Predicting discomfort glare in open-plan offices is a challenging problem since most of available glare metrics are developed for cellular offices which are typically daylight dominated. The problem with open-plan offices is that they are mainly dependent on electric lighting rather than daylight even when they have a fully glazed facade. In addition, the contrast between bright windows and the buildings interior can be problematic and may cause discomfort glare to the building occupants. These problems can affect occupant productivity and wellbeing. Thus, it is important to develop a predictive model to avoid discomfort glare when designing open plan offices. To the best of our knowledge, we are the first to adopt Machine Learning (ML) models to predict discomfort glare. In order to develop new glare predictive models for these types of offices, Post-Occupancy Evaluation (POE) and High Dynamic Range (HDR) images were collected from 80 occupants (n=80) in four different open-plan offices. Consequently, various multi-region luminance values, luminance and glare indices were calculated and used as input features to train ML models. The accuracy of the ML model was compared to the accuracy of 24 indices which were also evaluated using a Receiver Operating Characteristic (ROC) analysis to identify the best cutoff values (thresholds) for each index for open-plan configurations. Results showed that the ML glare model could predict glare in open-plan offices with an accuracy of 83.8% (0.80 true positive rate and 0.86 true negative rate) which outperformed the accuracy of the previously developed glare metrics.

Non-negative tensor factorization models enable predictive analysis on count data. Among them, Bayesian Poisson-Gamma models are able to derive full posterior distributions of latent factors and are less sensitive to sparse count data. However, current inference methods for these Bayesian models adopt restricted update rules for the posterior parameters. They also fail to share the update information to better cope with the data sparsity. Moreover, these models are not endowed with a component that handles the imbalance in count data values. In this paper, we propose a novel variational auto-encoder framework called VAE-BPTF which addresses the above issues. It uses multi-layer perceptron networks to encode and share complex update information. The encoded information is then reweighted per data instance to penalize common data values before aggregated to compute the posterior parameters for the latent factors. Under synthetic data evaluation, VAE-BPTF tended to recover the right number of latent factors and posterior parameter values. It also outperformed current models in both reconstruction errors and latent factor (semantic) coherence across five real-world datasets. Furthermore, the latent factors inferred by VAE-BPTF are perceived to be meaningful and coherent under a qualitative analysis.

Bayesian optimization (BO) is a popular approach for expensive black-box optimization, with applications in parameter tuning, experimental design, robotics, and so on. BO usually models the objective function by a Gaussian process (GP), and iteratively samples the next data point by maximizing some acquisition function. In this paper, we propose a new general framework for BO by generating pseudo-points (i.e., data points whose objective values are not evaluated) to improve the GP model. With the classic acquisition function, i.e., upper confidence bound (UCB), we prove a general bound on the cumulative regret, and show that the generation of pseudo-points can improve the instantaneous regret. Experiments using UCB and other acquisition functions, i.e., probability of improvement (PI) and expectation of improvement (EI), on synthetic as well as real-world problems clearly show the advantage of generating pseudo-points.

It's no secret that Disney films of yore had been plagued with racism and sexism. The company's movies have gotten more progressive in recent years --see Brave, Frozen, and Moana--but there's still lots more work to do. And now, Disney has pledged to tackle diversity in storytelling and gender bias with a little help from AI. The company has teamed up with Geena Davis and her Institute on Gender in Media to use GD-IQ: Spellcheck for Bias (Geena Davis Inclusion Quotient), a tool that analyzes TV and movie scripts to track gender and other biases. The software, codeveloped by the University of Southern California Viterbi School of Engineering, evaluates the number of male and female characters, how many characters are part of the LGBTQIA community, how many people of color are included, and how many disabled people are represented.

Conversational interfaces have gone mainstream. The technology behind keeps crossing new milestones, the result of which chatbots have transformed from simple Q&A systems to intelligent personal assistants. As a result, bots found widespread application in diverse areas, most recently in education. Although education stayed backward in terms of technology adoption, lately it took on a renewed quest to incorporate it. Educators are on the lookout for innovative ed-tech systems for efficient tutoring and students increasingly prefer personalized learning environments.

Amongst those who have, 82% answered the question they were asked. CLEARD LIFE RESPONSE: It's good to know the difference between Recruiters and the Cleard.life We can ask, in order to assess Trustworthiness and Loyalty. We can ask, in order to evaluate the potential for coercion, manipulation – assessing Trustworthiness, Maturity and Loyalty. We can ask in order to (think adult children involved in illegal conduct or having criminal associations placing the Candidate/Employer in harm) assess Trustworthiness and Loyalty.

Experts from Australia and around the world in the humanities, social and technological sciences are collaborating on a government-funded initiative to investigate how automated decision-making technologies can be used safely and ethically.

Nearly four in five (79%) Australian IT leaders believe that AI will be very or critically important to their business within two years, but only 34% have a comprehensive, company-wide AI strategy. These are among the key findings of new research by Deloitte into the state of AI in the enterprise in seven markets including Australia. According to the report, more than half (51%) of Australian respondents believe that AI will transform their business within three years. But the survey found that Australia is below the global average both in terms of the proportion of companies with a comprehensive AI strategy (35% globally) and in terms of the percentage of companies that are seasoned AI adopters (17%, compared to 21% globally). In addition, 50% of AI early adopters in Australia are still using AI to catch up or keep up with the competition rather than carve out a distinct advantage.

Rapidly maturing technologies such as 5G, artificial intelligence (AI), and quantum computing will "shift fundamentally the global balance of power", according to Dr Tobias Feakin, Australia's Ambassador for Cyber Affairs. "Those [nations] that really are at the forefront of AI and the way that it works will genuinely be at the forefront of the emerging 21st century economy," he told the Australian Cybersecurity Conference, or CyberCon, in Melbourne on Wednesday. Some nations are already positioning themselves to take advantage of these technologies. "Geopolitics now is being shaped and harnessed in a way that we probably didn't think conceivable a decade ago," Feakin said. "We need to be thinking about grand strategy in technology. How do we ensure that we're plugging into this conversation and the kinds of areas that are shaping technology, not only the technology development itself, but the kinds of legislation that shape the absorption of that technology into the global environment," he said.

For the first time ever, a human drug has been created entirely by artificial intelligence (AI). This news comes from a team at Flinders University in Australia, who claims to have created an enhanced influenza vaccine using an AI program known Search Algorithm for Ligands (SAM). Though computers have been used to make drugs before, this was the first time it was done independently by an AI system. The researchers described this drug as a flu vaccine with an added compound that better stimulates the human immune system. This addition causes more antibodies to be formed against the flu virus than with the traditional vaccination, increasing the vaccine's efficacy.