Recent blog posts
Responsible Artificial Intelligence in Practice
The ECAAI Responsible AI Lab The need for a responsible approach to AI has been recognized worldwide as reflected by the many manifestos and ethical guidelines that have been developed in the last few years [1]. The European Union for example, calls for Trustworthy AI [2] and defines a number of key requirements such as […]
AI for Social Good & Equality of Opportunity
On 25 September 2015 at the annual UN meeting, 193 countries signed an agreement on the 17 Sustainable Development Goals (SDG) and 169 associated targets. These goals aim to move us towards an inclusive, just and sustainable society by 2030. The first goal is to end extreme poverty. Other goals focus on health, education, sustainable […]
Europe needs its own Google!
The pros and cons of the existing Big Tech elite The US and Chinese Big Tech elite deliver us many benefits. Not only have they been a driver of innovation, but also a creator of thousands of jobs. The technology that has come out of these companies has enabled us to stay in touch with […]
Why we need the UvA Data Science Centre
Accelerating science through data As scientists and researchers, we are already immersed in what Turing Award winner Jim Gray termed the 4th paradigm: the use of data-intensive techniques to attack the hardest problems in research. This can clearly be seen in how researchers have tackled the many, sometimes unexpected, problems that have arisen from the […]
AI Research with China: to Collaborate or not to Collaborate – is that the Question?
The opinions in this blog item are the author’s own and do not necessarily reflect those of the organisations she represents. Amsterdam has a historically strong connection with Chinese culture, housing one of the oldest Chinatowns in the Netherlands. While our perception of Chinese culture is perhaps based predominantly on its cuisine, we have to […]
AI & Health in Amsterdam
This is surprising, given that the amount of data collected in the medical domain has been, and still is, increasing at a rapid pace. Of course, the need for a cautious approach, as people’s health is at stake, no doubt plays a role, but there is more to it than that. Challenges in using AI […]
Linked Data Innovation for a Smarter Government
Today is Prinsjesdag, the day that the Miljoenennota, the Dutch government’s yearly national budget is published. This budget determines how government funds will be allocated to achieve societal goals in the coming year. The Miljoenennota initiates the budget implementation cycle which consists of various phases: spending, adjusting, auditing and reporting. During this cycle, the Dutch […]
Increasing Data Science and AI Diversity
Amsterdam is home to 180 different nationalities and 45% of the population belong to ethnic minorities [1]. The region is a melting pot of different cultures, bringing together people with different socio-economic backgrounds, religions, (dis-)abilities, sexual preferences, genders, and ages. This diversity is palpable, where one can hear numerous languages on the metro, taste food […]
Safe & Flexible Testing of COVID-19 Treatments: flexibly combining results from many trials around the world
The BCG Trials The BCG vaccine has been in use since the 1920s as an effective vaccination against tuberculosis. There is substantial evidence that it provides a temporary boost of the immune system. Therefore, many virologists think that, within a limited period after vaccination, the chance of a (serious) corona infection would go down substantially. […]
A Practitioner’s Perspective on Fairness in AI
The use of machine learning models to support decision making can have harmful consequences on individuals, particularly, when such models are used in critical applications in the social domain such as justice, health, education and resource allocation (e.g. welfare). These harms can be caused by different types of biases that can originate at any point […]
Estimating Reproducibility of AI Research
There are a number of different ways to refer to reproducibility, in this piece we are actually referring to replicability using the standard ACM definition. It refers to research that reuses the data and/or analysis to hopefully get the same results. To this end, replication is considered to be a cornerstone of wider scientific reproducibility […]
Collaborative data analysis using SWISH DataLab
SWISH unites SWI-Prolog and R together behind a web based IDE that resembles Jupyter notebooks. The platform allows multiple data scientists to work on the same data simultaneously while rule sets can be reused and shared between users. This facilitates data scientists to provide more complex data transformation steps to domain experts. Most pipelines use […]
AI Technology for People
Amsterdam Data Science (ADS) has created a tremendous network in which academia, companies, and the municipality come together in meetups and other events. Furthermore, its newsletter reports news from the community, and ADS seed projects encourage collaboration between research and companies. These are powerful means to build an ecosystem around data science, a field in […]
The Data Studio: Innovating How We Learn and Teach Data Science and AI
The Data Studio, funded by a KNAW Comenius Senior Fellow grant, at the Amsterdam University of Applied Science is innovating education programmes for data scientists. We see this innovation as a key element in meeting the needs of students in the fields of Data Science and AI. The Data Studio Co-creation is the foundation of […]
The Role of Corporates in AI Ecosystems
Perhaps the most important role of international corporations in a regional ecosystem is to attract and retain talent. A number of my top PhD students and postdocs have decided to stay in Amsterdam because of the presence of the international giants, working at, for instance, Google Brain or Qualcomm AI Research. The presence of these […]
The Quest for Hybrid Intelligence
These are just two of the predictions of the type that have flooded the media over the past five years. Besides many (if not all) of them being wildly overoptimistic, they also reveal a hidden (and sometimes not so hidden) assumption underlying much mainstream work in current AI: artificially intelligent machines were going to replace […]
