Investigating active learning, we focus on the relation between the number of labeled examples (budget size), and suitable querying strategies. Our theoretical analysis shows a behavior reminiscent of phase transition: typical examples are best queried when the budget is low, while unrepresentative examples are best queried when the budget is large. Combined evidence shows that a similar phenomenon occurs in common classification models. Accordingly, we propose TypiClust -- a deep active learning strategy suited for low budgets. In a comparative empirical investigation of supervised learning, using a variety of architectures and image datasets, TypiClust outperforms all other active learning strategies in the low-budget regime. Using TypiClust in the semi-supervised framework, performance gets an even more significant boost. In particular, state-of-the-art semi-supervised methods trained on CIFAR-10 with 10 labeled examples selected by TypiClust, reach 93.2% accuracy -- an improvement of 39.4% over random selection. Code is available at https://github.com/avihu111/TypiClust.

In scientometrics, scientific collaboration is often analyzed by means of co-authorships. An aspect which is often overlooked and more difficult to quantify is the flow of expertise between authors from different research topics, which is an important part of scientific progress. With the Topic Flow Network (TFN) we propose a graph structure for the analysis of research topic flows between scientific authors and their respective research fields. Based on a multi-graph and a topic model, our proposed network structure accounts for intratopic as well as intertopic flows. Our method requires for the construction of a TFN solely a corpus of publications (i.e., author and abstract information). From this, research topics are discovered automatically through non-negative matrix factorization. The thereof derived TFN allows for the application of social network analysis techniques, such as common metrics and community detection. Most importantly, it allows for the analysis of intertopic flows on a large, macroscopic scale, i.e., between research topic, as well as on a microscopic scale, i.e., between certain sets of authors. We demonstrate the utility of TFNs by applying our method to two comprehensive corpora of altogether 20 Mio. publications spanning more than 60 years of research in the fields computer science and mathematics. Our results give evidence that TFNs are suitable, e.g., for the analysis of topical communities, the discovery of important authors in different fields, and, most notably, the analysis of intertopic flows, i.e., the transfer of topical expertise. Besides that, our method opens new directions for future research, such as the investigation of influence relationships between research fields.

Artificial intelligence is nowhere near good enough to address problems facing content moderation on Facebook, according to whistleblower Frances Haugen. Haugen appeared at an event in London Tuesday evening with Daniel Motaung, a former Facebook moderator who is suing the company in Kenya accusing it of human trafficking. Meta has praised the efficacy of its AI systems in the past. CEO Mark Zuckerberg told a Congressional hearing in March 2021 the company relies on AI to weed out over 95% of "hate speech content." In February this year Zuckerberg said the company wants to get its AI to a "human level" of intelligence.

Defence ministers from NATO countries and other parts of the world will convene to discuss weapons deliveries to Ukraine on Wednesday. The meeting comes as Kyiv seeks a significant increase in arms to fight off Russian forces. Ahead of the talks, NATO Secretary General Jens Stoltenberg said allies would continue to deliver heavy weapons and long-range systems to Ukraine. Since the Russian invasion on February 24, Ukraine has received billions of dollars' worth of weapons and military equipment from at least 28 countries. Twenty-five of the 28 nations providing military assistance to Ukraine are NATO members, including the US and UK, which are supplying Kyiv with sophisticated weapons such as multiple rocket launch systems (MLRS). Despite its growing arsenal, Ukraine, which has an active military personnel of just 200,000, is significantly outgunned by Russian forces.

We've all heard the adage, "Every coin has two sides." The same is true for AI; just as it has benefits, it may also have drawbacks if trained improperly. Microsoft discovered the dangers of developing racist AI, but what happens if the intelligence is actively directed at a poisonous forum? Yannic Kilcher, an AI researcher and YouTuber, trained an AI on 3.3 million postings from 4chan's infamously toxic Politically Incorrect /pol/ board. Kilcher released the AI on the board after implementing the model in 10 bots, which resulted in a wave of hatred.

A machine learning program has been used to distinguish between cancerous and healthy cells by identifying patterns in characteristic combinations of genes.

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The retail experience is evolving with artificial intelligence (AI) changing how items can be bought and sold. Inventory robots can automatically restock shelves and sensors can track customer traffic patterns to identify optimum store layout. Opportunities for cross-selling and digital signage can be edited for specific audiences, providing up-to-the-minute information to motivate consumers, such as alerting them to when stocks are running low. Augmented reality (AR) is also enhancing the retail experience. In homeware, a consumer can upload an image of their room and redecorate it using AR to view different colour schemes and choose suitable accessories, suggested by computers.

The graph represents a network of 3,357 Twitter users whose tweets in the requested range contained "smarthome", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Saturday, 28 May 2022 at 03:34 UTC. The requested start date was Saturday, 28 May 2022 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 10-day, 8-hour, 57-minute period from Tuesday, 17 May 2022 at 09:45 UTC to Friday, 27 May 2022 at 18:42 UTC.

Micronutrient deficiencies afflict more than two billion people worldwide, including 340 million children. This lack of vitamins and minerals can have serious health consequences. But diagnosing deficiencies early enough for effective treatment requires expensive, time-consuming blood draws and laboratory tests. New research provides a more efficient approach. Computer scientist Elizabeth Bondi and her colleagues at Harvard University used publicly available satellite data and artificial intelligence to reliably pinpoint geographical areas where populations are at high risk of micronutrient deficiencies.