It is a known fact that Artificial Intelligence (AI) has opened diverse opportunities in the world of technology, but it is no secret that the economic impact of AI has also been momentous for its adopters. Come 2030, AI based initiatives will pump 12.8 trillion Euros into the global economy!* Caught up in the global frenzy to prove dominance in the realm of AI, the European Union which was till very recently nascent in its AI advancements, has now set up extensive AI strategies to further boost its economy. With a united stance, the European Union took the big leap in the direction of AI when in 2014 they announced the launch of Horizon 2020. The biggest EU Research and Innovation program till date, Horizon 2020 was initiated in order to develop a conducive environment for producing world-class innovations within the continent.

Alphabet is shuffling some of its companies around as it works to better organize the health projects that are currently spread across its subsidiaries. So going forward, DeepMind's health unit will instead exist under the Google umbrella and it will be part of the company's recently formed Google Health initiative. Specifically, DeepMind's Streams app, which physicians in the UK have used to help treat their patients, will be moving over to Google, and the Google Health team will be working on expanding the app to more regions. We're excited to announce that the team behind Streams - our app supporting doctors and nurses to deliver faster, better care to patients - will be joining Google. Google recently brought in David Feinberg to lead the new Google Health group, with the goal of organizing Alphabet's health efforts and enhancing collaborations across its subsidiaries.

The UK government is actively funding the development of flying "killer robots" despite publicly stating it has no plans to develop them, a study claims. Research into fully autonomous drone weapons by the campaign group Drone Wars UK revealed the UK's Defence and Security Accelerator (Dasa) is funding research for developing weapons able to kill without direct human input. The report, titled Off the Leash: The Development of Autonomous Military Drones in the UK, highlighted the Taranis drone, which is capable of autonomously flying, plotting routes and locating targets. Uber has halted testing of driverless vehicles after a woman was killed by one of their cars in Tempe, Arizona. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

THE HAGUE, NETHERLANDS – The global chemical weapons watchdog will in February begin to assign blame for attacks with banned munitions in Syria's war, using new powers approved by member states but opposed by Damascus and its key allies Russia and Iran. The agency was handed the new task in response to an upsurge in the use of chemical weapons in recent years, notably in the Syrian conflict, where scores of attacks with sarin and chlorine have been carried out by Syrian forces and rebel groups, according to a joint United Nations-OPCW investigation. A core team of 10 experts charged with apportioning blame for poison gas attacks in Syria will be hired soon, Fernando Arias, the new head of the Organisation for the Prohibition of Chemical Weapons (OPCW), told the Foreign Press Association of the Netherlands on Tuesday. The Syria team will be able to look into all attacks previously investigated by the OPCW, dating back to 2014. The OPCW was granted additional powers to identify individuals and institutions responsible for attacks by its 193 member states at a special session in June.

Britain will not develop Terminator-style machines which can kill without human command because they are unethical, the chief scientist at the Ministry of Defence said. Countries worldwide are in a new arms race to develop lethal autonomous weapons (LAWS) which can kill in a war zone without a person having to push a button. But this has sparked major fears that some countries could develop a fleet of killer robots which are not reined in by humans. Simon Cholerton, the MoD's chief scientific adviser, has revealed that Britain is'doing no work and has no plans to develop fully automated weapons'. He said that Britain will snub the new technological field even if the UK's Armed Forces are put at a disadvantage on the battlefield, because it is immoral.

A robot that communicates with humans via facial expressions and understands people by scanning their face has been stumped when it met a person with botox. Furhat Robotics unveiled its'world's most advanced social robotics and conversational artificial intelligence platform' last week. The android can communicate with humans in the way we do with each other - by speaking, listening, showing emotions and reading changes to facial features. The Stockholm-based start-up were left scratching their heads when one test subject completely threw the eerily-lifelike robot. A Furhat insider said: 'We were at a loss as to why one of our robots wasn't interacting properly with a human test subject.

