Twenty IT leaders look into their crystal balls to predict the technologies and trends that will drive the sector in 2020. CIO Australia asked Australian technology bosses about their top line predictions for 2020, the technologies that will have the greatest impact next year, and what top trends will impact the IT and business landscape. Here are the predictions from IT leaders across vendor land to CIOs and CTOs across a host of industries. Intelligent systems (machine learning, artificial intelligence and automation) are the top trends in 2020. Intelligent systems will have a significant impact on increasing situational awareness (insights) and using these insights to enhance decision making – to deliver optimal outcomes for customers.

Road accidents constitute a major problem in our societies around the world. The World Health Organization(WHO) estimated that 1.25 million deaths were related to road traffic injuries in the year 2010. For the year 2016, the USA alone had recorded 37, 461 motor vehicle crash-related deaths, averaging around 102 people per day. In Europe, the statistics also indicate that each minute, there are 50 road deaths recorded in the year 2017. Can machine learning help us understand the causes and the factors that affect car crash severity?

An improved online learning algorithm for general fuzzy min-max neural network Thanh Tung Khuat Advanced Analytics Institute University of T echnology Sydney Sydney, Australia thanhtung.khuat@student.uts.edu.au Abstract --This paper proposes an improved version of the current online learning algorithm for a general fuzzy min-max neural network (GFMM) to tackle existing issues concerning expansion and contraction steps as well as the way of dealing with unseen data located on decision boundaries. These drawbacks lower its classification performance, so an improved algorithm is proposed in this study to address the above limitations. The proposed approach does not use the contraction process for overlapping hyperboxes, which is more likely to increase the error rate as shown in the literature. The empirical results indicated the improvement in the classification accuracy and stability of the proposed method compared to the original version and other fuzzy min-max classifiers. In order to reduce the sensitivity to the training samples presentation order of this new online learning algorithm, a simple ensemble method is also proposed. I NTRODUCTION Artificial neural networks (ANNs) are one of the most widely used methods for dealing with classification problems as well as real-world applications [1]. However, the main disadvantage of the original ANNs is that they do not have the capability of giving explanations of their predictive results to humans explicitly. This drawback restricts the widespread use of the ANNs for critical domains such as healthcare and criminal justice [2]. In a recent study, Rudin [2] has highlighted that there is a high demand for interpretable models to substitute black-box models in assisting decision-makers in areas with the requirement of high safety and trust.

The use of autonomous bots for automatic support in software development tasks is increasing. In the past, however, they were not always perceived positively and sometimes experienced a negative bias compared to their human counterparts. We conducted a qualitative study in which we deployed an autonomous refactoring bot for 41 days in a student software development project. In between and at the end, we conducted semi-structured interviews to find out how developers perceive the bot and whether they are more or less critical when reviewing the contributions of a bot compared to human contributions. Our findings show that the bot was perceived as a useful and unobtrusive contributor, and developers were no more critical of it than they were about their human colleagues, but only a few team members felt responsible for the bot.

Fatigue is the most vital factor of road fatalities and one manifestation of fatigue during driving is drowsiness . In this paper, we propose using deep Q - learning to analyze an electroencephalogram (EEG) dataset captured during a simulated endurance drivi ng test . By measur ing the correlation between drowsiness and driving performance, t h is experiment represents an important brain - computer interface (BCI) paradigm especially from an application perspective. We adapt the terminologies in the driving test to fit the reinforcement learning framework, thus formulate the drowsiness estimation problem as an optimization of a Q - learning task . B y referring to the latest deep Q - Learning technologies and attending to the characteristics of EEG data, we tailor a deep Q - network for action proposition that can indirectly estimate drowsiness . Our results show that the trained model can trace the variations of mind state in a satisfactory way against the testing EEG data, which demonstrates the feasibility and practicab ilit y of this new computation paradigm . We also show that our method outperforms the supervised learning counterpart and is superior for real applications. To the best of our knowledge, we are the first to introduce the deep reinforcement learning method to th is BCI scenario, and our method can be potentially generalized to other BCI cases . Fatigue is regarded as the most severe factor causing road fatalities [1] . To understand the correlation between fatigue and driving performance, both from theory to practice, is of persistent interest for researchers.

