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Awareness, Intention, Sentiment technologies in SAS – VI

A snapshot from the MIT Mood Detector showing in real time the level of excitement in various buildings on the MIT Campus. Mood detection in this case is based on counting smiles by analysing images flowing in from safety cameras on campus. Go and check the Mood Detector Live! Credit: MIT

Sentiment analyses usually refers to the analyses of Natural Language (including the sort of NL you find in SMS and Whatsapp) plus biometrics to identify and quantify the affective status of a person or a group of persons. A number of products, like IBM Watson Natural Language Understanding, are already on the market to support in text analyses aiming at sentiment detection. Interestingly a new boost of interest in sentiment analyses is coming from financial market evaluation, an area where the fleeting sentiment of investors leads to significant changes in the stock market. In this area blockchain technology is being considered to support sentiment analyses.

In Symbiotic Autonomous Systems it takes a broader view aiming at the analyses of the affective state of the system itself, of its components and of its environment.

NPL, Natural Language Processing is a cluster of technologies of its own that is largely benefitting from increased computational power and from huge mass of data. Machine Learning and Deep Learning can improve the NPL engine leading to the detection of subtle nuances in affective states. This goes beyond the polarity detection that is in many cases the object of sentiment analyses (to find if a community has a positive or negative feeling n a certain topic, from technical ones like writing software with certain tools to assessing the like or dislike in a political contest).

As machines will become more pervaded by artificial intelligence and in a way will assume unplanned behaviors (not in a negative sense, only in the factual sense of being self generated by AI in ways that have not been designed, like AlphaGo that played unexpected moves) it will become usual to associate “characters” to machines as it is done with humans (and other form of life) and this character might change depending on different situations. At that point it could make sense to apply sentiment analyses to machines as well.

In the shorter term humans will be (already are) conditioned by machines and sentiment analyses should take that into consideration. Today the relation with a machine may give rise to frustration, it does not work, or –sometimes- awe – it gives unexpected benefit. In the next decades as machines will match human behavior and intelligence the relation is bound to become much more complex and subtle.

In a symbiotic autonomous system with a human component, take as an example a person with an artificial limb, as the separation from the person component to the prosthetic components fades away (as it is bound to be the case with prosthetics that seamless integrate in the body, receive signals from the brain and provide sensory feedback to the brain) the sentiment analyses although targeting the human component has to take into account the whole system.

About Roberto Saracco

Roberto Saracco fell in love with technology and its implications long time ago. His background is in math and computer science. Until April 2017 he led the EIT Digital Italian Node and then was head of the Industrial Doctoral School of EIT Digital up to September 2018. Previously, up to December 2011 he was the Director of the Telecom Italia Future Centre in Venice, looking at the interplay of technology evolution, economics and society. At the turn of the century he led a World Bank-Infodev project to stimulate entrepreneurship in Latin America. He is a senior member of IEEE where he leads the Industry Advisory Board within the Future Directions Committee and co-chairs the Digital Reality Initiative. He teaches a Master course on Technology Forecasting and Market impact at the University of Trento. He has published over 100 papers in journals and magazines and 14 books.