This speech was given by Dr Larry Marshall at the AFR Innovation Summit on Monday 30 July 2018. I would like to begin by acknowledging the Gadigal people of the Eora nation as the Traditional Owners of the land that we are on today, and pay my respect to their Elders past and present. It's great to be back at the AFR Innovation Summit, and thank you to our morning speakers for setting a strong focus on the power of innovation to shape our future. Before I was the Chief Executive of Australia's national science agency, I was an inventor, an entrepreneur, a venture capitalist and of course a kid. I grew up in a time where people were obsessed with the power of the computer and its ability to replace humans, our parents told us to study computer programing.

We consider Markov models of stochastic processes where the next-step conditional distribution is defined by a kernel density estimator (KDE), similar to Markov forecast densities and certain time-series bootstrap schemes. The KDE Markov models (KDE-MMs) we discuss are nonlinear, nonparametric, fully probabilistic representations of stationary processes, based on techniques with strong asymptotic consistency properties. The models generate new data by concatenating points from the training data sequences in a context-sensitive manner, together with some additive driving noise. We present novel EM-type maximum-likelihood algorithms for data-driven bandwidth selection in KDE-MMs. Additionally, we augment the KDE-MMs with a hidden state, yielding a new model class, KDE-HMMs. The added state variable captures non-Markovian long memory and signal structure (e.g., slow oscillations), complementing the short-range dependences described by the Markov process. The resulting joint Markov and hidden-Markov structure is appealing for modelling complex real-world processes such as speech signals. We present guaranteed-ascent EM-update equations for model parameters in the case of Gaussian kernels, as well as relaxed update formulas that greatly accelerate training in practice. Experiments demonstrate increased held-out set probability for KDE-HMMs on several challenging natural and synthetic data series, compared to traditional techniques such as autoregressive models, HMMs, and their combinations.

Deep neural networks bring in impressive accuracy in various applications, but the success often relies on the heavy network architecture. Taking well-trained heavy networks as teachers, classical teacher-student learning paradigm aims to learn a student network that is lightweight yet accurate. In this way, a portable student network with significantly fewer parameters can achieve a considerable accuracy which is comparable to that of teacher network. However, beyond accuracy, robustness of the learned student network against perturbation is also essential for practical uses. Existing teacher-student learning frameworks mainly focus on accuracy and compression ratios, but ignore the robustness. In this paper, we make the student network produce more confident predictions with the help of the teacher network, and analyze the lower bound of the perturbation that will destroy the confidence of the student network. Two important objectives regarding prediction scores and gradients of examples are developed to maximize this lower bound, so as to enhance the robustness of the student network without sacrificing the performance. Experiments on benchmark datasets demonstrate the efficiency of the proposed approach to learn robust student networks which have satisfying accuracy and compact sizes.

We propose to incorporate adversarial dropout in generative multi-adversarial networks, by omitting or dropping out, the feedback of each discriminator in the framework with some probability at the end of each batch. Our approach forces the single generator not to constrain its output to satisfy a single discriminator, but, instead, to satisfy a dynamic ensemble of discriminators. We show that this leads to a more generalized generator, promoting variety in the generated samples and avoiding the common mode collapse problem commonly experienced with generative adversarial networks (GANs). We further provide evidence that the proposed framework, named Dropout-GAN, promotes sample diversity both within and across epochs, eliminating mode collapse and stabilizing training.

Social media has become prevalent and important for social networking and opinion sharing in recent years [1]. By changing the way we perceive and interact with the world, social media has changed our lives profoundly [2], [3]. With millions of posts and replies uploaded every day on social media such as Facebook, Twitters and YouTube, it is an abundant and informative data source of public opinions; thus, it has attracted lots of attention from both academia and industry to understand people and society [4]-[6]. Most previous text mining-based social media analysis focused on Twitter and Facebook [7]. YouTube, generally considered as a video platform, the values of its text comments below videos have long been underestimated. Being the second most popular website in the world [8] and having 1.9 billion active users [9], YouTube is an attractive source of research in social media analysis with immense potentials. Recent developments in autonomous vehicle technology have helped bring self-driving vehicles to the forefront of public interest [10].