Microsoft's CEO has said artificial intelligence has the potential to "change the trajectory of healthcare" if it can be scaled successfully across the NHS. Speaking at Microsoft Future Decoded at London's ExCel exhibition centre on 1 November, Satya Nadella suggested that readily-available machine learning tools could "change the way health is given". However, the chief executive said this would only happen if people used the digital tools at their disposal to fundamentally change ways of working. "It's not about the whizz-bang technologies, but the people behind it who take this technology and translate it into real action," Nadella said. "The biggest proviso is seeing this all in action – how is the technology all being used? I ask the hard question: is our participation really translating into people using our technology to shape outcomes that matter?"

Generating musical audio directly with neural networks is notoriously difficult because it requires coherently modeling structure at many different timescales. Fortunately, most music is also highly structured and can be represented as discrete note events played on musical instruments. Herein, we show that by using notes as an intermediate representation, we can train a suite of models capable of transcribing, composing, and synthesizing audio waveforms with coherent musical structure on timescales spanning six orders of magnitude ( 0.1 ms to 100 s), a process we call Wave2Midi2Wave. This large advance in the state of the art is enabled by our release of the new MAESTRO (MIDI and Audio Edited for Synchronous TRacks and Organization) dataset, composed of over 172 hours of virtuosic piano performances captured with fine alignment ( 3 ms) between note labels and audio waveforms. The networks and the dataset together present a promising approach toward creating new expressive and interpretable neural models of music. Since the beginning of the recent wave of deep learning research, there have been many attempts to create generative models of expressive musical audio de novo. These models would ideally generate audio that is both musically and sonically realistic to the point of being indistinguishable to a listener from music composed and performed by humans. However, modeling music has proven extremely difficult due to dependencies across the wide range of timescales that give rise to the characteristics of pitch and timbre (short-term) as well as those of rhythm (medium-term) and song structure (long-term). On the other hand, much of music has a large hierarchy of discrete structure embedded in its generative process: a composer creates songs, sections, and notes, and a performer realizes those notes with discrete events on their instrument, creating sound.

The occurrence of sleep passed through the evolutionary sieve and is widespread in animal species. Sleep is known to be beneficial to cognitive and mnemonic tasks, while chronic sleep deprivation is detrimental. Despite the importance of the phenomenon, a theoretical and computational approach demonstrating the underlying mechanisms is still lacking. In this paper, we show interesting effects of deep-sleep-like slow oscillation activity on a simplified thalamo-cortical model which is trained to encode, retrieve and classify images of handwritten digits. If spike-timing-dependent-plasticity (STDP) is active during slow oscillations, a differential homeostatic process is observed. It is characterized by both a specific enhancement of connections among groups of neurons associated to instances of the same class (digit) and a simultaneous down-regulation of stronger synapses created by the training. This is reflected in a hierarchical organization of post-sleep internal representations. Such effects favour higher performance in retrieval and classification tasks and create hierarchies of categories in integrated representations. The model leverages on the coincidence of top-down contextual information with bottom-up sensory flow during the training phase and on the integration of top-down predictions and bottom-up thalamo-cortical pathways during deep-sleep-like slow oscillations. Also, such mechanism hints at possible applications to artificial learning systems.

The verification problem for neural networks is verifying whether a neural network will suffer from adversarial samples, or approximating the maximal allowed scale of adversarial perturbation that can be endured. While most prior work contributes to verifying feed-forward networks, little has been explored for verifying recurrent networks. This is due to the existence of a more rigorous constraint on the perturbation space for sequential data, and the lack of a proper metric for measuring the perturbation. In this work, we address these challenges by proposing a metric which measures the distance between strings, and use deterministic finite automata (DFA) to represent a rigorous oracle which examines if the generated adversarial samples violate certain constraints on a perturbation. More specifically, we empirically show that certain recurrent networks allow relatively stable DFA extraction. As such, DFAs extracted from these recurrent networks can serve as a surrogate oracle for when the ground truth DFA is unknown. We apply our verification mechanism to several widely used recurrent networks on a set of the Tomita grammars. The results demonstrate that only a few models remain robust against adversarial samples. In addition, we show that for grammars with different levels of complexity, there is also a difference in the difficulty of robust learning of these grammars.