Authors: Riccardo Biella and Paolo Veronesi, software engineers at Paradox Engineering
World is increasingly complex and interconnected, generating more data traffic than ever before. A recent IDC report predicts connected IoT devices could reach almost 75 billion globally by 2025 and generate about 79.4 zettabytes of data.
This huge amount of information contains an incredible value, but it must be stored, processed, analyzed, and correlated to unlock its full potential. Is human effort enough for this incredible task? Probably not. That’s why Artificial Intelligence (AI) is increasingly being leveraged to elaborate such a large and growing amount of data, with more accurate and faster results than traditional computer programming.
Some years ago, Andrew Moore, Former-Dean of the School of Computer Science at Carnegie Mellon University, defined AI as “the science and engineering of making computers behave in ways that, until recently, we thought required human intelligence”. AI technology is inspired by human behaviors and cognitive processes, but it also leverages knowledge from other biotic processes such DNA mutations or the chemical decay of substances.
Although often mentioned as a synonym, Machine Learning is something different: it is a branch of AI focused on developing computer algorithms and building applications that learn from data and automatically improve their accuracy over time through experience.
Examples of Machine Learning applications are all around us. Every evening, while driving home after work, digital assistants search the web and play music in response to our voice commands, our watches monitor our heartbeat, our cars keep us on track and suggest stopping for a coffee if we are too tired.
Human brains have the extraordinary skill to learn from experience and, thanks to Machine Learning, algorithms will more and more sharpen this ability and provide better results through information. How far can Machine Learning go? Difficult question, but one thing is sure: data play a key role since their quality and quantity influence the accuracy of results, so they are a truly critical element in any Machine Learning progress.
Traditional algorithms convert input to output using some given rules, while Machine Learning algorithms build a model based on sample data to make predictions without being explicitly programmed to do so.
Machine Learning offers the best performance when processing and analyzing large and high-dimensional volumes of data, making it particularly useful and interesting in the Internet of Things (IoT) world due to the large number of devices collecting and sharing non-homogeneous data. After going through a statistical analysis process, those data are a treasure trove for the development of new smart applications.
In an IoT network we can acknowledge three different layers, each one characterized by the availability of different resources and data where Machine Learning is applicable: IoT devices, edge devices and the cloud.
With the advent of new technologies, it is possible to integrate the usage of neural networks and Machine Learning techniques at all IoT levels to perform complex data analysis locally. IoT devices, such as sensors typically constrained, can locally process data without sending them over the network, reducing latency and avoiding security problems. Moving to the cloud, it is possible to carry out more complex analysis due to the greater computing power and leveraging data coming from different sources, thus opening the IoT world to new and highly innovative use cases.
At Paradox Engineering, we have integrated the most recent Machine Learning techniques into our IoT platform components, enabling valuable use of Machine Learning at different levels, from cloud to edge and IoT devices, making the most of their peculiarities and functionality.
Our IoT solutions have been extended with a Machine Learning development framework to allow these technologies to be used in multiple application cases in a simple and transparent way. Developers can easily integrate Machine Learning as enabler for new information-driven functionalities or to enhance existing ones.
Want to learn more? Contact our Machine Learning experts!