Current mainstream SAR image object detection methods still lack robustness when dealing with unknown objects in open environments. Open-set detection aims to enable detectors trained on a closed set to detect all known objects and identify unknown objects in open-set environments. The key challenges are how to improve the generalization to potential unknown objects and reduce the empirical classification risk of known categories under strong supervision. To address these challenges, a novel open-set aircraft detector for SAR images is proposed, named Open-Set Aircraft Detection (OSAD), which is equipped with three dedicated components: global context modeling (GCM), location quality-driven pseudo labeling generation (LPG), and prototype contrastive learning (PCL). GCM effectively enhances the network's representation of objects by attention maps which is formed through the capture of long sequential positional relationships. LPG leverages clues about object positions and shapes to optimize localization quality, avoiding overfitting to known category information and enhancing generalization to potential unknown objects. PCL employs prototype-based contrastive encoding loss to promote instance-level intra-class compactness and inter-class variance, aiming to minimize the overlap between known and unknown distributions and reduce the empirical classification risk of known categories. Extensive experiments have demonstrated that the proposed method can effectively detect unknown objects and exhibit competitive performance without compromising closed-set performance. The highest absolute gain which ranges from 0 to 18.36% can be achieved on the average precision of unknown objects.

Recent advancements in artificial intelligence have led to the creation of highly capable large language models (LLMs) that can perform tasks in a human-like manner. However, LLMs exhibit only infant-level cognitive abilities in certain areas. One such area is the A-Not-B error, a phenomenon seen in infants where they repeat a previously rewarded behavior despite well-observed changed conditions. This highlights their lack of inhibitory control -- the ability to stop a habitual or impulsive response. In our work, we design a text-based multi-choice QA scenario similar to the A-Not-B experimental settings to systematically test the inhibitory control abilities of LLMs. We found that state-of-the-art LLMs (like Llama3-8b) perform consistently well with in-context learning (ICL) but make errors and show a significant drop of as many as 83.3% in reasoning tasks when the context changes trivially. This suggests that LLMs only have inhibitory control abilities on par with human infants in this regard, often failing to suppress the previously established response pattern during ICL.

Unlike its direct competitors DALL-E or Midjourney, Stable Diffusion makes its source code available and can be run on local hardware. Furthermore, Stable Diffusion claims no rights on the generated output images. The user owns the unique creations and is supposedly free to use them commercially.

Artificial Intelligence (AI) is the hottest trend at the moment, everyone is talking about how it may change our lives and even take our jobs. Potentially every industry will be affected by AI in the (near) future, but this doesn't mean it will be a negative effect. I have a background in Law so naturally I'm interested to see how AI might change the legal profession for the better. As AI continues to develop and learn it can be used to cut time in proof-reading and research. A study in America found that it took legal professionals on average one hour to proof a document for mistakes, but it only took the AI a matter of minutes.

We have written extensively about the potential of artificial intelligence for redesigning healthcare. How it could help medical professionals in designing treatment plans and finding the best-suited methods for every patient. How it could assist repetitive, monotonous tasks, so physicians and nurses can concentrate on their actual jobs instead of e.g. By what means A.I. could prioritize e-mails in doctors' inboxes or keep them up-to-date with the help of finding the latest and most relevant scientific studies in seconds. How its transformative power makes it as important as the stethoscope, the symbol of modern medicine, which appeared in the 19th century.

KPMG will hold a series of workshops over the next few months with IBM Watson staff to work out how to use the artificial intelligence and machine learning capabilities to carry out this new type of audit. "No-one knows exactly what the audit of the future will look like, but you can be sure it will involve two things - bright human beings and cognitive technology," said Duncan McLennan, the firm's national managing partner of audit. "Cognitive enables greater collaboration between humans and systems - so while it's a game-changer for audit in terms of depth of analysis, it will still require insights from talented people. We're being helped, not replaced."