Some of these meteorites fall in regions on Earth where fireball observatory networks are active, making it possible to record the trajectory of the fireball as it ablates material from the originating meteoroid. For some fireballs, this data can then be used to simulate both forward and backward in time to predict where the resulting meteorite landed on Earth and where the meteoroid originated in the solar system. Thus, recovering and analyzing these'orbital meteorites' with constrained, prior orbits provides an incredibly unique insight into the geology of the asteroid belt and the nature of mass transfer between the belt and the inner solar system. The Desert Fireball Network (DFN) (Bland et al. 2012; Howie et al. 2017) is one of many organizations (Oberst et al. 1998; Spurný et al. 2006; Trigo-Rodríguez et al. 2006; Olech et al. 2006; Colas et al. 2015; Devillepoix et al. 2020) that makes this possible.

Unleash the power of machine learning (ML) through hands-on learning and compete for prizes and glory. The AWS DeepRacer League is the world's first global autonomous racing competition driven by reinforcement learning; bringing together students, professionals, and enthusiasts from almost every continent. I'm Tomasz Ptak, a senior software engineer at Duco, an AWS Machine Learning Hero, an AWS DeepRacer competitor (named Breadcentric), a hobbyist baker, and a leader of the AWS Machine Learning Community on Slack, where we learn, race, and help each other start and grow our adventures in the cloud. It's my pleasure to unveil the exciting details of the upcoming 2022 AWS DeepRacer League season. It's a complete program that has helped over 175,000 individuals from over 700 businesses, educational institutions, and organizations begin their educational journey into machine learning through fun and rivalry.

Entrepreneurs are always looking for that next big idea to vault their business into the stratosphere. But just what is it that makes somebody want to start and grow their own company? It turns out it may be partly associated with a person's facial features, according to a team of researchers led by the University of Cyprus. They found that entrepreneurs chasing the prospect of becoming the next Sir Richard Branson or Elon Musk are more likely to have a symmetrical face and prominent cheekbones. However, although this makes them more likely to become an entrepreneur, it has no bearing on whether they will be a successful one, according to the researchers.

Read about how the Robotics industry is addressing key climate change issues highlighted by the IPCC's report in this editorial series. How can AI Prevent Fraud? Learn more about the potential of machine learning to help cut down on food waste. Read about how innovations in agricultural robotics are helping to reduce water waste with robotic irrigation. OcéanIA is providing hope for monitoring the effects of climate change on ocean systems.

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. Abbott said the test case was not about any sort of legal rights for machines, rather it was about trying to get a patent for "the inventive output from an AI" that lacks a traditional human inventor. Some firms were already using AI programmes to discover new drugs or to find ways to repurpose materials but the companies that many of the lawyers on the case represent wanted greater clarity on patent ownership before investing further, he said. The application was declined in Australia but later overturned by the Federal Court in 2021 which said the country's patent act had no specific provision excluding AI systems as inventors.

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, …