“2100 AI: Reflections on the mechanisation of scientific discovery” is a paper submitted to the RE-CODING BLACK MIRROR Workshop co-located with the International Semantic Web Conference (ISWC) 2017, 21-25 October 2017, Vienna, Austria.
Authors
Andrea Mannocci, Angelo Salatino, Francesco Osborne and Enrico Motta
Abstract
The pace of nowadays research is hectic. Datasets and papers are produced and made available on the Web at a rate so unprecedented that digesting the information conveyed by such a “data deluge” stretches far beyond human analytical capabilities. Data science, artificial intelligence, machine learning and big data analytics are providing researchers with new methodologies capable of coping and getting insight in an automated fashion from the overload of information conveyed. Nonetheless major advances in AI solutions for knowledge discovery risk to exacerbate some negative phenomena, which are already observable on a global scale and disrupt irremediably the way of doing science as we know it.
“Supporting Springer Nature Editors by means of Semantic Technologies” is a research paper accepted to the Industry Track at the International Semantic Web Conference (ISWC) 2017 , 21-25 October 2017, Vienna, Austria.
Authors
Francesco Osborne, Angelo Salatino, Thiviyan Thanapalasingam, Aliaksandr Birukou and Enrico Motta
Abstract
The Open University and Springer Nature have been collaborating since 2015 in the development of an array of semantically-enhanced solutions supporting editors in i) classifying proceedings and other editorial products with respect to the relevant research areas and ii) taking informed decisions about their marketing strategy. These solutions include i) the Smart Topic API, which automatically maps keywords associated with published papers to semantically characterized topics, which are drawn from a very large and automatically-generated ontology of Computer Science topics; ii) the Smart Topic Miner, which helps editors to associate scholarly metadata to books; and iii) the Smart Book Recommender, which assists editors in deciding which editorial products should be marketed in a specific venue.
“Smart Book Recommender: A Semantic Recommendation Engine for Editorial Products” is a poster paper that will be presented at the International Semantic Web Conference (ISWC) 2017, 21-25 October 2017, Vienna, Austria. Authors Francesco Osborne, Thiviyan Thanapalasingam, Angelo Salatino, Aliaksandr Birukou and Enrico Motta Abstract Academic publishers, such as Springer Nature, need to constantly make informed decisions … Read more
Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy.Everyday activities are more and more shifting to a digital environment. Digital gadgets such as smartphones and werable devices are becoming inseparable part of our lives promising mostly convenience. New digital technologies have been mainly seen as empowering technologies for the users. FitBit, for example, is claimed to be a motivating device to lead a healthy and active life enabling users to achieve their goals analysing their data [1]. The data collected by this kind of devices include sleeping patterns, the number of steps, the amount of time they are engaged in physical activities and so forth. However, these data are not available just to the users but also to companies that can use them for multiple purposes. Health insurance companies, such as Vitality [2], already exploit their customers’ data in an exchange of rewards such as free tickets to the cinema or hot beverages. The potential implications of the collection and manipulation of personal data on a personal and societal level though have been downgraded. Just imagine a National Health insurance business model that operates on the basis of the classification of citizens as high- or low- risk based on their data [3]. Citizens profiled as low-risk will be granted with lower health contributions, while high-risk profiled citizens will be paying expensive and unaffordable plans.
Imagine a society where decisions on public well-being, education and so forth will be dependent on algorithmic predictions. Cathy O’Neil’s book Weapon of Math Destruction; How Big Data Increases Inequality and Threatens Democracy explores exactly these societal consequences emerging from the abuse of big data predictions.
O’Neil gives insights of how algorithms can be misused in the sake of convenience and cost efficiency resulting in practices of discrimination and bias, amplifying inequality and threatening ultimately Democracy. Her book is written for the lay public drawing though upon her academic expertise and her working experience in the financial sector. O’Neil after earning a PhD in Mathematics at Harvard, worked for the D. E. Shaw hedge fund when she initially felt a sense of disillusionment towards mathematics for their part in the financial crisis in the U.S. in 2008. The financial sector was relying on algorithmic models based on mathematical formulas that, using her words, “were more to impress than clarify”. It is when similar incomprehensible models got adopted into other sectors that she started investigating on the matter.
Being able to rapidly recognise new research trends is strategic for many stakeholders, including universities, institutional funding bodies, academic publishers and companies. The literature presents several approaches to identifying the emergence of new research topics, which rely on the assumption that the topic is already exhibiting a certain degree of popularity and consistently referred to by a community of researchers. However, detecting the emergence of a new research area at an embryonic stage, i.e., before the topic has been consistently labelled by a community of researchers and associated with a number of publications, is still an open challenge.
On 16th May 2017, the STEM Faculty of my university organised a 3 Minutes Thesis (3MT) in which each candidate has a time slot of three minutes to describe their thesis. The speech can be supported by one static slide showing important features of the work. I wish I had shown the one above. In … Read more
“How are topics born? Understanding the research dynamics preceding the emergence of new areas” is a peer-reviewed paper submitted to PeerJ Computer Science. The paper has been submitted in July 2016 and accepted in May 2017. All the co-authors are thankful to the reviewers and the editor for providing insightful comments and thus improving the … Read more
I have recently been attending in Bari (IT) a winter school about Big Data: BigDat2017. At the moment, Big Data is gaining great attention in research, since it allows to provide data-driven solutions in several contexts. As part of my postgraduate research I decided to attend it and follow the new developments in this field. … Read more
This week I have been attending the 3rd edition of the Big Data winter school: BigDat2017. It was held in my former campus, at the University of Bari (IT). It was a really nice feeling to be back for a while, sitting on those benches and following courses, once again.
Big Data has recently gained a lot of interest in research and many believe that it will still play its leading role for many years. Nowadays, we live in a world in which all information seems to be available, we are surrounded by data-driven applications (Google, Facebook, Twitter, Spotify, just to name a few), which gather data and try to provide tailor-made solutions for their users. To this end, having such event like BigDat2017 with its clear mission —introduce and update new researchers into this fast advancing research area—is really important.
On the 8th February I delivered a seminar to my department (KMi @ OU) in which I described the work I have been doing in the last two years for my postgraduate research.
I started with a little bit of introduction about science. Shortly, I moved to the currently available technologies for keeping track of the development of the different research areas. I showed how this technologies were not satisfactory enough if we want to perform an early detection of research topics.
In presenting, the state of the art (including The Structure of Scientific Revolution by Kuhn), I could state my main hypothesis, regarding the existence of an embryonic stage that research areas face, and that it is possible to detect their emergence during this stage1.
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