One of the most promising applications of quantum computing is the processing of graphical data like images. Here, we investigate the possibility of realizing a quantum pattern recognition protocol based on swap test, and use the IBMQ noisy intermediate-scale quantum (NISQ) devices to verify the idea. We find that with a two-qubit protocol, swap test can efficiently detect the similarity between two patterns with good fidelity, though for three or more qubits the noise in the real devices becomes detrimental. To mitigate this noise effect, we resort to destructive swap test, which shows an improved performance for three-qubit states. Due to limited cloud access to larger IBMQ processors, we take a segment-wise approach to apply the destructive swap test on higher dimensional images. In this case, we define an average overlap measure which shows faithfulness to distinguish between two very different or very similar patterns when simulated on real IBMQ processors. As test images, we use binary images with simple patterns, greyscale MNIST numbers and MNIST fashion images, as well as binary images of human blood vessel obtained from magnetic resonance imaging (MRI). We also present an experimental set up for applying destructive swap test using the nitrogen vacancy centre (NVs) in diamond. Our experimental data show high fidelity for single qubit states. Lastly, we propose a protocol inspired from quantum associative memory, which works in an analogous way to supervised learning for performing quantum pattern recognition using destructive swap test.

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ANJA KASPERSEN: Today's podcast will focus on artificial intelligence (AI), neuroscience, and neurotechnologies. My guest today is Ricardo Chavarriaga. Ricardo is an electrical engineer and a doctor of computational neuroscience. He is currently the head of the Swiss office of the Confederation of Laboratories for AI Research in Europe (CLAIRE) and a senior researcher at Zurich University of Applied Sciences. Ricardo, it is an honor and a delight to share the virtual stage with you today. I am really happy and looking forward to a nice discussion today. ANJA KASPERSEN: Neuroscience is a vast and fast-developing field. Maybe you could start by providing our listeners with some background. When we think about the brain, this is something that has fascinated humanity for a long time. The question of how this organ that we have inside our heads can rule our behavior and can store and develop knowledge has been indeed one of the questions for science for many, many years. Neurotechnologies, computational neuroscience, and brain-machine interfaces are tools that we have developed to approach the understanding of this fabulous organ. When we talk about computational neuroscience it is the use of computational tools to create models of the brain. It can be mathematical models, it can be algorithms that try to reproduce our observations about the brain. It can be experiments on humans and on animals: these experiments can be behavioral, they can involve measurements of brain activity, and by looking at how the brains of organisms react and how the activity changes we will then try to apply our knowledge to create models for that. These models can have different flavors. We can for instance have very detailed models of electrochemical processes inside a neuron, and then we are looking at just a small part of the brain. We can have large-scale models with fewer details of how different brain structures interact among themselves, or even less-detailed models that try to reproduce behavior that we observe in animals and in humans as a result of certain mental disorders. We can even test these models using probes to tap into how can our brain construct representations of the world based on images, based on tactile, and based on auditory information.

The trustworthiness of neural networks is often challenged because they lack the ability to express uncertainty and explain their skill. This can be problematic given the increasing use of neural networks in high stakes decision-making such as in climate change applications. We address both issues by successfully implementing a Bayesian Neural Network (BNN), where parameters are distributions rather than deterministic, and applying novel implementations of explainable AI (XAI) techniques. The uncertainty analysis from the BNN provides a comprehensive overview of the prediction more suited to practitioners' needs than predictions from a classical neural network. Using a BNN means we can calculate the entropy (i.e. uncertainty) of the predictions and determine if the probability of an outcome is statistically significant. To enhance trustworthiness, we also spatially apply the two XAI techniques of Layer-wise Relevance Propagation (LRP) and SHapley Additive exPlanation (SHAP) values. These XAI methods reveal the extent to which the BNN is suitable and/or trustworthy. Using two techniques gives a more holistic view of BNN skill and its uncertainty, as LRP considers neural network parameters, whereas SHAP considers changes to outputs. We verify these techniques using comparison with intuition from physical theory. The differences in explanation identify potential areas where new physical theory guided studies are needed.

