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FREMONT, CA: AI and IoT are technologies that are becoming popular, with many businesses already embracing them and investing in making them, a part of their products and procedures. Both techniques have a broad variety of applications, ranging from private use to optimizing business activities. The two technologies together are sure to transform the world.
The device's complexity has nothing to do with its IoT classification. The device may be as easy as a thermometer, float switch, or as complicated as a gas chromatograph. The critical factor is that it is directly or indirectly linked, to the Internet. Directly connected is quite apparent— it could be linked via a conventional ethernet cable, wifi, or any other traditional web interface, while the later one connects to a gateway using techniques such as Bluetooth or Zigbee. There are several explanations for the migration of data processing to the IoT computer as the processor: one is to assist in restricting network traffic as an IoT device can produce a vast quantity of information. Compared to the information streaming from all the IoT systems being tracked. The other is that the data's value is highly transient. In other words, it is necessary to process the information instantly, or its value falls to nothing. The AI system could be used to streamline product output by installing IoT equipment to monitor all reactor circumstances that might influence the process, such as temperatures, pressures, and flow rates.
When it comes to evaluating the information being gathered, AI makes the machines much "smarter." This removes the need for experts to run the tools and analyze the data in all but rare cases. Another critical use of AI and IoT would be in multi-omics, where information from gas chromatographic-mass spectrometers and liquid chromatographic-mass spectrometers or other device combinations can be used to visualize the considerable quantity of data generated in proteomics, metabolomics, and flux analysis testing.
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The combination of the IoT and AI umbrellas indicates how one can speed up the analysis of complex data without the need of a field expert, while at the same time handling massive amounts of experimental data to extract meaning from data bits and provide different ways for imaging information.