In the future AI could develop a will of its own, a will that is in conflict with ours. These were some of the final words from the late, great Stephen Hawking that were published earlier this month when he warned about the consequences of unregulated artificial intelligence. Hawking also predicted the rise of the'superhuman' who will initially rely on AI and genetics and then eventually escape earth; hopefully to do a better job on a new planet than we have done to date on ours. It will be some time before superhuman are with us, but AI certainly is and it's completely transforming the world and how humans operate within it. This is not a episode of Black Mirror, these are the times we live in.

Apple boss Tim Cook said that being gay is "God's greatest gift to me" as he spoke of his pride at being the first CEO of a major company to come out. He revealed he made the decision to be open about his sexual orientation four years ago after hearing from children who were being bullied and abused and thinking of suicide. "I am a private person and so I kept me to my small circle, and I started thinking that's a selfish thing to do," he told CNN. Apple boss says data is being'weaponised with military efficiency' Tim Cook responds after Apple becomes first trillion-dollar company Apple boss Tim Cook denies Facebook privacy scandal ties Apple boss Tim Cook on the importance of kids learning to code Apple boss says data is being'weaponised with military efficiency' "I needed to be bigger than that, I need to do something for them and show them that you can be gay and still go on and do some big jobs in life. "I did not do it for other CEOs to come out.

Apple CEO, Tim Cook spoke up for privacy at a conference of European privacy commissioners in Brussels this morning. The themes of this year's conference is "Debating Ethics: Dignity and Respect in Data Driven Life", Cook is the first tech CEO to serve as the keynote speaker for the conference and was invited to speak. He talked about data, put in a bid for a bill of U.S. digital rights, slammed competitors for profiting while unleashing powerfully negative forces, and spoke up for a GDPR-style privacy protection in the U.S. What follows is the transcript of his speech. "It is an honor to be here with you today in this grand hall…a room that represents what is possible when people of different backgrounds, histories, and philosophies come together to build something bigger than themselves. "I am deeply grateful to our hosts.

ABB says its goal is to make the Shanghai facility the most advanced robotics factory in the world. It will even feature a Research and Development center to accelerate the firm's work in artificial intelligence. In addition, it will widen the types and variants of robots the company can build for Chinese companies, including automakers and electronics manufacturers. China is ABB's second biggest market after the United States, and the new factory could greatly expand its presence in the market. The company expects to open the 75,000-square-foot facility by late 2020.

Artificial Intelligence (AI) is the new buzzword in the technology world. It is becoming impossible to imagine the future of the healthcare sector without AI. Numerous startups and tech firms have already embarked on the AI hype wagon, promising a host of sophisticated healthcare solutions. In fact, AI is already significantly changing the healthcare landscape. AI has a symbiotic relationship with data, as well as learning from it. In 2016 alone, the amount of data generated in the world exceeded the total data ever generated by mankind.

Swiss robotics company ABB has revealed that it's spending $150 million to build an advanced robotics factory in Shanghai -- one that will use robots to build robots. The company will rely on its YuMi single-arm robots, which it once used to conduct an orchestra, for small parts assembly. It also plans to make extensive use"of its SafeMove2 software in the facility, which it says will allow its YuMi models and other automated machines to safely work in close proximity with human employees. ABB says its goal is to make the Shanghai facility the most advanced robotics factory in the world. It will even feature a Research and Development center to accelerate the firm's work in artificial intelligence.

There is huge demand for robots to work alongside humans in heterogeneous teams. To achieve a high degree of fluidity, robots must be able to (1) recognize their human co-worker's intent, and (2) adapt to this intent accordingly, providing useful aid as a teammate. The literature to date has made great progress in these two areas -- recognition and adaptation -- but largely as separate research activities. In this work, we present a unified approach to these two problems, in which recognition and adaptation occur concurrently and holistically within the same framework. We introduce Pike, an executive for human-robot teams, that allows the robot to continuously and concurrently reason about what a human is doing as execution proceeds, as well as adapt appropriately. The result is a mixed-initiative execution where humans and robots interact fluidly to complete task goals.Key to our approach is our task model: a contingent, temporally-flexible team-plan with explicit choices for both the human and robot. This allows a single set of algorithms to find implicit constraints between sets of choices for the human and robot (as determined via causal link analysis and temporal reasoning), narrowing the possible decisions a rational human would take (hence achieving intent recognition) as well as the possible actions a robot could consistently take (hence achieving adaptation). Pike makes choices based on the preconditions of actions in the plan, temporal constraints, unanticipated disturbances, and choices made previously (by either agent).Innovations of this work include (1) a framework for concurrent intent recognition and adaptation for contingent, temporally-flexible plans, (2) the generalization of causal links for contingent, temporally-flexible plans along with related extraction algorithms, and (3) extensions to a state-of-the-art dynamic execution system to utilize these causal links for decision making.

A theoretical performance analysis of the graph neural network (GNN) is presented. For classification tasks, the neural network approach has the advantage in terms of flexibility that it can be employed in a data-driven manner, whereas Bayesian inference requires the assumption of a specific model. A fundamental question is then whether GNN has a high accuracy in addition to this flexibility. Moreover, whether the achieved performance is predominately a result of the backpropagation or the architecture itself is a matter of considerable interest. To gain a better insight into these questions, a mean-field theory of a minimal GNN architecture is developed for the graph partitioning problem. This demonstrates a good agreement with numerical experiments.

Dynamic spectrum access (DSA) is regarded as an effective and efficient technology to share radio spectrum among different networks. As a secondary user (SU), a DSA device will face two critical problems: avoiding causing harmful interference to primary users (PUs), and conducting effective interference coordination with other secondary users. These two problems become even more challenging for a distributed DSA network where there is no centralized controllers for SUs. In this paper, we investigate communication strategies of a distributive DSA network under the presence of spectrum sensing errors. To be specific, we apply the powerful machine learning tool, deep reinforcement learning (DRL), for SUs to learn "appropriate" spectrum access strategies in a distributed fashion assuming NO knowledge of the underlying system statistics. Furthermore, a special type of recurrent neural network (RNN), called the reservoir computing (RC), is utilized to realize DRL by taking advantage of the underlying temporal correlation of the DSA network. Using the introduced machine learning-based strategy, SUs could make spectrum access decisions distributedly relying only on their own current and past spectrum sensing outcomes. Through extensive experiments, our results suggest that the RC-based spectrum access strategy can help the SU to significantly reduce the chances of collision with PUs and other SUs. We also show that our scheme outperforms the myopic method which assumes the knowledge of system statistics, and converges faster than the Q-learning method when the number of channels is large.

Dynamic spatiotemporal processes on the Earth can be observed by an increasing number of optical Earth observation satellites that measure spectral reflectance at multiple spectral bands in regular intervals. Clouds partially covering the surface is an omnipresent challenge for the majority of remote sensing approaches that are not robust regarding cloud coverage. In these approaches, clouds are typically handled by cherry-picking cloud-free observations or by pre-classification of cloudy pixels and subsequent masking. In this work, we demonstrate the robustness of a straightforward convolutional long short-term memory network for vegetation classification using all available cloudy and non-cloudy satellite observations. We visualize the internal gate activations within the recurrent cells and find that, in some cells, modulation and input gates close on cloudy pixels. This indicates that the network has internalized a cloud-filtering mechanism without being specifically trained on cloud labels. The robustness regarding clouds is further demonstrated by experiments on sequences with varying degrees of cloud coverage where our network achieved similar accuracies on all cloudy and non-cloudy datasets. Overall, our results question the necessity of sophisticated pre-processing pipelines if robust classification methods are utilized.