Episode 110

AI today is getting better at learning. In fact, learning is what differentiates what we call AI from advanced analytics. Machine learning algorithms minimize an error function by autonomously and iteratively adjusting their model variables. But what’s next for AI and machine learning?

Listen to this podcast (or read the transcript), where I speak with James Canton to hear predictions on where the predictive technology of machine learning will go in the Internet of Things.

The post Making Predictions About the Predictive Tech of ML appeared first on .

Episode 109

Convoluted Neural Networks or CNNs are a type of AI typically used in computer vision to process images, but they are also applicable to process the time series data we typically get from sensors in IoT.

Listen to this podcast (or read the transcript), where I speak with Simon Crosby about how these CNNs can be used to make predictions about the future and reduce the massive amounts of data we collect to just the important stuff.

The post Convolutional Neural Networks for IoT Time Series Data appeared first on .

Episode 108

AI and in particular, deep learning, is a powerful tool for uncovering useful relationships within data; but once found, can’t explain what they mean. Contrast this with humans, armed with tribal knowledge and more traditional analytics, who understand the data relationships but just can’t find as many of them.

Listen to this podcast (or read the transcript), where I speak with Drew Conway about how to find the balance between man and machine when looking for data value.

The post Balancing the Tension Between Informed Analytics and Blackbox AI appeared first on .

Episode 107

If you’re like me, before I started digging into AI, it all seemed so mysterious. Not only how it worked but also how it was put to work. But when thought of as a subset of analytics things come into focus – fast.

Listen to this podcast (or read the transcript), where I speak with Curtis Seare about the tools and frameworks used to incorporate AI into IoT projects.

The post Tools and Frameworks for Applying AI in IoT appeared first on .

Episode 106

Through its different techniques, machine learning allows us to look deep into our IoT data, giving us the hindsight, insight and foresight we need to transform that data into useful information, and ultimately value. But what’s the mechanism to do that?

Listen to this podcast (or read the transcript), where I speak with Vish Pai about the relationship between ML and the IoT platform.

The post Seeing Further into IoT Data with Machine Learning appeared first on .

Episode 105

When talking AI in IoT what we’re really talking about is machine learning in IoT, and the one thing machine learning needs above all else, is data. Lot’s and lots of data. Structured IoT data, when piped in properly can be transformed and loaded efficiently for machine learning to create beautiful, and more important, accurate models.

Listen to this podcast (or read the transcript), where I speak with Anand Rao about the symbiotic relationship between AI and IoT

The post AI & IoT – A Marriage Made in Data appeared first on .

Episode 104

Although AI has been around for over 60 years, it’s only been relatively recently that it’s been practical to apply it to real world problems such as those found in IoT. One, because the computational power is now available and two, because vast amounts of data are now available to train it.

Listen to this podcast (or read the transcript), where I speak with Richard Boire about the differences between artificial intelligence and analytics in the context of IoT

The post Comparing Machine Learning and Analytics in IoT appeared first on .

Episode 103

Is it just semantics or is there a real difference between artificial intelligence (AI) and analytics; between machine learning (ML) and AI; between deep learning (DL) and ML, and between analytics and DL? Well, it depends on how detailed you want to go.

Listen to this podcast (or read the transcript), where I speak with Bret Greenstein about using AI in IoT and how it’s different from using analytics

The post Illuminating the Enterprise’s Dark Data with AI in IoT appeared first on .

Episode 97

The technology of the software-defined product, containing the digital twin, is the most important technology in IoT – the most important because it’s directly related to creating value. Sometimes it can get a bit confusing.

Listen to this podcast (or read the transcript), where I share with you three lessons from the ICIP Technology course. The first, a simple example to demonstrate the software-defined product and digital twin. Then, an overview lesson on standards and protocols, and finally an overview lesson on IoT analytics and big data

The post Technology Lessons for the IoT Professional appeared first on .

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