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Edge AI is becoming increasingly important with the Internet of Things (IoT) boom, but security challenges are making it harder for companies to adopt edge editing. As the size of the IoT ecosystem has increased, so have the risks.
Edge AI has an advantage over cloud-based AI, said Celeste Freelick, chief data scientist at MAC Fee, to Fahmida Rashid, VentureBet’s executive editor, during a session of VentureBet’s Transform 2021 Summit. Backing up, processing, and then bringing down large amounts of data collected from various sensors to the cloud is costly and time consuming.
In the case of a farmer on a farm where there is no wireless network, getting online to access the cloud application and process the data from the sensor would not work well. But the same AI runs locally, so the same farmer can access the answers directly from the field. Edge AIA also avoids some of the privacy concerns associated with the cloud – which is especially important in areas such as health care.
“The virtues of the cloud, of course, are price, reliability and the ability to merge information very quickly,” Freck said. “But you also have the cons of the cloud, where you have the issue of your data privacy and security not as complete as you would like it to be with some companies.”
There is one particular disadvantage to edging AI, Freelick said, is that IoT devices – especially edge devices – Brittle. If the devices have a security flaw, the manufacturer is the one who needs to fix it.
“The risk on the edge is that it’s a lot more brittle and it’s more complex than it would be in the cloud. So your security risk is a lot higher,” Freelick said.
Due to the nature of Edge AI and IoT, it makes sense to keep your routers safe. “If you’re a consumer, you always have to make sure you’re updating your software,” Freelick said. “Of course, if you are an enterprise, a business, you have to constantly update your software. That is one of the most important things we can do. And as a home customer, making sure you have security on your router is also very important. “
Data Management and A.I.
People need to understand that there are different types of AI. “[You have] Simple statistics and machine learning, you have a deep education, and you definitely have neural language processing, ”said Freelick. At large conventions, Freckle was frequently asked to contact other booths to find out what type of AI was in their product. If anyone at the booth tried to claim 100% accuracy, Freelick knew they didn’t really understand AI.
“Make sure you have a monitor during your development process and go for 100% accuracy as much as you can. But the most important thing is to understand why you have false positives and false negatives, and to understand the root causes of why you have them, ”said Frilick.
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