
• The speaker mentions that Copilot uses a similar embedding model to the one used in the "Phi-3 y Semantic Kernel" video.

GitHub Models

• The speaker is impressed by the announcement of GitHub Models, which allows users to test models like Phi-3, LLaMa 3, and Mistral directly on GitHub.
• GitHub Models allows users to create and use their own Phi-3 models.
• The speaker encourages viewers to join the beta program for GitHub Models.

GitHub Playground for AI Models

• GitHub offers a playground for interacting with AI models like Phi-3 and Semantic Kernel. This playground allows users to experiment with these models and generate code examples.
• The playground is integrated with GitHub's existing environment, making it easy for developers to use. This eliminates the need to switch between different platforms or search for models elsewhere.
• The playground provides a frictionless path for experimentation and innovation. Users can easily try out different models and implement them into their projects.
• GitHub's playground is a valuable tool for developers who want to explore the potential of AI models. It provides a user-friendly environment for learning and experimenting with these powerful technologies.
• The playground is still in beta, but it has the potential to revolutionize the way developers work with AI. It offers a seamless and intuitive way to integrate AI models into existing projects.

Smart Components and AI Applications

• The text mentions "Smart Components" as an example of a project that can benefit from AI models. These components are designed to be intelligent and provide users with a more efficient and intuitive experience.
• Phi-3 and Semantic Kernel are powerful AI tools that can be used to create intelligent applications. One example is using Phi-3 to create a smart combo box that suggests relevant options based on the context of the user's input.

Encouraging Community Involvement

• The speaker is impressed by the capabilities of Phi-3 and Semantic Kernel. They believe that these tools are easy to use and can be used to create powerful applications.
• The speaker recommends the Phi-3 cookbook as a great starting point for learning about these tools. The cookbook provides tutorials and step-by-step instructions for getting started.
• The speaker encourages viewers to contribute to the Phi-3 community by submitting pull requests (PRs) or contacting them directly. This will help to improve the documentation and make it easier for others to learn about these tools.

Conclusion and Next Steps

• The speaker expresses gratitude to Bruno for joining the conversation and suggests scheduling a part two for their discussion.
• Bruno mentions he is currently traveling and unable to confirm any upcoming conferences or events.
• The speaker encourages viewers to check the announcements for the following week as Bruno will be present.
• Bruno suggests adding an example of how to use Phi-3 with GitHub models to the cookbook.
• The speaker encourages viewers to follow Bruno on Twitter to stay updated on his activities.
• The speaker highlights Bruno's YouTube channel as a valuable resource for learning.
• The speaker encourages viewers to explore the Phi-3 cookbook and contribute to the project.
• The speaker emphasizes the potential of Phi-3 and encourages viewers to explore its capabilities.
• The speaker encourages viewers to leave a star on the Phi-3 repository and contribute to the documentation.
• The speaker invites viewers to submit projects with a large Spanish-speaking community for potential inclusion in Open Source Friday.
• The speaker thanks viewers for their support and announces upcoming conversations with a startup and a potential appearance by Prensor Stride.
• Next week, the speaker will be joined by Dapper in English.
• The speaker is collaborating with the only Artificial Intelligence laboratory in Latin America, located in Montevideo, Uruguay.
• The speaker will be in Montevideo next week with some members of their team to give talks.
• Some of these talks will be streamed.
• The speaker encourages viewers to subscribe to the channel to support open source content.