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.

Today, we'll discuss the impact of data cleansing in a Machine Learning model and how it can be achieved in Azure Machine Learning (Azure ML) studio. It is an important part of the Data Science Process as I discussed in my previous blog post. In this example, I'm using a credit scoring data set which has the following columns: The Data Set also have a column Status which is the label, that is, the column that we want to predict. The first step is of course to explore the data in Azure ML studio. By using the visualize feature of the data set, we can go through each column of the data set and view properties of each column such as Mean, Unique Values and Missing Values.

Artificial Intelligence (AI) and its ability to replace human jobs over the next few years has been the buzzword around the valley in the recent past. If research and number were to be taken as any parameter a recent study by Obama administration pointed out that almost half of the US jobs would be taken over by AI by 2036. To add insult to injury, certain studies have pointed out that close to 6% of US jobs will be replaced by AI as early 2021. Recent behaviour of the Indian ecosystem has definitely shown a trend of following valley technology. Whether this poses more as a bane than boon is something we will have to wait and watch, but in a country like India which is majorly human resource driven, taking pride on its educated work force and the world largest population in the working age group, the possibility of losing out major employability to AI could pose serious problems.

Finance leaders are recognizing that their current ERP systems, often heavily customized and woefully behind on updates, are becoming an albatross--and they're moving quickly to explore new applications. An Accenture survey of some 250 finance executives found that their organizations have adopted blockchain, in-memory computing, artificial intelligence, and robotic process automation at nearly the same rates as mobile, data analytics, and cloud technologies. But implementing such modern technologies while clinging to old processes and organizational structures built on old ERP platforms is like replacing the engine in a Tesla with one from my first car (a 1972 Ford Pinto). You'll end up stranded on the side of the road while others speed by your expensive heap. "If you automate an inefficient process, you just get bad data faster," said David Axson, managing director of Accenture, during a session at Oracle's Modern Finance Experience conference in Boston recently.

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."

Traditional Kalman filter (KF) is derived under the well-known minimum mean square error (MMSE) criterion, which is optimal under Gaussian assumption. However, when the signals are non-Gaussian, especially when the system is disturbed by some heavy-tailed impulsive noises, the performance of KF will deteriorate seriously. To improve the robustness of KF against impulsive noises, we propose in this work a new Kalman filter, called the maximum correntropy Kalman filter (MCKF), which adopts the robust maximum correntropy criterion (MCC) as the optimality criterion, instead of using the MMSE. Similar to the traditional KF, the state mean and covariance matrix propagation equations are used to give prior estimations of the state and covariance matrix in MCKF. A novel fixed-point algorithm is then used to update the posterior estimations. A sufficient condition that guarantees the convergence of the fixed-point algorithm is given. Illustration examples are presented to demonstrate the effectiveness and robustness of the new algorithm.