It is well known that query-based attacks tend to have relatively higher successrates in adversarial black-box attacks. While research on black-box attacks is activelybeing conducted, relatively few studies have focused on pixel attacks thattarget only a limited number of pixels. In image classification, query-based pixelattacks often rely on patches, which heavily depend on randomness and neglectthe fact that scattered pixels are more suitable for adversarial attacks. Moreover, tothe best of our knowledge, query-based pixel attacks have not been explored in thefield of object detection. To address these issues, we propose a novel pixel-basedblack-box attack called Remember and Forget Pixel Attack using ReinforcementLearning(RFPAR), consisting of two main components: the Remember and Forgetprocesses. RFPAR mitigates randomness and avoids patch dependency byleveraging rewards generated through a one-step RL algorithm to perturb pixels.RFPAR effectively creates perturbed images that minimize the confidence scoreswhile adhering to limited pixel constraints.

We propose a learning algorithm for a variable memory length Markov process. Human communication, whether given as text, handwriting, or speech, has multi characteristic time scales. On short scales it is characterized mostly by the dynamics that gen(cid:173) erate the process, whereas on large scales, more syntactic and se(cid:173) mantic information is carried. For that reason the conventionally used fixed memory Markov models cannot capture effectively the complexity of such structures. On the other hand using long mem(cid:173) ory models uniformly is not practical even for as short memory as four. The algorithm we propose is based on minimizing the sta(cid:173) tistical prediction error by extending the memory, or state length, adaptively, until the total prediction error is sufficiently small.

Last week, I missed a real-life meeting because I hadn't set a reminder on my smartphone, leaving someone I'd never met before alone in a café. But on the same day, I remembered the name of the actor who played Will Smith's aunt in The Fresh Prince of Bel-Air in 1991 (Janet Hubert). Memory is weird, unpredictable and, neuroscientifically, not yet entirely understood. When memory lapses like mine happen (which they do, a lot), it feels both easy and logical to blame the technology we've so recently adopted. Does having more memory in our pockets mean there's less in our heads?

AI models don't have a memory: When you converse with a chatbot one day, it won't remember what you said the next day. Chatbots (and Language Models) typically work by looking at "context", which, for you, basically means a few sentences in the past. The limit will vary from model to model, but it's typically up to 1000 words or something (not sure what is it these days with super huge models, but there's always a limit). Even if a chatbot uses "RNN", it's still very limited (usually even more) as RNNs struggle with long-term memory where long [a few hundred words]. The point is that AI models have no idea what you said a few sentences back. Also, don't be confused by models like Neural Turing Machine, which have a "working memory" (like RAM) but still no permanent memory (like a hard disk).

My wife and kids have a tough time believing that I fear nothing supernatural. They fear all of them. When they tell me that this is strange, considering I'm the only one of us who goes to mass every Sunday and literally eats the body and blood of a man who came back from the dead, I distract them by showing them my latest Day-Glo Virgin Mary statue which plays Ave Maria. My youngest teen, Sharkie, gives me a list of her scariest games ever. Apparently one of them is bound to give me nightmares.

From the moment we are born, our brains are bombarded by an immense amount of information about ourselves and the world around us. So, how do we hold on to everything we've learned and experienced? Humans retain different types of memories for different lengths of time. We also have a working memory, which lets us keep something in our minds for a limited time by repeating it. Whenever you say a phone number to yourself over and over to remember it, you're using your working memory.

Companies of all kinds use machine learning to analyze people's desires, dislikes, or faces. Some researchers are now asking a different question: How can we make machines forget? A nascent area of computer science dubbed machine unlearning seeks ways to induce selective amnesia in artificial intelligence software. The goal is to remove all trace of a particular person or data point from a machine learning system, without affecting its performance. If made practical, the concept could give people more control over their data and the value derived from it.

In trying to create the best horror experience, you're instantly drawn into very interesting game design problems, almost philosophical questions, like how much free will the player needs to get a sense that they have agency?

A new machine learning approach added to conventional magnetic resonance imaging can identify the regions of the brain causing dissociative symptoms in people with post-traumatic stress disorder, researchers found in a study published Friday by the American Journal of Psychiatry. Although MRI has long been used to document changes in the brain that occur as a result of a number of neurological conditions, bolstering the approach with machine learning enabled researchers to uncover and measure changes in functional connections between different regions of the brain in women with PTSD. These altered connections correlated with their dissociative symptoms, including memory loss or amnesia, the researchers said. "This new work may help us to establish a new standard of care for traumatized patients with PTSD who struggle with significant symptoms of dissociation," study co-author Dr. Milissa Kaufman, director of the Dissociative Disorders and Trauma Research Program at McLean Hospital, said in a statement. PTSD is a mental health disorder that occurs following trauma -- violent personal assaults, natural or human-caused disasters, accidents and military combat, for example -- according to the National Institute of Mental Health.

When Jon was born prematurely at 26 weeks, he weighed around two pounds and had trouble breathing on his own. For two months he lived in an incubator and eventually grew into a healthy baby and toddler. At age four, he had two epileptic seizures. About a year later his parents began to notice that Jon couldn't remember things that happened in his daily life. He didn't recall watching TV or what happened at school or what book he read. Jon's IQ was normal, he could read and write, and did well at school.