Parameterized Quantum Circuits (PQC) are drawing increasing research interest thanks to its potential to achieve quantum advantages on near-term Noisy Intermediate Scale Quantum (NISQ) hardware. In order to achieve scalable PQC learning, the training process needs to be offloaded to real quantum machines instead of using exponential-cost classical simulators. One common approach to obtain PQC gradients is parameter shift whose cost scales linearly with the number of qubits. We present QOC, the first experimental demonstration of practical on-chip PQC training with parameter shift. Nevertheless, we find that due to the significant quantum errors (noises) on real machines, gradients obtained from naive parameter shift have low fidelity and thus degrading the training accuracy. To this end, we further propose probabilistic gradient pruning to firstly identify gradients with potentially large errors and then remove them. Specifically, small gradients have larger relative errors than large ones, thus having a higher probability to be pruned. We perform extensive experiments with the Quantum Neural Network (QNN) benchmarks on 5 classification tasks using 5 real quantum machines. The results demonstrate that our on-chip training achieves over 90% and 60% accuracy for 2-class and 4-class image classification tasks. The probabilistic gradient pruning brings up to 7% PQC accuracy improvements over no pruning. Overall, we successfully obtain similar on-chip training accuracy compared with noise-free simulation but have much better training scalability. The QOC code is available in the TorchQuantum library.

Lucas Rizzotto, a YouTuber from Brazil, had no idea what to expect when he gave his Alexa powered smart microwave a brain transplant, replacing the Amazon... 21.04.2022, What he created is a frightening abomination of a poet with an affinity towards Hitler, the British crown and the ending of what it calls the parasitic American empire. Oh, and it wants to kill its creator. There is that, too.Rizzotto, who makes humorous videos about technology projects he builds, used an imaginary friend he had as a child who happened to be embodied in his family's microwave as inspiration. "Magnetron" was a turn-of-the-century British poet who served in World War I, lost his family to the war and later became an expert StarCraft player.

TOKYO -- Zipline, an American company that specializes in using autonomously flying drones to deliver medical supplies, has taken off in Japan. Other parts of Japan may follow, including urban areas, although the biggest needs tend to be in isolated rural areas. Zipline, founded six years ago, already is in service in the U.S., where it has partnered with Walmart Inc. to deliver other products at the retail chain as well as drugs. It is also delivering medical goods in Ghana and Rwanda. Its takeoff in Japan is in partnership with Toyota Tsusho, a group company of Japan's top automaker Toyota Motor Corp. "You can totally transform the way that you react to pandemics, treat patients and do things like home health care delivery," Zipline Chief Executive Keller Rinaudo told The Associated Press.

Six years ago, Rwanda had a blood delivery problem. More than 12 million people live in the small East African country, and like those in other nations, sometimes they get into car accidents. Anemic children need urgent transfusions. You can't predict these emergencies. And when they do, the red stuff stored in Place A has to find its way to a patient in Place B--fast.

The remarkable progress in Natural Language Processing (NLP) brought about by deep learning, particularly with the recent advent of large pre-trained neural language models, is brought into scrutiny as several studies began to discuss and report potential biases in NLP applications. Bias in NLP is found to originate from latent historical biases encoded by humans into textual data which gets perpetuated or even amplified by NLP algorithm. We present a survey to comprehend bias in large pre-trained language models, analyze the stages at which they occur in these models, and various ways in which these biases could be quantified and mitigated. Considering wide applicability of textual affective computing based downstream tasks in real-world systems such as business, healthcare, education, etc., we give a special emphasis on investigating bias in the context of affect (emotion) i.e., Affective Bias, in large pre-trained language models. We present a summary of various bias evaluation corpora that help to aid future research and discuss challenges in the research on bias in pre-trained language models. We believe that our attempt to draw a comprehensive view of bias in pre-trained language models, and especially the exploration of affective bias will be highly beneficial to researchers interested in this evolving field. The examples provided in this paper may be offensive in nature and may hurt your moral beliefs.

Gaussian processes (GPs) are powerful but computationally expensive machine learning models, requiring an estimate of the kernel covariance matrix for every prediction. In large and complex domains, such as graphs, sets, or images, the choice of suitable kernel can also be non-trivial to determine, providing an additional obstacle to the learning task. Over the last decade, these challenges have resulted in significant advances being made in terms of scalability and expressivity, exemplified by, e.g., the use of inducing points and neural network kernel approximations. In this paper, we propose inducing Gaussian process networks (IGN), a simple framework for simultaneously learning the feature space as well as the inducing points. The inducing points, in particular, are learned directly in the feature space, enabling a seamless representation of complex structured domains while also facilitating scalable gradient-based learning methods. We consider both regression and (binary) classification tasks and report on experimental results for real-world data sets showing that IGNs provide significant advances over state-of-the-art methods. We also demonstrate how IGNs can be used to effectively model complex domains using neural network architectures.

Children and young people are growing up in an increasingly digital age, where technology pervades every aspect of their lives. From robotic toys and social media to the classroom and home, artificial intelligence (AI) is a ubiquitous part of daily life. It's vital therefore that ethical guidelines protect them and ensure they get the best from this emerging technology. Generation Z, who have grown up with AI, are uniquely placed to offer an insight into the potential issues of AI targeted at children and help create governance guidelines. With that in mind the World Economic Forum has set up the AI Youth Council, a global diverse group comprising young people interested in AI.

To make decisions more quickly and accurately, enterprises in the Middle East and Africa (MEA) are increasingly turning to Machine Learning, arguably today's most practical application of Artificial Intelligence (AI). How should CIOs and IT leaders ensure success and ROI from Machine Learning deployments in their organisations? Machine Learning is a type of AI that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine Learning algorithms use historical data as input to predict new output values. In addition, Machine Learning systems apply algorithms to data to glean insights into that data without explicit programming: It's about using data to answer questions.

This article was first published in the Virginia Mercury. Last week when Virginia's new Secretary of Transportation Sheppard Miller publicly declared his belief that flying cars will be a reality within the next 50 years as a reason that leaders across the commonwealth should "reexamine transit," some might have scoffed. But just as flying cars consumed the fantasies of many mid-century Americans, today plenty of people put their faith in another utopian technology replete with endlessly elusive promises of improved safety and unbridled freedom: autonomous vehicles. As is often the case in the United States, the regulation of autonomous vehicles is largely left to the states, resulting in a patchwork of conflicting and confusing policies where some sort of national approach ought to exist. Any state has the right to craft their own legal framework for the emerging technology but few have -- our commonwealth included.

The industry may be emerging from the Covid-19 pandemic, with e-commerce still thriving on it, but the supply chain crisis isn't going away. Infrastructure designed in a predictable pre-pandemic world isn't enough to clear the backlogs, and may even be making a bad situation worse. Could artificial intelligence (AI) be the technology that gets things moving again? Organisations certainly believe so, according to a new 3Gem report for Blue Yonder, which finds that more than half (53%) of UK supply chain decision-makers believe AI advances are key to managing disruption. This confidence in AI owes much to its promise of visibility.