Building AI models with data derived from IoT


You might ask what the difference is between most artificial intelligence (AI) companies and SparkCognition. Here it is: while at other firms, humans build models; SparkCognition puts them together with algorithms. Rather than roughing out one model and then doing a bunch of testing, SparkCognition continually tests and fits models to data accumulating in real time, an architecture that allows it to deal with big data.

But it’s not primarily a big-data company. It’s an AI company. Without foregone conclusions about what might be happening, SparkCognition algorithms keep probing for relationships and possible explanations without any a priori idea of what’s going on.

This fantastic flexibility, along with the speed of computer technology, allows SparkCognition to come to conclusions fast enough for real-time intervention.

The company’s early customers have mostly been infrastructure companies — oil and gas, pipeline and utility outfits — where sensors on expensive machinery generate a flood of data that SparkCognition interprets, looking for signs of potential failures, but its technology works equally well with cyber assets, which are also instrumented, but typically with virtual monitors like state bits and log files rather than with physical sensors. Thus, SparkCognition is an early player in analyzing the massive amount of data pouring off the Internet of Things (IoT).

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