Events:
Smart Manufacturing & Industrial IOT Summit
- Mon, Mar 13 – Tue, Mar 14
- London, UK
LoRaWAN
- Mon, Mar 13 – Wed, Mar 15
- Orlando International Airport, 1 Jeff Fuqua Blvd
- Orlando, FL
embedded world 2023
- Tue, Mar 14 – Thu, Mar 16
- NürnbergMesse GmbH, Messezentrum 1
- Nuremberg, Germany
- Smart City Expo - Australia
- Tue, Mar 14 – Wed, Mar 15
- Australian National Maritime Museum, 2 Murray St
- Sydney NSW, Australia
2023 Conexpo-Con/Agg
- Tue, Mar 14 – Sat, Mar 18
- Las Vegas Convention Center, 3150 Paradise Rd
- Las Vegas, NV
BIoT 2023: International Conference on Blockchain...
- Sat, Mar 18 – Sun, Mar 19
- Vienna
- Vienna, Austria
https://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=171135©ownerid=46167
Industry of Things World USA
- Sun, Mar 19 – Tue, Mar 21
- Paradise Point Resort & Spa, 1404 Vacation Rd
- San Diego, CA
NEWS
Technology: Merging IoT and AI
IoT and AI can merge into one solution
IoT and AI can merge into one solution by combining IoT devices and sensors with AI algorithms and machine learning models.
Here are some ways in which this integration can happen:
- Edge computing: IoT devices can collect vast amounts of data from sensors and other sources, but sending all of this data to a central server for processing can be impractical. By using edge computing, AI algorithms can be run directly on the IoT devices themselves, enabling real-time analysis and decision-making at the edge of the network.
- Cloud computing: IoT devices can also send data to the cloud for processing, where AI algorithms can be used to analyze the data and derive insights. This can enable more complex analysis, as well as the ability to combine data from multiple sources.
- Machine learning: IoT devices can be used to collect training data for machine learning models, which can then be used to make predictions or automate decision-making. For example, machine learning models can be trained on sensor data from manufacturing equipment to predict when maintenance is required.
- Natural language processing: IoT devices can be controlled using natural language commands, and AI algorithms can be used to process and interpret these commands. This can enable more intuitive and user-friendly interfaces, as well as the ability to automate certain tasks based on user input.
