The dynamics between AI and IoT
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
Impact of AI on IoT
AI techniques extend machine learning strategies in four ways:
- Complex decisions based on detecting a large number of hidden or hierarchical influencers
- Self learning
- Autonomous decision making
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:
- What decisions are suited for AI?
- Where can they be made?
- How can the decisions be made? (mechanism)
- How can they be propagated?
- What is the Impact?
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