Viewers around the world can see the site of the first ever Olympic Games as it looked in its prime more than 2,000 years ago thanks to a digital reconstruction. 'Ancient Olympia: Common Grounds' stems from collaboration between the Hellenic Ministry of Culture and Sport and Microsoft's AI for Cultural Heritage initiative. Microsoft teamed with tech firm Iconem to take hundreds of thousands of images of the ancient site as it lies today -- both with ground- and drone-based cameras. These were processed by Microsoft AI to create models so precise they are photo-realistic and from which the ancient monuments could be digitally reconstructed. The first games took place in Olympia in 776 BC, and recurred every four year until at least AD 393 and they perhaps continued until the Temple of Zeus burnt in 425 AD.

An expert in legal technology, Ope Olugasa, has said judges can use artificial intelligence (A.I) to easily review and evaluate written addresses and submissions in seconds. The Managing Director/Chief Executive Officer of LawPavilion Business Solutions, Ope Olugasa, said the firm will showcase its new A.I system geared towards smart justice delivery at the forthcoming biennial judges' conference in Abuja. According to him, the solutions are made by Nigerians to help solve some of the challenges facing the country's justice sector. Olugasa told reporters at a briefing in Lagos that the new solution, A.I Document Review, can identify a judge's previous judgments on similar issues. He said it makes adjudication easier and faster, without judges having to always reinvent the wheel in deciding each matter.

Two key assumptions shape the usual view of ranked retrieval: (1) that the searcher can choose words for their query that might appear in the documents that they wish to see, and (2) that ranking retrieved documents will suffice because the searcher will be able to recognize those which they wished to find. When the documents to be searched are in a language not known by the searcher, neither assumption is true. In such cases, Cross-Language Information Retrieval (CLIR) is needed. This chapter reviews the state of the art for cross-language information retrieval and outlines some open research questions.

Several studies have reported that biometric identification based on eye movement characteristics can be used for authentication. This paper provides an extensive study of user identification via eye movements across multiple datasets based on an improved version of method originally proposed by George and Routray. We analyzed our method with respect to several factors that affect the identification accuracy, such as the type of stimulus, the IVT parameters (used for segmenting the trajectories into fixation and saccades), adding new features such as higher-order derivatives of eye movements, the inclusion of blink information, template aging, age and gender.We find that three methods namely selecting optimal IVT parameters, adding higher-order derivatives features and including an additional blink classifier have a positive impact on the identification accuracy. The improvements range from a few percentage points, up to an impressive 9 % increase on one of the datasets.

Landing AI, which provides tools that make building and deploying AI systems in manufacturing faster and easier than ever, announced Series A funding of $57 million led by McRock Capital, the first investment firm focused exclusively on the Industrial IoT. In addition, New York-based global private equity and venture capital firm Insight Partners, Taiwania Capital, Canada Pension Plan Investment Board (CPP Investments), Intel Capital, Samsung Catalyst Fund, Far Eastern Group's DRIVE Catalyst, Walsin Lihwa, and AI Fund all participated in the round. Landing AI, led by artificial intelligence visionary, Andrew Ng, developed LandingLens, a fast, easy to use enterprise MLOps platform. It applies AI and deep learning to help manufacturers solve visual inspection problems, find product defects more reliably, and generate business value. Landing AI sees the next era of AI as one in which all companies access the benefits of AI--not just consumer internet companies like Google and Facebook--but legacy industries such as manufacturing, healthcare, and agriculture.

Back in 2018, the Google Arts & Culture app introduced a feature that looks your doppelgänger in works of art. It's searched for matches for more than 120 million selfies so far. Now, the app can look for animals in art that resemble your pets too. Using a machine learning algorithm, Pet Portraits matches a snap of your furry, finned or feathered friend against tens of thousands of works from Google's partner institutions. The app might determine that the best match for your pet is in a piece of street art from Mexico or a cat figurine from ancient Egypt.

