"... the research area that studies the operation and design of systems that recognize patterns in data." It includes statistical methods like discriminant analysis, feature extraction, error estimation, cluster analysis.
– Pattern Recognition Laboratory at Delft University of Technology
Artificial intelligence is growing in usage and capability, but it is still mostly a black box without guiding principles; Kinetic's Benjamin Lord argues that AI needs to develop ethical standards as it progresses. AI is dramatically changing the way we find and buy products. Brands have traditionally relied on targeted communications to stand out for customers, but they need to adapt to a world where people will fulfil their needs by simply chatting with Alexa or scanning an object with their smartphone's camera – no content involved. People seem to be fine for now with this kind of transaction-directed AI. After all, whether it's a voice assistant like Siri or a pattern recognition technology like Shazam, it's doing the job for you and saving you time.
Sheep can be trained to recognize celebrities like Barack Obama and Jake Gyllenhaal, new research shows. A University of Cambridge study found that sheep can learn to recognize human faces. The results were published Wednesday in the Royal Society's Open Science journal as part of research looking into cognitive ability and neurodegenerative disorders, like Huntington's disease, which can impair people's ability to recognize facial emotion. Researchers consider facial recognition as one of the most important human social skills. Sheep are known for their sociability, but this showed -- with some training -- the sheep could not only recognize fellow sheep and humans they knew, but process images of faces.
This article will propose an explanation of the conscious and subconscious minds, the purpose of dreaming and how/why we see optical illusions. All ideas are drawn from findings of modern computer science, machine learning and AI. The brain is made up of the conscious and the subconscious minds. The subconscious is the automated processing we do without conscious thinking and the conscious mind is the thoughtful deliberate processing of the results from the subconscious. We train our subconscious by repeating tasks and adjusting until we get it right.
Apple isn't making a special folder of your nude photos. But it does seem that way. A newly viral post is encouraging people to find out the "folder", and look at what is contained in there. And while some of the reports are true, they aren't all – or as intimate – they seem. The tweet – since reposted more than 10,000 times – instructs all women to go and search "brassiere" in their pictures.
Social media provide a low-cost alternative source for public health surveillance and health-related classification plays an important role to identify useful information. We summarized the recent classification methods using social media in public health. These methods rely on bag-of-words (BOW) model and have difficulty grasping the semantic meaning of texts. Unlike these methods, we present a word embedding based clustering method. Word embedding is one of the strongest trends in Natural Language Processing (NLP) at this moment.
Traditionally, template-matching algorithms have been used for things like digital image processing and visual pattern recognition. One simple example of this deals with taking a (typically very small) two-dimensional filter and sliding it across an image in order to detect low-level patterns of black-and-white pixels. Pattern recognition through template-matching is currently restricted in that it is only useful when dealing with vector spaces. However, problems of high complexity tend to deal with conceptually abstract relations and not with patterns dependent on space-time. In the following framework, I propose a feasible solution for extending template-matching methods to topological space, like graphs and networks.
As new technologies emerge, the battle over closed versus open systems continues to be one of the most important factors for a range of concerns that are critical to a healthy information ecosystem -- innovation, competition, privacy, security, consumer protection -- and even civil rights. With new advances in artificial intelligence -- particularly in the fields of machine learning and sensor technology -- questions of "open" versus "closed" have arisen again. In addition to code, algorithms, and data sets, this machine learning method requires immense computational power to discern complex patterns and data representations. While efforts to provide "open" code, algorithms and training data are laudable, the computation, competition, and accountability/audit concerns are unlikely to be answered with standard open source approaches.
Furthermore, these bots can also find the similar content on the social media and show you the performance of the content. Facebook's Research site range from studies of neural networks learning to predict hashtags, to pattern recognition algorithms that help you tag your friends in Facebook photos. Google-owned DeepMind is working on artificial intelligence (AI) that can imagine like humans and handle the unpredictable scenarios in real world. Pinterest has identified some areas in which deep learning will bring benefits to the network, particularly: object recognition to boost Pin and product recommendations; boost ad performance and relevance prediction; and detect spam users and content.
"In this book, aimed at senior undergraduates or beginning graduate students, Bishop provides an authoritative presentation of many of the statistical techniques that have come to be considered part of'pattern recognition' or'machine learning'. "Bishop (Microsoft Research, UK) has prepared a marvelous book that provides a comprehensive, 700-page introduction to the fields of pattern recognition and machine learning. "Author aims this text at advanced undergraduates, beginning graduate students, and researchers new to machine learning and pattern recognition. "This accessible monograph seeks to provide a comprehensive introduction to the fields of pattern recognition and machine learning.
As YARA sees more widespread use within the security community, we wanted to find a way to leverage YARA rules to scan for malicious files across our entire organization. Other security tools support YARA rule integration, but we could not find a private, low-cost, scalable, batteries-included solution that was easy to deploy and maintain. For example, VirusTotal supports YARA rule matching against file submissions, but it is a public service and not designed for analyzing internal files and documents with varying levels of confidentiality and sensitivity. Serverless architectures have proven effective for security tools due to the lower cost, simpler management, and scalability associated with serverless designs.