Dave Matchack’s Post

Discover the future of AI assistants in 2024 and how they're set to become even smarter and more personalized. Dive into this comprehensive guide: https://buff.ly/4gkJfes @[Top Apps AI](urn:li:organization:90548736)

Discover the future of AI assistants in 2024 and how they're set to become even smarter and more personalized. Dive into this comprehensive guide: https://buff.ly/3RRMUWl @TopAppsAI

Ten Wild Examples of Llama 3.1 Use Cases Meta’s recent release of Llama 3.1 has stirred excitement in the AI community, offering an array of remarkable applications. This groundbreaking model, particularly the 405B variant, stands out for its superior performance and open-source accessibility, outpacing even top-tier closed models. Here are ten wild examples showcasing the versatile use cases of Llama 3.1, from enhancing personal gadgets to innovative AI deployments. Efficient Task Automation: Llama 3.1 405B can be harnessed to teach the smaller 8B model how to execute tasks perfectly, reducing costs and latency. This setup allows users to train the 8B model to handle various operations, providing a cheaper alternative without compromising performance. Introducing `llama-405b-to-8b` Get the quality of Llama 3.1 405B, at a fraction of the cost and latency. Give one example of your task, and 405B will teach 8B (~30x cheaper!!) how to do the task perfectly. And it's open-source: https://t.co/H5590RiFhc pic.twitter.com/UyVJtFZH6V — Matt Shumer (@mattshumer_) July 26, 2024 Personal Phone Assistant: By turning Llama 3.1 into a phone assistant, users can enjoy quick and accurate responses to queries. This integration utilizes Groq’s API, demonstrating the model’s ability to provide instant intelligence, making daily tasks more manageable and interactive. I turned Llama 3.1 into my new phone assistant. It can answer anything, and look at how fast it does it using Groq’s API pic.twitter.com/dmlQ2gzSfu — Alvaro Cintas (@dr_cintas) July 25, 2024 Local Deployment of Chatbots: Building and deploying a chatbot that learns from user interactions is now possible in under ten minutes using Llama 3.1. This setup facilitates the creation of a personalized conversational agent that becomes more knowledgeable and efficient with each interaction. meta just released llama 3.1 you can now build & deploy a quick chatbot that learns more and more about you as you talk to it. in literally less than 10 minutes. here's how. there's no better time to build and ship stuff. these open models are incredible! pic.twitter.com/9fX0MMABNt — Dhravya Shah (@DhravyaShah) July 23, 2024 Distributed AI Clusters: Through the @exolabs_ home AI cluster, Llama 3.1 405B can be distributed across multiple devices, such as two MacBooks. This configuration enables users to run complex AI models efficiently at home, showcasing the model’s scalability and flexibility. Llama 3.1 70b beamed to my iPhone from my @exolabs_ home AI cluster of 2 MacBooks and 1 Mac Studio My own private GPT-4 assistant at home / on the go pic.twitter.com/0svmX39y4E — Alex Cheema – e/acc (@ac_crypto) July 26, 2024 Streamlit App Integration: With minimal code, users can create a Streamlit app to chat with Llama 3.1 8B locally via @ollama. This setup emphasizes the ease of integrating advanced AI into user-friendly applications, making sophisticated AI accessible to non-experts...

Learn the critical steps for deploying AI in businesses while overcoming common challenges in the AI adoption cycle.

