Cardinal Peak’s Post

Amazon Bedrock’s new embodied AI is taking #AI to the next level, using #GenAI to create autonomous chess players that learn and evolve just like humans. ♟️ With the AWS comprehensive suite of services, including Amazon Bedrock Custom Model Import, Amazon S3, AWS Amplify, AWS AppSync, AWS Step Functions, AWS IoT Core, and AWS IoT Greengrass, developers can create immersive chess experiences that bridge the digital and physical realms. 🏁 Read more about how Amazon Bedrock is (literally) changing the game:

Chat GPT took me to a light forest, Meta AI took me to the amazon forest, Gimini was determined to keep me in either forests. Why you may wonder? I made a silly mistake to rely on them for a first principle development, I wanted to integrate a Custom sign up and sign in form rather than leveraging on the built in withAuthenticator, aws-react-ui that comes with aws-amplify #aws. Never underestimate the power of documentation and the legendary stack-overflow #stackoverflow with a few line of pointer boom, my two days stress came to a solution within minutes. The demo below is a custom authentication form flow built on top of aws-amplify and aws-IoT-Core. The dashboard you see is design to simulate publish subscribe topics for Machine to Machine communication i.e Internet of Things communication flow. Watch out for the next demo which will be an actual machine to machine. Talk about: #iot #IoTCloudArchitect #Ajioz

Edge AI Computing 🚀 🌐 Problem: AI models hosted on the cloud can introduce latency in real-time applications, leading to delays in decision-making. ⏳ This can hinder performance and user experience. ❌ 💡 Solution: Utilize TensorFlow Lite to deploy models locally on edge devices, significantly reducing delays and enhancing responsiveness. Here’s a quick code snippet to get you started: 💻✨ code: import tensorflow as tf model = tf.lite.TFLiteConverter.from_saved_model('model/').convert() open('model.tflite', 'wb').write(model) 📱 With this approach, your Edge AI models are not only faster but also primed for real-time applications! 🚀 Let’s embrace the future of computing together! 🌟 #EdgeAI #TensorFlowLite #IoT #TechInnovation #AI #MachineLearning #PerformanceBoost 📈

Thoughts on this? >> Microsoft's Azure AI Speech lets Truecaller users create an AI assistant with their own voice >> Comment below! >>> lqventures.com #digitalhealth #socialmedia #digitalmarketing #healthtech #industry40 #IoT #mhealth #AI

Thoughts on this? >> Microsoft's Azure AI Speech lets Truecaller users create an AI assistant with their own voice >> Comment below! >>> lqventures.com #digitalhealth #socialmedia #digitalmarketing #IoT #AI #healthtech #industry40 #mhealth

Ready to dive deep at #AWSreInvent? Come join us at the "Build a vehicle insights assistant with AWS IoT and Amazon Q" builders' session where you'll use AWS #IoT to ingest vehicle data and then use #AmazonQ to build a conversational generative AI–powered assistant that helps analyze the data using natural language queries and responses. Learn how you can use Amazon Q to explore advanced querying and analysis capabilities like filtering, aggregations, and anomaly detection, and discover how Amazon Q can help you fine-tune your data collection campaigns or maintenance schedules based on collected data. Learn more about the session here: https://go.aws/4eoMRun

Attended the Video-based Driver Monitoring with AWS IoT and AI/ML workshop today at AWS re:Invent 2024, and it was an eye-opening experience! This session walked through the process of building an IoT device for edge computer vision and machine learning workloads, specifically designed to detect driver fatigue and distraction. 🔑 Key takeaways: 👉 Learn how to deploy CVML models at the edge for real-time driver event detection. 👉 Use generative AI in the cloud to search video content at scale with natural language. 👉 Practical steps on implementing the solution using AWS IoT Greengrass, Amazon Kinesis Video Streams, AWS IoT Core, and Amazon Bedrock. It was amazing to see how IoT and AI/ML can converge to create impactful real-time applications that enhance safety and decision-making on the road. 💡 Reflection: Edge computing combined with AI is transforming industries like automotive, and the ability to apply it in real time for driver monitoring is a game changer. zeb #AWSreInvent #AWSIoT #AIandML #GenerativeAI #EdgeComputing #IoT #CloudInnovation #zeb #AWS

Have you ever wondered how AI works inside your devices? Here is a brief glimpse to this technology 🔎

Edge AI, Cloud AI and Hybrid AI Edge AI and Cloud AI are two different approaches to deploying artificial intelligence (AI) systems, each with its own advantages and challenges. Here’s a breakdown of their differences: ### 1. **Location of Data Processing**:   - **Edge AI**: Data is processed locally on the device, close to where it is generated (e.g., smartphones, IoT devices, cameras, or sensors). The AI algorithms run directly on edge devices, with minimal need for cloud-based computation.   - **Cloud AI**: Data is sent over the internet to centralized cloud servers (e.g., AWS, Google Cloud, Microsoft Azure) for processing. The cloud servers run the AI models, process the data, and then send the results back to the device. ### 2. **Latency**:   - **Edge AI**: Lower latency because the data doesn't have to travel to and from the cloud. Decisions are made locally on the device, which is crucial for real-time applications such as autonomous vehicles, industrial robots, or healthcare monitoring.   - **Cloud AI**: Higher latency due to the need for data to be transmitted to the cloud, processed, and then sent back. This can be a disadvantage in situations that require immediate response times. ### Summary: - **Edge AI**: Processes data locally, offering low latency, higher privacy, and reduced dependence on internet connectivity. It's best for real-time applications in environments where immediate decision-making is critical. - **Cloud AI**: Leverages powerful cloud servers for processing, which can handle large volumes of data and complex models but comes with higher latency, bandwidth needs, and potential privacy concerns. In some cases, **Hybrid AI** models combine both approaches, using edge AI for real-time tasks and cloud AI for more complex tasks that require significant computational power.

The Tiny Brain Inside Your Gadget: Edge AI 🤖 Ever wonder how your phone can recognize your face, or how a smartwatch can monitor your heart rate? It's all thanks to Edge AI! What is Edge AI? Imagine tiny computers built into everyday devices, analysing #data on the spot without needing to send it to the cloud. That's edge AI. Why is it cool? 1. Faster decisions: Edge #AI processes data right on your device, so there's no waiting for info to travel across the web. 🚀 2. Privacy matters: Your data stays local, reducing the risk of it being shared elsewhere. 📲 3. Works offline: No internet connection? No problem! Edge AI keeps functioning even when you're not connected. 🌐 Where is it used? 1. Smartphones: Facial recognition, voice assistants, and fitness trackers all leverage Edge AI. 📲 2. Smart homes: Thermostats that adjust to your presence and security cameras that detect suspicious activity rely on Edge AI. 🏠 3. Manufacturing: Predicting equipment failures and optimizing production lines are just a few ways factories use edge AI. 🏭 Edge AI is making our devices smarter and more helpful. As technology evolves, expect to see even more innovative AI applications to emerge! If you want to learn more about Edge AI click the link below! #edgeAI #ArtificialIntelligence #FutureofTech #IoT #SmartDevices #TechInnovation