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Chaque semaine, nous sélectionnons des news Tech, Data et IA pertinentes que nous analysons grâce à l'intelligence artificielle et que nous transformons en podcast (10 minutes) dont les speakers sont des voix générées aussi par IA.
Venez écouter les actus Tech de la semaine ou faire votre veille grâce à ce mini podcast. Inscrivez-vous ici pour ne manquer aucun des prochains podcasts.
🎙️ Spotify’s AI Voice Translation Pilot Means Your Favorite Podcasters Might Be Heard in Your Native Language
📦 Expanding access to safer AI with Amazon
🦾 ChatGPT can now see, hear, and speak
😻 GitHub Copilot Chat beta now available for all individuals
💻 Next.js 13.5
🪢 Retrieval Augmented Generation (RAG): What, Why and How?
Les précédents épisode du podcast Tech Press Review est toujours disponible.
Spotify is launching a new feature called Voice Translation for podcasts, using AI to translate podcasts into different languages while retaining the original speaker's voice. The tool leverages OpenAI's voice generation technology to match the speaker's style, creating a more authentic listening experience. In collaboration with podcasters Dax Shepard, Monica Padman, Lex Fridman, Bill Simmons, and Steven Bartlett, Spotify has generated AI-powered voice translations in Spanish, French, and German for select catalog episodes. The company plans to include other shows, such as Dax Shepard's "eff won with DRS," The Ringer's "The Rewatchables," and Trevor Noah's upcoming podcast. The voice-translated episodes will be available to both Premium and Free users worldwide. Initially, episodes in Spanish will be released, with French and German translations coming soon. Users can access these translations through the Now Playing View of supported episodes or visit the dedicated Voice Translations Hub for more translated episodes. Spotify aims to use this feature to connect listeners and creators on a deeper level and plans to expand access for more creators and languages based on feedback from the pilot. As the number of podcast listeners on Spotify continues to grow, the company is committed to exploring new ways to overcome barriers to storytelling.
Source 👉 https://newsroom.spotify.com/2023-09-25/ai-voice-translation-pilot-lex-fridman-dax-shepard-steven-bartlett/
Amazon has announced that it will invest up to $4 billion in Anthropic to develop reliable and high-performing AI models. This collaboration will make Anthropic's safe and steerable AI accessible to Amazon Web Services (AWS) customers. AWS will become Anthropic's primary cloud provider, offering leading compute infrastructure with AWS Trainium and Inferentia chips. They will also expand support for Amazon Bedrock, allowing organizations to customize and optimize performance while minimizing potential harm.
Developers and engineers will be able to build on top of Anthropic's models through Amazon Bedrock, incorporating generative AI capabilities into their work. Enterprises will benefit from this technology, using it for a wide range of tasks such as dialogue generation, content creation, reasoning, and instruction. Anthropic models are already being used by companies like LexisNexis, which uses Claude 2 for conversational search and legal drafting, and Bridgewater Associates, which utilizes Claude 2 for investment analysis.
Both Anthropic and Amazon are committed to the responsible development and use of AI. They actively participate in organizations such as the Global Partnership on AI (GPAI) and the Partnership on AI (PAI) to promote safety and trust in AI technologies. Amazon's investment in Anthropic includes a minority stake, but Anthropic's corporate governance structure remains unchanged. They will continue to conduct pre-deployment tests to manage the risks associated with AI systems.
With Amazon's investment and supply of technology, Anthropic is equipped to advance AI safety and research. Their goal is to responsibly scale the adoption of Claude, offering safe AI solutions worldwide.
Source 👉https://www.anthropic.com/index/anthropic-amazon
New voice and image capabilities are being introduced in ChatGPT, providing users with a more intuitive way to interact with the system. These features allow for voice conversations and image sharing, expanding the ways in which ChatGPT can be used in daily life.
With the new voice capability, users can engage in live conversations with ChatGPT by speaking to it. This opens up possibilities such as discussing and learning about landmarks while traveling. Users can snap a picture of a landmark and have a real-time conversation with ChatGPT about what makes it interesting. Additionally, this feature can be used at home to determine what ingredients are available in the fridge and pantry for dinner. Users can simply snap pictures and ask follow-up questions for a step-by-step recipe.
The image capability, available on all platforms, allows users to share images with ChatGPT. This feature can be utilized to seek assistance in various scenarios. For example, parents can capture a photo of a math problem, circle the specific set, and have ChatGPT provide hints to both the child and parent.
These voice and image capabilities will be gradually rolled out to Plus and Enterprise users of ChatGPT over the next two weeks. Voice functionality will be available on iOS and Android devices, with the option to opt-in through settings. Image sharing will be accessible across all platforms.
Stay tuned as ChatGPT enhances its capabilities, providing a more engaging and interactive experience for its users.