Working on AI product features? In this third edition of this DoP Deep dive series, we’re picking up where we left off back in February to explore what new features top tier tech companies have released since then. This fresh batch of companies we’ve chosen this time includes new features from 20+ companies including Chrome, Slack, Docusign, Yelp, Google Maps, Pinterest, Airbnb, Replit, Airtable and others. The new AI features include the use of Assistants / chatbots, machine learning, UX enhancers and other categories - with notes and guidance on how you can use these features to help shape your own product’s AI strategy and principles. • How AI features are categorised - from ML powered insights to AI assistants and standalone APIs • New features shipped by Chrome, Slack, Docusign, Yelp, Google Maps, Pinterest, Airbnb, Replit, Airtable and others • How to use these new features to help shape your product’s AI strategy • The full list of the AI features recently released from 20+ top tier companies https://lnkd.in/eKrbBJ-4

Introducing Nexa: MIBT’s New AI Assistant Dear MIBT Team, We’re thrilled to introduce Nexa, our AI assistant, built to enhance user engagement, streamline support, and elevate the experience we deliver across the MIBT community. Nexa will be made available on the MIBT Official Website and other platforms, including StartShield, providing users an interactive way to access information, navigate resources, and stay connected with MIBT’s offerings. How Nexa Enhances Our Operations 1. 24/7 Support for Users: Nexa is ready to assist with frequently asked questions, provide guidance on MIBT resources, and support users with platform navigation, offering continuous assistance across our platforms. 2. Empowering Our Community: By handling immediate inquiries, Nexa enables our team to focus on strategic initiatives while keeping users informed, engaged, and well-supported. 3. Future Growth and Flexibility: Nexa’s capabilities are adaptable, allowing us to introduce new features like event reminders, personalized support, and deeper integrations with our platforms, including StartShield, as we gather insights from user interactions. 4. Voice Capabilities: Looking ahead, we’re planning to add voice functionality for a more intuitive experience. This will allow users to interact with Nexa through voice commands, expanding accessibility and enhancing personalized support across platforms. What’s Next? We’ll begin rolling out Nexa gradually, starting with FAQs and site navigation guidance. This initial phase will allow us to gather valuable feedback to refine Nexa’s responses and explore additional features, such as proactive notifications, personalized recommendations, and voice integration. How You Can Help Your feedback is crucial to ensure Nexa serves our community effectively. We encourage you to test Nexa’s features, share your insights, and suggest improvements. Together, we’ll shape Nexa into an essential part of MIBT’s digital ecosystem, enhancing both our website and StartShield. Thank you for your support and innovation as we bring MIBT’s vision to life. Best regards, MIBT CEO

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into mobile applications is revolutionizing the way businesses operate and interact with their customers. Whether you are looking to develop chatbots, implement predictive analytics, or create personalized user experiences, OZVID Technologies has the expertise to guide you through every step of the process. #ozvid #chatbot #ArtificialIntelligence #developchatbot #MachineLearning

Not all pharmacy savings apps are created equal. Is your app delivering the results you need? Levrx's advanced Machine Learning AI is delivering personalized, actionable recommendations right to your member's fingertips. Learn how we make it happen here: https://lnkd.in/eD2AywmB

AI is transforming applications across industries. At Canary Speech, we're using the power of Azure AI to drive breakthroughs in health technology. Read more about how AI is impacting industries and how Canary Speech is at the forefront of health tech innovation. https://lnkd.in/eTYY9Ujg Share your thoughts on AI in health tech in the comments below. How do you think AI will shape the future of healthcare? #AI #HealthTech #EarlyDetection #CanarySpeech #AzureAI

Screen-aware AI assistants that proactively provide suggestions based on what the user sees, even before they ask, might become a big thing. This concept enhances the traditional context-aware assistant by leveraging multimodal capabilities. Instead of relying solely on conversation history or structured inputs, it analyzes screenshots in real time to offer relevant suggestions on the fly. For a specific example of multimodal AI applied to mobile UIs, check out this research paper: https://lnkd.in/eKJYy2a2 While it focuses on understanding mobile app interfaces (icons, texts, buttons), it provides a glimpse into how screen-aware models can be developed for more specialized tasks. You can experiment with such functionality by using one of the many multimodal LLMs on Azure Ai Studio and providing it with screenshots (can also be parts of the UI). However, deploying this in a production environment may require additional guardrails, for example, by adding structured inputs and likely also fine-tuning the model. Has anyone tried this approach? I'd love to hear your thoughts and experiences!