The day is coming--some say has already arrived--when artificial intelligence starts to invent things that its human creators could not. But our laws are lagging behind this technology, UNSW experts say. It's not surprising these days to see new inventions that either incorporate or have benefitted from artificial intelligence (AI) in some way, but what about inventions dreamt up by AI--do we award a patent to a machine? This is the quandary facing lawmakers around the world with a live test case in the works that its supporters say is the first true example of an AI system named as the sole inventor. In commentary published in the journal Nature, two leading academics from UNSW Sydney examine the implications of patents being awarded to an AI entity.

The images are so crafted and "painterly" that you may not realise at first they have been dreamed up by a machine in just a few minutes. Maybe you've seen one already, but not realised what it was. It may have looked like something you'd seen before in an art book or a museum. These images are the product of a new AI-generated art scene that's exploded thanks to the development of free and easy-to-use tools that require (at the very least) short text prompts to create unique pictures. The image in the tweet above, for example, was created by giving the text prompt "a summer day" to an AI tool.

Analysis - Two years ago this weekend, GPT-3 was introduced to the world and although you may not have heard of it there's a good chance you've read its work. It is likely that you have already read work composed by AI model, GPT-3. Or you may have used a website that runs GPT-3 code, or even conversed with it through a chatbot or a character in a game. GPT-3 is an AI model - a type of artificial intelligence - and its applications have quietly trickled into our everyday lives over the past couple of years. In recent months, that trickle has picked up force: more and more applications are using AI like GPT-3, and these AI programmes are producing greater amounts of data, from words, to images, to code.

Two years ago this weekend, GPT-3 was introduced to the world. You may not have heard of GPT-3, but there's a good chance you've read its work, used a website that runs its code, or even conversed with it through a chatbot or a character in a game. GPT-3 is an AI model -- a type of artificial intelligence -- and its applications have quietly trickled into our everyday lives over the past couple of years. In recent months, that trickle has picked up force: more and more applications are using AI like GPT-3, and these AI programs are producing greater amounts of data, from words, to images, to code. A lot of the time, this happens in the background; we don't see what the AI has done, or we can't tell if it's any good.

Artificial Intelligence (AI) systems have been involved in numerous scandals in recent years. For instance, take the COMPAS recidivism algorithm. The algorithm evaluated the likelihood that defendants will commit another crime in the future. It was widely used in the US criminal justice system to inform decisions about who can be set free at all stages of the process. In 2016, ProPublica exposed that COMPAS's predictions were biased: its mistakes favored white over black defendants. Black defendants were twice as likely to be labeled as high risk to reoffend but not actually reoffend.

Several techniques have been proposed over the years for automatic hypocenter localization. While those techniques have pros and cons that trade-off computational efficiency and the susceptibility of getting trapped in local minima, an alternate approach is needed that allows robust localization performance and holds the potential to make the elusive goal of real-time microseismic monitoring possible. Physics-informed neural networks (PINNs) have appeared on the scene as a flexible and versatile framework for solving partial differential equations (PDEs) along with the associated initial or boundary conditions. We develop HypoPINN -- a PINN-based inversion framework for hypocenter localization and introduce an approximate Bayesian framework for estimating its predictive uncertainties. This work focuses on predicting the hypocenter locations using HypoPINN and investigates the propagation of uncertainties from the random realizations of HypoPINN's weights and biases using the Laplace approximation. We train HypoPINN to obtain the optimized weights for predicting hypocenter location. Next, we approximate the covariance matrix at the optimized HypoPINN's weights for posterior sampling with the Laplace approximation. The posterior samples represent various realizations of HypoPINN's weights. Finally, we predict the locations of the hypocenter associated with those weights' realizations to investigate the uncertainty propagation that comes from those realisations. We demonstrate the features of this methodology through several numerical examples, including using the Otway velocity model based on the Otway project in Australia.

Recent advances in the areas of multimodal machine learning and artificial intelligence (AI) have led to the development of challenging tasks at the intersection of Computer Vision, Natural Language Processing, and Embodied AI. Whereas many approaches and previous survey pursuits have characterised one or two of these dimensions, there has not been a holistic analysis at the center of all three. Moreover, even when combinations of these topics are considered, more focus is placed on describing, e.g., current architectural methods, as opposed to also illustrating high-level challenges and opportunities for the field. In this survey paper, we discuss Embodied Vision-Language Planning (EVLP) tasks, a family of prominent embodied navigation and manipulation problems that jointly use computer vision and natural language. We propose a taxonomy to unify these tasks and provide an in-depth analysis and comparison of the new and current algorithmic approaches, metrics, simulated environments, as well as the datasets used for EVLP tasks. Finally, we present the core challenges that we believe new EVLP works should seek to address, and we advocate for task construction that enables model generalizability and furthers real-world deployment.

Alessandra Rossi is a member of both the technical and organising committees for the RoboCup Humanoid League. We spoke to her about the Humanoid League Virtual Season, which concluded with the grand final of the virtual soccer competition, and a three day workshop. The Humanoid League Virtual Season (HLVS) has been driven by two main core motivations: firstly to allow teams to have support for continuous testing while making progresses and changes to their software, and secondly, to keep the teams connected throughout the year, thus strengthening the community and collaboration between teams. We wanted to let teams use the longer periods between games, and the continuous games throughout the year to test novel approaches, with less risk, and to aid their success in the overall tournament. In addition, this way, teams can thoroughly analyse the collected data between games, and make informed decisions on how to improve and implement their approaches for the following match.

Technology has completely infiltrated the built environment. Between IoT connectivity in buildings, indoor wayfinding and virtual tours, real estate is no longer the tech-averse industry it once was. In the context of digitization, there are new uses for Augmented Reality (AR). The technology has evolved from a marketing gimmick to a solid strategy for asset managers to improve their built environments. The premise of AR technology is the real-time integration of digital information to "augment" the user's physical experience.

The Indian Ministry of Science and Technology said this unique approach uses the predictive power of AI to identify road hazards and a collision warning system to communicate timely alerts to drivers, to make various safety-related improvements. Artificial intelligence (AI)-powered solutions may soon make roads in India a safer place to drive. The Indian government announced on Tuesday that an AI-powered technology could reduce the risk of road accidents in the country, which have killed more than a lakh people in 2020. In a bid to prevent this from happening, the Indian government said the AI approach will use a first-of-its-kind dataset consisting of 10,000 images. He said that this dataset is finely annotated with 34 classes collected from 182 driving sequences on Indian roads obtained from a front-facing camera attached to a car driving through the cities of Hyderabad, Bangalore and their outskirts.