Answer Set Programming (ASP) is a well-known declarative problem solving approach based on nonmonotonic logic programs, which has been successfully applied to a wide range of applications in artificial intelligence and beyond. To address the needs of modern applications, HEX-programs were introduced as an extension of ASP with external atoms for accessing information outside programs via an API style bi-directional interface mechanism. To evaluate such programs, conflict-driving learning algorithms for SAT and ASP solving have been extended in order to capture the semantics of external atoms. However, a drawback of the state-of-the-art approach is that external atoms are only evaluated under complete assignments (i.e., input to the external source) while in practice, their values often can be determined already based on partial assignments alone (i.e., from incomplete input to the external source). This prevents early backtracking in case of conflicts, and hinders more efficient evaluation of HEX-programs. We thus extend the notion of external atoms to allow for three-valued evaluation under partial assignments, while the two-valued semantics of the overall HEX-formalism remains unchanged. This paves the way for three enhancements: first, to evaluate external sources at any point during model search, which can trigger learning knowledge about the source behavior and/or early backtracking in the spirit of theory propagation in SAT modulo theories (SMT). Second, to optimize the knowledge learned in terms of so-called nogoods, which roughly speaking are impossible input-output configurations. Shrinking nogoods to their relevant input part leads to more effective search space pruning. And third, to make a necessary minimality check of candidate answer sets more efficient by exploiting early external evaluation calls. As this check usually accounts for a large share of the total runtime, optimization is here particularly important. We further present an experimental evaluation of an implementation of a novel HEX-algorithm that incorporates these enhancements using a benchmark suite. Our results demonstrate a clear efficiency gain over the state-of-the-art HEX-solver for the benchmarks, and provide insights regarding the most effective combinations of solver configurations.

Queensland artificial intelligence technology has joined forces with the state's world-leading medical research institute to tailor cancer patients' treatment and improve its effectiveness. The collaboration is an Australian first, according to the researchers involved, and will span at least two years after receiving $2.6 million in funding from the federal government last week. The project will bring together medical institutes and specialised Brisbane research companies. Brisbane AI company Max Kelsen has partnered with the internationally renowned QIMR Berghofer Medical Research Institute, precision-analytics start-up genomiQa, genomics researcher BGI Australia and the Royal Brisbane and Women's Hospital. Cancer patients' genetics will be analysed by AI in the hope of finding more complex patterns and therefore helping doctors to decide which treatment would be most effective for the individuals.

Original and learnt clauses in Conflict-Driven Clause Learning (CDCL) SAT solvers often contain redundant literals. This may have a negative impact on performance because redundant literals may deteriorate both the effectiveness of Boolean constraint propagation and the quality of subsequent learnt clauses. To overcome this drawback, we propose a clause vivification approach that eliminates redundant literals by applying unit propagation. The proposed clause vivification is activated before the SAT solver triggers some selected restarts, and only affects a subset of original and learnt clauses, which are considered to be more relevant according to metrics like the literal block distance (LBD). Moreover, we conducted an empirical investigation with instances coming from the hard combinatorial and application categories of recent SAT competitions. The results show that a remarkable number of additional instances are solved when the proposed approach is incorporated into five of the best performing CDCL SAT solvers (Glucose, TC_Glucose, COMiniSatPS, MapleCOMSPS and MapleCOMSPS_LRB). More importantly, the empirical investigation includes an in-depth analysis of the effectiveness of clause vivification. It is worth mentioning that one of the SAT solvers described here was ranked first in the main track of SAT Competition 2017 thanks to the incorporation of the proposed clause vivification. That solver was further improved in this paper and won the bronze medal in the main track of SAT Competition 2018.

New research proves that eyes might in fact be windows to the soul. Over the past few years, eye tracking technology has emerged as a field of much academic and corporate interest. With major acquisitions by Apple (SMI) and Oculus (The Eye Tribe), it's clear that major international companies regard eye tracking technology as an vital facet of Industry 4.0 -- particularly in its integration with virtual and augmented reality technologies (VR/AR), which involve persistent interaction with the human eye. In the study, researchers tracked 42 participants' eye movements while going about their day on a university campus, and matrixed these findings against user questionnaires. The results assert that machine learning can in fact deduce important personality traits with appropriate datasets -- with the algorithm reliably identifying four of the "Big Five" human personality traits: agreeableness, conscientiousness, extroversion, and neuroticism. In a statement from UniSA, Senior Lecturer of Psychology Dr. Tobias Loetscher explained that this research establishes a meaningful link between our eye motions and our innate and learned characteristics: People are always looking for improved, personalised [sic] services.

President Donald Trump's July 19 executive order establishing the President's National Council for the American Worker is directed at preparing Americans for the workplace of the future. Although short on specifics, the order sends a powerful message about the need for revitalizing educational opportunities if Americans are to thrive in the era of big data, robots and artificial intelligence. The president's intent is to lay the groundwork for tackling a national "skills crisis." His order accepts that Americans need additional skills to fill the current 6.7 million job vacancies. In fact, the executive order gives official imprimatur to what many in industry and academia have feared for some time: "The economy is changing at a rapid pace because of the technology, automation, and artificial intelligence," and existing programs have "prepared Americans for the economy of the past."