In a newly published thought leadership paper from SAP entitled "European Prosperity Through Human-Centric Artificial Intelligence," prepared by Andreas Tegge, head Global Public Policy, SAP addresses some anxieties and proposes measures for ensuring AI's rapid assimilation and advancement in Europe. It is probably fair to say that no other technology currently inspires such fascination nor stirs up such heated debate as AI and the branch of AI known as machine learning. Algorithms that are capable of autonomously gaining insight from data without being explicitly programmed are already empowering machines to see, read, listen, and interact. In the intelligent enterprise, machine learning allows better and more efficient processes, which means that enterprises benefit from increased productivity levels and employees have more time to devote to tasks that add greater value. Machine learning will one day have applications in almost every branch and sector of industry.

As a digital marketer, it is critical to stay on top of all the latest trends and tactics. And when it comes to selecting conferences to attend, you need to choose carefully because not all of them are equally useful. Be sure to look closely at the agenda for each to determine what kinds of skills and education they offer. SEMrush has assembled the full calendar of 2018 digital marketing conferences. Outbrain has taken it one step further and narrowed it down to the 13 must-attend SEO, PPC and digital marketing conferences in 2018.

Scientists Alan Winfield, professor of robot ethics at the University of the West of England in Bristol, and Marina Jirotka, professor of human-centered computing at Oxford University, believe robots should be fitted with an "ethical black box." This would be the ethics equivalent of the aviation safety measure of the same name, designed to track a pilot's decisions and enable investigators to follow those actions in the event of accidents. As robots leave the controlled settings of factories and laboratories to interact more with humans, safety measures of this nature will become increasingly important. Winfield and Jirotka argue that robotics firms should emulate the example provided by the aviation industry, which owes its safety record not just to technology and design, but also to stringent safety protocols and accident investigation. That industry introduced both black boxes and cockpit voice recorders to ensure accident investigators would be able to determine both causes of crashes and obtain critical lessons in prevention and safety.

It is not much to look at: the nematode C. elegans is about one millimetre in length and is a very simple organism. But for science, it is extremely interesting. C. elegans is the only living being whose neural system has been analysed completely. It can be drawn as a circuit diagram or reproduced by computer software, so that the neural activity of the worm is simulated by a computer program. Such an artificial C. elegans has now been trained at TU Wien (Vienna) to perform a remarkable trick: The computer worm has learned to balance a pole at the tip of its tail. The Worm's Reflexive behaviour as Computer Code C. elegans has to get by with only 300 neurons.

The University of Southampton in the U.K. has launched the Center for Machine Intelligence to develop a coherent approach to research and technology transfer. The University of Southampton in the U.K. recently launched the Center for Machine Intelligence (CMI), bringing together researchers and practitioners in artificial intelligence, machine learning, and autonomous systems to develop a coherent approach to research and technology transfer. Discussions at the launch event focused on these various technologies' application in large-scale Internet of Things systems and in the insurance and social care sectors. Research groups within the CMI will focus on the theoretical aspects of machine intelligence, including the Agents, Interaction, and Complexity group, and the Vision, Learning, and Control group. "The formation of the CMI is an important next step at a time of great advances in this field and we look forward to working with industry, policymakers and the general public as we address both national and global challenges," says Southampton professor Sarvapali Ramchurn, who will head the CMI.

Think that human augmentation is still decades away? This week, government leaders met with experts and innovators ahead of the World Government Summit in Dubai. To determine the future of artificial intelligence. It was an event that attracted some of the biggest names in AI. Managers from IBM Watson, Microsoft, Facebook, OpenAI, Nest, Drive.ai, and Amazon AI.

"I have read and understood…" has got to be one of the biggest lies people commit on a regular basis. It's the typical ending for the long-winded customer agreements or privacy policies attached to every online service, which few ever read. When humankind finds something difficult, we typically build a gizmo to do it for us -- and this case is no different. It turns out, reading lengthy fine-print is the expertise of a machine-learning artificial intelligence (AI) designed by researchers from the Federal Institute of Technology at Lausanne, Switzerland (EPFL), the University of Wisconsin, and the University of Michigan. Their research began with a question, said lead researcher Hamza Harkous from EPFL.

We study the role of latent space dimensionality in Wasserstein auto-encoders (WAEs). Through experimentation on synthetic and real datasets, we argue that random encoders should be preferred over deterministic encoders. We highlight the potential of WAEs for representation learning with promising results on a benchmark disentanglement task.

In spoken dialogue systems, we aim to deploy artificial intelligence to build automated dialogue agents that can converse with humans. A part of this effort is the policy optimisation task, which attempts to find a policy describing how to respond to humans, in the form of a function taking the current state of the dialogue and returning the response of the system. In this paper, we investigate deep reinforcement learning approaches to solve this problem. Particular attention is given to actor-critic methods, off-policy reinforcement learning with experience replay, and various methods aimed at reducing the bias and variance of estimators. When combined, these methods result in the previously proposed ACER algorithm that gave competitive results in gaming environments. These environments however are fully observable and have a relatively small action set so in this paper we examine the application of ACER to dialogue policy optimisation. We show that this method beats the current state-of-the-art in deep learning approaches for spoken dialogue systems. This not only leads to a more sample efficient algorithm that can train faster, but also allows us to apply the algorithm in more difficult environments than before. We thus experiment with learning in a very large action space, which has two orders of magnitude more actions than previously considered. We find that ACER trains significantly faster than the current state-of-the-art.

AI applications pose increasing demands on performance, so it is not surprising that the era of client-side distributed software is becoming important. On top of many AI applications already using mobile hardware, and even browsers for computationally demanding AI applications, we are already witnessing the emergence of client-side (federated) machine learning algorithms, driven by the interests of large corporations and startups alike. Apart from mathematical and algorithmic concerns, this trend especially demands new levels of computational efficiency from client environments. Consequently, this paper deals with the question of state-of-the-art performance by presenting a comparison study between native code and different browser-based implementations: JavaScript, ASM.js as well as WebAssembly on a representative mix of algorithms. Our results show that current efforts in runtime optimization push the boundaries well towards (and even beyond) native binary performance. We analyze the results obtained and speculate on the reasons behind some surprises, rounding the paper off by outlining future possibilities as well as some of our own research efforts.