As a graduate student in neuroscience at the University of California at Berkeley, Bilenko met students Asako Miyakawa and James Gao, who would become co-creators of Dr. Brainlove and the Cognitive Technologies exhibit. She became a researcher in Professor Jack Gallant's lab, collaborating with him on a project to train a computer to reconstruct a person's visual experience by interpreting EEG signals from the brain. The researchers created a computer algorithm that could interpret the brain impulses, match them against visuals collected from thousands of hours of YouTube videos, and generate movies that illustrate likely images based on the impulses. To outside observers, the results looked freakishly like computer-assisted mind reading. While the resulting visualizations were hazy, the work made a big splash in the tech community. It even prompted Mark Zuckerberg to declare that in the future "you're going to be able to capture a thought."

"Artificial intelligence is around us, and it will change medicine, including ophthalmology. Come and learn about recent developments in different subfields of ophthalmology, based on AI technology!" Andrzej Grzybowski, Professor of Ophthalmology and Chair of the Department of Ophthalmology, University of Warmia and Mazury, Olsztyn, Poland, and Head of the Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznań, Poland, talks about the inspiration behind the virtual event, the impressive speaker list, and his own work in the field. When did you first decide to organize this online event; what was the inspiration behind it? I have thought about it for some time. However, the final argument for going ahead with the event was to receive the support from the Polish Ministry of Science and Education.

For the past three decades, geologist Carlos Souza has worked at the Brazil-based nonprofit Imazon, exploring ways he and the teams he coordinates can use applied science to protect the Amazon rainforest. For much of that time, satellite imagery has been a big part of his job. In the early 2000s, Souza and colleagues came to understand that 90 percent of deforestation occurs within 5 kilometers of newly created roads. While satellites have long been able to track road expansion, the old way of doing things required people to label those findings by hand, amassing what would eventually become training data. Those years of labor paid off last fall with the release of an AI system that Imazon says reveals 13 times more roadway than the previous method, with an accuracy rate of between 70 and 90 percent.

Move to Leeds and benefit from the jobs boom, says Melissa Berthelot, boss of medical appliance maker WarnerPatch, who relocated her business from London two years ago to benefit from a burgeoning deep tech industry in the West Yorkshire city. With skilled data science and software engineers in short supply across the south-east – and most other parts of the country – Leeds has proved a happy hunting ground for Berthelot, an engineer turned chief executive who used the first lockdown to make the jump north. Deep tech refers to sectors including artificial intelligence, robotics and bio-technologies. Its Blade Runner-like image may seem worlds away from the Emmerdale village tour on offer just west of town, but Leeds is managing to straddle old and new as it jumps up the UK rankings for job creation and productivity. The city has gained a reputation for developing the skilled staff and financial muscle needed to fund startups and innovation, especially in healthcare, but also in the city's more traditional areas of expertise – financial and legal services, manufacturing and retail.

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Although partially autonomous driving (AD) systems are already available in production vehicles, drivers are still required to maintain a sufficient level of situational awareness (SA) during driving. Previous studies have shown that providing information about the AD's capability using user interfaces can improve the driver's SA. However, displaying too much information increases the driver's workload and can distract or overwhelm the driver. Therefore, to design an efficient user interface (UI), it is necessary to understand its effect under different circumstances. In this paper, we focus on a UI based on augmented reality (AR), which can highlight potential hazards on the road. To understand the effect of highlighting on drivers' SA for objects with different types and locations under various traffic densities, we conducted an in-person experiment with 20 participants on a driving simulator. Our study results show that the effects of highlighting on drivers' SA varied by traffic densities, object locations and object types. We believe our study can provide guidance in selecting which object to highlight for the AR-based driver-assistance interface to optimize SA for drivers driving and monitoring partially autonomous vehicles.

How do different presidents present themselves differently in different contexts? How do different ways of presenting oneself influence presidential popularity? This paper aims to answer these questions by implementing a multi-modal deep learning pipeline, extracting information from the text, audio, and image data from presidential speeches. This paper will first walk through the motivations and (brief) literature review. Then, we will introduce in order the seven models we ran for encoding the text (FastText and BERT), audio (CNN audio classifier, CNN emotion recognition), image models (EfficientNet, CNN emotion recognition), and multimodal prediction (self-defined RankNet).

I grew up in Western New York, and studied Math & Computer Science at University in the early 1980s. After my time as an undergraduate I took a variety of programming jobs available in the local area. I started my career coding payroll applications, which was well paying but dull work. I later took a job with DARPA, programming avionics for military aircraft. After a few years I moved to AT&T Bell Labs in Homdel, New Jersey, where I programmed Expert Systems.

Mental health issues have come into a clearer focus amid the pandemic. Depression became endemic, but it still too often goes undetected. Even when it does, healthcare providers struggle to meet demand. Two women engineers -- both of whom experienced depression and had trouble finding therapy -- thought the answer might be helping medical pros detect depression. Kintsugi is a startup that wants to put technology to work on the problem.

Pogrebna is a pioneer in behavioural data science – a field that combines behavioural science and data science techniques to better understand, model and predict the behaviour of humans, algorithms and complex systems in the face of risk and uncertainty. "I warmly welcome Professor Pogrebna to the University and look forward to working with her to establish the Cybersecurity and Data Science Institute," Charles Sturt pro vice-chancellor (Research and Innovation) Professor Mark Evans said. "Professor Pogrebna comes to the University with a wealth of experience. This includes being the Lead of the Behavioural Data Science strand at The Alan Turing Institute – the national centre for AI and data science in London where she is also a Fellow working on hybrid modelling approaches between behavioural science and data science (e.g. According to Evans, Pogrebna helps leaders in businesses, charities and the public sector better understand why they make the decisions they make and how they can optimise their behaviour to achieve higher profit, better social and commercial outcomes, and bolster the wellbeing of their teams. Pogrebna said her goal as executive director of the Cybersecurity and Data Science Institute is to avoid'building a silo'. "I have spent several weeks visiting our researchers across the University's campuses and became aware of the incredible work they are doing," she said. "The new Institute will aim to support our local talent and build on it, seeing how we can develop new research collaborations in Australia as well as internationally.