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Episode Info:

On this IoT For All podcast episode, Dr. Roger Brooks, Chief Scientist at Guavus, defines artificial intelligence (AI), Machine Learning (ML) and Machine Intelligence (MI) and how each of these impact whether or not an IoT solution is successful.

Roger introduces us to Guavus and how they utilize AI, big data and ML to analyze time-series data at the edge. He explains how Guavus delivers monitoring use cases that measure key performance indicators (KPIs) and how these KPIs can determine whether or not devices will fail to allow robust optimized IoT solutions.

The episode concludes with a discussion about the influx of collected data from connected devices and how this data has changed the way service providers are seeking AI/ML help to organize and analyze it without manual involvement. Lastly, Roger shares what the future holds for AI and ML.

If you're interested in connecting with Roger, check out his LinkedIn!

About Guavus (a Thales company): Guavus is at the forefront of streaming big data analytics, artificial intelligence and machine learning innovation. Guavus processes half a trillion records every day for over 450 million individuals, enabling enterprises to analyze data the instant it’s captured, driving faster decision making, lower costs and higher growth.

Key Question and Topics from this Episode:

(6:15) What use cases does Guavus focus on? 

(12:14) How do you monitor devices and determine if they are going to fail? 

(14:57) What does a typical customer engagement look like for Guavus?

(20:43) How well do customers understand where ML fits into their solutions? 

(25:08) What is AI and ML? 

(32:45) What is Machine Intelligence? 

(34:43) How has the influx in data from connected devices changed the way communication service providers are using AI and ML to handle huge volumes of data into their network without having to manually get involved? 

(38:14) What role do AI and ML play in the success of an IoT solution? How does it influence the ROI? 

(41:30) What does the future of AI/ML look like over the next couple of years? 

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