You are head of talent acquisition. You're about to make contact with what recruiters call a cold prospect, someone not actively looking for a job. You know this because their CV isn't on any job boards. Neither have they registered with LinkedIn's Open Candidate tool, which basically says "call me". And yet far from this being the shot in the dark it used to be, you're calling safe in the knowledge that the recipient will be seven times more likely to be interested in what you say.

The growing capabilities of Artificial Intelligence (AI) are changing the world as we know it. Ideas once confined to the imagination are now becoming a reality, with AI technology creating outputs either largely or entirely independent from human intervention. In 2018, an album called I AM AI was the first of its kind to be entirely composed and produced by AI technology, through a music composition software called Amper. Deep learning networks allow Amper to analyse data to learn chords, notes, genres, tempo and song length to independently compose melodies. A qualified person is an Australian citizen or a person resident in Australia.[1]

Gaussian Belief Propagation (BP) algorithm is one of the most important distributed algorithms in signal processing and statistical learning involving Markov networks. It is well known that the algorithm correctly computes marginal density functions from a high dimensional joint density function over a Markov network in a finite number of iterations when the underlying Gaussian graph is acyclic. It is also known more recently that the algorithm produces correct marginal means asymptotically for cyclic Gaussian graphs under the condition of walk summability. This paper extends this convergence result further by showing that the convergence is exponential under the walk summability condition, and provides a simple bound for the convergence rate.

In this document, we explore in more detail our published work (Komorowski, Celi, Badawi, Gordon, & Faisal, 2018) for the benefit of the AI in Healthcare research community. In the above paper, we developed the AI Clinician system, which demonstrated how reinforcement learning could be used to make useful recommendations towards optimal treatment decisions from intensive care data. Since publication a number of authors have reviewed our work (e.g. Given the difference of our framework to previous work, the fact that we are bridging two very different academic communities (intensive care and machine learning) and that our work has impact on a number of other areas with more traditional computer-based approaches (biosignal processing and control, biomedical engineering), we are providing here additional details on our recent publication. We acknowledge the online comments by Jeter et al (https://arxiv.org/abs/1902.03271). The sections of the present document are structured so as to address some of their questions. For clarity, we label figures from our main Nature Medicine publication as "M", figures from Jeter et al.'s arXiv paper as "J" and figures from our response here as "R". Jeter et al. state "the only possible response we can afford is a more aggressive and open dialogue".

Recent work in decentralized, schedule-driven traffic control has demonstrated the ability to significantly improve traffic flow efficiency in complex urban road networks. However, in situations where vehicle volumes increase to the point that the physical capacity of a road network reaches or exceeds saturation, it has been observed that the effectiveness of a schedule-driven approach begins to degrade, leading to progressively higher network congestion. In essence, the traffic control problem becomes less of a scheduling problem and more of a queue management problem in this circumstance. In this paper we propose a composite approach to real-time traffic control that uses sensed information on queue lengths to influence scheduling decisions and gracefully shift the signal control strategy to queue management in high volume/high congestion settings. Specifically, queue-length information is used to establish weights for the sensed vehicle clusters that must be scheduled through a given intersection at any point, and hence bias the wait time minimization calculation. To compute these weights, we develop a model in which successive movement phases are viewed as different states of an Ising model, and parameters quantify strength of interactions. To ensure scalability, queue information is only exchanged between direct neighbors and the asynchronous nature of local intersection scheduling is preserved. We demonstrate the potential of the approach through microscopic traffic simulation of a real-world road network, showing a 60% reduction in average wait times over the baseline schedule-driven approach in heavy traffic scenarios. We also report initial field test results, which show the ability to reduce queues during heavy traffic periods.

Many areas of AI today use benchmarks and competitions with larger and wider sets of tasks. This tries to deter AI systems (and research effort) from specialising to a single task, and encourage them to be prepared to solve previously unseen tasks. It is unclear, however, whether the methods with best performance are actually those that are most general and, in perspective, whether the trend moves towards more general AI systems. This question has a striking similarity with the analysis of the so-called positive manifold and general factors in the area of human intelligence. In this paper, we first show how the existence of a manifold (positive average pairwise task correlation) can also be analysed in AI, and how this relates to the notion of agent generality, from the individual and the populational points of view. From the populational perspective, we analyse the following question: is this manifold correlation higher for the most or for the least able group of agents? We contrast this analysis with one of the most controversial issues in human intelligence research, the so-called Spearman's Law of Diminishing Returns (SLODR), which basically states that the relevance of a general factor diminishes for most able human groups. We perform two empirical studies on these issues in AI. We analyse the results of the 2015 general video game AI (GVGAI) competition, with games as tasks and "controllers" as agents, and the results of a synthetic setting, with modified elementary cellular automata (ECA) rules as tasks and simple interactive programs as agents. In both cases, we see that SLODR doesnot appear. The data, and the use of just two scenarios, does not clearly support the reverse either, a Universal Law of Augmenting Returns (ULOAR), but calls for more experiments on this question.

In this work, a heuristic as operational tool to estimate the lactate threshold and to facilitate its integration into the training process of recreational runners is proposed. To do so, we formalize the principles for the lactate threshold estimation from empirical data and an iterative methodology that enables experience based learning. This strategy arises as a robust and adaptive approach to solve data analysis problems. We compare the results of the heuristic with the most commonly used protocol by making a first quantitative error analysis to show its reliability. Additionally, we provide a computational algorithm so that this quantitative analysis can be easily performed in other lactate threshold protocols. With this work, we have shown that a heuristic %60 of 'endurance running speed reserve', serves for the same purpose of the most commonly used protocol in recreational runners, but improving its operational limitations of accessibility and consistent use.

Human-robot interaction is easy to do badly, and very difficult to do well. One approach that has worked well for robots from R2-D2 to Kuri is to avoid the problem of language--rather than use real words to communicate with humans, you can do pretty well (on an emotional level, at least) with a variety of bleeps and bloops. But as anyone who's watched Star Wars knows, R2-D2 really has a lot going on with the noises that it makes, and those noises were carefully designed to be both expressive and responsive. Most actual robots don't have the luxury of a professional sound team (and as much post-production editing as you need), so the question becomes how to teach a robot to make the right noises at the right times. At Georgia Tech's Center for Music Technology (GTCMT), Gil Weinberg and his students have a lot of experience with robots that make noise of various sorts, and they've used a new deep learning-based technique to teach their musical robot Shimi a basic understanding of human emotions, and how to communicate back to those humans in just the right way, using music.

There has been a lot of hand-wringing in certain circles that European businesses are not exploiting advanced technologies such as AI anything like as well as US or Chinese companies. It is true we haven't (yet) spawned global giants like Google or Baidu. But O think there's a more nuanced reality. Back in November 2018, I was delighted to be invited by IBM to be a judge at its European IBM Watson Challenge event. This was a "Dragon's Den" style event where 32 IBM business partners (from an initial submission of 155 prototypes) were each invited to present an innovative AI-based business solution and associated business plan to a panel of judges (the Dragons!) over two, exhausting and intensive (but exhilarating) days.

We were hosting a Meetup on robotics in Australia and it was question time. "How do I get into artificial intelligence and machine learning from a different background?" Nick turned and called my name. I was backstage and talking to Alex. "Here he is," Nick continued, "Dan comes from a health science background, he studied nutrition, then drove Uber, learned machine learning online and has now been with Max Kelsen as a machine learning engineer for going on a year." Nick is the CEO and Co-founder of Max Kelsen, a technology company in Brisbane.