Visual active search (VAS) has been proposed as a modeling framework in which visual cues are used to guide exploration, with the goal of identifying regions of interest in a large geospatial area. Its potential applications include identifying hot spots of rare wildlife poaching activity, search-and-rescue scenarios, identifying illegal trafficking of weapons, drugs, or people, and many others. State of the art approaches to VAS include applications of deep reinforcement learning (DRL), which yield end-to-end search policies, and traditional active search, which combines predictions with custom algorithmic approaches. While the DRL framework has been shown to greatly outperform traditional active search in such domains, its end-to-end nature does not make full use of supervised information attained either during training, or during actual search, a significant limitation if search tasks differ significantly from those in the training distribution. We propose an approach that combines the strength of both DRL and conventional active search approaches by decomposing the search policy into a prediction module, which produces a geospatial distribution of regions of interest based on task embedding and search history, and a search module, which takes the predictions and search history as input and outputs the search distribution. In addition, we develop a novel meta-learning approach for jointly learning the resulting combined policy that can make effective use of supervised information obtained both at training and decision time. Our extensive experiments demonstrate that the proposed representation and meta-learning frameworks significantly outperform state of the art in visual active search on several problem domains.

Last week, the United Kingdom began requiring residents to verify their ages before accessing online pornography and other adult content, all in the name of protecting children. Almost immediately, things did not go as planned--although, they did go as expected. As experts predicted, UK residents began downloading virtual private networks (VPNs) en masse, allowing them to circumvent age verification, which can require users to upload their government IDs, by making it look like they're in a different country. The UK's Online Safety Act is just one part of a wave of age-verification efforts around the world. And while these laws may keep some kids from accessing adult content, some experts warn that they also create security and privacy risks for everyone.

Imagine, for a moment, that your most clandestine internet searches--anxiety-riddled deep dives on WebMD, Google queries wondering if your cat is trying to kill you, or why farts smell the way they do--were the key to finding a soulmate. Would you sign up for a dating site that guaranteed connection in return for your browser history? For more than a decade, developers have tried to perfect the science of compatibility. Bumble let women make the first move. Grindr was a gay utopia (until it became overrun with ads).

Visual active search (VAS) has been proposed as a modeling framework in which visual cues are used to guide exploration, with the goal of identifying regions of interest in a large geospatial area. Its potential applications include identifying hot spots of rare wildlife poaching activity, search-and-rescue scenarios, identifying illegal trafficking of weapons, drugs, or people, and many others. State of the art approaches to VAS include applications of deep reinforcement learning (DRL), which yield end-to-end search policies, and traditional active search, which combines predictions with custom algorithmic approaches. While the DRL framework has been shown to greatly outperform traditional active search in such domains, its end-to-end nature does not make full use of supervised information attained either during training, or during actual search, a significant limitation if search tasks differ significantly from those in the training distribution. We propose an approach that combines the strength of both DRL and conventional active search approaches by decomposing the search policy into a prediction module, which produces a geospatial distribution of regions of interest based on task embedding and search history, and a search module, which takes the predictions and search history as input and outputs the search distribution.

This past spring, a man in Washington State worried that his marriage was on the verge of collapse. "I am depressed and going a little crazy, still love her and want to win her back," he typed into ChatGPT. With the chatbot's help, he wanted to write a letter protesting her decision to file for divorce and post it to their bedroom door. "Emphasize my deep guilt, shame, and remorse for not nurturing and being a better husband, father, and provider," he wrote. In another message, he asked ChatGPT to write his wife a poem "so epic that it could make her change her mind but not cheesy or over the top." The man's chat history was included in the WildChat data set, a collection of 1 million ChatGPT conversations gathered consensually by researchers to document how people are interacting with the popular chatbot.

The U.S. Patent and Trademark Office (USPTO) recently added a new artificial intelligence (AI)-based "Similarity Search" feature to the prior art search tools available to examiners. This Similarity Search feature is designed to be an enhanced replacement of the Patent Linguistic Utility Service (PLUS) search tool and provides examiners with optional new search capabilities to access prior art alongside traditional document retrieval approaches. The PLUS search tool received as input a keyword list generated from scanned portions of the Specification being searched and produced a list of only U.S. patents and published U.S. applications that most closely match the Specification. The new AI-based Similarity Search tool receives the full text of the Specification as input and outputs a list of both domestic and foreign patent documents that are similar to the Specification being searched. Further, an examiner can refine the AI-based Similarity Search queries by emphasizing certain Cooperative Patent Classification (CPC) symbols and certain paragraphs, sentences, or words in a Specification to focus on specific concepts in the Specification being searched.

Everything Google knows about you can be laid bare with a few simple clicks on your smartphone or computer. It goes far beyond where you live, your age, your interests and your favorite stores - the search engine knows more than some of the people closest to you. Google Maps knows all the countries, cities, attractions and local routes you've ever visited. And its Ad Personalization system uses your search history, browsing data and screen time to generate. If you have Location History enabled on your Google account, take a trip down memory lane with the Google Maps Timeline.

A geography professor shared his method to detect AI-generated plagiarism with Fox News. He developed it after noticing that ChatGPT produced fake citations. Chatbots are the latest new tech that everyone seems to be obsessing over. They are computer programs that use artificial intelligence and natural language processing to simulate human conversations. These conversational assistants can be accessed through various messaging platforms such as ChatGPT, Bing Chat and Bard.

Before starting the article, it is important to understand what an agent in AI is. The agent is basically an entity that helps the AI, machine learning, or deep reinforcement learning to make a decision or trigger the AI to make a decision. In terms of software, it is defined as the entity which can take decisions and can make different decisions on the basis of changes in the environment, or after getting input from the external environment. In simpler words, the quick agent perceives external change and acts against it the better the results obtained from the model. Hence the role of the agent is always very important in artificial intelligence, machine learning, and deep learning.

Fueled by a great increase in online shopping, e-commerce platforms have been utilizing AI to improve the customer experience and optimize their business performance. AI has the power to customize websites for individual users, power chatbots which can understand and respond to complex questions, and get a detailed understanding of business performance. Fueled by platforms like Amazon, which have simplified online shopping and the Covid-19 pandemic, e-commerce has seen a significant rise in the last year with no sign of slowing down. According to the United Nations Conference on Trade and Development, e-commerce increased from 14% of global retail trade in 2019 to 17% in 2020. In a pre-pandemic world, retailers were already utilizing machine learning (ML) tools such as targeted advertisements on mobile devices and experimenting with different ways to incorporate artificial intelligence (AI) into their systems.