The graph matching problem seeks to find an alignment between the nodes of two graphs that minimizes the number of adjacency disagreements. Solving the graph matching is increasingly important due to it's applications in operations research, computer vision, neuroscience, and more. However, current state-of-the-art algorithms are inefficient in matching very large graphs, though they produce good accuracy. The main computational bottleneck of these algorithms is the linear assignment problem, which must be solved at each iteration. In this paper, we leverage the recent advances in the field of optimal transport to replace the accepted use of linear assignment algorithms. We present GOAT, a modification to the state-of-the-art graph matching approximation algorithm "FAQ" (Vogelstein, 2015), replacing its linear sum assignment step with the "Lightspeed Optimal Transport" method of Cuturi (2013). The modification provides improvements to both speed and empirical matching accuracy. The effectiveness of the approach is demonstrated in matching graphs in simulated and real data examples.

This study proposes a novel method to assess damages in the built environment using a deep learning workflow to quantify it. Thanks to an automated crawler, aerial images from before and after a natural disaster of 50 epicenters worldwide were obtained from Google Earth, generating a 10,000 aerial image database with a spatial resolution of 2 m per pixel. The study utilizes the algorithm Seg-Net to perform semantic segmentation of the built environment from the satellite images in both instances (prior and post-natural disasters). For image segmentation, Seg-Net is one of the most popular and general CNN architectures. The Seg-Net algorithm used reached an accuracy of 92% in the segmentation. After the segmentation, we compared the disparity between both cases represented as a percentage of change. Such coefficient of change represents the damage numerically an urban environment had to quantify the overall damage in the built environment. Such an index can give the government an estimate of the number of affected households and perhaps the extent of housing damage.

International collaboration has become imperative in the field of AI. However, few studies exist concerning how distance factors have affected the international collaboration in AI research. In this study, we investigate this problem by using 1,294,644 AI related collaborative papers harvested from the Microsoft Academic Graph (MAG) dataset. A framework including 13 indicators to quantify the distance factors between countries from 5 perspectives (i.e., geographic distance, economic distance, cultural distance, academic distance, and industrial distance) is proposed. The relationships were conducted by the methods of descriptive analysis and regression analysis. The results show that international collaboration in the field of AI today is not prevalent (only 15.7%). All the separations in international collaborations have increased over years, except for the cultural distance in masculinity/felinity dimension and the industrial distance. The geographic distance, economic distance and academic distances have shown significantly negative relationships with the degree of international collaborations in the field of AI. The industrial distance has a significant positive relationship with the degree of international collaboration in the field of AI. Also, the results demonstrate that the participation of the United States and China have promoted the international collaboration in the field of AI. This study provides a comprehensive understanding of internationalizing AI research in geographic, economic, cultural, academic, and industrial aspects.

Multimodal sentiment analysis (MSA) draws increasing attention with the availability of multimodal data. The boost in performance of MSA models is mainly hindered by two problems. On the one hand, recent MSA works mostly focus on learning cross-modal dynamics, but neglect to explore an optimal solution for unimodal networks, which determines the lower limit of MSA models. On the other hand, noisy information hidden in each modality interferes the learning of correct cross-modal dynamics. To address the above-mentioned problems, we propose a novel MSA framework \textbf{M}odulation \textbf{M}odel for \textbf{M}ultimodal \textbf{S}entiment \textbf{A}nalysis ({$ M^3SA $}) to identify the contribution of modalities and reduce the impact of noisy information, so as to better learn unimodal and cross-modal dynamics. Specifically, modulation loss is designed to modulate the loss contribution based on the confidence of individual modalities in each utterance, so as to explore an optimal update solution for each unimodal network. Besides, contrary to most existing works which fail to explicitly filter out noisy information, we devise a modality filter module to identify and filter out modality noise for the learning of correct cross-modal embedding. Extensive experiments on publicly datasets demonstrate that our approach achieves state-of-the-art performance.