During the past two decades, epileptic seizure detection and prediction algorithms have evolved rapidly. However, despite significant performance improvements, their hardware implementation using conventional technologies, such as Complementary Metal-Oxide-Semiconductor (CMOS), in power and area-constrained settings remains a challenging task; especially when many recording channels are used. In this paper, we propose a novel low-latency parallel Convolutional Neural Network (CNN) architecture that has between 2-2,800x fewer network parameters compared to SOTA CNN architectures and achieves 5-fold cross validation accuracy of 99.84% for epileptic seizure detection, and 99.01% and 97.54% for epileptic seizure prediction, when evaluated using the University of Bonn Electroencephalogram (EEG), CHB-MIT and SWEC-ETHZ seizure datasets, respectively. We subsequently implement our network onto analog crossbar arrays comprising Resistive Random-Access Memory (RRAM) devices, and provide a comprehensive benchmark by simulating, laying out, and determining hardware requirements of the CNN component of our system. To the best of our knowledge, we are the first to parallelize the execution of convolution layer kernels on separate analog crossbars to enable 2 orders of magnitude reduction in latency compared to SOTA hybrid Memristive-CMOS DL accelerators. Furthermore, we investigate the effects of non-idealities on our system and investigate Quantization Aware Training (QAT) to mitigate the performance degradation due to low ADC/DAC resolution. Finally, we propose a stuck weight offsetting methodology to mitigate performance degradation due to stuck RON/ROFF memristor weights, recovering up to 32% accuracy, without requiring retraining. The CNN component of our platform is estimated to consume approximately 2.791W of power while occupying an area of 31.255mm$^2$ in a 22nm FDSOI CMOS process.

Calibrated confidence estimates obtained from neural networks are crucial, particularly for safety-critical applications such as autonomous driving or medical image diagnosis. However, although the task of confidence calibration has been investigated on classification problems, thorough investigations on object detection and segmentation problems are still missing. Therefore, we focus on the investigation of confidence calibration for object detection and segmentation models in this chapter. We introduce the concept of multivariate confidence calibration that is an extension of well-known calibration methods to the task of object detection and segmentation. This allows for an extended confidence calibration that is also aware of additional features such as bounding box/pixel position, shape information, etc. Furthermore, we extend the expected calibration error (ECE) to measure miscalibration of object detection and segmentation models. We examine several network architectures on MS COCO as well as on Cityscapes and show that especially object detection as well as instance segmentation models are intrinsically miscalibrated given the introduced definition of calibration. Using our proposed calibration methods, we have been able to improve calibration so that it also has a positive impact on the quality of segmentation masks as well.

Want to see a picture of Jesus Christ laughing at a meme on his phone, Donald Trump as the Nevermind baby, or Karl Marx being slimed at the Nikelodeon Kid's Choice awards? If you've been on Twitter or Instagram in the past couple of weeks, it's been hard to miss odd-looking formulations of these kinds of scenarios in the form of AI art. DALL-E (and DALL-E mini), the creator of these artworks, is a neural network that can take a text phrase and transform it an image. It was trained by looking at millions of images on the internet along with accompanying text and it learned to create pictures of things you'd never expect to be combined, such as an avocado armchair. Text to image technology is proceeding at a rapid pace, and the full DALL-E model is able to produce scarily clear images based on the input you provide, while the mini version is still clunky enough to capture the weird internet style that makes them instantly meme-able.

You're at the wheel of your car but you're exhausted. Your shoulders start to sag, your neck begins to droop, your eyelids slide down. As your head pitches forward, you swerve off the road and speed through a field, crashing into a tree. But what if your car's monitoring system recognised the tell-tale signs of drowsiness and prompted you to pull off the road and park instead? The European Commission has legislated that from this year, new vehicles be fitted with systems to catch distracted and sleepy drivers to help avert accidents.

While Artificial intelligence (AI) has been developing for decades, recent years have seen increasing attention to its various societal impacts. These impacts range from positive and helpful to harmful and even life-threatening in some cases. Parliaments have responded to such developments by undertaking various programmes of work. What have they done, and what can Scotland learn from these approaches? This short review provides a snapshot of the work that various Parliaments around the world have undertaken on AI. It outlines the various approaches adopted by Parliaments and highlights common themes. In noting the key points for Scotland, it is designed to inform and guide the Scottish Parliament and others, as Scotland considers its own approach to the many opportunities and challenges AI presents. The report was written by Robbie Scarff on an internship supported by the Scottish Graduate School of Social Science. From this work, here are some key areas and questions for the Scottish Parliament to consider.

Marine biologists have a new AI tool for monitoring and protecting coral reefs. The project--a collaboration between Google and Australia's Commonwealth Scientific and Industrial Research Organization (CSIRO)--employs computer vision detection models to pinpoint damaging outbreaks of crown-of-thorns starfish (COTS) through a live camera feed. Keeping a closer eye on reefs helps scientists address growing populations quickly, to protect the valuable Great Barrier Reef ecosystem. Despite covering less than 1% of the vast ocean floor, coral reefs support about 25% of sea species including fish, invertebrates, and marine mammals. When healthy, these productive marine environments provide commercial and subsistence fishing and income for tourism and recreational businesses.

Do you feel bad about the current cryptocurrency and NFT market? But there is a plan. This whole period can be turned into a very profitable activity. There is no need to call or persuade anyone to order something, then participate in the pyramid and end up disappointed. Creativity is the essential ingredient.

Today there are more incredible uses for robots than ever before. Innovators and inventors worldwide are applying countless types of robots to fascinating tasks. You may have heard of mechanized surgeons and AI cars, but have you heard of these six exciting uses? More eateries, especially fast-food businesses, are facing difficulties with staffing today. Developers have devised innovative solutions and unique new types of robotics.

Artificial Intelligence (AI) often reminds us of Hollywood with films such as The Terminator and The Matrix presenting dystopic futures where humans and robots face off in epic battles. However, AI and automated decision-making (ADM) are increasingly being adopted in organisations across Australia. It is estimated digital technologies, including AI, will be worth $A315b to the Australian economy by 2028. The Federal Government recently completed consultations regarding potentially regulating AI and ADM, with Governance Institute of Australia lodging a submission in response to the issues paper. This article discusses the technologies, the potential they offer and the challenges they pose.

With millions of boats across the world, it is difficult for regulators to stop fishers who exceed catching limits. Even those with no malicious intent can accidentally catch too much because of manual reporting practices. Enter OnDeck Fisheries AI, a Vancouver, B.C.-based startup developing software to automatically scan how many fish are brought onto a ship. The company's machine learning and computer vision technology tracks the precise biomass and type of fish without requiring human observers. The idea is to help regulators and fishers rely on an automated solution to ensure they are complying with fishing regulations.