A popular approach for dealing with the large label space is to arrange the labels into a shallow tree-based index and then learn an ML model to efficiently search this index via beam search.

For almost two years now, the world's biggest tech companies have been at war over generative AI. Meta may be known for social media, Google for search, and Amazon for online shopping, but since the release of ChatGPT, each has made tremendous investments in an attempt to dominate in this new era. Along with start-ups such as OpenAI, Anthropic, and Perplexity, their spending on data centers and chatbots is on track to eclipse the costs of sending the first astronauts to the moon. To be successful, these companies will have to do more than build the most "intelligent" software: They will need people to use, and return to, their products. Everyone wants to be Facebook, and nobody wants to be Friendster.

A wide range of imaging techniques and data formats available for medical images make accurate retrieval from image databases challenging. Efficient retrieval systems are crucial in advancing medical research, enabling large-scale studies and innovative diagnostic tools. Thus, addressing the challenges of medical image retrieval is essential for the continued enhancement of healthcare and research. In this study, we evaluated the feasibility of employing four state-of-the-art pretrained models for medical image retrieval at modality, body region, and organ levels and compared the results of two similarity indexing approaches. Since the employed networks take 2D images, we analyzed the impacts of weighting and sampling strategies to incorporate 3D information during retrieval of 3D volumes. We showed that medical image retrieval is feasible using pretrained networks without any additional training or fine-tuning steps. Using pretrained embeddings, we achieved a recall of 1 for various tasks at modality, body region, and organ level.

March 24 (Reuters) - Microsoft Corp (MSFT.O) has threatened to cut off access to its internet-search data, which it licenses to rival search engines, if they do not stop using it as the basis for their own artificial intelligence chat products, Bloomberg News reported on Friday. The company has told at least two customers that using its Bing search index - a map of the internet that can be scanned in real time - to feed their AI chat tools violates the terms of their contract, the news agency said, citing people familiar with the dispute. Redmond, Washington-based Microsoft may also terminate licenses providing access to its search index, Bloomberg added. "We've been in touch with partners who are out of compliance as we continue to consistently enforce our terms across the board," a Microsoft spokesperson told Reuters, adding that the company will continue to work with them directly and give information needed to find a path forward. The maker of the Windows operating system had said in February it was revamping its Bing search engine and Edge Web browser with artificial intelligence, signaling its ambition to retake the lead in consumer technology markets where it has fallen behind.

Microsoft Corp. has threatened to cut off access to its internet-search data, which it licenses to rival search engines, if they don't stop using it as the basis for their own artificial intelligence chat products, according to people familiar with the dispute. The software maker licenses the data in its Bing search index - a map of the internet that can be quickly scanned in real time - to other companies that offer web search, such as Apollo Global Management Inc.'s Yahoo and DuckDuckGo. In February, Microsoft integrated a cousin of ChatGPT, OpenAI's AI-powered chat technology, into Bing. Rivals quickly moved to roll out their own AI chatbots as hype built around the buzzy technology. This week, Alphabet Inc.'s Google publicly released Bard, its conversational AI product.

Extreme multi-label classification (XMC) is a popular framework for solving many real-world problems that require accurate prediction from a very large number of potential output choices. A popular approach for dealing with the large label space is to arrange the labels into a shallow tree-based index and then learn an ML model to efficiently search this index via beam search. Existing methods initialize the tree index by clustering the label space into a few mutually exclusive clusters based on pre-defined features and keep it fixed throughout the training procedure. This approach results in a sub-optimal indexing structure over the label space and limits the search performance to the quality of choices made during the initialization of the index. In this paper, we propose a novel method ELIAS which relaxes the tree-based index to a specialized weighted graph-based index which is learned end-to-end with the final task objective. More specifically, ELIAS models the discrete cluster-to-label assignments in the existing tree-based index as soft learnable parameters that are learned jointly with the rest of the ML model. ELIAS achieves state-of-the-art performance on several large-scale extreme classification benchmarks with millions of labels. In particular, ELIAS can be up to 2.5% better at precision@1 and up to 4% better at recall@100 than existing XMC methods.

As of writing this paper, COVID-19 (Coronavirus disease 2019) has spread to more than 220 countries and territories. Following the outbreak, the pandemic's seriousness has made people more active on social media, especially on the microblogging platforms such as Twitter and Weibo. The pandemic-specific discourse has remained on-trend on these platforms for months now. Previous studies have confirmed the contributions of such socially generated conversations towards situational awareness of crisis events. The early forecasts of cases are essential to authorities to estimate the requirements of resources needed to cope with the outgrowths of the virus. Therefore, this study attempts to incorporate the public discourse in the design of forecasting models particularly targeted for the steep-hill region of an ongoing wave. We propose a sentiment-involved topic-based latent variables search methodology for designing forecasting models from publicly available Twitter conversations. As a use case, we implement the proposed methodology on Australian COVID-19 daily cases and Twitter conversations generated within the country. Experimental results: (i) show the presence of latent social media variables that Granger-cause the daily COVID-19 confirmed cases, and (ii) confirm that those variables offer additional prediction capability to forecasting models. Further, the results show that the inclusion of social media variables introduces 48.83--51.38% improvements on RMSE over the baseline models. We also release the large-scale COVID-19 specific geotagged global tweets dataset, MegaGeoCOV, to the public anticipating that the geotagged data of this scale would aid in understanding the conversational dynamics of the pandemic through other spatial and temporal contexts.

Ontologies comprising of concepts, their attributes, and relationships, form the quintessential backbone of many knowledge based AI systems. These systems manifest in the form of question-answering or dialogue in number of business analytics and master data management applications. While there have been efforts towards populating domain specific ontologies, we examine the role of document structure in learning ontological relationships between concepts in any document corpus. Inspired by ideas from hypernym discovery and explainability, our method performs about 15 points more accurate than a stand-alone R-GCN model for this task.

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