Justin Herring’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/3VKEXEI @[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.

Level up your productivity with these 5 powerful Google AI tools, from Workspace AI integration to custom model development with AI Studio! #GoogleAI #Productivity #TechTools Google Google Workspace Admins Read More: https://lnkd.in/gcctXS5H 

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

Five fundamentals to re design AI transformative experience as competitive advantage https://lnkd.in/drSWWsV

Microsoft study analyzes #AI adoption trends, and real-world #ROI across the most common use cases, while hundreds of Microsoft customer examples provide validation of AI's benefits. Kieron Allen breaks down the details in this #CloudWars analysis. https://lnkd.in/djDQN2rk

Excellent article by Kate Moran and Maria Rosala of the Nielsen Norman Group. This sentence captures the current state well: "Current AI tools have many limitations. Like interns, they work best when you provide ample instructions, context, constraints, and corrections" but "have the potential to accelerate your UX-research workflows." https://lnkd.in/dcziqqr4

AppWorld: Advancing AI Agents for Real-World App Interactions As the founder of QuantaLogic, I'm always on the lookout for advancements that bridge the gap between AI's theoretical potential and its practical applications. Today, I share insights from a research paper that's set to reshape how we evaluate AI agents in real-world scenarios. 👉 Introducing AppWorld: A New Frontier in AI Benchmarking Researchers have unveiled "AppWorld," a novel benchmark for testing AI agents' ability to interact with multiple apps via APIs. This isn't just another theoretical test – it's a leap towards creating AI assistants that can truly navigate our complex digital lives. Why AppWorld Matters 1. Realistic Simulation:    - Mimics 9 real-world apps with 457 APIs   - Populated with data from ~100 fictitious users   - Replicates typical digital activities we all engage in daily 2. Complex Task Suite:    - 750 diverse, challenging tasks   - Requires using multiple apps (avg. 1.8, max 6) per task   - Involves intricate API flows (avg. 9.5, max 26 API calls) 3. Robust Evaluation:    - Uses state-based programmatic evaluation   - Allows for different valid solutions   - Detects unintended changes or "collateral damage" 👉 The Digital Sandbox: How AppWorld Works Imagine a digital sandbox where AI agents can play with real-world apps without real-world consequences. That's AppWorld. It's like giving an AI a set of digital LEGO blocks – apps, APIs, and user data – and challenging it to build complex structures (complete tasks) that mirror our everyday digital interactions. Examples of AppWorld Tasks: - Scheduling a meeting by checking multiple calendars and sending invites - Splitting expenses among roommates using finance apps and messaging platforms - Planning a trip by coordinating flights, accommodations, and itineraries across various apps 👉 Beyond Simple Metrics: A New Way to Evaluate AI AppWorld's evaluation method is like a meticulous inspector checking not just if the job is done, but how it's done: 1. State-Based Checks: Examines the final state of the digital environment 2. Multiple Solutions: Recognizes that there's often more than one way to complete a task 3. Collateral Damage Detection: Ensures the AI didn't accidentally mess up other parts of the digital environment 👉 Current AI Performance: A Reality Check Even the most advanced AI models struggle with AppWorld's challenges: - Best performer (GPT4O with ReAct):   - 48.8% task completion on normal tasks  - 30.2% on more challenging tasks This reveals a significant gap between current AI capabilities and the complexity of real-world digital interactions. 👉 Why This Matters for Businesses and Developers 1. Realistic Expectations: Helps set accurate expectations for AI assistant capabilities 2. Development Focus: Highlights specific areas where AI needs improvement for practical use 3. Integration Challenges: Illustrates the complexities of integrating AI into multi-app ecosystems