Microsoft’s Post

This is the type of AI hype that kills people OpenAI has had their first dev day of the year. One of the use cases as pictured in the image of a new feature showcases the detection of traffic signs, lanes etc by fine tuning their vision models for gpt-4o. I've previously worked at Veoneer, (later Arriver, and now Qualcomm) and can debunk that use case as a disaster. If people honestly believe that something like OpenAI API's would be used for self-driving cars, they're naive. It's ridiculous. The safety needed in cars is so strict, that vehicles such as Mercedes, BMW and Volvo (that Qualcomm delivers cameras and software for) need to have a system run inside the vehicle for safety, using onboard GPUs for critical use cases that you can depend on. There is no way you'd run an image model in the cloud. People would die when the software would freeze or break in ways you'd not be able to foresee and for instance not send a correct panic signal when (in Sweden) a moose is running out on the road, or a child is jaywalking! Otherwise, cool feature! But some of this AI-hype is unreal (toward the bad side!). There is however a good use case for generating datasets, which would be used for training the onboard models with more data (maybe the human annotators are threatened now?) Where is talk of safe AI systems?! #aigonewrong #selfdrivingcars

Denso, a leader in automotive parts and a pioneer in technology innovation, is breaking new ground with Microsoft’s Azure OpenAI Service, Azure AI, and Machine Learning platforms. This partnership is centered around a bold vision: to create autonomous AI-powered robots that not only follow instructions but adapt, learn, and collaborate seamlessly with humans in real-time. Denso's generative AI-powered robots aim to transcend current robotic limitations. Rather than simply responding to pre-programmed commands, these robots interpret human language and adjust actions dynamically. For example, a robot could discern that “the black drink” means coffee, using context rather than explicit instructions—a leap toward intuitive human-robot interaction. By harnessing advanced Azure AI capabilities, Denso is developing a multifunctional robot ecosystem designed for diverse industries, from manufacturing to retail. GitHub Copilot accelerates development, enabling swift prototyping and a robust development environment. Looking ahead, Denso’s collaboration with Microsoft envisions a society where humans and robots work side by side, enriching lives and enhancing productivity. As we explore open innovation and social implementation, we’re excited about what this transformative journey holds. Read more: https://lnkd.in/gWA4Ra2p #Denso #Microsoft #Azure #OpenAI #GenerativeAI #AI

Here's the latest in AI this week: Tesla, HeyGen, NVIDIA, Apple, and OpenAI just dropped some mind-blowing announcements. Let's dive into the details 🤿 Tesla's unveiling Optimus, Robotaxi, and Robovan. We're looking at the future of personal robotics and autonomous transport here. Optimus for $20k-$30k? That's cheaper than most cars. Impressive. HeyGen's new avatar looks are here. Create your digital twin, dress them up, pose them, and set the scene. Personalized video content just got a major upgrade. Your move, TikTok. NVIDIA's Blackwell is here, and OpenAI's already got their hands on the DGX B200 system. This next-gen AI architecture is about to make your current setup look outdated. Expect some significant AI breakthroughs soon. Apple's changing their approach. No more yearly "upgrades" that barely upgrade anything. Looks like they're working on some real innovations between releases. Get ready for a new era in consumer electronics. OpenAI's MLE-bench is here to improve the AI agent game. 75 Kaggle competitions to test AI's real-world ML skills? Interesting. Automated machine learning and AI-assisted data science are about to accelerate. More on Tesla's Optimus: • Your personal robot assistant • Teach, babysit, walk dogs, mow lawns, grocery shop • "Party mode" for when you're feeling fancy The future is now, and it includes robot helpers.

🚀 Exciting Times Ahead in AI & Machine Learning! 🚀 As an AI & ML Engineer, I’ve been passionate about using data to drive innovative solutions in the transportation and autonomous systems industries. 🌍 From image analysis and object detection to developing intelligent computer vision systems, I’m constantly amazed by the impact AI has on reshaping the future. 🔧 Currently, I’m sharpening my skills in cloud computing to scale AI models, learning platforms like AWS, Google Cloud, and Azure to take my solutions from the lab to the real world. Model deployment at scale is an essential skill for modern AI engineers, and I’m excited to implement cutting-edge technologies to do just that. 💡 My vision is to apply AI in creating safer, smarter transport systems, helping industries optimize operations while improving safety and efficiency on a global scale. 📈 If you’re working in AI/ML or interested in collaborating on AI-based projects for autonomous vehicles or smart transport systems, I’d love to connect and discuss how we can push the boundaries of what’s possible! #AI #MachineLearning #DataScience #AutonomousSystems #DeepLearning #ComputerVision #CloudComputing #Innovation #Technology #Transport

Imagine dumping your entire codebase after 8 years and 11 versions of development. Make the hard decisions, folks--A lesson from Elon and Tesla self-driving team: Tesla changed their self-diving architecture from a rule-based algorithm approach to a full neural net approach and it changed the game for them. It just goes to show the power of simplicity and bold decision-making. Until 2023 (yes you read that right), Tesla's autonomous driving was powered by complicated human programming that defined each driving scenario and objects on the road and how to handle them one by one. The problem with this approach is that it's complicated, and labor intensive and you can never program for ALL the edge cases that can exist in the real world. Tesla launched 11 versions of their self-driving software in their cars until they decided to scrap the entire approach for version 12 in 2023. In version 12, they adopted an "End-to-end Neural Net Approach" which involves no hardcoding or "if-then" logic. Instead in this approach, the pixels from Tesla's car cameras go into an inference engine, and control (how to steer the wheels, use throttle/break etc) instructions come out. The training dataset of the deep learning model for the inference engine came from thousands of Tesla owners who drive around in their cars every day. Since there are 5+ million Teslas on the road, they will have enough data to train their model and perfect it.

