Why are so many AI researchers so worried about lethal autonomous weapons? What makes autonomous weapons so much worse than any other weapons we have today? And why is it so hard for countries to come to a consensus about autonomous weapons? Not surprisingly, the short answer is: it's complicated. In this month's podcast, Ariel spoke with experts from a variety of perspectives on the current status of LAWS, where we are headed, and the feasibility of banning these weapons. Guests include ex-Pentagon advisor Paul Scharre (3:40), artificial intelligence professor Toby Walsh (40:51), Article 36 founder Richard Moyes (53:30), Campaign to Stop Killer Robots founders Mary Wareham and Bonnie Docherty (1:03:38), and ethicist and co-founder of the International Committee for Robot Arms Control, Peter Asaro (1:32:39). You can listen to the podcast above, and read the full transcript below. You can check out previous podcasts on SoundCloud, iTunes, GooglePlay, and Stitcher. If you work with ...

Kiwi companies should be upfront with customers about what their data-harvesting artificial intelligence programmes do, a new report finds. The review, just published by Chartered Accountants Australia and New Zealand (CAANZ), said these guidelines should be shared with consumers so they could better decide which businesses they chose to deal with. The report also suggested data-trawling AI algorithms should be designed so they could be reviewed by a third party. From smart phones to smart cars, AI has invaded every aspect of our lives, but the hottest field of AI right now was machine learning – the notion of using statistical techniques to help systems learn from data. There was now heightening concern around how own personal information was being collected and used, and not just by global giants like Google and Facebook, but also Government ministries and agencies.

The Director-General of the Australian Signals Directorate, Mike Burgess, has unloaded on technology hype mongers, warning IT security practitioners and businesses they need to think in one and five year cycles rather than "just the next product or service you will buy". In a frank and direct speech delivered to the SINET61 conference in Melbourne, Australia's chief cyber spook bluntly cautioned those charged with upholding cybersecurity should not be dazzled by shiny new concepts. Instead they should maintain a relentless focus on hygiene and knowing "what is important to your business and your customers." "Don't get caught up in the hype and excitement in this technology-enabled world. AI is a great example of this – peak hype comes to mind," Burgess said.

AI and automation can be useful for data analysis and efficiency but not every part of the world is as ready as others for it. Where should you be looking to do your AI business? The Economist and ABB Group recently put together an Automation Readiness Index. Estonia barely misses the top 5 followed by France, the UK, the US and Australia. And the ABB Group notes that even the top countries aren't fully prepared.

As the success of the iPhone X's Face ID confirms, lots of us are thrilled to bits at the idea of a machine that can identify us based on our facial features. But how happy would you be if a computer used your facial features to start making judgments about your age, your gender, your race, your attractiveness, your trustworthiness, or even how kind you are? Chances are that, somewhere down the line, you'd start to get a bit freaked out. Especially if the A.I. in question was using this information in a way that controlled the opportunities or options that are made available to you. Exploring this tricky (and somewhat unsettling) side of artificial intelligence is a new project from researchers at the University of Melbourne in Australia. Taking the form of a smart biometric mirror, their device uses facial-recognition technology to analyze users' faces, and then presents an assessment in the form of 14 different characteristics it has "learned" from what it's seen.

Neuro-inspired recurrent neural network algorithms, such as echo state networks, are computationally lightweight and thereby map well onto untethered devices. The baseline echo state network algorithms are shown to be efficient in solving small-scale spatio-temporal problems. However, they underperform for complex tasks that are characterized by multi-scale structures. In this research, an intrinsic plasticity-infused modular deep echo state network architecture is proposed to solve complex and multiple timescale temporal tasks. It outperforms state-of-the-art for time series prediction tasks.

Professional sports are developing towards increasingly scientific training methods with increasing amounts of data being collected from laboratory tests, training sessions and competitions. In cycling, it is standard to equip bicycles with small computers recording data from sensors such as power-meters, in addition to heart-rate, speed, altitude etc. Recently, machine learning techniques have provided huge success in a wide variety of areas where large amounts of data ("big data") is available. In this paper, we perform a pilot experiment on machine learning to model physical response in elite cyclists. As a first experiment, we show that it is possible to train a LSTM machine learning algorithm to predict the heart-rate response of a cyclist during a training session. This work is a promising first step towards developing more elaborate models based on big data and machine learning to capture performance aspects of athletes.

We use reinforcement learning (RL) to learn dexterous in-hand manipulation policies which can perform vision-based object reorientation on a physical Shadow Dexterous Hand. The training is performed in a simulated environment in which we randomize many of the physical properties of the system like friction coefficients and an object's appearance. Our policies transfer to the physical robot despite being trained entirely in simulation. Our method does not rely on any human demonstrations, but many behaviors found in human manipulation emerge naturally, including finger gaiting, multi-finger coordination, and the controlled use of gravity. Our results were obtained using the same distributed RL system that was used to train OpenAI Five. We also include a video of our results: https://youtu.be/jwSbzNHGflM

Google is working on some nifty new features for Google Maps, including a short list of your favorite places, the possibility of a'virtual positioning system' and more. Assistant is coming to Google Maps in a big way, with a ton of new shortcuts, as well as the ability for the digital assistant to text your friend when you're on your way. Google is rolling out a tool called'Your Match', which uses machine learning to determine your location and interests, serving up targeted suggestions for new businesses opening up in your area and more.

Developed by the University of South Australia in partnership with the University of Stuttgart, Flinders University and the Max Planck Institute for Informatics in Germany, the research uses state-of-the-art machine-learning algorithms to demonstrate a link between personality and eye movements. Findings show that people's eye movements reveal whether they are sociable, conscientious or curious, with the algorithm software reliably recognising four of the Big Five personality traits: neuroticism, extroversion, agreeableness, and conscientiousness. Researchers tracked the eye movements of 42 participants as they undertook everyday tasks around a university campus, and subsequently assessed their personality traits using well-established questionnaires. UniSA's Dr Tobias Loetscher says the study provides new links between previously under-investigated eye movements and personality traits and delivers important insights for emerging fields of social signal processing and social robotics. "There's certainly the potential for these findings to improve human-machine interactions," Dr Loetscher says. "People are always looking for improved, personalised services.