Developing a cutting-edge data-driven methodology for identifying early signals of new trends & technologies.
Mapping the ecosystems & networks surrounding these key topics, evaluating their social, legal, technological, ethical & economic contexts.
Creating a value-driven vision for what the future internet could and should look like, involving a wide variety of voices across Europe.
Hover on a topic to show keywords and a short description
Artificial Intelligence and machine learning algorithms are among the most important computer science fields, with huge social implications. The top trending terms include both specific algorithms (e.g. reinforcement learning), tools (e.g. PyTorch) and also various controversial implementations, as deep fakes or Google’s project Maven. Moreover, AI and ML may be crucial in solving many social challenges, as in the case of the content crisis on social media.
Internet of Things, along with various related technologies (AR/VR), has large potential to transform consumer electronics and production systems as well (industrial IoT). On the other hand, IoT devices raise cybersecurity and privacy concerns (e.g. smart speakers).
Blockchain has been long regarded as a transformative technology with large disruptive potential. Blockchain technologies may play a central role in the future of social media, financial services and in other intermediation services. As of today, the most widespread implementation of blockchain is related to cryptocurrencies. As an emerging technology, blockchain raises pressing regulatory issues.
Quantum computing, although there are promising developments, is not likely to become a mature technology in the next few years. However, quantum computing provides an opportunity for Europe to regain its competitive edge in advanced technologies. Therefore, mapping of quantum technology areas and developments has large value added.
As discussions about the potential transformative impact of AI and Machine Learning have come to dominate public debate in recent years, so have concerns about the potential negative side-effects of allowing these kinds of technologies to play an ever-larger role in decision-making and the governing of our societies. The development of ethical AI and ML tools doesn’t only involve the use of responsibly managed data (make sure we have a representative sample, privacy and anonymity is ensured) and algorithms that don’t further existing societal biases (around gender and ethnicity, for example), but also that the tools themselves are used for purposes we consider ethically just. Ensuring we have solutions that are fair and inclusive along the value chain (from data generation to the impact of the decisions being made or tasks replaced).
Europe has been at the forefront of online regulations with GDPR, while the copyright directive (especially Article 11 and 13) has been more polarising among stakeholders. In the US, recent discussion has been focused on online content and Section 230 (platforms are not liable for the user generated content) or the controversial repeal of net neutrality rules.
The spread of fake news, misinformation and the decline of trust in reliable sources create a profound challenge for the functioning of democracies and societies. While regulating platforms or implementing advanced topic filtering algorithms are among possible solutions, bringing back trust to written words may be far more complicated.
The giants of digital economy (GAFA: Google, Amazon, Facebook and Apple) are all functioning as platforms with incredible market power. While the US has been less active in regulating market competition, e.g. in the case of Facebook acquisition of rival Instagram and Whatsapp, the EU is leading the discussion on ensuring competition in the Digital Single Market.
China has managed to build a vibrant ecosystem in such key technologies as AI or 5G. The increasing position of the Chinese tech sector has brought a momentous challenge for both Europe and the US. China may be the forerunner in developing advanced AI systems and 5G networks, while advocating an approach to citizen rights and privacy that is in stark contrast to European values.
This study presents an innovative methodology for analysing technology news using various text mining methods. News articles provide a rich source of information to track promising emerging technologies, relevant social challenges or policy issues. Our goal is to support the Next Generation Internet initiative by providing data science tools to map and analyse the developments of the tech word. Based on more than 200 000 articles from major media outlets, we are going to:
To meet these goals, a number of machine learning techniques are combined. The major steps can be summarised as follows:
The topics selected for the deep dives are:
The Policy topic groups together 3 areas: Social media crisis, Privacy and 5G.
Wide areas selected for deep-dive analyses
The 17 umbrella topics are identified using the topic modelling technique Latent Dirichlet Allocation. Besides the topics selected for deep dives, such areas are highlighted as Smartphones, CPU and other hardware, Digital ecosystems or Space.
Next, various maps are created based on the t-SNE algorithm. The example below presents the news stories in two-dimensions: articles that report on the same subject are clustered together. We demonstrate that this technique is highly useful to discover more narrow, domain-specific areas within the umbrella topics. Moreover, the distance between clusters is also meaningful, enabling the analysis of relationships between topics as well.
As an example, within the AI and robots topic, the map reveals groups of articles focused on such issues as:
It is also visible that articles on social and ethical issues are closer to each other, while articles on AI in self-driving cars are placed near business news on ride-sharing apps. It shows that our methodology is efficient in decreasing the complexity of text data, enabling to analyse and map topics.
All maps are interactive, inviting users to explore the headline of articles. Click here for an interactive version.
Social and economic aspects of robotics
Ethical aspects of AI
Self-driving cars technologies
The presented methodology provides intuitive, easily understandable results. To enhance the exploration of results, the study is presented as an interactive guide. This report has been designed with different readers in mind, offering various journeys. To analyse and understand the results, it is sufficient to read the introduction and results sections. We also prepared a guide briefly explaining various text-mining methods for anyone interested. Finally, detailed description of methods are included for proper reproducibility of the study in the methods section.Click to see topic modelling deliverable