Change is hard, but necessary

Machine learning is a branch of artificial intelligence (AI) that empowers computers to learn from data and improve over time without explicit programming. It involves algorithms that enable systems to identify patterns, make predictions, and adapt to changing circumstances. Machine learning finds applications across various industries, including healthcare, finance, marketing, and more. It powers recommendation systems, fraud detection algorithms, autonomous vehicles, and personalized customer experiences. #MachineLearning #AI #DataScience #ArtificialIntelligence #ML #DeepLearning #DataAnalytics #PredictiveAnalytics #Algorithm #BigData #NeuralNetworks #ComputerVision #NaturalLanguageProcessing #MLAlgorithms #AIinBusiness #Automation #MLModels #AIApplications #MLResearch #AIProgress

🚀 The AI Supercomputer Market is poised for exponential growth, projected to reach a staggering USD 45.3 billion by 2032! This surge is driven by the increasing demand for advanced computational capabilities to power AI-driven innovations across various industries. The rise in AI applications, from healthcare to autonomous vehicles, highlights the critical role of supercomputing in accelerating data processing and decision-making. As businesses and researchers continue to push the boundaries of what's possible with AI, the supercomputer market is set to be a cornerstone of future technological advancements. https://lnkd.in/g8gHGeeR 🌐💻 #AI #Supercomputing #Innovation #Technology #FutureTrends

🚗 Revolutionizing the Automotive Industry with Machine Learning 🤖 Machine learning (ML) is transforming the automotive industry, enhancing manufacturing processes, vehicle performance, and user experiences. Here's how ML is making a significant impact: - Autonomous Driving: ML processes data for object and lane detection, enabling autonomous vehicles. - Predictive Maintenance: ML predicts potential component failures, optimizing maintenance schedules. - Manufacturing and Quality Control: ML inspects parts for defects and enhances production efficiency. - Supply Chain Management: ML forecasts demand and optimizes logistics for cost and time efficiency. - In-Vehicle Experience: ML-driven systems offer intuitive interfaces and personalized settings. - Safety and Driver Assistance: ML powers features like adaptive cruise control and driver monitoring. - Energy Management: ML optimizes battery usage in electric vehicles and fuel efficiency in combustion engines. Implementation Steps: 1. Data Collection: Gather data from sensors and user interactions. 2. Data Preprocessing: Prepare data for training ML models. 3. Model Selection: Choose appropriate ML models for deployment. 4. Training and Validation: Train and validate models on historical data. 5. Deployment: Integrate models into existing systems. 6. Monitoring and Maintenance: Continuously retrain models with new data. Tools and Technologies: - Frameworks: TensorFlow, PyTorch, Scikit-learn, Keras - Platforms: AWS SageMaker, Google Cloud ML Engine, Microsoft Azure ML - Libraries: OpenCV for computer vision, NLTK for natural language processing Machine learning is driving efficiency, safety, and customer satisfaction in the automotive industry. The future of driving is here, powered by ML! 🚀 #MachineLearning #Automotive #Innovation #AI #AutonomousDriving #PredictiveMaintenance #SmartManufacturing #SupplyChain #InVehicleExperience #ADAS #EnergyManagement #FutureOfDriving Connect and share your thoughts on how ML is shaping the automotive industry! 👇

Last week (Nov 3-9) was packed with exciting developments in AI and robotics and I've put together a summary of some key announcements from champions, movers and shakers of AI community such as OpenAI, XPENG, Nvidia, Microsoft, Hume, and Anthropic. Here's an insight to the major updates that may interest you: 1. OpenAI has launched Predicted Outputs to cut down latency on GPT-4o and GPT-4o-mini models. By using reference strings, developers can now accelerate tasks such as document editing, response iteration, and code updates. This is a significant enhancement for real-time AI applications!👇👇 https://lnkd.in/dSXSKYH2 2. XPENG has unveiled Iron, a 5’10" humanoid robot weighing 153 lbs. This impressive robot is already operational in their vehicle factory, powered by XPENG’s in-house Turing AI chip. Chairman and CEO He Xiaopeng showcased XPENG’s latest breakthroughs, revealing advancements in AI vehicles, robotics, and flying cars. Highlights included the eagerly awaited XPENG Kunpeng Super Electric System and the Turing AI Intelligent Driving System. 👇👇👇 https://lnkd.in/dh6tfi9C 3. Nvidia has announced exciting new updates to Project GR00T, their all-encompassing humanoid robot development suite. The suite now features environment generation with over 2,500 3D assets, motion learning, and advanced dexterity training. All training is done in simulation and can be deployed to real robots. 👇👇👇 https://lnkd.in/dXkbwsS4 4. Microsoft has launched Magentic-One, an agent framework designed to coordinate multiple agents for real-world tasks. This versatile agent can write code, navigate a browser, and even place food orders from restaurants. 👇👇👇 https://lnkd.in/dhewfzZi 5. Hume AI has launched a new app featuring AI assistants powered by the EVI 2 speech model. By integrating Claude 3.5 models and web search, the app offers a range of services from quick answers to emotional support and storytelling, complete with voice generation and distinct personalities. 👇👇👇 https://lnkd.in/dSA7phMV 6. Anthropic has introduced Claude 3.5 Haiku on their API, Amazon Bedrock, and Google Cloud’s Vertex AI. This new model not only outperforms GPT-4o on SWE-bench Verified but also surpasses Claude 3 Opus on numerous benchmarks. 👇👇👇 https://lnkd.in/d3wPQ5DB This is it for the week! While a new week starts today, expect another exciting weekly updates on developments in the AI world next week. Have a great week ahead. See you next week! 👍👍👍 #artificiaintelligence #ai #aiworld #AI #responsibleai