Just look at the reports on AI. When I asked one expert last week how many of these documents are out there, she said at least 120. The von der Leyen Commission's much anticipated White Paper dropped on February 19th. It follows the statement on AI in her own political guidelines, and the previous Commission's Communication on Artificial Intelligence for Europe from last spring. And it's accompanied by the Commission's Report on the safety and liability implications of Artificial Intelligence, the Internet of Things and robotics.

When the European Commission released the long awaited white paper "On Artificial Intelligence - A European approach to excellence and trust" on February 19, much of the initial public reaction focused on potential AI regulation further challenging the EU's position in light of fierce technological competition from China and the United States. Few discussed the European Commission's document mention of gender and ethical guidelines. Importantly, the white paper calls for "requirements to take reasonable measures aimed at ensuring that [the] use of AI systems does not lead to outcomes entailing prohibited discrimination." This is not simply about a theoretical approach to discrimination. It is largely also about saving (women's) lives - and ensuring that essential products and services meet the needs of both women and men.

One area that the Commission is particularly concerned about is facial recognition. At the moment, the processing of biometric data in order to identify people is illegal in most cases, under data privacy laws. However, the EU is now looking at whether there should be certain exceptions. Speaking to journalists in Brussels, Margrethe Vestager, the EU's head of competition policy, said: "Artificial intelligence is not good or bad in itself, it all depends on why and how it is used." In an exclusive interview with CNBC Tuesday, Vestager said that the EU is taking a "double-sided" approach where it will enable this technology, while also ensuring it's not harmful to EU citizens.

The golden age for artificial intelligence may have just dawned, but the course is not without its challenges. A plethora of technology glitches seems to indicate that it is not quite there yet. Perhaps machines cannot be not perfect either. Although AI is meant to solve problems, as it turns out, it can create new ones as well. These accounts may alarm or amuse consumers but are very embarrassing for the companies involved.

We describe a new approach to estimating relative risks in time-to-event prediction problems with censored data in a fully parametric manner. Our approach does not require making strong assumptions of constant baseline hazard of the underlying survival distribution, as required by the Cox-proportional hazard model. By jointly learning deep nonlinear representations of the input covariates, we demonstrate the benefits of our approach when used to estimate survival risks through extensive experimentation on multiple real world datasets with different levels of censoring. We further demonstrate advantages of our model in the competing risks scenario. To the best of our knowledge, this is the first work involving fully parametric estimation of survival times with competing risks in the presence of censoring.

High-dimensional observations and unknown dynamics are major challenges when applying optimal control to many real-world decision making tasks. The Learning Controllable Embedding (LCE) framework addresses these challenges by embedding the observations into a lower dimensional latent space, estimating the latent dynamics, and then performing control directly in the latent space. To ensure the learned latent dynamics are predictive of next-observations, all existing LCE approaches decode back into the observation space and explicitly perform next-observation prediction---a challenging high-dimensional task that furthermore introduces a large number of nuisance parameters (i.e., the decoder) which are discarded during control. In this paper, we propose a novel information-theoretic LCE approach and show theoretically that explicit next-observation prediction can be replaced with predictive coding. We then use predictive coding to develop a decoder-free LCE model whose latent dynamics are amenable to locally-linear control. Extensive experiments on benchmark tasks show that our model reliably learns a controllable latent space that leads to superior performance when compared with state-of-the-art LCE baselines.

Artificial Moral Agents (AMA's) is a field in computer science with the purpose of creating autonomous machines that can make moral decisions akin to how humans do. Researchers have proposed theoretical means of creating such machines, while philosophers have made arguments as to how these machines ought to behave, or whether they should even exist. Of the currently theorised AMA's, all research and design has been done with either none or at most one specified normative ethical theory as basis. This is problematic because it narrows down the AMA's functional ability and versatility which in turn causes moral outcomes that a limited number of people agree with (thereby undermining an AMA's ability to be moral in a human sense). As solution we design a three-layer model for general normative ethical theories that can be used to serialise the ethical views of people and businesses for an AMA to use during reasoning. Four specific ethical norms (Kantianism, divine command theory, utilitarianism, and egoism) were modelled and evaluated as proof of concept for normative modelling. Furthermore, all models were serialised to XML/XSD as proof of support for computerisation.

While AI and robots are changing jobs in many industries, there are some positions that will always require human creativity and judgment. Automation and artificial intelligence are prominent buzzwords that are getting integrated in everyday life more and more with each passing year. The rise of automation and AI has allowed machines to supplant humans in certain tasks and make processes more efficient. However, there are still many jobs deemed incompatible with automation. Featured below are some of the occupations unlikely to be occupied by robots or machines, at least in the foreseeable future. Robots are used in medicine including surgeries.

Since this article will probably come out during Income tax season, let me start with the following example: Suppose we would like to build a program that calculates income tax for people. According to US federal income tax rules: "For single filers, all income less than $9,875 is subject to a 10% tax rate. Therefore, if you have $9,900 in taxable income, the first $9,875 is subject to the 10% rate and the remaining $25 is subject to the tax rate of the next bracket (12%)". This is an example of rules or an algorithm (set of instructions) for a computer. Let's look at this from a formal, pragmatic point of view. A computer equipped with this program can achieve the goal (calculate tax) without human help.

Everything starts with a good business case that aligns with urban strategy. Business cases should start with the needs of the citizen or the business.