Lendi is Australia's fastest-growing Fintech business and we're building a technology-enabled platform to take the hard work out of home loans. We're passionate about how technology can revolutionise our industry and solve a key pain point in peoples' lives. In an ever-changing regulatory environment, Lendi Group is leading the charge in developing industry-first technology designed to offer Australian homeowners transparency, simplicity and convenience in their home loan experience. Engineering is forefront in this charge as we look to automate and build out scalable and reliable systems to support our customers and counterparty banks. Lendi is looking for an experienced BI Analyst to join our BI team for an initial 6 month contract.

(Sivanantham and Seran, 2019). It is widely spoken in the southern state of Tamil Nadu in India, Combating offensive content is crucial for different Sri Lanka, Malaysia, and Singapore. Tamil is an entities involved in content moderation, which official language of Tamil Nadu, Sri Lanka, Singapore, includes social media companies as well as individuals and the Union Territory of Puducherry in (Kumaresan et al., 2021; Chakravarthi and India. Significant minority speak Tamil in the four Muralidaran, 2021). To this end, moderation is other South Indian states of Kerala, Karnataka, often restrictive with either usage of human content Andhra Pradesh, and Telangana, as well as the moderators, who are expected to read through Union Territory of the Andaman and Nicobar Islands the content and flag the offensive mentions (Arsht (Sakuntharaj and Mahesan, 2021, 2017, 2016; and Etcovitch, 2018). Alternatively, there are Thavareesan and Mahesan, 2019, 2020a,b, 2021).

We consider event extraction in a generative manner with template-based conditional generation. Although there is a rising trend of casting the task of event extraction as a sequence generation problem with prompts, these generation-based methods have two significant challenges, including using suboptimal prompts and static event type information. In this paper, we propose a generative template-based event extraction method with dynamic prefix (GTEE-DynPref) by integrating context information with type-specific prefixes to learn a context-specific prefix for each context. Experimental results show that our model achieves competitive results with the state-of-the-art classification-based model OneIE on ACE 2005 and achieves the best performances on ERE. Additionally, our model is proven to be portable to new types of events effectively.

We introduce a conditional compression problem and propose a fast framework for tackling it. The problem is how to quickly compress a pretrained large neural network into optimal smaller networks given target contexts, e.g. a context involving only a subset of classes or a context where only limited compute resource is available. To solve this, we propose an efficient Bayesian framework to compress a given large network into much smaller size tailored to meet each contextual requirement. We employ a hypernetwork to parameterize the posterior distribution of weights given conditional inputs and minimize a variational objective of this Bayesian neural network. To further reduce the network sizes, we propose a new input-output group sparsity factorization of weights to encourage more sparseness in the generated weights. Our methods can quickly generate compressed networks with significantly smaller sizes than baseline methods.

A small island in the South Pacific Ocean recently shot to fame by becoming the first territory on our planet to derive its energy needs from the Sun. Covering a small area of 10 square kilometers, Tokelau is a part of New Zealand and lies to the North of Samoan islands . Funded by the government of New Zealand, Tokelau spent about $7 million to put in place three solar grids that will now enable its 1500 residents to harness and utilize solar energy for their daily needs. Why spend $7 million for a power plant in the middle of nowhere you might ask! While the small island generates a small sum of $ 500,000 every year by selling agricultural produce, it spends over $2.8 million, most of which is spent of food and fuel.

We study COMP-AMS, a distributed optimization framework based on gradient averaging and adaptive AMSGrad algorithm. Gradient compression with error feedback is applied to reduce the communication cost in the gradient transmission process. Our convergence analysis of COMP-AMS shows that such compressed gradient averaging strategy yields same convergence rate as standard AMSGrad, and also exhibits the linear speedup effect w.r.t. the number of local workers. Compared with recently proposed protocols on distributed adaptive methods, COMP-AMS is simple and convenient. Numerical experiments are conducted to justify the theoretical findings, and demonstrate that the proposed method can achieve same test accuracy as the full-gradient AMSGrad with substantial communication savings. With its simplicity and efficiency, COMP-AMS can serve as a useful distributed training framework for adaptive gradient methods.

Eileen Yu began covering the IT industry when Asynchronous Transfer Mode was still hip and e-commerce was the new buzzword. Currently an independent business technology journalist and content specialist based in Singapore, she has over 20 years of industry experience with various publications including ZDNet, IDG, and Singapore Press Holdings. If applied inappropriately, artificial intelligence (AI) can bring more harm than good. But, it can offer a much-needed helping hand when humans are unable to find comfort from their own kind. AI hasn't always gotten a good rep.

China has developed a remote sensing satellite powered by the latest artificial intelligence technology that helps the People's Liberation Army (PLA) trace the movements of U.S. aircraft carriers. A new study by Chinese space scientists said the technology was put into use last year in June to detect the movements of the USS Harry S. Truman. The satellite, which has not been named in the study, is said to have alerted Beijing with the precise coordinates of the carrier as it headed to a strait transit drill off the coast of Long Island in New York, reported South China Morning Post. According to the study published by the domestic peer-reviewed journal Spacecraft Engineering last month, the drill held on June 17 involved a joint action of seven warships and planes beside the USS Harry S Truman. Before this satellite, the PLA had to go through a large amount of raw satellite data on the ground to get a clue about such drills happening in the U.S. home waters, and the results usually came after the event was over, the report added. But, with the AI-powered satellites, China could now "live stream" military activities or assets of interest on the other side of the planet, the report quoted the study by space scientist Yang Fang and her colleagues with DFH Satellite.

Notorious facial recognition company Clearview AI has agreed to permanently halt sales of its massive biometric database to all private companies and individuals in the United States as part of a legal settlement with the American Civil Liberties Union, per court records. Monday's announcement marks the close of a two-year legal dispute brought by the ACLU and privacy advocate groups in May of 2020 against the company over allegations that it had violated BIPA, the 2008 Illinois Biometric Information Privacy Act. This act requires companies to obtain permission before harvesting a person's biometric information -- fingerprints, gait metrics, iris scans and faceprints for example -- and empowers users to sue the companies who do not. "Fourteen years ago, the ACLU of Illinois led the effort to enact BIPA – a groundbreaking statute to deal with the growing use of sensitive biometric information without any notice and without meaningful consent," Rebecca Glenberg, staff attorney for the ACLU of Illinois, said in a statement. "BIPA was intended to curb exactly the kind of broad-based surveillance that Clearview's app enables. Today's agreement begins to ensure that Clearview complies with the law. This should be a strong signal to other state legislatures to adopt similar statutes."

Previous work has shown promising results in performing entity linking by measuring not only the affinities between mentions and entities but also those amongst mentions. In this paper, we present novel training and inference procedures that fully utilize mention-to-mention affinities by building minimum arborescences (i.e., directed spanning trees) over mentions and entities across documents in order to make linking decisions. We also show that this method gracefully extends to entity discovery, enabling the clustering of mentions that do not have an associated entity in the knowledge base. We evaluate our approach on the Zero-Shot Entity Linking dataset and MedMentions, the largest publicly available biomedical dataset, and show significant improvements in performance for both entity linking and discovery compared to identically parameterized models. We further show significant efficiency improvements with only a small loss in accuracy over previous work, which use more computationally expensive models.