I am lucky to have a large collection of historical family photos. Scanned by my relatives in Finland, I have photos dating back a century showing my great-grandparents with my grandparents as children. Identifying who's who in these photos is a fascinating trip back in time. I've been exploring using People tags in Mylio Photos to catalog and organize my collection. The People tags in Mylio Photos uses AI to match faces. The more people you tag, the more accurate it gets.

High-precision vehicle positioning is key to the implementation of modern driving systems in urban environments. Global Navigation Satellite System (GNSS) carrier phase measurements can provide millimeter- to centimeter-level positioning, provided that the integer ambiguities are correctly resolved. Abundant code measurements are often used to facilitate integer ambiguity resolution (IAR), however, they suffer from signal blockage and multipath in urban canyons. In this contribution, a lidar-aided instantaneous ambiguity resolution method is proposed. Lidar measurements, in the form of 3D keypoints, are generated by a learning-based point cloud registration method using a pre-built HD map and integrated with GNSS observations in a mixed measurement model to produce precise float solutions, which in turn increase the ambiguity success rate. Closed-form expressions of the ambiguity variance matrix and the associated Ambiguity Dilution of Precision (ADOP) are developed to provide a priori evaluation of such lidar-aided ambiguity resolution performance. Both analytical and experimental results show that the proposed method enables successful instantaneous IAR with limited GNSS satellites and frequencies, leading to centimeter-level vehicle positioning.

Digital transformation has become a business imperative in the wake of the pandemic. Companies that have not modernized their processes and integrated cloud and analytics capabilities are severely disadvantaged. Genpact's latest study, "Data-Driven Business Transformation," reveals that only 8% of enterprises have fully modernized. This digital divide will result in those companies who can move quickly and take advantage of new opportunities, leaving behind those who are not. In addition to Genpact, Peak's latest report, "State of AI 2022," revealed that 55% of enterprises will be AI-First by 2025.

Eyal Ben-Ari just took his first shot on a goal of bringing professional-class analytics to amateur soccer players. The CEO of startup Track160, in Tel Aviv, has seen his company's AI-powered sports analytics software tested and used in the big leagues. Now he's turning his attention to underserved amateurs in the clubs and community teams he says make up "the bigger opportunity" among the world's 250 million soccer players. "Almost everyone in professional sports uses data analytics today. Now we're trying to enable any team at any level to capture their own data and analytics, and the only way to do it is leveraging AI," he said.

The combination of Reinforcement Learning (RL) with deep learning has led to a series of impressive feats, with many believing (deep) RL provides a path towards generally capable agents. However, the success of RL agents is often highly sensitive to design choices in the training process, which may require tedious and error-prone manual tuning. This makes it challenging to use RL for new problems and also limits its full potential. In many other areas of machine learning, AutoML has shown that it is possible to automate such design choices, and AutoML has also yielded promising initial results when applied to RL. However, Automated Reinforcement Learning (AutoRL) involves not only standard applications of AutoML but also includes additional challenges unique to RL, that naturally produce a different set of methods. As such, AutoRL has been emerging as an important area of research in RL, providing promise in a variety of applications from RNA design to playing games, such as Go. Given the diversity of methods and environments considered in RL, much of the research has been conducted in distinct subfields, ranging from meta-learning to evolution. In this survey, we seek to unify the field of AutoRL, provide a common taxonomy, discuss each area in detail and pose open problems of interest to researchers going forward.

Back translation is one of the most widely used methods for improving the performance of neural machine translation systems. Recent research has sought to enhance the effectiveness of this method by increasing the 'diversity' of the generated translations. We argue that the definitions and metrics used to quantify 'diversity' in previous work have been insufficient. This work puts forward a more nuanced framework for understanding diversity in training data, splitting it into lexical diversity and syntactic diversity. We present novel metrics for measuring these different aspects of diversity and carry out empirical analysis into the effect of these types of diversity on final neural machine translation model performance for low-resource English$\leftrightarrow$Turkish and mid-resource English$\leftrightarrow$Icelandic. Our findings show that generating back translation using nucleus sampling results in higher final model performance, and that this method of generation has high levels of both lexical and syntactic diversity. We also find evidence that lexical diversity is more important than syntactic for back translation performance.

Coral reefs are among Earth's most stunning and biodiverse ecosystems. Yet, due to human-induced climate change resulting in warmer oceans, we are seeing growing numbers of these living habitats dying. The urgency of the crisis facing coral reefs around the world was highlighted in a recent study that showed that 91% of Australia's Great Barrier Reef had experienced coral bleaching in the summer of 2021–22 due to heat stress from rising water temperatures. Determining reef health is key to gauging the extent of the problem and developing ways of intervening to save these ecosystems, and a new artificial intelligence (AI) tool has been developed to measure reef health using… sound. Research coming out of the UK is using AI to study the soundscape of Indonesian reefs to determine the health of the ecosystems.

Have you ever gone to the dentist and been unsure if that spot on your tooth the doctor is looking at is really a cavity? Or maybe you've gone to get a second opinion, only to have the new practice tell you that you need a crown on a completely different tooth? The next wave of IT innovation will be powered by artificial intelligence and machine learning. We look at the ways companies can take advantage of it and how to get started. Unfortunately, this story is all too common in dentistry -- in fact, there's a well-known story about a Reader Digest reporter who went to see 50 different dentists and received nearly 50 different diagnoses.

It was the veritable search for a needle in a haystack. With drug-resistant bacteria on the rise, researchers at MIT were sifting through a database of more than 100 million molecules to identify a few that might have antibacterial properties. Fortunately, the search proved successful. But it wasn't a human who found the promising molecules. It was a machine learning program .

Virtual children that play with you, cuddle you, and even look like you will be commonplace in 50 years, and could help to combat overpopulation, an artificial intelligence expert has claimed. These computer-generated offspring will only exist in the immersive digital world known as the'metaverse', which is accessed using virtual reality technology such as a headset to make a user feel as if they're face-to-face with the child. They will cost next to nothing to bring up, as they will require minimal resources, according to Catriona Campbell, one of the UK's leading authorities on AI and emerging technologies. In her new book, AI by Design: A Plan For Living With Artificial Intelligence, she argues that concerns about overpopulation will prompt society to embrace digital children. She describes them as the'Tamagotchi generation' -- a reference to the handheld digital pets that became wildly popular among Western youngsters in the late 1990s and the 2000s.