In our high-vocational stress household, the most volcanic tension usually erupts over control of the PlayStation. I'm still – still – absorbed in the game of Fallout 4 I started a year ago, with thousands of hours spent on perfecting the aesthetics of post-apocalyptic settlement-building. My partner prefers a wordless immersion in the splattery worlds of first-person shooters and war games but we reconcile over rounds of two-player Diablo, fighting demons and hoarding treasure together. I've come a long way from the handheld Donkey Kong I cherished as a child, or the Pitfall caves I explored on a home PC, or the small parties of teens that gathered to play Sonic the Hedgehog on the loungeroom TV. The demands of fun are more complex now – but the need for fun remains the same.

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.

A study of Google Trends will show you a huge spike in the mentions of'InsurTech' since February 2016, and that's because people and investors are beginning to take note of how state-of-the-art AI can enhance the insurance sphere and disrupt specific elements of the insurance value chain. Indeed, according to PwC's 2017 Global InsurTech Report, 45% of insurers are currently partnered with InsurTech players, whilst a staggering 94% have aligned their priorities closer to emerging trends and AI-led risk insights and customer engagement. Whilst there is budding support from all camps to evolve the traditionally fairly inertial insurance industry, there is little to suggest that innovation will come from incumbent insurers. The insurance disruption space hasn't seen nearly as much activity as fintech, but 2017 has seen the trinity of technological trends - machine learning, AI and Big Data - cross over and fuel the motor of change within InsurTech. But how fast is the industry moving and how worried should incumbent insurers be?

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.

We are surrounded by hysteria about the future of Artificial Intelligence and Robotics. There is hysteria about how powerful they will become how quickly, and there is hysteria about what they will do to jobs. As I write these words on September 2nd, 2017, I note just two news stories from the last 48 hours. Yesterday, in the New York Times, Oren Etzioni, chief executive of the Allen Institute for Artificial Intelligence, wrote an opinion piece titled How to Regulate Artificial Intelligence where he does a good job of arguing against the hysteria that Artificial Intelligence is an existential threat to humanity. He proposes rather sensible ways of thinking about regulations for Artificial Intelligence deployment, rather than the chicken little "the sky is falling" calls for regulation of research and knowledge that we have seen from people who really, really, should know a little better. Today, there is a story in Market Watch that robots will take half of today's jobs in 10 to 20 years. It even has a graphic to prove the numbers. How many robots are currently operational in those jobs? How many realistic demonstrations have there been of robots working in this arena? Similar stories apply to all the other job categories in this diagram where it is suggested that there will be massive disruptions of 90%, and even as much as 97%, in jobs that currently require physical presence at some particular job site. Mistaken predictions lead to fear of things that are not going to happen. Why are people making mistakes in predictions about Artificial Intelligence and robotics, so that Oren Etzioni, I, and others, need to spend time pushing back on them? Below I outline seven ways of thinking that lead to mistaken predictions about robotics and Artificial Intelligence. We find instances of these ways of thinking in many of the predictions about our AI future. I am going to first list the four such general topic areas of such predictions that I notice, along with a brief assessment of where I think they currently stand. Research on AGI is an attempt to distinguish a thinking entity from current day AI technology such as Machine Learning. Here the idea is that we will build autonomous agents that operate much like beings in the world. This has always been my own motivation for working in robotics and AI, but the recent successes of AI are not at all like this.

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.

A chatbot is one of the oldest envisioned use cases for Artificial Intelligence. Alan Turing suggested that instead of debating whether a machine could think, we should measure how it imitates human language. Microsoft's Xiaoice already passes the Turing test for 10 minutes. Do you already have a customer service chat bot or are you looking to build a new one? Here we have a comprehensive list of vendors your enterprise can partner with to build an intelligent chat bot.

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.