Voice-activated home and travel assistants, intelligent prosthesis, augmented reality … Digital transformation is reaching every single corner of our daily lives. What makes all these electronic devices so desirable? – the back-end.
The theoretical concepts underlying AI are empowering conventional design and manufacturing with “cognitive” attributes. Several aspects of digital innovation can be here discussed. However, looking at the world surrounding us, I’ve chosen to focus on the large amount of data humans produce every minute.
According to the Met Office, the UK Historic Station Data database counts on climate time series records ranging from 50 to 100 years in length – plenty of unstructured data for intelligent algorithms to explore. Similarly, the UK climate projections from 2009 (UKCP09) reports provide both quantitative and qualitative data illustrating how the UK’s climate could vary in the 21st century, in response to the human activity. This includes the increasing emission of atmospheric pollutants generated by man.
How to govern all this varied data efficiently?
The mathematical foundations of AI technology may date from 14th-17th centuries [Gil Press, “A Very Short History Of Artificial Intelligence (AI)”, Forbes, 2016], with conceptual correlation and combinatorics being idealised. I would go even deeper in the past, referring to the foundations of the Method of Archimedes for deriving the volume of different geometric structures via correlation. In that, decomposition and complexity reduction played a fundamental role.
“Essentially, Archimedes considered an n-dimensional figure to consist of an infinite number of n-1-dimensional cross sections;” [Jeff Suzuki, “Mathematics in Historical Context”, The Mathematical Association of America, 2009]
Archimedes decomposed the initially n-dimensional problem into sub-problems, which were less complex in nature; with the volume of the resulting figures being potentially known. This can potentially be associated with the “divide & conquer” paradigm, in its very basic premises. The resulting method is broadly applied in efficient recursive algorithms; being explored in inductive rule learning processes, over the last 2 decades.
Since its foundation, the AI paradigm has grown, matured, and are nowadays intrinsically immersed in almost all corners of disruptive technology. AI methods are used in data and insights discovery, automatically searching for relevant patterns on which to support both learning – e.g. in machine learning; and knowledge generation – e.g. in natural language processing-based machine learning. AI is backing several technological branches. Voice analysis and intelligent searches (data correlation), along with time-series generation can be found in numerous gadgets, as home and travel assistants, which are pieces of hardware designed to follow voice command, performing searches that result in either query responses or the definition of actions to be taken. In robotics, AI is promising. The paradigm connects patterns learned, reinforce or award successful decisions, and classify inputs, to control behavior. The more we progress, the more data we produce, the more complex the systems we seek to mimic, the more technology evolves. However, it worth stressing that epigenetics and evolution [Charles Darwin, “On the Origin of Species by Means of Natural Selection”, John Murray, 1859] work in parallel, promoting “disruptive biogenesis”.
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