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Microsoft confirms that OpenAI’s Sora will be integrated with Copilot in the future. The integration could offer developers a new way of creating and testing their applications. In a recent tweet exchange, Mikhail Parakhin, CEO of Advertising & Web Services at Microsoft, confirmed that OpenAI’s Sora will be integrated with Copilot, but it will take some time. This news comes after a user tweeted asking if Sora would be coming to Copilot, to which Parakhin replied, “Eventually, but it will take time.” Sora is a large language model chatbot developed by OpenAI; you can type out any word prompts however detailed you want, and then the AI model will return with a 60-second highly-detailed video. For example, you can ask Sora to show you how a car engine works, or how a neural network learns, or how a rocket launches, and it will generate a video that explains it visually. Integrating Sora with Copilot could potentially create a powerful tool for developers, allowing them to leverage the capabilities of both AI models in their work. Copilot is an AI-powered coding assistant that helps developers write better code faster. It can suggest code completions, refactor code, generate tests, and more. By combining Copilot with Sora, developers could have a new way of creating and testing their applications. They could use Sora to generate realistic scenarios, inputs, outputs, and feedback for their code, and use Copilot to improve their code quality and performance. OpenAI said Sora is taught to use a network that simplifies videos. This network takes regular videos as its starting point and makes them smaller, keeping only the most important parts. Then, Sora learns from these simplified videos to create new ones. Microsoft is possibly still working on the technical aspects of this integration. It will take some time before Sora is available in Copilot, given that it was just revealed to the public a few days ago. It is important to note that Parakhin’s tweet does not provide a specific timeline for when Sora will be integrated with Copilot. However, his response suggests that Microsoft is committed to making this happen eventually.

🚀 Exciting AI Product Integrations to Keep Up with the Times! 👴🏻👨🔧 Stay ahead in the world of AI and ML with these easy-to-build AI product integrations. They can give your projects magical powers! ✨ 1️⃣ CopilotPortal: Embed an Actionable LLM Chatbot Into Your app. #AI #Chatbot 2️⃣ LinguiJS: Automatic & Simple Internationalization for React Apps. #Internationalization #React 3️⃣ Pezzo.ai: Observability, Cost & Prompt Engineering Platform for OpenAI. #OpenAI #Optimization 4️⃣ CopilotTextarea: AI-powered Writing in React Apps. #React #AI 5️⃣ LangChain: Easy-to-use API and library for adding LLMs into apps. #API #LLM 6️⃣ SwirlSearch: AI-powered search, summaries, and output. #Search #AI 7️⃣ ReactAgent: Experimental LLM agent for generating React components from user stories. #GPT4 #React Don't miss out on these amazing integrations! Check them out on GitHub and start enhancing your projects today. ⭐️ #AIIntegration #MachineLearning #DeepLearning #ComputerVision #NLP #DataScience #BigData

🚀 Exciting news! Learn how to build a powerful AI assistant to chat with your documents using Next.js and OpenAI. Discover how AI is transforming document understanding and collaboration. Check out the open-source alternative to DocSend, Papermark. Get started with configuring the OpenAI Assistant API and creating a chat interface. Don't miss out on this insightful article! #AI #ML #NextJS #OpenAI #DocumentAssistant #Papermark #VercelAI #TechInnovation

🚀 Exploring AI and Having Fun with the Vercel AI SDK! 🚀 I built an AI chatbot with Next.js and had a blast doing it! In my latest blog post, I share my journey from understanding the basics to implementing a practical application. If you're curious about AI, love hands-on projects, or just want to see how easy it can be to integrate various LLM providers, check it out! 📚 👉 https://lnkd.in/g64qERjK I'd love for you to read through it, try it out yourself, and share your thoughts! 🤖💬 #AI #VercelAI #Nextjs #LearningInPublic #SoftwareEngineering #Chatbot

Artificial intelligence (AI) and machine learning (ML) are revolutionizing mobile app development in today’s fast-paced digital landscape. Understanding the technical intricacies of AI and ML is crucial for developers and businesses alike. This knowledge not only helps in making informed decisions but also in leveraging these technologies to their full potential. This article delves into the technical aspects of AI and ML in mobile apps, exploring the technologies, algorithms, and frameworks that drive their development along with key differences between AI vs Machine Learning, Generative AI vs Machine Learning.

