Google's entry into health diagnostics has alarmed health experts who fear a new artificial intelligence tool to identify skin conditions could lead to overdiagnosis, or rare and complex skin conditions being missed. At a technology conference in the US on Tuesday, Google revealed there are almost 10bn Google searches related to skin, nail and hair issues every year. In response, Google has developed an artificial intelligence "dermatology assist tool" for people with concerns about their skin. Users of the app can use their phone to take three images of their skin, hair or nails from different angles. The app will then ask users questions about their skin type, how long they have had the issue, and for other symptoms that help narrow down the possibilities.

The Australian government is strengthening the country's digital economy through new investments in artificial intelligence (AI), cyber security and digital government services, among other areas. The investments, aimed at bolstering Australia's competitiveness in the global technology sector, are part of the government's 2021-2022 budget, which was unveiled last week. The centrepiece of the budget is arguably the A$1.2bn Digital Economy Strategy, a set of policies and actions the government is taking to grow Australia's future as a leading digital economy by 2030. But to put that expenditure into perspective, it is less than half of the A$2.6bn earmarked for a single 6km road project in Adelaide – even as it is welcomed by some quarters of the technology industry.

We introduce Dynaboard, an evaluation-as-a-service framework for hosting benchmarks and conducting holistic model comparison, integrated with the Dynabench platform. Our platform evaluates NLP models directly instead of relying on self-reported metrics or predictions on a single dataset. Under this paradigm, models are submitted to be evaluated in the cloud, circumventing the issues of reproducibility, accessibility, and backwards compatibility that often hinder benchmarking in NLP. This allows users to interact with uploaded models in real time to assess their quality, and permits the collection of additional metrics such as memory use, throughput, and robustness, which -- despite their importance to practitioners -- have traditionally been absent from leaderboards. On each task, models are ranked according to the Dynascore, a novel utility-based aggregation of these statistics, which users can customize to better reflect their preferences, placing more/less weight on a particular axis of evaluation or dataset. As state-of-the-art NLP models push the limits of traditional benchmarks, Dynaboard offers a standardized solution for a more diverse and comprehensive evaluation of model quality.

This study aims to optimize Deep Feedforward Neural Networks (DFNNs) training using nature-inspired optimization algorithms, such as PSO, MTO, and its variant called MTOCL. We show how these algorithms efficiently update the weights of DFNNs when learning from data. We evaluate the performance of DFNN fused with optimization algorithms using three Wisconsin breast cancer datasets, Original, Diagnostic, and Prognosis, under different experimental scenarios. The empirical analysis demonstrates that MTOCL is the most performing in most scenarios across the three datasets. Also, MTOCL is comparable to past weight optimization algorithms for the original dataset, and superior for the other datasets, especially for the challenging Prognostic dataset.

It's a question that has baffled most men for years – what do women want? Now, a new survey has revealed exactly what females rate the highest when it comes to sexual attraction, as well as what men's priorities are. The findings suggest that while women rate age, income and personality highly, men are more focused on looks. The researchers suggest that these differences may occur as a result of the fact that women's window for reproduction is more limited than men's, so they'can't risk choosing poorly.' In the study, researchers from Queensland University of Technology in Brisbane surveyed 7,325 users of dating websites about what they look for in a potential partner.

Last week, our trending stock lists collected a motley crew of companies ranging from biotech to regular tech to home entertainment tech. In general, there was just a lot of tech. For the week of May 16, many of those same stocks hit our trending roundup again – for good reason. From a 49 million square foot downgrade to a pilot program intended to put credit cards in the hands of the credit-less, here's an inside look at what's making the market pop. Q.ai runs daily factor models to get the most up-to-date reading on stocks and ETFs.

The planet is faced with overwhelming environmental problems. Rising environmental pollution is wreaking havoc on nature and endangering the lives of millions of humans. Evolving digital technologies offer a bottom-up solution to tackling climate change. These digital technologies have a revolutionary way to involve citizens in addressing local and global issues. Young people are generally the most worried regarding the consequences of climate change. Early findings of ongoing projects suggest a high potential for leveraging digital technology in joint measures to preserve the world for ourselves and future generations.

The 3rd edition of the Montreal AI Ethics Institute's The State of AI Ethics captures the most relevant developments in AI Ethics since October 2020. It aims to help anyone, from machine learning experts to human rights activists and policymakers, quickly digest and understand the field's ever-changing developments. Through research and article summaries, as well as expert commentary, this report distills the research and reporting surrounding various domains related to the ethics of AI, including: algorithmic injustice, discrimination, ethical AI, labor impacts, misinformation, privacy, risk and security, social media, and more. In addition, The State of AI Ethics includes exclusive content written by world-class AI Ethics experts from universities, research institutes, consulting firms, and governments. Unique to this report is "The Abuse and Misogynoir Playbook," written by Dr. Katlyn Tuner (Research Scientist, Space Enabled Research Group, MIT), Dr. Danielle Wood (Assistant Professor, Program in Media Arts and Sciences; Assistant Professor, Aeronautics and Astronautics; Lead, Space Enabled Research Group, MIT) and Dr. Catherine D'Ignazio (Assistant Professor, Urban Science and Planning; Director, Data + Feminism Lab, MIT). The piece (and accompanying infographic), is a deep-dive into the historical and systematic silencing, erasure, and revision of Black women's contributions to knowledge and scholarship in the United Stations, and globally. Exposing and countering this Playbook has become increasingly important following the firing of AI Ethics expert Dr. Timnit Gebru (and several of her supporters) at Google. This report should be used not only as a point of reference and insight on the latest thinking in the field of AI Ethics, but should also be used as a tool for introspection as we aim to foster a more nuanced conversation regarding the impacts of AI on the world.

As AI systems are increasingly involved in decision making, it also becomes important that they elicit appropriate levels of trust from their users. To achieve this, it is first important to understand which factors influence trust in AI. We identify that a research gap exists regarding the role of personal values in trust in AI. Therefore, this paper studies how human and agent Value Similarity (VS) influences a human's trust in that agent. To explore this, 89 participants teamed up with five different agents, which were designed with varying levels of value similarity to that of the participants. In a within-subjects, scenario-based experiment, agents gave suggestions on what to do when entering the building to save a hostage. We analyzed the agent's scores on subjective value similarity, trust and qualitative data from open-ended questions. Our results show that agents rated as having more similar values also scored higher on trust, indicating a positive effect between the two. With this result, we add to the existing understanding of human-agent trust by providing insight into the role of value-similarity.

Digital Twin is an emerging technology at the forefront of Industry 4.0, with the ultimate goal of combining the physical space and the virtual space. To date, the Digital Twin concept has been applied in many engineering fields, providing useful insights in the areas of engineering design, manufacturing, automation, and construction industry. While the nexus of various technologies opens up new opportunities with Digital Twin, the technology requires a framework to integrate the different technologies, such as the Building Information Model used in the Building and Construction industry. In this work, an Information Fusion framework is proposed to seamlessly fuse heterogeneous components in a Digital Twin framework from the variety of technologies involved. This study aims to augment Digital Twin in buildings with the use of AI and 3D reconstruction empowered by unmanned aviation vehicles. We proposed a drone-based Digital Twin augmentation framework with reusable and customisable components. A proof of concept is also developed, and extensive evaluation is conducted for 3D reconstruction and applications of AI for defect detection.