A mechatronic engineering student from Australia's Curtin University had developed an intelligent system capable of reading people's emotions in real-time. This project offers potential benefits for national security as well as health authorities. According to a recent press release, three artificial intelligence (AI) algorithms were used to create the system. The algorithms are capable of assessing emotional reactions via the monitoring of real-time video footage. Final-year student, Mr. Jordan Vice, was invited to present his work to the First IEEE International Conference on Cognitive Machine Intelligence held in Los Angeles recently.

Virtual avatars might well be the future of customer support. According to Juniper Research, conversational assistants will drive cost savings of over $8 billion annually by 2022 (up from $20 million in 2017). In fact, chatbots are anticipated to power 85% of all customer service interactions by year-end 2020 -- already, 42% of consumers use them on the regular. It was with this in mind that Greg Cross and Mark Sagar, a former special projects supervisor at director Peter Jackson's Weta Digital, founded Soul Machines in 2016. The goal was to develop a suite enabling clients to build interactive customer experiences.

The connectivity structure of graphs is typically related to the attributes of the nodes. In social networks for example, the probability of a friendship between two people depends on their attributes, such as their age, address, and hobbies. The connectivity of a graph can thus possibly be understood in terms of patterns of the form 'the subgroup of individuals with properties X are often (or rarely) friends with individuals in another subgroup with properties Y'. Such rules present potentially actionable and generalizable insights into the graph. We present a method that finds pairs of node subgroups between which the edge density is interestingly high or low, using an information-theoretic definition of interestingness. This interestingness is quantified subjectively, to contrast with prior information an analyst may have about the graph. This view immediately enables iterative mining of such patterns. Our work generalizes prior work on dense subgraph mining (i.e. subgraphs induced by a single subgroup). Moreover, not only is the proposed method more general, we also demonstrate considerable practical advantages for the single subgroup special case.

We propose to tackle the mode collapse problem in generative adversarial networks (GANs) by using multiple discriminators and assigning a different portion of each minibatch, called microbatch, to each discriminator. We gradually change each discriminator's task from distinguishing between real and fake samples to discriminating samples coming from inside or outside its assigned microbatch by using a diversity parameter $\alpha$. The generator is then forced to promote variety in each minibatch to make the microbatch discrimination harder to achieve by each discriminator. Thus, all models in our framework benefit from having variety in the generated set to reduce their respective losses. We show evidence that our solution promotes sample diversity since early training stages on multiple datasets.

Aviation Maintenance Administrationman 3rd Class Shea Wright, assigned to the Skinny Dragons of Patrol Squadron (VP) 4, recovers a squadron P-8A Poseidon maritime patrol and reconnaissance aircraft following an anti-submarine warfare mission over the Atlantic Ocean, Nov. 30, 2019. The increasingly global reach of Chinese nuclear-armed ballistic missile submarines, armed with JL-2 weapons reportedly able to hit parts of the U.S., continues to inspire an ongoing Navy effort to accelerate production of attack submarines, prepare long-dwell drones for deployment to the Pacific and continue acquisition of torpedo-armed sub-hunting planes such as the P-8/A Poseidon. The Navy has been moving quickly to increase its fleet of Poseidon's on an accelerated timetable; in the Navy's 2020 budget, the service was authorized for a near term increase in Poseidon production by three, moving funding for the year up for nine Poseidons, as cited in a report from USNI news. Last year, the Navy awarded Boeing a $2.4 billion deal to produce 19 more P-8A Poseidon surveillance and attack planes. The Poseidon increase appears to align with the service's overall Pacific theater strategy, which makes a point to sustain peaceful, yet vital surveillance and Freedom of Navigation missions in the region.

Artificial intelligence and machine learning (AI/ML) systems are growing exponentially around the world and is estimated to generate AU$22.17 trillion to the global economy by 2030. The Australian Government's Artificial Intelligence Technology Roadmap, developed by Data61, identified Australia's need for up to 161,000 new specialist AI workers by 2030. Stuart Ayres, NSW Minister for Jobs, Investment, Tourism and Western Sydney, said: "NeurIPS 2021 will propel Australia's research and innovative discoveries to the forefront – bringing with it opportunities for trade and investment and talent attraction as well as helping to build Sydney's brand as an intellectual capital." Dr. Terrence Sejnowski, President of the NeurIPS Foundation, agreed that it was a "significant step" bringing the conference to Australia. It will be the first time NeurIPS is held in the Asia-Pacific region, and only the third time it has been held outside North America.

Innovation ecosystems can be naturally described as a collection of networked entities, such as experts, institutions, projects, technologies and products. Representing in a machine-readable form these entities and their relations is not entirely attainable, due to the existence of abstract concepts such as knowledge and due to the confidential, non-public nature of this information, but even its partial depiction is of strong interest. The representation of innovation ecosystems incarnated as knowledge graphs would enable the generation of reports with new insights, the execution of advanced data analysis tasks. An ontology to capture the essential entities and relations is presented, as well as the description of data sources, which can be used to populate innovation knowledge graphs. Finally, the application case of the Universidad Politecnica de Madrid is presented, as well as an insight of future applications.

Arguably, the most important scenario for search technology is lifelong learning and education, both for students and all citizens. Human learning is a complex multidimensional activity, which includes procedural learning (e.g., activity patterns associated with cooking, sports) and knowledge-based learning (e.g., mathematics, genetics). It also includes different levels of learning, such as the ability to solve an individual math problem correctly. It also includes the development of meta-cognitive self-regulatory abilities, such as recognizing the type of problem being solved and whether one is in an error state. These latter types of awareness enable correctly regulating ones approach to solving a problem, and recognizing when one is off track by repairing momentary errors as needed. Later stages of learning enable the generalization of learned skills or information from one context or domain to others such as applying math problem solving to calculations in the wild (e.g., calculation of garden space, engineering calculations required for a structurally sound building).

Mark Wallace 1 and Aldeida Aleti 1 1 Monash University, Wellington Road, Clayton, Vic and 3800, Australia Abstract For most practical optimisation problems local search outperforms random sampling - despite the "No Free Lunch Theorem". This paper introduces a property of search landscapes termed Neighbours' Similar Fitness (NSF) that underlies the good performance of neighbourhood search in terms of local improvement . Though necessary, NSF is not sufficient to ensure that searching for improvement among the neighbours of a good solution is better than random search. The paper introduces an additional (natural) property which supports a general proof that, for NSF landscapes, neighbourhood search beats random search. 1 Introduction Local Search is a successful class of methods used to solve many large complex optimisation problems. A problem (S,f) is defined as a set S of candidate solutions, termed its search space, and a fitness function f that maps candidate solutions to a fitness measure. Many researchers have explored why different forms of local search [ Burke and Kendal, 2014 ] are so effective, and deep theoretical studies have been published on the performance of algorithms on specific classes of problems [ Michiels et al., 2007 ] . Our focus is on challenging problems for which it is hard to find optimal (or just "good") solutions. In section 5 it will also be shown that all the example hard problems (classed as PLS-Complete) in [ Michiels et al., 2007 ] have this same property that solutions thin out towards the optimum.