Conditions of employment and salary are based on the Dutch Collective Labour Agreement for Hospitals (CAO-Ziekenhuizen). You will receive a fulltime contract (36 hours/week) for an initial period of one year. Your salary will be according to the salary scale FWG 50 (starting with € 2.336,- depending on your relevant experience). Within the collective labor agreement, there is an extensive package of fringe benefits, including a good pension arrangement, a 8.33% holiday allowance and end-of-year bonus and an excellent pension provision. In addition, MAASTRO offers various discount schemes with regard to (healthcare) insurance, bicycle purchase and sports subscriptions.

This paper applies a genetic algorithm and fuzzy markup language to construct a human and smart machine cooperative learning system on game of Go. The genetic fuzzy markup language (GFML)-based Robot Agent can work on various kinds of robots, including Palro, Pepper, and TMUs robots. We use the parameters of FAIR open source Darkforest and OpenGo AI bots to construct the knowledge base of Open Go Darkforest (OGD) cloud platform for student learning on the Internet. In addition, we adopt the data from AlphaGo Master sixty online games as the training data to construct the knowledge base and rule base of the co-learning system. First, the Darkforest predicts the win rate based on various simulation numbers and matching rates for each game on OGD platform, then the win rate of OpenGo is as the final desired output. The experimental results show that the proposed approach can improve knowledge base and rule base of the prediction ability based on Darkforest and OpenGo AI bot with various simulation numbers.

Last week I attended the Oracle Open World Europe 2019 in London. At this event Andrew Sutherland VP of technology told us that security was one of the main reasons why customers were choosing the Oracle autonomous database. This is interesting for two reasons firstly it shows that security is now top of mind amongst the buyers of IT systems and secondly that buyers have more faith in technology than their own efforts. The first of these reasons is not surprising. The number of large data breaches disclosed by organizations continues to grow and enterprise databases contain the most valuable data.

"You've got a perfect storm, really," says Rose Luckin, a professor at University College London who has studied AIEd for the past 20 years. "You can do things that you weren't able to do before." AIEd now helps investigate the steps students go through when learning subjects from calculus to chemistry, shining a light on what individual learners need to progress. To get there, an AI program is first trained on hundreds or thousands of students' work, gaining a knowledge base of the common areas that give learners trouble. Then over time, as an individual uses the system, the AI homes in on specifics to focus on, usually offering bespoke lessons to brush up on skills, and, in some cases, offer pep talks through bots.

There is a disconnect between explanatory artificial intelligence (XAI) methods and the types of explanations that are useful for and demanded by society (policy makers, government officials, etc.) Questions that experts in artificial intelligence (AI) ask opaque systems provide inside explanations, focused on debugging, reliability, and validation. These are different from those that society will ask of these systems to build trust and confidence in their decisions. Although explanatory AI systems can answer many questions that experts desire, they often don't explain why they made decisions in a way that is precise (true to the model) and understandable to humans. These outside explanations can be used to build trust, comply with regulatory and policy changes, and act as external validation. In this paper, we focus on XAI methods for deep neural networks (DNNs) because of DNNs' use in decision-making and inherent opacity. We explore the types of questions that explanatory DNN systems can answer and discuss challenges in building explanatory systems that provide outside explanations for societal requirements and benefit.

A US federal judge has blocked new Trump administration regulations on birth control from applying across the entire country. The rules allow employers and insurers to decline to provide birth control if doing so violates their "religious beliefs" or "moral convictions". The rules were to come into effect nationwide from Monday. But the judge in Philadelphia granted an injunction requested by attorneys general in Pennsylvania and New Jersey. Judge Wendy Beetlestone ruled that the new rules would make it more difficult for many women to obtain free contraception and would be an undue burden on US states. Her decision follows a similar verdict by a judge in California on Sunday.

A federal health official says a nationwide injunction on new Trump administration rules that allowed more employers to opt out of providing women with no-cost birth control will force Americans to "violate" their consciences.

U.S. District Judge Wendy Beetlestone in Philadelphia issued a nationwide injunction preventing the rules from taking effect, a day after another judge issued a more limited ruling blocking their enforcement in 13 states and the District of Columbia.

U.S. District Judge Haywood Gilliam in Oakland granted a request by 14 Democratic attorneys general for a preliminary injunction. The rules, which are set to go into effect Jan. 14, allow businesses or nonprofits to obtain exemptions to an Obamacare requirement for contraceptive coverage on moral or religious grounds.

Cognitive scientists of the embodied cognition tradition have been providing evidence that a large part of our creative reasoning and problemsolving processes are carried out by means of conceptual metaphor and blending, grounded on our bodily experience with the world. In this talk I shall aim at fleshing out a mathematical model that has been proposed in the last decades for expressing and exploring conceptual metaphor and blending with greater precision than has previously been done. In particular, I shall focus on the notion of aptness of a metaphor or blend and on the validity of metaphorical entailment. Towards this end, I shall use a generalisation of the category-theoretic notion of colimit for modelling conceptual metaphor and blending in combination with the idea of reasoning at a distance as modelled in the Barwise-Seligman theory of information flow. I shall illustrate the adequacy of the proposed model with an example of creative reasoning about space and time for solving a classical brainteaser. Furthermore, I shall argue for the potential applicability of such mathematical model for ontology engineering, computational creativity, and problem-solving in general.