We present a new method to detect anomalies in texts (in general: in sequences of any data), using language models, in a totally unsupervised manner. The method considers probabilities (likelihoods) generated by a language model, but instead of focusing on low-likelihood tokens, it considers a new metric introduced in this paper: oddballness. Oddballness measures how ``strange'' a given token is according to the language model. We demonstrate in grammatical error detection tasks (a specific case of text anomaly detection) that oddballness is better than just considering low-likelihood events, if a totally unsupervised setup is assumed.

Low Probability of Detection (LPD) communication aims to obscure the very presence of radio frequency (RF) signals, going beyond just hiding the content of the communication. However, the use of Unmanned Aerial Vehicles (UAVs) introduces a challenge, as UAVs can detect RF signals from the ground by hovering over specific areas of interest. With the growing utilization of UAVs in modern surveillance, there is a crucial need for a thorough understanding of their unknown nonlinear dynamic trajectories to effectively implement LPD communication. Unfortunately, this critical information is often not readily available, posing a significant hurdle in LPD communication. To address this issue, we consider a case-study for enabling terrestrial LPD communication in the presence of multiple UAVs that are engaged in surveillance. We introduce a novel framework that combines graph neural networks (GNN) with Koopman theory to predict the trajectories of multiple fixed-wing UAVs over an extended prediction horizon. Using the predicted UAV locations, we enable LPD communication in a terrestrial ad-hoc network by controlling nodes' transmit powers to keep the received power at UAVs' predicted locations minimized. Our extensive simulations validate the efficacy of the proposed framework in accurately predicting the trajectories of multiple UAVs, thereby effectively establishing LPD communication.

Johnson spends millions every year in order to find a way to make his organs similar to that of an 18-year-old male. 'IT'S HUMANS I FEAR': Tech billionaire on journey to immortality welcomes AI as a solution. NO PRESSURE: IG report calls on VA to fix automated system that led to faulty claims decisions. Radiologist Bhavik Patel, M.D. (pictured here) has been named chief AI officer at Mayo Clinic Arizona. Businessman chatting through chatbot Online customer service with chat bots for support.

Johnson spends millions every year in order to find a way to make his organs similar to that of an 18-year-old male. A tech billionaire on a quest to reverse the aging process believes that it is unlikely humanity will survive without the assistance of artificial intelligence (AI). Bryan Johnson, a 46-year-old tech entrepreneur, spends millions yearly on a team of experts monitoring his health and conducting experiments. The goal: Get his organs to look and act like that of an 18-year-old. Some of his regiments include a strict bedtime of 8:30 p.m., taking 111 pills daily, collecting his stool samples, and having a small device attached to his penis to monitor nighttime erections.

Low probability of detection (LPD) has recently emerged as a means to enhance the privacy and security of wireless networks. Unlike existing wireless security techniques, LPD measures aim to conceal the entire existence of wireless communication instead of safeguarding the information transmitted from users. Motivated by LPD communication, in this paper, we study a privacy-preserving and distributed framework based on graph neural networks to minimise the detectability of a wireless ad-hoc network as a whole and predict an optimal communication region for each node in the wireless network, allowing them to communicate while remaining undetected from external actors. We also demonstrate the effectiveness of the proposed method in terms of two performance measures, i.e., mean absolute error and median absolute error.

Identifying a malfunction in the nation's power grid can be like trying to find a needle in an enormous haystack. Hundreds of thousands of interrelated sensors spread across the U.S. capture data on electric current, voltage, and other critical information in real time, often taking multiple recordings per second. Researchers at the MIT-IBM Watson AI Lab have devised a computationally efficient method that can automatically pinpoint anomalies in those data streams in real time. They demonstrated that their artificial intelligence method, which learns to model the interconnectedness of the power grid, is much better at detecting these glitches than some other popular techniques. Because the machine-learning model they developed does not require annotated data on power grid anomalies for training, it would be easier to apply in real-world situations where high-quality, labeled datasets are often hard to come by.

Artificial Intelligence (or AI) is a field in computer science that focuses on solving problems by applying learning techniques (and some math). In some ways, AI and the field, in general, focuses on building programs that try and imitate the way your own brain works. But let's talk about learning some more because it's important in understanding artificial intelligence. There are so many ways we as humans or even other animals learn. Let's take my dog, Buster. When Buster was a pup I wanted to teach him to roll over, but I had two main problems.

If you want to create convincing output for some purpose, something that isn't real but seems to be, how do you do it? Generative Adversarial Networks (GANs) are an approach which holds a lot of promise. They provide a form of unsupervised learning, where a system can improve its performance over time without human feedback. Imagine that you're an artist hoping to get rich by creating fake "previously undiscovered" Rembrandts. Your works have to be good enough to fool the experts.

In this tutorial, we'll be using a GA to find a solution to the traveling salesman problem (TSP). Let's start with a few definitions, rephrased in the context of the TSP: Now, let's see this in action. While each part of our GA is built from scratch, we'll use a few standard packages to make things easier: We first create a City class that will allow us to create and handle our cities. These are simply our (x, y) coordinates. Within the City class, we add a distance calculation (making use of the Pythagorean theorem) in line 6 and a cleaner way to output the cities as coordinates with __repr__ in line 12. We'll also create a Fitness class.