plugins don't exist anymore If you missed the OpenAI Dev Day stream, here's a summary. Transcript provided by OpenAI Whisper and summarized by gpt-3.5-turbo-16k. > * OpenAI launched GPT-4 Turbo, a new model that addresses developer feedback and offers improvements such as longer context length, more control over model responses, better world knowledge through retrieval capabilities, support for new modalities like vision and text-to-speech, customization options through fine-tuning and custom models, increased rate limits, and copyright shield for legal protection. > * OpenAI introduced GPTs, tailored versions of ChatGPT for specific purposes, which combine instructions, expanded knowledge, and actions to make them more helpful and personalized. > * OpenAI launched the Assistants API, which includes features like persistent threads, built-in retrieval, code interpreter, and improved function calling, making it easier to build assisted experiences in apps. > * OpenAI introduced the GPT Store, where GPTs can be listed and shared publicly, with revenue sharing for creators. > * OpenAI showcased examples of GPTs built by partners, including Code.org, Canva, and Zapier. > * OpenAI demonstrated how the Assistance API can be used to create voice-based assistants and perform real-time actions. > * OpenAI deepened their partnership with Microsoft, focusing on infrastructure development and product integration. > Overall, these changes aim to enhance developer experiences, provide more capabilities, and make AI more accessible and customizable.

OpenAI DevDay! 🎤 Key announcements 🎤 * GPT4 Turbo is live in ChatGPT and API - there was rumor that GPT4 Turbo saw some sort of setback, but OpenAI proved that wrong with actions. GPT4 Turbo is now live and has longer context length (128K), fresher knowledge (until April 2023), DALLE3 integration, new text to speech integration, image input from API, is open for fine tuning, and pricing about 2.5x cheaper. Speed improvement is being teased for future. There is also a set of improvements for people who uses GPT4 to generate code or JSON in complex AI workflows. * Interestingly, Microsoft (Satya) was given spotlight in today's event. It has nothing to announce really, just there to reiterate its commitment to the partnership. There was rumor of frictions between the two partners, and Satya was specifically asked to address the state of the partnership, so perhaps the rumor has some truth? Microsoft is betting very heavily on GPT4 for its Copilots across its product lines. It really has the most to lose if GPT4 doesn't improve fast enough. The only parallel to this level of mutual dependency I could think of was probably the wintel partnership. Nothing lasts forever but this partnership probably has leg for another decade. * More ecosystem building - GPT is the custom chatbots that can be built with low technical skills, these chatbots are hosted and distributed from OpenAI's marketplace and there is revenue sharing. It simplified knowledge, history management and has access to GPT4's full array of tricks - plugins (now rebranded actions), image generation, vision, code interpreter, browsing, etc. The API counterpart is also available called Assistant API that allowed for greater controls and integration into custom UI (woven into complex application flow and not just a bolted on chatbot). And I think it's very important that OpenAI followed Microsoft's footstep to introduce Copyright Shield, legal protection for its API customers from potential copyright claim against the models' training data. Overall, very strong showing to sustain OpenAI's leadership position in model capability and strengthening its ecosystem by improving performance / price, flexibility, removing pain points. There is investment for both enterprise and grass root developers.

OpenAI's GPT-4 reveals major leaps in AI capabilities, but also signals consolidation as broad platforms like ChatGPT integrate more features. While exciting, this means narrow AI applications could face challenges. Long-term vision is key - innovative AI builders should focus on specialization and strategic planning amidst progress.

Artificial intelligence (AI) and machine learning (ML) are revolutionizing mobile app development in today’s fast-paced digital landscape. Understanding the technical intricacies of AI and ML is crucial for developers and businesses alike. This knowledge not only helps in making informed decisions but also in leveraging these technologies to their full potential. This article delves into the technical aspects of AI and ML in mobile apps, exploring the technologies, algorithms, and frameworks that drive their development along with key differences between AI vs Machine Learning, Generative AI vs Machine Learning.

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