This article is written by Betty Feenstra, head of a department within the CIO office of the central public administration in the Netherlands. Around the world, public institutions are experimenting with these new technologies -- but with new tools also comes new questions. For example, how will we protect privacy and data rights? Throughout 2019 I have published a monthly newsletter about Data and AI in government. Governments increasingly use data and AI in their activities.

AILabPage defines – Artificial neural networks (ANNs) as "Biologically inspired computing code with the number of simple, highly interconnected processing elements for simulating (only an attempt) human brain working & to process information model".

It was the beginning of 2014 and I was in Hallstatt, Austria, on BNCI Horizon 2020 Retreat, event aimed to discuss the future of BCIs (brain-computer interfaces) with over 60 experts from the field. Among the hot topics was the state of brainwave reading by means of electroencephalography (EEG). The special thing about this event is that I had a "secret agenda": without announcing it, I brought up with me the just assembled first working small Smarting mobile EEG device AND the Android phone with the (first beta version of) application able to display signals in real time! It may not sound impressive -- but believe me, it was. No one had such ability (!) and it is still rather unique today.

Similar to that at the RSNA, AI developed at the University of Toronto and the Center for Addiction and Mental Health trained their Al deep learning algorithm with data from the Alzheimer's Disease Neuroimaging Initiative through the National Institutes of Health's National Institute on Aging using data from over 800 geriatric patients ranging from healthy to mild cognitive impairment to Alzheimer's disease. Their algorithm was found to be able to accurately predict cognitive decline leading to AD in cohorts by analyzing brain scans, clinical data, and genetics by up to 5 years before symptoms appear; research was published in PLOS Computational Biology.

Welcome to the 300th episode of the Robohub podcast! You might not know that the podcast has been going in one form or another for 14 years. Originally called "Talking Robots," the podcast was started in 2006 by Dario Floreano, now Director of the Laboratory of Intelligent Systems at EPFL in Switzerland, who started out interviewing his robotics Ph.D. students. Some of those students, alongside others, eventually took over the running of the podcast, then called "Robots Podcast", interviewing a number of researchers, entrepreneurs, and engineers involved in robotics. The official first episode of the Robots Podcast was published back in 2008 and is still available to listen to at robohub.org/podcast.

People give massive amounts of their personal data to companies every day and these data are used to generate tremendous business values. Some economists and politicians argue that people should be paid for their contributions--but the million-dollar question is: by how much? This article discusses methods proposed in our recent AISTATS and VLDB papers that attempt to answer this question in the machine learning context. This is joint work with David Dao, Boxin Wang, Frances Ann Hubis, Nezihe Merve Gurel, Nick Hynes, Bo Li, Ce Zhang, Costas J. Spanos, and Dawn Song, as well as a collaborative effort between UC Berkeley, ETH Zurich, and UIUC. More information about the work in our group can be found here.

Yes!!! Our Data Science and Artificial Intelligence Masterclass Cohort 1 will commence with three tracks for January -February 2020. Members who participate at our cohorts will have the opportunity of doing an internship programme with Reispar Analytics Academy.

We are only at the beginning of a rapid period of transformation of our economy and society due to the convergence of many digital technologies. Artificial Intelligence (AI) is central to this change and offers major opportunities to improve our lives. The recent developments in AI are the result of increased processing power, improvements in algorithms and the exponential growth in the volume and variety of digital data. Many applications of AI have started entering into our every-day lives, from machine translations, to image recognition, and music generation, and are increasingly deployed in industry, government, and commerce. Connected and autonomous vehicles, and AI-supported medical diagnostics are areas of application that will soon be commonplace.

The rise of populist politicians across the rich world has led to a profound rethinking of the way developed economies work. In particular, the impact of automation on the labor market, and the disappearance of routine manufacturing jobs, has been blamed for the electoral successes of leaders, such as US President Donald Trump and Italy's Matteo Salvini. Yet, there are profound differences in what determines the economic winners and losers on the two sides of the Atlantic. In the US, the main factor deciding whether a worker can prosper in the age of robots appears to be education. Conversely, in the European Union, it seems to be whether staff have strong protection in their employment contracts--as many older industrial workers do here. It would be foolish for any government to dissuade companies from investing in machines that are more productive.

In recent years researchers in the busy deep learning, computer vision and natural language processing communities have all become increasingly interested in vision and language (V&L). A compelling reason to study language and vision jointly is the promise of language as a universal and natural interface for visual reasoning problems -- useful in both specifying a wide range of problems and communicating AI responses. However, previous research in visually-grounded language understanding have been mostly task-specific. Researchers from the Facebook AI Research, Georgia Institute of Technology, and Oregon State University found that the skills required for different V&L tasks such as visual question answering and caption-based image retrieval overlap significantly, thanks mainly to the rise of V&L general architectures. The wide variety of independent V&L tasks motivated these researchers explore ways to consolidate some of them -- and the result of their efforts is an all-in-one model that learns from 12 supporting datasets of four broad categories of V&L tasks.