Source 👉 https://openai.com/blog/chatgpt-can-now-see-hear-and-speak
In an exciting update, GitHub has released a public beta of GitHub Copilot Chat for individual users, marking a significant milestone in their AI-powered software development journey. This powerful AI-assistant, when integrated with GitHub Copilot pair programmer, becomes an invaluable tool for developers, allowing them to build at the speed of their thoughts using natural language. This cohesive experience aims to revolutionize software development by reducing mundane tasks and establishing natural language as a universal programming language for all developers.
For individual users, accessing GitHub Copilot Chat beta is now free. It is currently supported in Visual Studio and Visual Studio Code editors. Users will receive an email notification with instructions on how to get started. If you haven't joined the beta program yet, a comprehensive guide is conveniently linked in the email.
GitHub Copilot Chat offers a range of powerful features to enhance developer productivity and happiness. It can provide real-time guidance, suggesting best practices, tips, and solutions tailored to specific coding challenges. Code analysis is simplified, enabling users to break down complex concepts and understand code snippets. It can also help address security issues by offering suggestions for remediation and reducing vulnerabilities found during security scans. Additionally, GitHub Copilot Chat assists with troubleshooting, identifying issues and offering explanations and alternative approaches.
This release aims to democratize software development by empowering developers globally, across various industries and backgrounds. Whether you're a beginner in Brazil learning unit tests or a professor in Germany needing documentation assistance, GitHub Copilot Chat supports learning and building code in your natural language. By elevating natural language to a universal programming language, GitHub envisions accelerating human progress and enabling developers to build the innovation of tomorrow.
To join this innovative development journey and experience the power of GitHub Copilot Chat, individual users can now explore its features and receive guidance in their preferred language.
Source 👉https://github.blog/2023-09-20-github-copilot-chat-beta-now-available-for-all-individuals/
In the latest release of Next.js, version 13.5, significant improvements have been made to local development performance and reliability. The focus has been on optimizing the App Router applications, resulting in faster server startup times, faster Hot Module Replacement (HMR), and reduced memory usage. These improvements have been achieved through various optimizations, including caching, minimizing slow operations, and optimizing file system operations. Additionally, large icon libraries are now automatically configured, leading to better performance.
Furthermore, the release introduces the optimizePackageImports feature, which automatically optimizes package imports for libraries like @mui/icons-material, @mui/material, and react-bootstrap. This feature only loads the modules that are actually being used, enhancing both local development performance and production cold starts.
Next.js 13.5 also includes various bug fixes, documentation updates, and performance enhancements. For example, there have been improvements in server actions, support for scroll behavior changes, and better Jest support for the App Router.
Overall, this release of Next.js brings significant performance and reliability improvements, making it easier and faster for developers to build web applications. The Next.js team, along with contributions from individual developers and industry partners, have worked tirelessly to deliver these updates. The community can join in discussions on GitHub, Reddit, and Discord to learn and engage with the Next.js ecosystem.
Source 👉 https://nextjs.org/blog/next-13-5
In recent years, a new architecture called retrieval augmented generation (RAG) has gained popularity for enhancing the performance of language models (LLMs). Popularized by Meta in 2020, RAG involves passing relevant information along with the task details to the model, improving its ability to generate accurate output.
LLMs are trained on vast amounts of data and possess parameterized memory, enabling them to answer questions and complete tasks. However, their knowledge is limited to a specific cutoff date, and they may "hallucinate" when asked about events beyond this date. Meta researchers discovered that by providing relevant information about the task, the model's performance significantly improves.
Fine-tuning is a common approach to improve the performance of LLMs. However, it requires a large dataset and is not suitable for dynamic datasets. This is where RAG shines, as it can be used to improve LLM performance on tasks like summarization and translation, which may not be possible to fine-tune.
The RAG pipeline consists of three stages: data preparation, retrieval, and generation. During data preparation, various databases such as vector stores, search engines, and graph databases are used to store and organize the data. For example, vector stores are ideal for unstructured data, while graph databases are useful for structured data and relationships between entities.
Retrieval involves querying the chosen database based on the task at hand. Hybrid approaches that combine semantic search and keyword matches have shown promising results. Once the relevant data is retrieved, it is passed to the generator (LLM) along with the user's query or task. The quality of the output depends on the data and retrieval strategy.
Several techniques can be used to improve RAG performance, such as hybrid search, summarization of data, overlapping chunks for context, fine-tuned embedding models, metadata inclusion, re-ranking of results, and re-ordering of context snippets.
RAG is already integrated into LLMStack, which handles data chunking, embedding generation, retrieval, and generation. Various applications, such as chatbots, search engines, and copy checkers, utilize the RAG pipeline in LLMStack.
RAG has proven to be a powerful approach, and ongoing research and development are expected to lead to more use cases in the future. To leverage RAG in your work, you can try out LLMStack through the Promptly cloud offering or the open-source GitHub repository.
Source 👉 https://llmstack.ai/blog/retrieval-augmented-generation
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