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Elon Musk's Neuralink rival Synchron begins human trials of brain implant

Daily Mail - Science & tech

Elon Musk's Neuralink rival Synchron has begun human trials of its brain implant that lets the wearer control a computer using thought alone. The firm's Stentrode brain implant, about the size of a paperclip, will be implanted in six patients in New York and Pittsburgh who have severe paralysis. Stentrode will let patients control digital devices just by thinking and give them back the ability to perform daily tasks, including texting, emailing and shopping online. Although the implant has already been implanted and tested in Australian patients, the new clinical trial marks the first time it will be tested in the US. If successful, the Stentrode brain implant could be sold as a commercial product aimed at paralysis patients to regain their independence and quality of life.

Mental Stress Detection using Data from Wearable and Non-wearable Sensors: A Review Artificial Intelligence

This paper presents a comprehensive review of methods covering significant subjective and objective human stress detection techniques available in the literature. The methods for measuring human stress responses could include subjective questionnaires (developed by psychologists) and objective markers observed using data from wearable and non-wearable sensors. In particular, wearable sensor-based methods commonly use data from electroencephalography, electrocardiogram, galvanic skin response, electromyography, electrodermal activity, heart rate, heart rate variability, and photoplethysmography both individually and in multimodal fusion strategies. Whereas, methods based on non-wearable sensors include strategies such as analyzing pupil dilation and speech, smartphone data, eye movement, body posture, and thermal imaging. Whenever a stressful situation is encountered by an individual, physiological, physical, or behavioral changes are induced which help in coping with the challenge at hand. A wide range of studies has attempted to establish a relationship between these stressful situations and the response of human beings by using different kinds of psychological, physiological, physical, and behavioral measures. Inspired by the lack of availability of a definitive verdict about the relationship of human stress with these different kinds of markers, a detailed survey about human stress detection methods is conducted in this paper. In particular, we explore how stress detection methods can benefit from artificial intelligence utilizing relevant data from various sources. This review will prove to be a reference document that would provide guidelines for future research enabling effective detection of human stress conditions.

AI Can Identify People Even in Anonymized Datasets


Advancements in AI might soon render phrases such as "hidden in the crowd" or "stay hidden in plain sight" a curious relic of the past, according to new research published last week on Nature Communications. In a paper titled "Interaction data are identifiable even across long periods of time," researchers used geometric deep learning and triplet loss optimization to successfully identify a majority of individuals from an anonymized mobile phone dataset of 40,000 people. The research is notable because fine-grained records of people's interactions, both offline and online, are collected at scale today. Tech giants such as Facebook and Google, telecommunication operators, and other businesses are known to collect and either resell data wholesale or leverage it to power data-centric services. The technique relies on how people tend to stick to established social circles and that such regular interactions form a stable pattern over time.

How AI can identify people even in anonymized datasets


How you interact with a crowd may help you stick out from it, at least to artificial intelligence. When fed information about a target individual's mobile phone interactions, as well as their contacts' interactions, AI can correctly pick the target out of more than 40,000 anonymous mobile phone service subscribers more than half the time, researchers report January 25 in Nature Communications. The findings suggest humans socialize in ways that could be used to pick them out of datasets that are supposedly anonymized. It's no surprise that people tend to remain within established social circles and that these regular interactions form a stable pattern over time, says Jaideep Srivastava, a computer scientist from the University of Minnesota in Minneapolis who was not involved in the study. "But the fact that you can use that pattern to identify the individual, that part is surprising."

CTIN: Robust Contextual Transformer Network for Inertial Navigation Artificial Intelligence

Recently, data-driven inertial navigation approaches have demonstrated their capability of using well-trained neural networks to obtain accurate position estimates from inertial measurement units (IMU) measurements. In this paper, we propose a novel robust Contextual Transformer-based network for Inertial Navigation~(CTIN) to accurately predict velocity and trajectory. To this end, we first design a ResNet-based encoder enhanced by local and global multi-head self-attention to capture spatial contextual information from IMU measurements. Then we fuse these spatial representations with temporal knowledge by leveraging multi-head attention in the Transformer decoder. Finally, multi-task learning with uncertainty reduction is leveraged to improve learning efficiency and prediction accuracy of velocity and trajectory. Through extensive experiments over a wide range of inertial datasets~(e.g. RIDI, OxIOD, RoNIN, IDOL, and our own), CTIN is very robust and outperforms state-of-the-art models.

