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contents/introduction/introduction.qmd

@@ -14,7 +14,7 @@ In the early 1990s, [Mark Weiser](https://en.wikipedia.org/wiki/Mark_Weiser), a
 
 In the vision of ubiquitous computing [@weiser1991computer], the integration of processors into everyday objects is just one aspect of a larger paradigm shift. The true essence of this vision lies in creating an intelligent environment that can anticipate our needs and act on our behalf, enhancing our experiences without requiring explicit commands. To achieve this level of pervasive intelligence, it is crucial to develop and deploy machine learning systems that span the entire ecosystem, from the cloud to the edge and even to the tiniest IoT devices.
 
-By distributing machine learning capabilities across the computing continuum, we can harness the strengths of each layer while mitigating their limitations. The cloud, with its vast computational resources and storage capacity, is ideal for training complex models on large datasets and performing resource-intensive tasks. Edge devices, such as gateways and smartphones, can process data locally, enabling faster response times, improved privacy, and reduced bandwidth requirements. Finally, the tiniest IoT devices, equipped with machine learning capabilities, can make quick decisions based on sensor data, enabling highly responsive and efficient systems.
+By distributing machine learning capabilities across the "computing continuum," from cloud to edge to embedded systems that surround us, we can harness the strengths of each layer while mitigating their limitations. The cloud, with its vast computational resources and storage capacity, is ideal for training complex models on large datasets and performing resource-intensive tasks. Edge devices, such as gateways and smartphones, can process data locally, enabling faster response times, improved privacy, and reduced bandwidth requirements. Finally, the tiniest IoT devices, equipped with machine learning capabilities, can make quick decisions based on sensor data, enabling highly responsive and efficient systems.
 
 This distributed intelligence is particularly crucial for applications that require real-time processing, such as autonomous vehicles, industrial automation, and smart healthcare. By processing data at the most appropriate layer of the computing continuum, we can ensure that decisions are made quickly and accurately, without relying on constant communication with a central server.