While you are busy giving Alexa commands to play your favourite song or book an Uber, the intelligent virtual assistant from Amazon is helping the US space agency organise daily tasks while making sense of intrinsic data-sets. According to Tom Soderstrom, IT Chief Technology and Innovation Officer at NASA's Jet Propulsion Laboratory (JPL), voice as a platform will become the next big thing once we learn to talk to digital assistants and chatbots in a fashion we do with friends and family. "If you have Alexa-controlled Amazon Echo smart speaker at home, tell her to enable the'NASA Mars' app. Once done, ask Alexa anything about the Red Planet and she will come back with all the right answers," Soderstrom said during the Amazon Web Services' (AWS) public sector summit in Washington. "This enables serverless computing where we don't need to build for scale but for real-life work cases and get the desired results in a much cheaper way. Remember that voice as a platform is poised to give 10 times faster results," Soderstrom noted on the inaugural "Earth and Space Day".

Artificial intelligence could give internet giants like Facebook and Amazon even more power to reshape the Canadian economy, threatening the viability of domestic businesses, researchers warn. A December presentation to senior civil servants said that Canadian companies were losing ownership of – and access to – data to the likes of Facebook, Amazon, Netflix and Google, requiring a federal policy response. Artificial intelligence "will reinforce this trend," presenters from the National Research Council warned top officials, adding that a national data strategy would be necessary to prevent Canada from becoming "a nation of'data cows' for other countries." The presentation, among other documents obtained by The Canadian Press under the Access to Information Act, provides a window into the scale of the problem the Liberals are trying to tackle by crafting a national data strategy, and the breadth of departments involved in its creation. The Liberals took another step towards the creation of the strategy by launching online and in-person consultations that will run through the summer in order to inform a final policy.

Paediatric and child trauma experts are sounding the alarm that separating migrant children from their parents at the US border can cause serious physical and psychological damage. As more stories emerge about children being separated from their parents at the border between Mexico and the US, doctors and scientists are warning that there could be long-term, irreversible health impacts on children if they're not reunited expediently. The head of the American Academy of Pediatrics went so far as to call the policy "child abuse" and against "everything we stand for as paediatricians". "This is completely ridiculous and I'm approaching that not as someone who's taking a position in the politics, but as a scientist," says Charles A Nelson III, a professor of paediatrics and neuroscience at Harvard Medical School. "We just know the science does not support that this is good for kids."

"Humans tend to overestimate technology in the short term but underestimate it in the long term," said Tom Foster, editor at large for Inc. magazine, during a panel he moderated on innovations in machine learning at South by Southwest (SXSW) in March. Artificial intelligence (AI) was a recurring theme across panels at SXSW 2018's Interactive Conference held in Austin, Texas. The topic was particularly popular in tracks titled "Intelligent Future" and "Startup & Tech Sectors." Many AI experts marveled at recent advances in the technology while pondering its future. "I've been working in AI for now more than 30 years and in the past eight years there are things that have occurred that I never thought would happen in my lifetime," said Adam Cheyer, co-founder of Viv Labs, during a discussion on innovations in AI.

Microsoft is the latest tech giant to find itself in the crosshairs over a controversial government contract. In January, it was announced that Microsoft's Azure Government arm was working with U.S. Immigration and Customs Enforcement (ICE) to assist with'facial recognition and identification', providing access to its deep learning AI technology. Along with that, Microsoft said it was'proud to support' ICE - a section that was deleted and, ultimately, restored after it was made public, according to BuzzFeed. In January, it was announced that Microsoft's Azure Government arm was working with ICE to assist with'facial recognition and identification,' providing access to its deep learning AI tech That hasn't stopped many from calling on Microsoft employees to resign from the company or urge CEO Satya Nadella to speak about the firm's dealings with ICE. Microsoft has since responded that deleting the content was a'mistake' and issued a follow up blog post further detailing the matter. 'In response to questions we want to be clear: Microsoft is not working with U.S. Immigration and Customs Enforcement or U.S. Customs and Border Protection on any projects related to separating children from their families at the border, and contrary to some speculation, we are not aware of Azure or Azure services being used for this purpose,' the firm wrote in a statement.

Researchers at Harvey Mudd College have developed a novel autonomous underwater vehicle. Harvey Mudd College researchers working on a long-term project to unite robotics and archaeology have developed a novel autonomous underwater vehicle (AUV) that can explore the sea floor looking for signs of wrecked ships. The researchers also created a new artificial intelligence (AI) system to help better analyze the images of the sea bottom, as well as algorithms to improve the search and navigation of a target area. The team initially plots a large area for the AUV to explore by beaming sound waves down from the water's surface, creating acoustic images of what lies beneath. The AI then ranks the most promising areas for further exploration, looking for indicators such as long shadows and sharp corners that could indicate a manmade object.

Several researchers have argued that a machine learning system's interpretability should be defined in relation to a specific agent or task: we should not ask if the system is interpretable, but to whom is it interpretable. We describe a model intended to help answer this question, by identifying different roles that agents can fulfill in relation to the machine learning system. We illustrate the use of our model in a variety of scenarios, exploring how an agent's role influences its goals, and the implications for defining interpretability. Finally, we make suggestions for how our model could be useful to interpretability researchers, system developers, and regulatory bodies auditing machine learning systems.

In this study, we explore capsule networks with dynamic routing for text classification. We propose three strategies to stabilize the dynamic routing process to alleviate the disturbance of some noise capsules which may contain "background" information or have not been successfully trained. A series of experiments are conducted with capsule networks on six text classification benchmarks. Capsule networks achieve competitive results over the strong baseline methods on 4 out of 6 datasets, which shows the effectiveness of capsule networks for text classification. We additionally show that capsule networks exhibit significant improvement when transfer single-label to multi-label text classification over the strong competitors. To the best of our knowledge, this is the first work that capsule networks have been empirically investigated for text modeling.

Vehicles of higher automation levels require the creation of situation awareness. One important aspect of this situation awareness is an understanding of the current risk of a driving situation. In this work, we present a novel approach for the dynamic risk assessment of driving situations based on images of a front stereo camera using deep learning. To this end, we trained a deep neural network with recorded monocular images, disparity maps and a risk metric for diverse traffic scenes. Our approach can be used to create the aforementioned situation awareness of vehicles of higher automation levels and can serve as a heterogeneous channel to systems based on radar or lidar sensors that are used traditionally for the calculation of risk metrics.

To generate trust with their users, Explainable Artificial Intelligence (XAI) systems need to include an explanation model that can communicate the internal decisions, behaviours and actions to the interacting humans. Successful explanation involves both cognitive and social processes. In this paper we focus on the challenge of meaningful interaction between an explainer and an explainee and investigate the structural aspects of an explanation in order to propose a human explanation dialog model. We follow a bottom-up approach to derive the model by analysing transcripts of 398 different explanation dialog types. We use grounded theory to code and identify key components of which an explanation dialog consists. We carry out further analysis to identify the relationships between components and sequences and cycles that occur in a dialog. We present a generalized state model obtained by the analysis and compare it with an existing conceptual dialog model of explanation.