According to Dennett, the same system may be described using a `physical' (mechanical) explanatory stance, or using an `intentional' (belief- and goal-based) explanatory stance. Humans tend to find the physical stance more helpful for certain systems, such as planets orbiting a star, and the intentional stance for others, such as living animals. We define a formal counterpart of physical and intentional stances within computational theory: a description of a system as either a device, or an agent, with the key difference being that `devices' are directly described in terms of an input-output mapping, while `agents' are described in terms of the function they optimise. Bayes' rule can then be applied to calculate the subjective probability of a system being a device or an agent, based only on its behaviour. We illustrate this using the trajectories of an object in a toy grid-world domain.

We propose Episodic Backward Update - a new algorithm to boost the performance of a deep reinforcement learning agent by a fast reward propagation. In contrast to the conventional use of the experience replay with uniform random sampling, our agent samples a whole episode and successively propagates the value of a state to its previous states. Our computationally efficient recursive algorithm allows sparse and delayed rewards to propagate efficiently through all transitions of a sampled episode. We evaluate our algorithm on 2D MNIST Maze environment and 49 games of the Atari 2600 environment and show that our method improves sample efficiency with a competitive amount of computational cost.

Robustness to adversarial perturbation has become an extremely important criterion for applications of machine learning in security-sensitive domains such as spam detection [25], fraud detection [6], criminal justice [3], malware detection [13], and financial markets [27]. Systematic methods for generating adversarial examples by small perturbations of original input data, also known as "attack," have been developed to operationalize this criterion and to drive the development of more robust learning systems [4, 26, 7]. Most of the work in this area has focused on differentiable models with continuous input spaces [26, 7, 14, 14]. In this setting, the proposed attack strategies add a gradient-based perturbation to the original input. It has been shown that such perturbations can result in a dramatic decrease in the predictive accuracy of the model. Thus this line of research has demonstrated the vulnerability of deep neural networks to adversarial examples in tasks like image classification and speech recognition. We focus instead on adversarial attacks on models with discrete input data, such as text data, where each feature of an input sample has a categorical domain. While gradient-based approaches are not directly applicable to this setting, variations of gradient-based approaches have been shown effective in differentiable models. For example, Li et al. [15] proposed to locate the top features with the largest gradient magnitude of their embedding, and Papernot et al. [20] proposed to modify randomly selected features of an input through perturbing each feature by signs of the gradient, and project them onto the closest vector in the embedding space.

Maverick Romanian serial entrepreneur Radu Negulescu, 30, is the founder of Trencadis, an IT company that develops software solutions for governments worldwide to streamline their interactions with the public. Magazine have recently nominated Trencadis as one of the fastest growing companies in Europe. Negulescu is also a co-owner of Autonomous Flight Technologies (AFT), a pioneer in Romania's unmanned aerial vehicle (UAV) industry. AFT has been developing and producing UAV systems since 2004. Today, it is the leading UAV producer in Central and Eastern Europe, delivering multifaceted solutions with civilian and military applications.

ANYbotics led the way in the ICRA 2018 Robot Launch Startup Competition on May 22, 2018 at the Brisbane Conference Center in Australia. Although ANYbotics pitched last out of the 10 startups presenting, they clearly won over the judges and audience. As competition winners, ANYbotics received a $3,000 prize from QUT bluebox, Australia's robotics accelerator (currently taking applications for 2018!), plus Silicon Valley Robotics membership and mentoring from The Robotics Hub. ANYbotics is a Swiss startup creating fabulous four legged robots like ANYmal and the core component, the ANYdrive highly integrated modular robotic joint actuator. Founded in 2016 by a group of ETH Zurich engineers, ANYbotics is a spin-off company of the Robotic Systems Lab (RSL), ETH Zurich.

As of 1/15, based on historical growth rates, it is estimated that 90% of USA Uber driver-partners drive for UberX.

When it comes to cyber-risk, the most exposed part of any organization is also the least protected; endpoints. Much of the data on laptops and other devices is unique and not stored on storage owned by the organization, but endpoints are often the first casualties of a cyber-attack. Given the new threat organizations need to provide complete protection, which not only includes protection from device failure but also ransomware attacks, device theft and litigation response. The challenge for IT is providing this protection without disrupting the user. If the protection solution gets in the way of the user, they will disable it, putting not only their data at risk but leaving the organization open to data loss and cyber-breach.

The Mapbox Vision SDK provides augmented reality (AR) navigation, driver alerts for speed limits, pedestrians, vehicles and other event-based triggers for responsive apps. The integration with Azure IoT Hub will provide developers with a holistic solution that aggregates cloud data using artificial intelligence (AI) and machine-learning technologies to send reports. Mapbox plans to integrate the open-sourced Azure IoT Edge runtime, which provides custom logic, management and communications functions for edge devices. Events detected from the Vision SDK integrated with Azure IoT Edge will enable developers to build responsive applications that both provide immediate feedback to the driver as well as stream semantic event data into Microsoft Cognitive Services for additional analysis. Reports include sending collision incidents to an insurance platform, providing information about heavy traffic or blocked roadway alerts to a dispatch network, or activity about a crossing intersection to a business intelligence platform analyzing route paths.

Moscow-based search giant Yandex has revealed a Russian speaking smart speaker to take on Amazon. The $160 device will work with its digital assistant'Alice', becoming the latest challenger to take on the leading voice-activated home helpers from Silicon Valley. Yandex is targeting the Russian-speaking market with its Yandex.Station speaker, as well as a platform on which third-party developers can programme Alice to order pizza, check mobile phone bills or buy plane tickets. The $160 device will work with its digital assistant'Alice', becoming the latest challenger to take on the leading voice-activated home helpers from Silicon Valley. The Yandex Station has Bluetooth capabilities and an HDMI port for streaming television on connected displays, and has including two 10W audio drivers and a 30W woofer.

The CERN Summer Student Programme offers once-in-a-lifetime opportunity for undergraduate students of physics, computing and engineering to join one of their research projects with top scientists in multicultural teams at CERN in Geneva, Switzerland. In June 2017, I was very fortunate to be accepted to join the programme. I literally burst with joy as particle physics have always been my research interest and being able to conduct the research at CERN was simply a dream-come-true-experience for me! During the 2 months internship period, I did some analysis and simulation on the event reconstruction of terabytes of data via Worldwide LHC Computing Grid & Cloud Computing for Compact Muon Solenoid (CMS) Experiment. Besides, summer students also attended a series of lectures, workshops and visits to CERN facilities that covered a wide range of topics in the fields of theoretical and experimental particle physics and computing.