The increasingly wide use of deep neural networks (DNNs) for such computer vision tasks as facial recognition, medical imaging, object detection, and autonomous driving is going to, if not already, catch the attention of cybercriminals. DNNs have become foundational to deep learning and to the larger field of artificial intelligence (AI). They're a multi-layered class of machine learning algorithms that essentially try to mimic how a human brain works and are becoming more popular in developing modern applications. That use is expected to increase rapidly in the coming years. According to analysts with Emergen Research, the worldwide market for DNN technology will grow from $1.26bn in 2019 to $5.98bn by 2027, with demand in such industries as healthcare, banking, financial services and insurance surging.

Multimodal sentiment analysis has attracted increasing attention and lots of models have been proposed. However, the performance of the state-of-the-art models decreases sharply when they are deployed in the real world. We find that the main reason is that real-world applications can only access the text outputs by the automatic speech recognition (ASR) models, which may be with errors because of the limitation of model capacity. Through further analysis of the ASR outputs, we find that in some cases the sentiment words, the key sentiment elements in the textual modality, are recognized as other words, which makes the sentiment of the text change and hurts the performance of multimodal sentiment models directly. To address this problem, we propose the sentiment word aware multimodal refinement model (SWRM), which can dynamically refine the erroneous sentiment words by leveraging multimodal sentiment clues. Specifically, we first use the sentiment word position detection module to obtain the most possible position of the sentiment word in the text and then utilize the multimodal sentiment word refinement module to dynamically refine the sentiment word embeddings. The refined embeddings are taken as the textual inputs of the multimodal feature fusion module to predict the sentiment labels. We conduct extensive experiments on the real-world datasets including MOSI-Speechbrain, MOSI-IBM, and MOSI-iFlytek and the results demonstrate the effectiveness of our model, which surpasses the current state-of-the-art models on three datasets. Furthermore, our approach can be adapted for other multimodal feature fusion models easily. Data and code are available at https://github.com/albertwy/SWRM.

Preserving the performance of a trained model while removing unique characteristics of marked training data points is challenging. Recent research usually suggests retraining a model from scratch with remaining training data or refining the model by reverting the model optimization on the marked data points. Unfortunately, aside from their computational inefficiency, those approaches inevitably hurt the resulting model's generalization ability since they remove not only unique characteristics but also discard shared (and possibly contributive) information. To address the performance degradation problem, this paper presents a novel approach called Performance Unchanged Model Augmentation~(PUMA). The proposed PUMA framework explicitly models the influence of each training data point on the model's generalization ability with respect to various performance criteria. It then complements the negative impact of removing marked data by reweighting the remaining data optimally. To demonstrate the effectiveness of the PUMA framework, we compared it with multiple state-of-the-art data removal techniques in the experiments, where we show the PUMA can effectively and efficiently remove the unique characteristics of marked training data without retraining the model that can 1) fool a membership attack, and 2) resist performance degradation. In addition, as PUMA estimates the data importance during its operation, we show it could serve to debug mislabelled data points more efficiently than existing approaches.

A legal challenge is being prepared to overturn the Intellectual Property Office's (IPONZ) decision not to recognise a machine as an inventor. It is being led by University of Surrey law professor Ryan Abbott, who has been testing patent law around the world, including New Zealand, to see if an invention created by an artificial intelligence (AI) programme could receive a patent. The test case centres around a "creativity machine" or AI inventor programme, known as DABUS, which was developed by US-based physicist Stephen Thaler. Abbott approached Thaler about using the AI as the basis of the case and with a team of lawyers, all working pro bono, they filed patent applications in more than a dozen countries listing DABUS as the inventor of a beverage container it created. New Zealand's Assistant Commissioner of Patents rejected the initial application in January, ruling that the term "inventor" intrinsically refers to a natural person.

… and borrowing from McGregor’s repertoire, the Living Archive exploits machine learning to generate new movements in the dancer’s style, …

Imagine the benefits to communities and organizations that have access to improved bandwidth, reliability, and reduced latency, while leveraging the rich capabilities of cloud-to-edge technology without compromise to security, critical services, or key workloads. With the most complete offerings for the telecommunications industry, Microsoft is the ideal cloud provider to help operators with their digital transformation journey and enable them to deliver these innovative services to their consumer, enterprise, and public sector customers. Today, we’re announcing the next wave of Azure for Operators solutions and services.

The US scientific workforce is not representative of the population. Barriers to entry and participation have been well-studied; however, few have examined the effect of these disparities on the advancement of science. Furthermore, most studies have looked at either race or gender, failing to account for the intersection of these variables. Our analysis utilizes millions of scientific papers to study the relationship between scientists and the science they produce. We find a strong relationship between the characteristics of scientists and their research topics, suggesting that diversity changes the scientific portfolio with consequences for career advancement for minoritized individuals. Science policies should consider this relationship to increase equitable participation in the scientific workforce and thereby improve the robustness of science. The US scientific workforce is primarily composed of White men. Studies have demonstrated the systemic barriers preventing women and other minoritized populations from gaining entry to science; few, however, have taken an intersectional perspective and examined the consequences of these inequalities on scientific knowledge. We provide a large-scale bibliometric analysis of the relationship between intersectional identities, topics, and scientific impact. We find homophily between identities and topic, suggesting a relationship between diversity in the scientific workforce and expansion of the knowledge base.

For several years we have been hearing about the potential of Artificial Intelligence (AI) to improve traditional drug discovery and development. In the last two years, clinical trials have begun. The UK's Exscientia made headlines last April by announcing the start of a Phase 1 clinical trial for a drug it designed using AI for an established protein target. Recursion Pharmaceuticals in Utah uses AI to find new uses for the drugs owned by other companies. Insilico Medicine has now announced the crucial next step: the start of the world's first Phase 1 clinical trial of a drug developed from scratch using AI.

Quantum computing may sound like science fiction (especially the weird principles on which it relies); nevertheless, businesses are beginning to pay attention. According to the Venture Beat staff, "Sixty-nine percent of global enterprises have already adopted or plan to adopt quantum computing in the near term, according to a new survey of enterprise leaders commissioned by Zapata Computing. The findings suggest that quantum computing is quickly moving from the fringes and becoming a priority for enterprise digital transformation, as 74% of enterprise leaders surveyed agreed that those who fail to adopt quantum computing will fall behind."[1] The staff goes on to report, "Adoption thus far is highest in the transportation sector, where 63% of respondents reported being in the early stages of quantum adoption. This may be a reaction to the ongoing supply chain crisis, which quantum could help relieve through its potential to solve complex optimization problems common in shipping and logistics."

We have a remote-first culture and this position can be based anywhere from Australia or New Zealand. This role can be fully remote or a hybrid if you are in Melbourne. We encourage you to work in the way that best suits you. Culture Amp revolutionizes how over 25 million employees across 5,000 companies create a better world of work. As the global platform leader for employee experience, we empower companies of all sizes and industries to transform employee engagement, develop high performing teams, and retain talent via cutting-edge research, powerful technology, and the largest employee dataset in the world.