Building a Decision Support System for Automated Mobile Asthma Monitoring in Remote Areas Artificial Intelligence

Advances in mobile computing have paved the way for the development of several health applications using smartphone as a platform for data acquisition, analysis and presentation. Such areas where mhealth systems have been extensively deployed include monitoring of long term health conditions like Cardio Vascular Diseases and pulmonary disorders, as well as detection of changes from baseline measurements of such conditions. Asthma is one of the respiratory conditions with growing concern across the globe due to the economic, social and emotional burden associated with the ailment. The management and control of asthma can be improved by consistent monitoring of the condition in realtime since attack could occur anytime and anywhere. This paper proposes the use of smartphone equipped with embedded sensors, to capture and analyze early symptoms of asthma triggered by exercise. The system design is based on Decision Support System techniques for measuring and analyzing the level and type of patients physical activity as well as weather conditions that predispose asthma attack. Preliminary results show that smartphones can be used to monitor and detect asthma symptoms without other networked devices. This would enhance the usability of the health system while ensuring users data privacy, and reducing the overall cost of system deployment. Further, the proposed system can serve as a handy tool for a quick medical response for asthmatics in low income countries where there are limited access to specialized medical devices and shortages of health professionals. Development of such monitoring systems signals a positive response to lessen the global burden of asthma.

Artificial Intellgence -- Application in Life Sciences and Beyond. The Upper Rhine Artificial Intelligence Symposium UR-AI 2021 Artificial Intelligence

The TriRhenaTech alliance presents the accepted papers of the 'Upper-Rhine Artificial Intelligence Symposium' held on October 27th 2021 in Kaiserslautern, Germany. Topics of the conference are applications of Artificial Intellgence in life sciences, intelligent systems, industry 4.0, mobility and others. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, Offenburg and Trier, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.

Roadmap on Signal Processing for Next Generation Measurement Systems Artificial Intelligence

Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.

Modelling and Optimisation of Resource Usage in an IoT Enabled Smart Campus Artificial Intelligence

University campuses are essentially a microcosm of a city. They comprise diverse facilities such as residences, sport centres, lecture theatres, parking spaces, and public transport stops. Universities are under constant pressure to improve efficiencies while offering a better experience to various stakeholders including students, staff, and visitors. Nonetheless, anecdotal evidence indicates that campus assets are not being utilised efficiently, often due to the lack of data collection and analysis, thereby limiting the ability to make informed decisions on the allocation and management of resources. Advances in the Internet of Things (IoT) technologies that can sense and communicate data from the physical world, coupled with data analytics and Artificial intelligence (AI) that can predict usage patterns, have opened up new opportunities for organisations to lower cost and improve user experience. This thesis explores this opportunity via theory and experimentation using UNSW Sydney as a living laboratory.

Podcast: How pricing algorithms learn to collude

MIT Technology Review

Algorithms now determine how much things cost. It's called dynamic pricing and it adjusts according to current market conditions in order to increase profits. The rise of e-commerce has propelled pricing algorithms into an everyday occurrence--whether you're shopping on Amazon, booking a flight, hotel or ordering an Uber. In this continuation of our series on automation and your wallet, we explore what happens when a machine determines the price you pay. This episode was reported by Anthony Green and produced by Jennifer Strong and Emma Cillekens. We're edited by Mat Honan and our mix engineer is Garret Lang, with sound design and music by Jacob Gorski. Jennifer: Alright so I'm in an airport just outside New York City and just looking at the departures board here seeing all these flights going different places… It makes me think about how we decide how much something should cost… like a ticket for one of these flights. Because where the plane is going is just part of the puzzle. The price of airfare is highly personalized.