Perhaps you've bumped into Mildred, Carla or Oscar on your recent travels. They're not real people but avatars of chatbots – concocted by Lufthansa, Avianca and Air New Zealand respectively – or artificial intelligence (AI) powered computer programs accessed on your smartphone that enable you to have a simulated conversation of sorts. Now airports are getting in on the act, and it's all part of a paradigm shift towards self-service and interactions with technologies that offer "personal" information to help us on our way through the terminal. It's a shift confirmed in the findings of the Passenger IT Trends Survey released by Sita, the provider of much of the digital infrastructure that underpins airport and airline communications and operations worldwide. The survey found that face-to-face check-in is now down to 46 per cent of passengers, and since last year's survey, self-service bag-tagging has risen from 31 per cent to 47 per cent. Almost a fifth of passengers now use self-service bag drop, and when it comes to ID control, 57 per cent of passengers would definitely use biometrics instead of a passport or boarding pass across the journey.

The International Joint Conference of Artificial Intelligence (or in short IJCAI) is the most established, important and leading scientific event in Artificial Intelligence. Established in 1969 as the first ever international conference on Artificial Intelligence (AI), it is an extension of the seminal (AI first) Dartmouth workshop in 1956 (for the interested, read the inspirational first papers on AI). Practically, much of the leading AI science and technology was presented during previous IJCAI conferences. Before we talk more about the upcoming IJCAI-ECAI-18 event in Stockholm, Sweden (July 13-19, 2018), let us share with you our first-hand experience of IJCAI 2017 in Melbourne, Australia. IJCAI 2017 brings together the brightest researcher and technologists from around the world.

SELF-DRIVING, personal assistants, cleaning robots, smart homes - these are just some examples of autonomous systems. With many such systems already in use or under development, a key question concerns trust. My central argument is that having trustworthy, well-working systems is not enough. To enable trust, the design of autonomous systems also needs to consider other requirements, including a capacity to explain decisions and to have recourse options when things go wrong. The past few years have seen dramatic advances in the deployment of autonomous systems. These are essentially software systems that make decisions and act on them, with real-world consequences.

Kaggle is an AirBnB for Data Scientists – this is where they spend their nights and weekends. It's a crowd-sourced platform to attract, nurture, train and challenge data scientists from all around the world to solve data science and predictive analytics problems through machine learning. It has over 536,000 active members from 194 countries and it receives close to 150,000 submissions per month. Started from Melbourne, Australia Kaggle moved to Silicon Valley in 2011, raised some 11 million dollars from the likes of Hal Varian (Chief Economist at Google), Max Levchin (Paypal), Index and Khosla Ventures and then ultimately been acquired by the Google in March of 2017. Kaggle is the number one stop for data science enthusiasts all around the world who compete for prizes and boost their Kaggle rankings.

Deep learning neural networks -- the likes of which power AlphaGo as well as the current generation of image recognition and language translation systems -- are the best machine learning systems we've developed to date. They're capable of incredible feats but still face significant technological hurdles, like the fact that in order to be trained on a specific skill they require upfront access to massive data sets. What's more if you want to retrain that neural network to perform a new skill, you've essentially got to wipe its memory and start over from scratch -- a process known as "catastrophic forgetting". Compare that to the human brain, which learns incrementally rather than bursting forth fully-formed from a sea of data points. It's a fundamental difference: deep learning AIs are generated from the top down, knowing everything it needs to from the get-go, while the human mind is built from the ground up with previous lessons learned being applied to subsequent experiences to create new knowledge.

Driverless vehicles could build a "gold mine" of personal data for private companies and would make it easier for them to target people as consumers, an Australian law professor has warned. Des Butler, of the Queensland University of Technology, said the privacy risks involved in driverless vehicles were a "sleeper issue" that regulators were yet to fully consider, even though car manufacturers say the technology could be on roads in Australia by 2020. "These vehicles will know where you like to frequent, which businesses, and may very well build a profile of you," Butler said. "People will go into these things not realising just how much data the vehicle will be generating about them and not knowing the extent to which the data can be used." On Thursday, the federal government formally launched a $55m bid to answer some of the questions that surround the nascent technology.

Generative Adversarial Networks (GANs) produce systematically better quality samples when class label information is provided., i.e. in the conditional GAN setup. This is still observed for the recently proposed Wasserstein GAN formulation which stabilized adversarial training and allows considering high capacity network architectures such as ResNet. In this work we show how to boost conditional GAN by augmenting available class labels. The new classes come from clustering in the representation space learned by the same GAN model. The proposed strategy is also feasible when no class information is available, i.e. in the unsupervised setup. Our generated samples reach state-of-the-art Inception scores for CIFAR-10 and STL-10 datasets in both supervised and unsupervised setup.

The term "industrial Internet of Things" has a more muted-sounding promise of driving operational efficiencies through automation, connectivity and analytics. But the focus of IIoT -- on industry at large -- is broader. Here, we take a comprehensive view, rounding up 20 IIoT leaders and pioneers, drawing on the feedback from industry analysts and consultants. The focus here is not on vendors offering, say, a cloud-based platform for monitoring industrial machines but on the companies that themselves are using IIoT technology to drive their business forward. For the sake of this feature, we focus on organizations that use connected technology in tandem with cloud-based analytics to drive efficiencies and launch new business models.

This paper considers a discrete-time opinion dynamics model in which each individual's susceptibility to being influenced by others is dependent on her current opinion. We assume that the social network has time-varying topology and that the opinions are scalars on a continuous interval. We first propose a general opinion dynamics model based on the DeGroot model, with a general function to describe the functional dependence of each individual's susceptibility on her own opinion, and show that this general model is analogous to the Friedkin-Johnsen model, which assumes a constant susceptibility for each individual. We then consider two specific functions in which the individual's susceptibility depends on the \emph{polarity} of her opinion, and provide motivating social examples. First, we consider stubborn positives, who have reduced susceptibility if their opinions are at one end of the interval and increased susceptibility if their opinions are at the opposite end. A court jury is used as a motivating example. Second, we consider stubborn neutrals, who have reduced susceptibility when their opinions are in the middle of the spectrum, and our motivating examples are social networks discussing established social norms or institutionalized behavior. For each specific susceptibility model, we establish the initial and graph topology conditions in which consensus is reached, and develop necessary and sufficient conditions on the initial conditions for the final consensus value to be at either extreme of the opinion interval. Simulations are provided to show the effects of the susceptibility function when compared to the DeGroot model.

If you've got a friend you're dying to set up on a date, then there's good news as a new online dating app lets you play'virtual Cupid.' Hinge has unveiled a new standalone app called Hinge Matchmaker that lets people help their single friends to find love by suggesting matches based on your Facebook friends. The beta test version of the app is free to download, and is available to all Hinge markets in the UK, US, Canada, Australia and India. Hinge Matchmaker gives people in a relationship a chance to set up their single friends, based on your Facebook friends who have a profile on Hinge. As the matchmaker, the power is left in your hands to decide whether your friend should connect on Hinge, with the option to send an icebreaker message to get the conversation started. As well as randomly recommending potential couples, Hinge Matchmaker also has an option for you to take control and select a specific friend you'd like to help by selecting the'lock' option on their profile.