A tough day at the office changes our voices, a study suggests. They also asked them to report on the stressors they had experienced that day and their perceived stress levels. When they analysed the voice recordings using computer software they noticed some distinct changes on the days people reported more stressors. They found that people talked more quickly and with more intensity when they'd had more strains that day, regardless of how stressed they actually felt. A tough day at the office changes our voices, a study suggests.

Knowing "what is happening" and "what will happen" of the mobility in a city is the building block of a data-driven smart city system. In recent years, mobility digital twin that makes a virtual replication of human mobility and predicting or simulating the fine-grained movements of the subjects in a virtual space at a metropolitan scale in near real-time has shown its great potential in modern urban intelligent systems. However, few studies have provided practical solutions. The main difficulties are four-folds. 1) The daily variation of human mobility is hard to model and predict; 2) the transportation network enforces a complex constraints on human mobility; 3) generating a rational fine-grained human trajectory is challenging for existing machine learning models; and 4) making a fine-grained prediction incurs high computational costs, which is challenging for an online system. Bearing these difficulties in mind, in this paper we propose a two-stage human mobility predictor that stratifies the coarse and fine-grained level predictions. In the first stage, to encode the daily variation of human mobility at a metropolitan level, we automatically extract citywide mobility trends as crowd contexts and predict long-term and long-distance movements at a coarse level. In the second stage, the coarse predictions are resolved to a fine-grained level via a probabilistic trajectory retrieval method, which offloads most of the heavy computations to the offline phase. We tested our method using a real-world mobile phone GPS dataset in the Kanto area in Japan, and achieved good prediction accuracy and a time efficiency of about 2 min in predicting future 1h movements of about 220K mobile phone users on a single machine to support more higher-level analysis of mobility prediction.

Using machine learning and analytical behaviour, the system focuses on … "Combining this intelligence with our internal data allows us to …

In the past few months, there has been a suite of new artificial intelligence products that go far beyond what has been made available to the public before. Last week, the high-profile suspension of a Google employee after he went public about an AI chat bot that he thought was (almost certainly incorrectly) sentient put a spotlight on just how far AI has come. One major advancement has been the new AI model Generative Pre-trained Transformer-3 (GPT-3) by research firm OpenAI, released in 2020. Since its initial release, OpenAI has slowly rolled out access to the model for various uses -- carefully allowing access to it due to fear of the powerful technology being misused. Just how powerful is this technology? Rather than telling you, why don't we get the AI to tell you?

In the past few months, there has been a suite of new artificial intelligence products that go far beyond what has been made available to the public before. Last week, the high-profile suspension of a Google employee after he went public about an AI chat bot that he thought was (almost certainly incorrectly) sentient put a spotlight on just how far AI has come. One major advancement has been the new AI model Generative Pre-trained Transformer-3 (GPT-3) by research firm OpenAI, released in 2020. Since its initial release, OpenAI has slowly rolled out access to the model for various uses -- carefully allowing access to it due to fear of the powerful technology being misused. Just how powerful is this technology?

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.