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Advances in machine learning and NLP have cracked the flood gates open and AI is now emerging as the definitive transformational technology of our times. A recent IDG study found 93% of IT professionals have already deployed or started to explore AI to augment their ITSM and ITOM modernization efforts. Clearly, AI is making its way into our workplaces faster than we imagined.
The ‘Internet of Things’ was first coined to help people understand the concept of digital appliances that could communicate with an app or central hub. If you’ve been to CES at any point in the last decade, you could see many examples on the show floor, such as connected weight scales or a connected fridge. But these early examples of ‘connected’ tech felt more gimmicky than useful, and were largely contained to the consumer electronics industry.
We've found a pattern to mock external client libraries while keeping code simple, reducing the number of injection spots and ensuring all the code down a callstack uses the same mock client. Establishing patterns like these is what makes test suites great, and improves developer productivity when writing tests. Here's how it works.
At re:Invent this year, AWS announced its new digital twin service, AWS IoT TwinMaker (in preview), which allows users to create digital twins of real-world systems like buildings, factories, industrial equipment, and production lines. Using a digital twin to monitor and improve operations for a physical system requires ingesting data from IoT sensors, process instruments, cameras, and enterprise systems, and curating and associating data from these disparate sources.
Researchers discovered that diagnostic artificial intelligence models used to detect cancer were fooled by cyberattacks that falsify medical images. Diagnostic artificial intelligence (AI) models hold promise in clinical research, but a new study conducted by University of Pittsburgh researchers and published in Nature Communications found that cyberattacks using falsified medical images could fool AI models.
Buckle up, this one isn’t short…but I’m hoping it will be thoroughly informative! This post is about Telegraf as a consumer of MQTT messages in the context of writing them to InfluxDB. If you are interested in and unfamiliar with Telegraf, you can view docs here. Unsure if Telegraf aligns with your needs? I make a case for it in the Optimizing Writes section of this blog post. It may also help to have an understanding of Line Protocol, InfluxDB’s default accepted format.
It’s true that AI and machine learning have already provided us with some opportunities to transform entrenched methods of recording and monitoring communications in regulated industries. However, to date, most companies’ injection of AI has been limited and solutions have been piecemeal. But that’s all about to change as the rapid expansion in the applications of AI in compliance is just around the corner.