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The dynamics between AI and IoT

How does Artificial Intelligence apply to the Internet of Things? #AI #IoT.

  • We see the need for a new type of Engineer who will combine knowledge from Electronics & IoT with Machine learning, AI, Robotics, Cloud and Data management (devops).
  • AI / Machine Learning for Data Science isfar more than applying predictive algorithms to an IoT dataset.
  • Indeed, as we shall see below:  I believe that we are seeing the need for a new type of Engineer who will combine learnings from Electronics(IoT) with  Machine learning, AI, Robotics, Cloud  and Data management (devops)

    AI is based on Deep Learning algorithms.

  • With this background,let us see how these ideas apply to IoT

    I have written previously about the Ten ways Data Science for IoT is different from traditional Data Science

    By considering more complex decisions (many more influencers), AI techniques extend traditional Machine learning strategies(like Anomaly detection).

  • The domains most likely to be impacted by the deployment of AI with IoT include: Automotive – Self driving cars; Smart cities and Cloud robotics

    We are seeing the need for a new type of Engineer  who will combine  learnings from Electronics(IoT) with  Machine learning, AI, Robotics, Cloud  and Data management (devops).


We see the need for a new type of Engineer who will combine knowledge from Electronics & IoT with Machine learning, AI, Robotics, Cloud and Data management (devops).


@Wipro: How does Artificial Intelligence apply to the Internet of Things? #AI #IoT.

How does Artificial Intelligence (AI)  apply to Internet of Things(IoT)?

AI / Machine Learning for Data Science isfar more than applying predictive algorithms to an IoT dataset. Indeed, as we shall see below:  I believe that we are seeing the need for a new type of Engineer who will combine learnings from Electronics(IoT) with  Machine learning, AI, Robotics, Cloud  and Data management (devops)

AI is based on Deep Learning algorithms. Deep Learning involves automatic feature detection from data. AI techniques can be applied to a range of Data types including: Images and sound (CNNs), Transactional data, Sequences (LSTMs), Text (Natural Language Processing) and Behaviour (Reinforcement learning). With this background,let us see how these ideas apply to IoT

AI techniques extend machine learning strategies in four ways:

By considering more complex decisions (many more influencers), AI techniques extend traditional Machine learning strategies(like Anomaly detection). We have to now address questions such as:

This involves implementation of models like the Digital Twin and Continuous learning. The domains most likely to be impacted by the deployment of AI with IoT include: Automotive – Self driving cars; Smart cities and Cloud robotics

We are seeing the need for a new type of Engineer  who will combine  learnings from Electronics(IoT) with  Machine learning, AI, Robotics, Cloud  and Data management (devops). This blog is based on my teaching. I teach a course on Implementing Enterprise AI and also teach Data Science for IoT at the University of Oxford.

The dynamics between AI and IoT