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- Research Report > New Finding (0.68)
- Research Report > Experimental Study (0.46)
Police admit overstating Maccabi fan ban evidence
West Midlands Police has admitted it overstated the evidence used to make the decision to ban Israeli fans from a match in Birmingham. Craig Guildford, its former chief constable, retired earlier this month after damning criticism of the ban on Maccabi Tel Aviv fans from the Europa League match against Aston Villa, last November. In newly released documents, the force also said we did not engage early enough with the local Jewish community, and indicated there was now a ban on AI use after its evidence included a match that did not take place. Furthermore, it said its operations would have lasted four days, involved multiple forces, and cost more than £5m, if 2,500 away fans had attended. The documents were released ahead of a public meeting on Tuesday, at which Police and Crime Commissioner for the West Midlands, Simon Foster, will discuss at his accountability and governance board, the decision to ban the Maccabi fans.
- Europe > United Kingdom > England > Surrey > Guildford (0.28)
- Asia > Middle East > Israel > Tel Aviv District > Tel Aviv (0.27)
- North America > United States (0.16)
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- Leisure & Entertainment > Sports > Soccer (0.55)
- Government > Regional Government > Europe Government > United Kingdom Government (0.49)
One Explanation is Not Enough: Structured Attention Graphs for Image Classification
Attention maps are popular tools for explaining the decisions of convolutional neural networks (CNNs) for image classification. Typically, for each image of interest, a single attention map is produced, which assigns weights to pixels based on their importance to the classification. We argue that a single attention map provides an incomplete understanding since there are often many other maps that explain a classification equally well. In this paper, we propose to utilize a beam search algorithm to systematically search for multiple explanations for each image. Results show that there are indeed multiple relatively localized explanations for many images. However, naively showing multiple explanations to users can be overwhelming and does not reveal their common and distinct structures.
- Europe > Spain > Catalonia > Barcelona Province > Barcelona (0.04)
- Europe > France > Île-de-France (0.04)
The Vision Pro Was An Expensive Misstep. Now Apple Has to Catch Up With Smart Glasses
The Vision Pro Was an Expensive Misstep. Having reportedly shelved work on a cheaper Vision Pro, Apple is apparently pivoting its focus to smart glasses--and hoping it's not too late. If Apple wants to match the grip Meta has on the smart glasses market, it just might have to simplify its face computers. According to an internal announcement reported in Bloomberg by serial Apple leaker Mark Gurman, Apple has deprioritized efforts to make a lighter, more affordable version of its Vision Pro headset in favor of focusing on AI-enabled smart glasses . Apple now seems to be aiming to launch a pair of Meta-style smart glasses in 2027, with another pair featuring a display on the lens aimed for release in 2028--if not before.
- North America > United States > California > San Francisco County > San Francisco (0.05)
- Europe > Slovakia (0.05)
- Europe > Czechia (0.05)
- Research Report > New Finding (0.68)
- Research Report > Experimental Study (0.46)
- Information Technology > Sensing and Signal Processing > Image Processing (1.00)
- Information Technology > Artificial Intelligence > Natural Language (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Search (0.96)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.48)
ASkDAgger: Active Skill-level Data Aggregation for Interactive Imitation Learning
Luijkx, Jelle, Ajanović, Zlatan, Ferranti, Laura, Kober, Jens
Human teaching effort is a significant bottleneck for the broader applicability of interactive imitation learning. To reduce the number of required queries, existing methods employ active learning to query the human teacher only in uncertain, risky, or novel situations. However, during these queries, the novice's planned actions are not utilized despite containing valuable information, such as the novice's capabilities, as well as corresponding uncertainty levels. To this end, we allow the novice to say: "I plan to do this, but I am uncertain." We introduce the Active Skill-level Data Aggregation (ASkDAgger) framework, which leverages teacher feedback on the novice plan in three key ways: (1) S-Aware Gating (SAG): Adjusts the gating threshold to track sensitivity, specificity, or a minimum success rate; (2) Foresight Interactive Experience Replay (FIER), which recasts valid and relabeled novice action plans into demonstrations; and (3) Prioritized Interactive Experience Replay (PIER), which prioritizes replay based on uncertainty, novice success, and demonstration age. Together, these components balance query frequency with failure incidence, reduce the number of required demonstration annotations, improve generalization, and speed up adaptation to changing domains. We validate the effectiveness of ASkDAgger through language-conditioned manipulation tasks in both simulation and real-world environments. Code, data, and videos are available at https://askdagger.github.io.
- Europe > Netherlands > South Holland > Delft (0.05)
- North America > United States > Wisconsin > Dane County > Madison (0.04)
Satellite Internet Will Let Us Put AI in Everything
Satellite internet is blasting off right now. Nations and states are inking deals with satellite providers to fill in service gaps for their residents and keep their critical infrastructure connected. Phone makers are building satellite capabilities into their handsets. Airlines are partnering with satellite operators to keep your in-flight Netflix stream stutter-free. And the race to blast the satellites powering these networks into orbit is helping the rocket business thrive.
- North America > United States (0.32)
- Asia > Myanmar (0.06)
- Transportation > Air (0.37)
- Information Technology (0.37)
- Aerospace & Defense (0.37)
- Government (0.34)