Artificial intelligence (AI) stepped out of the research labs and into the limelight at the 17th Cebit exhibition and conference held in Sydney, Australia. You forgot to provide an Email Address. This email address doesn't appear to be valid. This email address is already registered. You have exceeded the maximum character limit.

Spectral dimensionality reduction methods enable linear separations of complex data with high-dimensional features in a reduced space. However, these methods do not always give the desired results due to irregularities or uncertainties of the data. Thus, we consider aggressively modifying the scales of the features to obtain the desired classification. Using prior knowledge on the labels of partial samples to specify the Fiedler vector, we formulate an eigenvalue problem of a linear matrix pencil whose eigenvector has the feature scaling factors. The resulting factors can modify the features of entire samples to form clusters in the reduced space, according to the known labels. In this study, we propose new dimensionality reduction methods supervised using the feature scaling associated with the spectral clustering. Numerical experiments show that the proposed methods outperform well-established supervised methods for toy problems with more samples than features, and are more robust regarding clustering than existing methods. Also, the proposed methods outperform existing methods regarding classification for real-world problems with more features than samples of gene expression profiles of cancer diseases. Furthermore, the feature scaling tends to improve the clustering and classification accuracies of existing unsupervised methods, as the proportion of training data increases.

To power RoboFly, the engineers pointed an invisible laser beam (shown here in red laser) at a photovoltaic cell, which is attached above the robot and converts the laser light into electricity.Mark Stone/University of Washington Insect-sized flying robots could help with time-consuming tasks like surveying crop growth on large farms or sniffing out gas leaks. These robots soar by fluttering tiny wings because they are too small to use propellers, like those seen on their larger drone cousins. Small size is advantageous: These robots are cheap to make and can easily slip into tight places that are inaccessible to big drones. But current flying robo-insects are still tethered to the ground. The electronics they need to power and control their wings are too heavy for these miniature robots to carry.

When you consider the dominant trends in information technology, they don't come much bigger than artificial intelligence (AI) and machine learning (ML). Both undergoing rapid development, they have the potential to reshape many areas of both business and daily life. Analyst company IDC forecasts that revenues from AI and ML systems will total (US) $46 billion by 2020. Specifically in Australia, Forrester predicts the AI market to be worth AU$2bn. The potential of these technologies are so vast that even the Australian government is investing in AI and ML. In its recent Budget 2018-19 announcement, the government has committed to invest AU$29.9 million over four years in AI and ML.

Miniscule robotic drones might be the future, but they've been tricky to get off the ground. Until now, any wing-flapping insect robot had to have a power source, making it too heavy to lift off with its tiny wings. Now, however, researchers at the University of Washington have found a way to transmit power to a flying robotic insect (lovingly dubbed RoboFly) via laser, obviating the need for a separate power supply. The team is set to present its findings in a paper at the International Conference on Robotics and Automation in Brisbane, Australia on May 23rd. "Before now, the concept of wireless insect-sized flying robots was science fiction, said co-author and assistant professor Sawyer Fuller in a statement. "Would we ever be able to make them work without needing a wire?

We introduce the open-ended, modular, self-improving Omega AI unification architecture which is a refinement of Solomonoff's Alpha architecture, as considered from first principles. The architecture embodies several crucial principles of general intelligence including diversity of representations, diversity of data types, integrated memory, modularity, and higher-order cognition. We retain the basic design of a fundamental algorithmic substrate called an "AI kernel" for problem solving and basic cognitive functions like memory, and a larger, modular architecture that re-uses the kernel in many ways. Omega includes eight representation languages and six classes of neural networks, which are briefly introduced. The architecture is intended to initially address data science automation, hence it includes many problem solving methods for statistical tasks. We review the broad software architecture, higher-order cognition, self-improvement, modular neural architectures, intelligent agents, the process and memory hierarchy, hardware abstraction, peer-to-peer computing, and data abstraction facility.

For robots of all sizes, power is a fundamental problem. Any robot that moves is constrained in one way or another by power supply, whether it's relying on carrying around heavy batteries, combustion engines, fuel cells, or anything else. It's particularly tricky to manage power as your robot gets smaller, since it's much more straightforward to scale these things up rather than down--and for really tiny robots (with masses in the hundreds of milligrams range), especially those that demand a lot of power, there really isn't a good solution. In practice, this means that on the scale of small insects robots often depend on tethers for power, which isn't ideal for making them practical in the long term. At the IEEE International Conference on Robotics and Automation in Brisbane, Australia, next week, roboticists from the University of Washington, in Seattle, will present RoboFly, a laser-powered insect-sized flapping wing robot that performs the first (very brief) untethered flight of a robot at such a small scale.

MOA is the most popular open source framework for data stream mining, with a very active growing community (blog). It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation. Related to the WEKA project, MOA is also written in Java, while scaling to more demanding problems. MOA can be extended with new mining algorithms, and new stream generators or evaluation measures. The goal is to provide a benchmark suite for the stream mining community.

Recent years have seen rising needs for location-based services in our everyday life. Aside from the many advantages provided by these services, they have caused serious concerns regarding the location privacy of users. An adversary such as an untrusted location-based server can monitor the queried locations by a user to infer critical information such as the user's home address, health conditions, shopping habits, etc. To address this issue, dummy-based algorithms have been developed to increase the anonymity of users, and thus, protecting their privacy. Unfortunately, the existing algorithms only consider a limited amount of side information known by an adversary which may face more serious challenges in practice. In this paper, we incorporate a new type of side information based on consecutive location changes of users and propose a new metric called transition-entropy to investigate the location privacy preservation, followed by two algorithms to improve the transition-entropy for a given dummy generation algorithm. Then, we develop an attack model based on the Viterbi algorithm which can significantly threaten the location privacy of the users. Next, in order to protect the users from Viterbi attack, we propose an algorithm called robust dummy generation (RDG) which can resist against the Viterbi attack while maintaining a high performance in terms of the privacy metrics introduced in the paper. All the algorithms are applied and analyzed on a real-life dataset.

Periodicity is often studied in timeseries modelling with autoregressive methods but is less popular in the kernel literature, particularly for higher dimensional problems such as in textures, crystallography, and quantum mechanics. Large datasets often make modelling periodicity untenable for otherwise powerful non-parametric methods like Gaussian Processes (GPs) which typically incur an $\mathcal{O}(N^3)$ computational burden and, consequently, are unable to scale to larger datasets. To this end we introduce a method termed \emph{Index Set Fourier Series Features} to tractably exploit multivariate Fourier series and efficiently decompose periodic kernels on higher-dimensional data into a series of basis functions. We show that our approximation produces significantly less predictive error than alternative approaches such as those based on random Fourier features and achieves better generalisation on regression problems with periodic data.