Deep Neural Networks (DNNs) are finding important applications in safety-critical systems such as Autonomous Vehicles (AVs), where perceiving the environment correctly and robustly is necessary for safe operation. Raising unique challenges for assurance due to their black-box nature, DNNs pose a fundamental problem for regulatory acceptance of these types of systems. Robust training --- training to minimize excessive sensitivity to small changes in input --- has emerged as one promising technique to address this challenge. However, existing robust training tools are inconvenient to use or apply to existing codebases and models: they typically only support a small subset of model elements and require users to extensively rewrite the training code. In this paper we introduce a novel framework, PaRoT, developed on the popular TensorFlow platform, that greatly reduces the barrier to entry. Our framework enables robust training to be performed on arbitrary DNNs without any rewrites to the model. We demonstrate that our framework's performance is comparable to prior art, and exemplify its ease of use on off-the-shelf, trained models and on a real-world industrial application: training a robust traffic light detection network.

Automation services for complex business processes usually require a high level of information technology literacy. There is a strong demand for a smartly assisted process automation (IPA: intelligent process automation) service that enables even general users to easily use advanced automation. A natural language interface for such automation is expected as an elemental technology for the IPA realization. The workflow targeted by IPA is generally composed of a combination of multiple tasks. However, semantic parsing, one of the natural language processing methods, for such complex workflows has not yet been fully studied. The reasons are that (1) the formal expression and grammar of the workflow required for semantic analysis have not been sufficiently examined and (2) the dataset of the workflow formal expression with its corresponding natural language description required for learning workflow semantics did not exist. This paper defines a new grammar for complex workflows with chaining machine-executable meaning representations for semantic parsing. The representations are at a high abstraction level. Additionally, an approach to creating datasets is proposed based on this grammar.

At the Last Futurist, we enjoy looking at AI Trends and digital transformation trends. In between those two are more broad technology trends. In fact these topics make up the mission statement of this new news site. However the last decade had a lot of technology and gadgets that didn't fare so well in the real world. The decade was mobile all the way, with mass adoption taking place the way we might expect the brain-computer interface (BCI) to achieve mass adoption in a future decade years from now. In the decade ahead the move to automated stores and electric vehicles are real trends, but it's important to differentiate the hype from the reality. Autonomous vehicles, quantum computing going mainstream, better self-learning AI, hang on a second! Even mass adoption of digital currencies is coming faster. From computers to the internet and smart phones, a few generations shows a lot of progress. But technology never stands still. Advertising has scaled a world of surveillance capitalism normalization and an AI-arms race is now taking place. Most technology trends and AI listicles only touch the surface of how humans are embedding technology increasingly into their lives. However looking at it from the perspectives of many industries and across technology and innovation stacks gives a more complete picture. The real world and customer experience are the real tests for new technological innovations and pivots. It will take decades for 3D printing, quantum computing and an AGI to even become mature, but an age of biotechnology and AI in healthcare, education and finance is inevitable. From Huawei, to ByteDance (TikTok), to Didi, China will wage major battles for global market share in 5G, consumer apps, E-commerce, mobile payments and ride sharing, among others. Chinese led tech companies -- with the support of the Chinese Government and venture funds such as Softbank Vision Fund -- can mean that in the 2020s China's ecosystem fully replaces Silicon Valley as the leader of innovation. In 2019, some believe this has already occurred.

These six enterprise tech trends defined 2019As we kick off another year, ZDNet's global team of editors has put together a list of five tech trends that will have a significant impact on the enterprise in 2020. The effects could be negative or positive, but will undoubtedly be substantial. Our panel of editors includes -- TechRepublic's Bill Detwiler, Larry Dignan, Chris Duckett, and Steve Ranger. Take a look back at best of the decade: ZDNet's top enterprise CEOs of the 2010s The PC was supposed to die a decade ago. Instead, this happened A decade of malware: Top botnets of the 2010s A decade of hacking: The most notable cyber-security events of the 2010s Device of the decade: Why did it take nine years for the iPad to get its own operating system?

Existing Recommender Systems mainly focus on exploiting users' feedback, e.g., ratings, and reviews on common items to detect similar users. Thus, they might fail when there are no common items of interest among users. We call this problem the Data Sparsity With no Feedback on Common Items (DSW-n-FCI). Personality-based recommender systems have shown a great success to identify similar users based on their personality types. However, there are only a few personality-based recommender systems in the literature which either discover personality explicitly through filling a questionnaire that is a tedious task, or neglect the impact of users' personal interests and level of knowledge, as a key factor to increase recommendations' acceptance. Differently, we identifying users' personality type implicitly with no burden on users and incorporate it along with users' personal interests and their level of knowledge. Experimental results on a real-world dataset demonstrate the effectiveness of our model, especially in DSW-n-FCI situations.