One of the most pressing issues facing cities and urban spaces are the future impacts of climate change, and how to make critical decisions related to mitigation and response. This month, ITU's AI for Good Innovation Factory series kicked-off with a challenge on smart and sustainable cities. Sipremo, a Brazilian-based start-up applying artificial intelligence (AI) for smart decision making, was awarded the top prize for start-ups making our cities safe, clean and sustainable. We talked to Gabriel Savio, CEO of Sipremo, about his solution. Sipremo addresses the massive impact of climate change events on business in various industries and all of our society. In recent years, Brazil has faced some of its worst natural disasters, such as what happened in Petropolis, Minas Gerais, and others.

Luis Vera believes the third time is the charm. The self-proclaimed serial entrepreneur admits that his vision for digitizing retail was a decade or two early when he started his journey in the 90s. Through a pair of startups -- Prospect and SCOPIX -- he tried a variety of methods to help capture store inventory, including placings cameras on shelves and a ceiling-based system where one ran on tracks. He was, effectively, attempting to compete with Amazon well before Amazon was, well, Amazon -- at least in any meaningful sense. Computer vision, machine learning and the like have caught up a lot since then, of course.

The development of state-of-the-art (SOTA) Natural Language Processing (NLP) systems has steadily been establishing new techniques to absorb the statistics of linguistic data. These techniques often trace well-known constructs from traditional theories, and we study these connections to close gaps around key NLP methods as a means to orient future work. For this, we introduce an analytic model of the statistics learned by seminal algorithms (including GloVe and Word2Vec), and derive insights for systems that use these algorithms and the statistics of co-occurrence, in general. In this work, we derive -- to the best of our knowledge -- the first known solution to Word2Vec's softmax-optimized, skip-gram algorithm. This result presents exciting potential for future development as a direct solution to a deep learning (DL) language model's (LM's) matrix factorization. However, we use the solution to demonstrate a seemingly-universal existence of a property that word vectors exhibit and which allows for the prophylactic discernment of biases in data -- prior to their absorption by DL models. To qualify our work, we conduct an analysis of independence, i.e., on the density of statistical dependencies in co-occurrence models, which in turn renders insights on the distributional hypothesis' partial fulfillment by co-occurrence statistics.

We believe in the everyday hero. Those who have the courage to follow their passion and who have the strength and determination to realize their dreams. Small business owners are at the heart of all we do, so we create powerful, easy-to-use financial solutions to help them run their businesses with a founder's mentality and a'team-first' attitude. We have a diverse teams across Europe, South America and the United States working together to ensure that the small business owners we partner with can be successful doing what they love. We are looking for professionals interested in turning data and information into business insights to help to improve payouts operations and who like to go beyond analyses by actually implementing data-driven solutions and process automations.

According to a new KAUST study, machine learning approaches can achieve an assumption-free analysis of epidemic case data with amazingly good prediction accuracy and the flexibility to incorporate new data dynamically. Yasminah Alali, an intern in KAUST's 2021 Saudi Summer Internship (SSI) program, developed a proof of concept that reveals a possible alternative to traditional parameter-driven mechanistic models by removing human bias and assumptions from analysis, revealing the underlying story of the data. Using publicly released COVID-19 incidence and recovery data from India and Brazil, Alali leveraged her experience working with artificial intelligence models to design a framework to fit the characteristics and time-evolving nature of epidemic data in collaboration with KAUST's Ying Sun and Fouzi Harrou. To create an effective Gaussian process regression (GPR) based model for forecasting recovered and confirmed COVID-19 cases in two significantly impacted countries, India and Brazil, the researchers first used Bayesian optimization to modify the Gaussian process regression (GPR) hyperparameters. However, the time dependency in the COVID-19 data series is ignored by machine learning models.

By Joyjit Chatterjee and Nina Dethlefs, University of Hull Cottingham Artificial Intelligence (AI) is much more than just a buzzword nowadays. It powers facial recognition in smartphones and computers, translation between foreign languages, systems which filter spam emails and identify toxic content on social media, and can even detect cancerous tumours. These examples, along with countless other existing and emerging applications of AI, help make people's daily lives easier, especially in the developed world. As of October 2021, 44 countries were reported to have their own national AI strategic plans, showing their willingness to forge ahead in the global AI race. These include emerging economies like China and India, which are leading the way in building national AI plans within the developing world.